DEV Community: TongWu

Visual ETL Orchestration with qData:Drag and Drop to Connect, Transform, and Output Data

TongWu — Mon, 28 Sep 2026 07:49:12 +0000

In an enterprise data platform, synchronizing data from a business system to a target database is often only the first step in the data processing pipeline.

Before business data enters a data warehouse or analytics system, it often needs to go through a series of processing steps, including:

Field selection;
Format adjustments;
Anomaly handling;
Deduplication;
Value mapping.

At the same time, a data task that runs continuously needs to answer several additional questions:

When should it run? Which execution engine should it use? Did the task complete successfully? How can issues be identified when something goes wrong?

Therefore, a complete ETL task is not simply:

Input → Transform → Output

It also needs to form a complete process:

Task Creation → Runtime Configuration → ETL Workflow Orchestration → Scheduled Execution → Run Result Monitoring

For this scenario, qData Open-Source Edition provides visual data integration capabilities.

Users can create data integration tasks, build data ingestion, transformation, and output workflows through drag-and-drop components, and combine execution engine and scheduling configurations to put the designed ETL workflow into continuous operation.

Using a real water level anomaly handling task as an example, let’s look at how an ETL workflow is created and executed step by step.

Step 1: Create a Data Integration Task

In qData, ETL workflows are first managed as data integration tasks.

When creating a task, in addition to basic information such as the task name, task category, and owner, users also need to determine how the task will be executed later.

qData currently supports different execution engines for different task scenarios.

For relatively lightweight data reading, writing, and synchronization tasks, users can select DataX and configure:

Initial JVM memory;
Maximum JVM memory;
Channel concurrency;
Byte throttling;
Record throttling;
Dirty data limit.

These parameters do not determine how the data is processed. Instead, they control how relevant resources are used while the task is running.

For larger-scale offline data processing scenarios, users can select Spark and configure parameters such as:

Driver cores;
Driver memory;
Number of Executors;
Executor cores;
Executor memory;
Yarn queue.

The current page also reserves an entry for the Flink execution engine.

However, in the version shown in the document, it is marked as not yet available, so the currently available execution methods remain the primary options.

Once the execution engine is determined, another question needs to be answered:When should the task run?

Therefore, the task creation process also allows users to configure the scheduling system and corresponding scheduling method.

It can be simply understood as:

Execution Engine: How the task runs；
Scheduling Configuration: When the task runs；
ETL Workflow: What the task actually does during execution。

After completing these basic settings, select Save and Configure Workflow to enter the actual data processing workflow design stage.

Step 2: Build the ETL Workflow on a Visual Canvas

After entering the workflow configuration page, available data processing components are provided on the left, while the center is a visual workflow canvas.

Users can drag different components onto the canvas according to the data processing logic, then connect them to define the order of data flow.

For the water level anomaly handling example, the basic workflow consists of three core nodes:

Table Input → Transformation → Table Output

Each node has a different responsibility:

Table Input: Where does the data come from?
Transformation: What processing and cleansing should be performed after the data enters the workflow?
Table Output: Where should the processed data be written?

The value of this approach is not simply turning scripts into several graphical components.

For subsequent maintenance, opening a task allows users to understand its data flow directly from the workflow structure:

Where does the data come from? Which nodes does it pass through? Where does it ultimately go?

When business rules need to be adjusted, users can also locate the corresponding processing node first and then modify its configuration, rather than searching through large amounts of scripts to find the relevant logic.

Step 3: Configure the Input — Where Does the Data Come From?

The first question an ETL workflow needs to solve is:

Where is the data that needs to be processed?

After opening the Table Input component, users can configure the data source for the current node.

In this example, the water level data comes from:

Water Resources Management System (Third-Party Database)

After selecting the corresponding data connection and then specifying the WATER_LEVEL table, the data source for this ETL task is defined.

The system then reads and displays the table structure, including fields such as:

ID、STATION_CODE、OBS_TIME、WATER_LEVEL

These fields are then passed to the subsequent transformation and output nodes.

In addition to selecting a database table directly, the current Table Input component also provides other integration methods, including:

SQL；
Data connections；
Asset tables.

For this example, because the task only needs to read water level data from an existing business system database, the data connection and corresponding table can be selected directly.

At this point, the first question in the ETL workflow is clearly defined:Where does the data come from, and which data needs to be processed next?

Step 4: Data Transformation — What Happens After the Data Is Connected?

If a table is simply copied as-is from one table to another, the process is closer to data synchronization.

The more important part of ETL is:

What processing should be performed after the data is read?

Therefore, transformation nodes can be added between the Table Input and Table Output components.

qData currently provides multiple transformation components, including:

Sort Records,Field Derivation,Remove Duplicate Records,Add Constant,Field Selection and Modification,Value Mapping.

These components are designed for different data processing scenarios.

For example:

If the source data contains fields that are not needed later, field selection can be used.
If the same batch of data contains duplicate records, deduplication can be performed.
If field values from different business systems use inconsistent definitions, value mapping can be used for conversion.
If a new field needs to be calculated based on existing fields, field derivation can be used.

In the current water level data example, the key issue is:Handling anomalous water level values.

Why can’t the water level data simply be written directly into the downstream data warehouse?

Suppose the source system’s WATER_LEVEL field contains values that are clearly outside the normal business range.

If these anomalous values are written directly into the downstream data warehouse without processing, they may continue to participate in statistics, analysis, and metric calculations, ultimately affecting the results of data usage.

Therefore, an anomaly handling rule can be added for the WATER_LEVEL field in the transformation node, together with the corresponding data cleansing method.

At this point, the workflow no longer simply states:

“Perform data transformation here.”

It further defines:

Which field should be processed, and according to which rule?

This allows data cleansing logic to be applied to specific fields and specific rules.

Step 5: Turn Common Cleansing Logic into Configurable Rules

In enterprise data governance, many data quality issues tend to occur repeatedly.

If a new processing logic has to be developed every time a problem appears, maintenance costs will increase as the number of tasks grows.

Therefore, qData’s transformation components can also use existing, maintained data cleansing rules.

For example:

Date Format Standardization：Convert dates from different sources into a specified format.

Decimal Place Standardization：Standardize the decimal precision of data from different sources.

Remove Field Spaces：Process unnecessary spaces before and after strings.

Enumeration Value Mapping Standardization：Convert field values from different systems into a unified representation.

Numeric Boundary Adjustment：Process data that falls outside a specified range.

Returning to the water level anomaly handling example, corresponding numeric processing rules can be configured for the WATER_LEVEL field according to actual business requirements.

In this way, the source data is not written directly into the target table.

Instead:

Read → Cleanse → Output

The role of ETL therefore extends beyond simple data movement to also include the necessary data processing and standardization during data flow.

Step 6: Configure the Output — Where Does the Processed Data Go?

After data transformation is complete, the endpoint of the entire workflow needs to be determined:

Where should the processed data be written?

This is handled by the Table Output component.

If Table Input answers:

“Where do we read from?”

then Table Output answers:

“Where do we write to?”

In this example, the processed water level data is ultimately written to:

DWD_WATER_LEVEL_CLEAN_OUTLIER

But selecting the target table is only the first step.

A mapping relationship also needs to be established between the source and target fields, for example:

ID → ID

STATION_CODE → STATION_CODE

OBS_TIME → OBS_TIME

WATER_LEVEL → WATER_LEVEL

Through field mapping, users can directly see where each source field will ultimately be written in the target table.

If the source and target table structures are not exactly the same, field selection, modification, or processing can first be completed through transformation nodes before the final field mapping.

At this point, the core data processing path of the ETL workflow has been established:

Data Reading → Data Transformation and Cleansing → Field Mapping → Data Output

Step 7: The Workflow Is Ready. How Does It Keep Running?

Completing the visual workflow configuration does not mean the data will automatically start being processed.

The previous steps answered:

“How should this task process data?”

The next step is the actual execution stage.

Because the execution engine and scheduling method were already configured when the task was created, once the scheduled time is reached, the scheduling system triggers the current data integration task.

The configured DataX or Spark execution engine then runs the corresponding data processing workflow.

The responsibilities of the three parts are clear:

Scheduling System: When to execute；
DataX / Spark: How to execute；
ETL Workflow: What to process during execution.

Taking the water level task as an example, when the scheduled time arrives, the system reads the new WATER_LEVEL data, applies the configured anomaly handling rules, and writes the cleansed results to the target table.

The same ETL workflow does not need to be configured again each time.

It can continue running according to the predefined rules.

Step 8: Once the Task Is Running, You Also Need to Know How It Is Running

For an actual data platform, being able to execute a task is only the foundation.

During long-term operation, users also need to know:

Is the task enabled?
Has scheduling been enabled?
What execution cycle is being used?
Did the most recent execution succeed or fail?

Therefore, after execution, data integration tasks can still be viewed and managed centrally from the task list.

If the most recent task execution succeeded, the corresponding status can be viewed directly.

If an exception occurs, task runtime information can be used to further locate the problem.

This is especially important for data tasks that run over the long term.

A data integration task may continue running for months or even longer.

During that time, issues such as changes to source data structures, adjustments to business rules, or abnormal data may occur and require maintenance.

Therefore, from task creation and workflow configuration to scheduled execution and runtime monitoring, what is ultimately established is a complete data integration task management process, rather than a one-time ETL configuration.

Back to the Example: How Does the Water Level ETL Task Actually Run?

Connecting the previous steps together, the complete process of the water level anomaly handling task becomes clear:

① Create the Data Integration Task

Configure the task name, category, owner, and other information. Select DataX or Spark, and set the scheduling strategy.

② Build the Visual ETL Workflow

Use Table Input, Transformation, and Table Output components to establish the basic processing flow.

③ Connect the Source Data

Connect to the third-party Water Resources Management System and read the WATER_LEVEL table, including fields such as:ID、STATION_CODE、OBS_TIME、WATER_LEVEL.

④ Process Anomalous Water Level Values

Apply the corresponding anomaly handling rules to the WATER_LEVEL field.

⑤ Configure Output and Field Mapping

Map source fields to target fields and write the processed data to:DWD_WATER_LEVEL_CLEAN_OUTLIER。

⑥ Trigger Execution Through Scheduling

Once the scheduled time is reached, the scheduling system triggers the task, and DataX or Spark processes the data according to the configured ETL workflow.

⑦ Check the Runtime Status

Return to the data integration task list to view the latest execution result and task runtime status.

At this point, the complete data processing chain has been established—from raw water level data in a third-party business system, through anomaly handling, to the target data table.

The Value of Drag and Drop Is More Than “Writing Less Code”

When people talk about visual ETL, one of the first questions that comes to mind is:Can it reduce the amount of SQL or scripting required?

But for a data platform, the more important value of visual orchestration is that it organizes what was previously scattered across:

Data reading;
Data transformation;
Data cleansing;
Field mapping;
Data output;
Task execution.

into a data processing workflow that can be viewed directly.

By opening a task, users can answer several questions more intuitively:

Where does the data come from?
What processing does it go through?
Where does it ultimately go?
How does the workflow continue to run?

For users who are just beginning to work with data platforms, they can also start with the most basic:

Input → Transform → Output

and build their first ETL workflow, then gradually add data cleansing, transformation rules, and scheduling configurations based on actual business requirements.

Conclusion

Data integration is not simply about synchronizing data from one system to another.

A truly long-running ETL task needs to address:

Data integration;
Data processing and transformation;
Data cleansing;
Field mapping;
Data output;
Execution engines;
Scheduled execution;
Runtime management.

qData Open-Source Edition connects these stages through visual ETL orchestration:

Create the task → Configure the workflow → Bring the data in, process it, and write it out → Use execution engines and scheduling capabilities to keep the configured workflow running as planned

The water level anomaly handling example also shows that the key value of visual ETL is not simply reducing the amount of code required for workflow configuration.

It is making the source, processing steps, output direction, and execution method of a data processing task more explicit.

For data platform development, this complete management approach—from data flow to task execution—also provides a more intuitive foundation for continuously adding data cleansing rules, expanding processing workflows, and maintaining long-running data tasks.

Build Knowledge Graphs for Real Business

TongWu — Mon, 28 Sep 2026 06:53:03 +0000

When enterprises build knowledge graphs, the real challenge is often not whether a graph can be built, but whether it can support real business needs after it is built.

Simply converting data from existing databases, business systems, and documents into nodes and relationships can easily result in a large and visually complex network.

But when business users actually start asking questions, they may find that:

The same device exists as multiple entities.Relationship directions are inconsistent.Data sources cannot be traced.It is unclear which node users should start from when querying.

The value of a knowledge graph does not lie in how many nodes and relationships it contains.

It lies in whether it can organize fragmented data according to unified business semantics and ultimately support real enterprise queries, analysis, and business decisions.

Therefore, when building an enterprise knowledge graph in the qKnow Intelligent Agent Building Platform, the starting point is not “How much data should be imported?”

Instead, it begins with two questions:

First: How should an enterprise plan a knowledge graph that is genuinely usable?
Second: Once the model is defined, how can qKnow gradually transform data into knowledge that can be queried, verified, and continuously updated?

1. Think Like an Enterprise Product Designer: Design the Graph Model Before Connecting the Data

Building a knowledge graph does not end once data has been imported into the platform.

A knowledge graph that can be used continuously needs to go through a complete process:

Business Problem Definition → Knowledge Graph Model Design → Data Integration → Knowledge Extraction → Manual Review → Entity Normalization → Knowledge Fusion → Graph Validation → Application → Feedback and Iteration

Only when this loop is established can a knowledge graph evolve from a one-time implementation into a continuously optimized system, improving its accuracy and usability through ongoing business use.

01. Start with One Question: What Problem Is This Knowledge Graph Supposed to Solve?

The first step in building a knowledge graph is not connecting databases or importing files in bulk.

What should be determined first is:

Who will use this graph? When will they use it? What questions do they need to answer? And what business action should be taken after the query is completed?

For example, in a pumping station equipment operation and maintenance scenario, when maintenance personnel receive an equipment alarm, they may need to continue querying:

Which equipment or components are associated with the current fault symptom?
What possible causes could have led to it?
Has similar equipment experienced similar problems in the past?
What maintenance measures have been used historically?
Who should be responsible for handling the current issue?

Only around this business process should the first-stage graph be planned to include concepts such as:

Main equipment units;
Mechanical components;
Electrical equipment;
Sensors;
Fault symptoms;
Fault causes;
Operating conditions;
Maintenance strategies;
Personnel;
Historical cases.

In other words:

The scope of the knowledge graph should be determined by business questions, not by the enterprise’s existing data catalog.

If a certain type of data cannot currently help users complete a query, make a judgment, or take a subsequent business action, there is no need to include all of that data in the first phase simply because the volume is large.

02. Understand the Business Through Scenarios, Time, Space, Objects, and Processes

Enterprise business is naturally multi-dimensional.

A knowledge graph for equipment failures may contain business objects such as equipment, personnel, and components, while also involving time information such as alarm time and maintenance time.

It may also involve spatial information such as the pumping station and equipment area where the equipment is located, as well as business processes such as anomaly detection, diagnosis, work assignment, maintenance, and acceptance.

These dimensions can be understood as follows:

Scenario dimension

Where is the graph used?
Examples: fault diagnosis, operation monitoring, inspection, maintenance.

Time dimension

How do objects and events change over time?
Examples: alarm time, maintenance time, equipment status changes.

Spatial dimension

Where is an object located, and which area does it belong to?
Examples: pumping station, equipment area, workshop, pipeline location.

Object dimension

What are the core entities managed by the enterprise?
Examples: pumping station, main equipment unit, motor, sensor, personnel.

Process dimension

How are business actions connected in sequence?
Examples: detecting an anomaly, diagnosis, work assignment, maintenance, acceptance.

Organizational dimension

Who is responsible, who uses the knowledge, and who reviews it?
Examples: operation and maintenance team, maintenance personnel, equipment supervisor.

These dimensions should not be turned into several unrelated graphs.

For example, fault diagnosis can use equipment objects as the main axis, use time to describe when the fault occurred and how it was handled, use space to determine equipment location, and use process relationships to connect diagnosis, maintenance, and acceptance.

Multiple dimensions should be organized around the same business question.

03. Do Not Simply Divide Knowledge Graphs by Department or Business System

A common problem in enterprise knowledge graph development is to build separate graphs directly according to ERP, MES, document systems, or departmental boundaries.

This may appear clear, but it can easily create new knowledge silos.

For example, the same main equipment unit may exist simultaneously in the asset management system, maintenance system, and technical documentation.

If separate graphs are built for each system, the same equipment may eventually become three entities with no direct connection.

A more practical approach is to:

First identify the enterprise’s core business objects and establish unified identifiers, then associate attributes, records, documents, and business events from different systems with the same object.

Systems and departments can continue to serve as:

Data sources;
Management scopes;
Permission boundaries.

But they should not automatically become the boundaries of the knowledge graph model.

04. Clearly Define Concepts, Attributes, Relationships, and Identifiers

In qKnow, the graph model provides the common foundation for:

Structured extraction;
Unstructured extraction;
Knowledge fusion;
Application queries.

A basically usable model should clearly define at least:

Concepts：Business objects such as equipment, components, personnel, faults, and work orders.
Attributes：Information such as equipment codes, models, locations, statuses, and timestamps.
Relationships：Connections such as contains, belongs to, is responsible for, causes, affects, and requires.
Unique identifiers：Used to determine whether two records represent the same entity.
Relationship constraints：Define which types of concepts can establish certain relationships.
Model versions：Define how changes to the model affect historical data and existing applications.

The first-stage model does not need to pursue a “large and comprehensive” structure.

Compared with establishing hundreds of concepts at once, it is more important to ensure that the names of core concepts, unique identifiers, and relationship directions remain stable, and that every newly added concept or relationship corresponds to a real business question.

A practical approach is to organize a set of real questions before modeling.

For example:

“What could be causing abnormal vibration in the main equipment unit?”

This requires equipment, fault symptoms, fault causes, and their relationships.

“What alarms have occurred in this area over the past month?”

This requires the graph to represent the area, events, and time together.

“Who is responsible for this maintenance task, and which spare parts were used?”

This requires a complete path connecting the maintenance task, personnel, equipment, and spare parts.

If a real business question cannot be transformed into a clear path of entities, attributes, and relationships, the model should be adjusted first rather than relying on manual interpretation after the data enters the platform.

Once model planning is complete, the actual data construction stage can begin.

2. Operational Workflow: Creating and Using a Knowledge Graph in qKnow

Using a Pumping Station Equipment Fault Diagnosis and Maintenance Knowledge Graph as an example, let’s look at how an enterprise knowledge graph can be gradually built in the qKnow Intelligent Agent Building Platform—from the model and data to the final business query.

The specific concepts, fields, relationships, data scope, and review roles should all be designed according to the enterprise’s own business requirements.

Step 1: Create the Target Knowledge Graph

Enter Knowledge Graph at the top of qKnow and create a new graph from the graph list.

The graph name should ideally reflect both the business object and the application purpose.

For example:

Pumping Station Equipment Fault Diagnosis and Maintenance Knowledge Graph

At the same time, configure the label, owner, and publication status.

During the first stage, it is recommended to keep the graph unpublished.

Only after the model, data extraction, and relationship checks are completed should it be formally opened for use.

When creating the graph, the initial scope should also be defined:

What types of equipment are included?
Which areas are covered?
What time range is covered?
Which business problems will the first phase solve?
Which content will not be included for now?

This helps prevent the graph from expanding without clear boundaries during implementation.

Step 2: Establish a Unified Model Through Graph Model Management

Go to:

Graph Model → Graph Model Management → Add

Enter the model name, labels, department, and model description.

Under the same business topic, regardless of whether the data later comes from databases or technical documents, structured and unstructured extraction should, as far as possible, reuse the same graph model.

Before configuring the platform, the enterprise can first prepare a model list that clearly defines:

Concept Name → Business Definition → Unique Identifier → Core Attributes → Data Source → Owner

Special attention should be paid to differences in how the same business object is understood by different departments.

For example, if the equipment department, operation and maintenance department, and production department have different interpretations of the same object, the definition should be unified before entering the platform configuration stage.

Otherwise, when data from different departments enters the graph, those original differences in data definitions will also be introduced into the knowledge system.

Step 3: Configure Concepts and Attributes in the Graph

Enter Concept Configuration on the model details page.

Build the concepts required for the initial business scenario, such as:

Operation and maintenance personnel,Main equipment units,Mechanical components,Electrical cabinets,Valves and pipe fittings,Sensors,Fault symptoms,Maintenance strategies,Operating conditions

Five points require particular attention here.

1. Distinguish Between Objects and Categories

For example, Main Equipment Unit can be a concept, while Main Equipment Unit No. 1 is a specific entity.

Concepts and instances should not be mixed within the model.

2. Give Core Objects Stable Unique Identifiers

Equipment can use equipment codes.

Personnel can use employee IDs.

Materials can use material codes.

Otherwise, when data from different systems is integrated, duplicate entities can easily be created.

3. Attributes Do Not Need to Be as Numerous as Possible

Keep only the attributes that genuinely support business queries and decision-making.

For example, equipment model, operating status, installation location, and update time are more important than a large number of fields that will rarely be used by the business.

4. Standardize Time, Space, and Status Formats

For example, date formats, area hierarchies, and equipment status enumerations should be standardized in advance.

5. Concept Definitions Need to Be Understandable Across Roles

When model designers, data processing personnel, and business reviewers see the same concept, they should understand it as the same business object.

Step 4: Configure Relationships and Directions Between Entities

After completing the concepts, enter Relationship Configuration.

When configuring relationships, clearly define:

Start → Relationship → End

For example:

Operation and maintenance personnel → Responsible for → Main equipment unit
Mechanical component → Belongs to → Main equipment unit
Fault symptom → Causes → Maintenance strategy

At the same time, determine whether the relationship should be reversible based on the business scenario.

Relationship names should preferably use verbs with clear business meaning rather than relying heavily on vague relationships such as “related to,” “associated with,” or “other.”

Another easily overlooked issue is query direction.

If users frequently need to “search from a fault to its cause,” the relationship paths in the model should allow them to continue querying from the fault symptom to the fault cause.

Relationship definitions are also part of the subsequent knowledge retrieval experience.

Step 5: Prepare Databases and Enterprise Knowledge Files

Once the model is complete, the process moves into the data integration stage.

The knowledge sources handled in qKnow can generally be divided into two categories.

1. Structured Data

Database connections can be configured through:

Data Management → Data Sources

The following should also be clearly defined:

Connection information;
Data tables;
Primary keys;
Update times;
Subsequent synchronization frequency.

2. Unstructured Knowledge

Examples include:

Fault reports
Maintenance summaries;
Technical manuals;
Acceptance records;
Operation and maintenance documents.

These materials can first enter the Knowledge Center or Knowledge Files, where file versions, applicable scope, and parsing quality should be confirmed.

Before formal extraction, it is recommended to first establish a data mapping:

Which table or file → Which concept → Which field corresponds to which attribute → Which fields generate which relationships

Do not simply turn a database table name directly into a concept and a field name directly into a graph attribute.

Database structures solve data storage problems.Graph models express business semantics.The two are not completely equivalent.

Step 6: Create Unstructured Knowledge Extraction Tasks

For technical documents, fault reports, and maintenance records, go to:

Knowledge Extraction → Unstructured Extraction → Add

Enter the task name, select the corresponding files from the Knowledge Center, and import or configure the triples that need to be extracted.

The same extraction task should, as far as possible, process files with similar content structures and business types.

For example:

Fault reports can form one task.
Technical manuals can form another task.
Maintenance summaries can form a separate task.

After the task is executed, check the results in:

Extraction Results and Execution Logs

Pay particular attention to:

Whether entity boundaries are correct;
Whether relationship directions are correct;
Whether a large number of synonymous entities have been created;
Whether knowledge has been generated without support from the source content;

Completing unstructured extraction does not mean the knowledge is ready to enter the formal graph directly.

Results that have not undergone business review should not be published for direct use.

Step 7: Create Structured Knowledge Extraction Tasks

For data such as equipment records, alarm records, and maintenance work orders in databases, structured extraction can be used.

Go to:

Knowledge Extraction → Structured Extraction → Add

The process generally includes three core stages:

Basic Information → Table Mapping → Relationship Mapping

Basic Information:Select the data source and configure the data update method.Depending on actual business requirements, you can configure full updates or incremental updates, as well as the execution frequency for subsequent tasks.
Table Mapping:Map business tables in the database to the graph concepts that have already been designed, while mapping fields to concept attributes.
Relationship Mapping：Establish relationships between entities based on primary keys, foreign keys, or other associated fields.After configuration is completed, it is not recommended to synchronize all production data immediately for the first run.

A more cautious approach is:

Test Connection → Sample Review → Small-Scale Execution → Check Entities and Relationships → Expand the Synchronization Scope

After the task is completed, check the extraction logs, including:

Success status;
Failure status;
Start time;
End time.

Even if the task status shows “Success,” it is still necessary to sample the generated entities, attributes, and relationships to verify whether they actually conform to the business model.

Step 8: Conduct Business Review of Knowledge Extraction Results

Once a knowledge graph enters enterprise business use, technical “successful task execution” and business-level “correct knowledge” are two different concepts.

Therefore, extraction results need to undergo further review.

Business users should focus on checking:

Whether entity names are complete;
Whether unique identifiers are accurate;
Whether duplicate entities have been generated;
Whether attribute values, units, time, and data sources are correct;
Whether relationship names and directions conform to the model design;
Whether the knowledge actually comes from the corresponding original data or files.

Role boundaries also need to be clearly defined.

Platform administrators can be responsible for:

Connection status;
Task status;
Platform operation issues.

Business roles such as equipment experts and operation and maintenance personnel should be responsible for whether the knowledge itself is correct.

Technical review and business review cannot replace each other.

Step 9: Use Entity Normalization and Knowledge Fusion to Resolve Different Names for the Same Object

Once enterprise knowledge is truly aggregated, a very common problem appears:

The same object may have different names in different data sources.

For example, the same equipment may be described as:

Main equipment unit;
Pump unit;
Water pump unit.

Without normalization, these may become three separate entities.

In qKnow, users can go to: Knowledge Fusion → Entity Normalization to maintain standard names and corresponding aliases.

A knowledge fusion task can then be established to identify duplicate or similar entities.

The system can display candidate entities, attributes, and their associated triples.

Reviewers can then determine based on the actual data:

Same entity / Different entities / Temporarily unable to determine

However, entity fusion should not rely only on name similarity.

For example:

Equipment should primarily be checked against equipment codes, models, and locations.
Personnel should primarily be checked against employee IDs and departments.
Materials should primarily be checked against material codes and specifications.

If attributes from different sources conflict, the enterprise also needs to define in advance:

Which source has priority, and how should data with different update times be handled?

Only after these rules are defined can multi-source enterprise data truly be organized around unified business objects.

Step 10: Use Graph Exploration to Check the Model and Data

After the knowledge is published, enter Graph Exploration.

qKnow provides:General View,Relationship View,Timeline View,Table View.

These views are not simply different ways of displaying the graph.

They can also support different types of checks.

For example:

General View:Check the overall graph structure.
Relationship View:Check the components, faults, personnel, and maintenance strategies surrounding a specific equipment entity.
Timeline View:Check how equipment status, alarms, and maintenance events change over time.
Table View:Directly verify subjects, relationships, objects, and data sources.

Here again, it is recommended to start from real business questions rather than simply checking whether the graph is “complex enough.”

For example, search for a known main equipment unit and:

Check whether its core attributes are complete.
Continue expanding to its associated components.
Check which faults have occurred.
Check which causes and maintenance measures correspond to those faults.
Check which personnel are responsible.
Check whether different events have the correct timestamps.
Check whether the equipment is associated with the correct area.

If isolated nodes, duplicate nodes, or incorrect relationship directions are discovered, return to the model, knowledge extraction, or fusion stages for further adjustment.

Step 11: Use Entity-Relationship Search to Verify Whether the Graph Is Truly Usable

At this stage, the acceptance criterion should not simply be:

“The data has entered the graph.”

The process still needs to return to the business questions defined at the beginning.

Go to:

Application Center → Cross-Functional General Applications → Entity-Relationship Search

Enter entity or relationship keywords.

Multiple keywords can be separated by commas, while the search scope, labels, and advanced search conditions can also be used.

For example, the original set of business questions can be used for first-stage validation.

Enter Main Equipment Unit, Abnormal Vibration to check whether the relevant entities and associated graph can be found.
Enter Communication/Sensor, Loose Wiring to check whether the corresponding fault and “causes” relationship can be found.
Enter an area and time condition to check whether the corresponding alarms and maintenance events can be located.
Enter a specific component to continue viewing its associated equipment, historical faults, and maintenance strategies.

If there are too many results, advanced conditions such as concepts, relationships, and labels can be used to narrow the search.

If the expected object cannot be found, troubleshooting can follow a relatively clear path:

Has the data been integrated → Is the mapping correct → Was the extraction task executed → Has the result been published → Was the entity incorrectly fused → Has the retrieval index been updated

This is also an important step in moving knowledge graph development from a construction problem toward engineering-oriented troubleshooting.

Step 12: Continuously Adjust the Graph Based on Real Usage

An enterprise knowledge graph does not remain unchanged after a one-time data import.

New equipment will be added.

Documents will continue to be updated.

Business rules will change.

Users will also raise new query requirements.

Therefore, application feedback needs to continuously flow back into the knowledge construction process.

For example:

Cannot find an entity or case:Add data sources, files, or synchronization scope.
Incorrect entity or relationship recognition:Adjust extraction rules and review again.
Different names for the same object / same name for different objects:Improve primary keys, aliases, and fusion rules.
Unable to represent real business questions:Adjust concepts, attributes, relationships, and relationship directions.
Inaccurate time or spatial queries:Standardize time formats, location hierarchies, and mappings.
Data is not updated for a long period of time:Clarify update frequency, task status, and responsible personnel.

For important model changes, their impact on existing data, extraction tasks, and application queries should also be evaluated.

Model version records should be maintained, and corresponding tasks should be executed again when necessary.

The Final Acceptance Criterion Should Not Be the Number of Nodes

Whether a knowledge graph has been successfully built should not be judged only by:

Number of nodes;
Number of relationships;
Complexity of the visualization page.

More importantly, enterprises should check whether:

Real business questions can be transformed into clear entity and relationship paths.
Core entities have unified identifiers and trustworthy data sources.
Time, space, objects, and business processes can be correctly associated.
Extraction results have been reviewed and errors can continue to be traced.
Entity-relationship search can support real troubleshooting and analysis.
New data can continuously enter the graph according to the defined process.

If a graph can only display a complex network of nodes but cannot help the enterprise complete queries, make judgments, or take subsequent business actions, then it has still not solved the problem of “building a graph just for the sake of building one.”

Conclusion

For enterprise intelligent agents, the significance of a knowledge graph is not simply to add another form of knowledge storage.

What it truly addresses is:

How to organize structured data in databases, unstructured knowledge in enterprise documents, and business objects distributed across different systems through unified semantics and clearly defined relationships.

In qKnow, this process can start with business questions and graph model design, then gradually move through:

Data Integration → Knowledge Extraction → Manual Review → Entity Normalization → Knowledge Fusion → Graph Exploration → Entity-Relationship Search

Issues discovered during application can then be fed back into the model and data processing stages.

Plan the model before connecting the data.
Ensure knowledge is trustworthy before validating actual applications.

When this closed loop can operate continuously, the knowledge graph is no longer merely a visualization result.

It can gradually become the knowledge foundation that enables enterprise intelligent agents to understand business objects, connect enterprise knowledge, and support subsequent applications.

Why Do Report Data Keep Failing to Match?qData Professional Edition Starts with Data Standards to Unify Enterprise Data Definitions

TongWu — Mon, 28 Sep 2026 05:33:09 +0000

In the process of building an enterprise data platform, inconsistent data definitions are a very common challenge.

Even when different teams are analyzing the same type of business data, the results from different systems and departments can still vary.

Sometimes, the differences come from:

Different statistical periods；
Different metric calculation methods；
Different data update times；
Different data processing logic。

But there is another issue that is easier to overlook in the early stages:

The data itself was never defined and used according to the same set of rules from the beginning.

For example, the same concept—river section code—might be named:

RSCD in one system；
river_code in another。

When these data points exist independently within different business systems, the difference may not immediately create a problem.

But as data is gradually consolidated into a data platform and begins to support cross-system analysis, statistics, and reporting, questions like these continue to emerge:

“Do these two fields mean the same thing?”
“Does the value in one system represent the same status as in another system?”
“Which standard should be used to interpret this field?”

Therefore, gradually unifying enterprise-wide data definitions cannot be achieved only at the reporting and metric layers by solving the question of how to calculate.

The process needs to trace back further:

Do the underlying data itself have a unified definition and a consistent set of usage rules?

From this perspective, data standards address a more fundamental issue:

Enabling data from different systems to be understood through the same enterprise language.

1.Before Unifying Data, First Manage Where the Standards Come From

When talking about data standards, it is easy to start directly with field definitions.

But in actual implementation, another question needs to be answered first:

What are these rules based on?

Enterprise data standards are usually not created entirely from scratch.

Many industries already have:

National standards；
Industry standards；
Local standards；
Group standards。

Enterprises may also develop their own internal data specifications based on their business requirements.

These standards may already define terminology, data structures, coding methods, and business rules.

The real question is:

Are different teams within the enterprise referring to the same set of standards?

If one team uses an industry standard while another uses a local standard, or if different versions of the same standard are being used, then even when everyone is building “data standards,” differences may still exist at the source.

Therefore, in qData, data standard development starts with standard registration and standard search.

The platform can centrally manage the national, industry, local, and group standards that need to be referenced throughout enterprise data development.

Through standard search, users can further view:

Standard name；
Standard category；
Implementation status；
Publication date；
Implementation date。

This step is not primarily about answering:

“What should this field be called?”

It first answers:

“Which standard is the enterprise using as the basis for developing this data rule?”

Turning a PDF or Word Standard into Platform-Based Rules

In traditional standard management, many standards ultimately remain stored as:：

PDF files；
Word documents；
File servers；
Shared folders。

When employees need to use them, they search for and review the relevant documents.

But for data governance, simply being able to view a standard document is not enough.

Because the real questions that need to be managed later are:

Which fields are derived from this standard?
Which code values are defined by this standard?
Which models follow this standard?

Therefore, in qData, the standard details can also associate:

Logical models

Data elements

Code tables

with the corresponding standards.

In this way, a specification that originally existed as a static PDF or Word document can gradually become a data rule that can be associated and managed within the platform.

In other words:

Standard registration answers: What is the basis for the rule?；

The subsequent management of data elements and code tables answers:

“How should this standard actually be implemented in enterprise data?”

2. From Standard Documents to Standard Data Elements: Defining What a Field Means”

Once the standard basis is clear, the next step is to turn the rules in those standard documents into data definitions that the platform can actually use.

One particularly important object is：

the standard data element

A standard data element can be simply understood as a unified field definition established by the enterprise for a specific category of foundational data.**

For example, an enterprise may need to standardize the data item “river section code.”

Simply deciding that its Chinese name should be “river section code” is not enough.

The enterprise also needs to define:

What is its English name?What is the field type?What is the length?Does it have decimal places?Which data element category does it belong to?Which standard is it based on?Who is responsible for it?What does it mean from a business perspective?Is it currently enabled?

In qData, a standard data element can maintain:

Chinese name；
English name；
Field type；
Data length；
Decimal places；
Standard data element category；
Standard type；
Standard registration；
Owner；
Description；
Enablement statu。

For example, “river section code” can be defined as:

Chinese name: River Section Code
English name: RSCD
Field type: VARCHAR
Data length: 50
Description: Used to consistently describe coding information for river sections.

In this way, even when the physical field names used by different business systems are not exactly the same, the enterprise can still establish a unified definition at the data standard level.

A Standard Data Element Is Not Just About Naming a Field

As this example shows, a standard data element is not simply about standardizing what a field is called.

It needs to answer more fundamental questions:

What does this data mean? What field type should be used? What should its length be? Which standard is it based on?

A standard data element is therefore more like a unified data language within the enterprise.

The physical fields in business systems can continue to retain their existing designs.

For example:

System A continues to use RSCD；
System B continues to use river_code；
System C may use river_id。

This does not mean that all production databases must immediately be redesigned and renamed.

Once these data points enter the data platform, however, they can be consistently interpreted in terms of their business meaning and technical attributes through unified standard data elements.

This is an important point in data standard development:

Unifying the way data is understood does not necessarily mean forcing every system to immediately adopt the same physical field names.

3. Standardizing the Field Is Not Enough. The Meaning of Each Value Also Needs to Be Unified

For many business fields, standardizing only the field name, type, and length still cannot fully resolve differences in data definitions.

A typical example is enumerated data.

Take cross-section type as an example.

Even if every system has standardized the field name as “cross-section type,” the problem remains if different business systems still use different code definitions internally.

Once the data is aggregated, the values still need to be reinterpreted.

For example, one system may define:

0 = Hydrological cross-section
1 = Ecological flow cross-section
2 = Control cross-section
3 = Assessment cross-section

If another system uses completely different business definitions for 0，1，2 and 3 , directly combining the two systems for statistical analysis may create another data definition issue.

So, simply standardizing what the field is is not enough.

We also need to standardize:

What does each value inside the field represent?

Code Tables: Standardizing Value Domains and Business Meaning

For this scenario, qData can further build and manage code tables.

Code tables are mainly used to standardize the allowed values of enumerated data and clearly define the business meaning corresponding to each code.

For example, cross-section type can be standardized as:

0 — Hydrological cross-section
1 — Ecological flow cross-section
2 — Control cross-section
3 — Assessment cross-section

In this way, data standards move beyond the field level and further into the management of the value domain within the field.

It can be understood simply as:

Standard data elements define how the data itself should be defined；
Code tables define which standard values the data may contain and what each value means.

Together, these extend enterprise data standards beyond:

Standardizing a field name

to also standardizing:

Data meaning + technical structure + standard value rules

4. Once Standards Are Defined, the More Important Step Is Making Sure Real Data Actually Uses Them

This is a critical step in the data standard development process.

Suppose an enterprise has already established:

Hundreds of standard data elements；
Dozens of standard code tables；
Multiple standard documents.

But if all of these remain within the data standards module, while the fields in the actual databases have no relationship with those standards, then the standards and real data are still disconnected.

What the enterprise may ultimately have is:

One set of standards and one set of actual data—but no real connection between them.

Therefore, the key to making data standards effective is:

Establishing relationships between standards and actual data.

Find the Enterprise Standard Corresponding to an Actual Field

For example, a business system already contains a river data table.

RSCD

In the database, it may simply be：VARCHAR2(50)

But after entering qData’s data asset management, this actual field can be further associated with the corresponding standard data element:River Section Code

After the association is completed, the platform can clearly identify that:

RSCD in the current business table represents “River Section Code” within the enterprise data standard system.

If other business systems also contain:

river_code or river_id

and business teams confirm that they represent the same business meaning, they can also be associated with the same standard data element:

River Section Code

In this way, when an enterprise needs to determine whether two fields from different systems represent the same type of data, it no longer has to rely only on whether their field names are identical.

It can further determine:

Whether they are associated with the same standard data element.

For enterprises with a large number of legacy systems, this approach is more practical.

Legacy systems cannot realistically have every database field renamed immediately simply because a data platform is being built.

What data standards do first is establish a unified interpretation layer.

5. Actual Fields Can Also Be Associated with Code Tables

For enumerated fields, standard associations can be extended further.

For example, a business table may contain a cross-section type field.

In qData, this field can be further linked to the centrally managed cross-section type code table.

This allows the values 0，1，2 and 3in real data to correspond to standardized business meanings within the platform.

Ultimately, a relatively clear standard association chain can be established:

Actual Field → Standard Data Element → Standard Code Table → Standard Code Value and Meaning

This chain addresses consistency at two levels:

First level:How should this actual field be understood within the enterprise?

Second level:How should the specific values inside this field be interpreted?

This is a key step in moving data standards from rule definition into real data.

6. Standard Implementation Is Not Just About Creating a Field Mapping

Once a data field has been associated with a standard data element, the implementation of the standard is not finished.

Suppose the enterprise standard specifies that:

The field type should be VARCHAR；
The maximum length should be 50；
An enumerated field can only contain 0，1，2 and 3.

The platform then needs to consider:

Is the actual data being used according to these rules?

Therefore, in qData’s data element details, data cleansing rules and inspection rules can also be associated.

For example, once a standard data element has clearly defined:

Field type；
Field length；
Allowed code values.

corresponding data checks and cleansing requirements can then be configured around these standards.

In this way, the role of the standard extends from:

“Telling everyone what the data should look like”

to:

“Helping the platform check whether actual data is being used according to the standard.”

This is also an important step in bringing data standards into the actual data governance process.

7. Can Data Standards Solve Every “Report Data Doesn’t Match” Problem?

No.

This point is particularly important.

Differences in final report results may also be affected by:

Metric calculation formulas；
Statistical periods；
Data update times；
Data processing logic；
Business statistical scope。

Therefore, data standards are not the only way to address all report discrepancies.

What they solve is a more fundamental issue:

Before data enters statistical analysis and calculations, are different teams actually using data to express the same meaning?

For example, two departments may both be counting the number of cross-sections.

But if one system interprets type 1 as an ecological flow cross-section, while another system interprets 1 as a different type, then even when exactly the same statistical formula is used, the business meaning behind the results may already be different.

Therefore, enterprise-wide data definition consistency can be divided into several layers:

Data standards: What does the data mean?
Metric systems: How is the data calculated?
Statistical rules: When should it be calculated, and what scope should be included?

Data standards address the most fundamental layer of consistency at the beginning of this process.

How Does a Data Standard Actually Become Operational?

Looking back at the entire process, a relatively complete qData data standard development path can be summarized as follows:

① Standard Registration：Bring national standards, industry standards, local standards, group standards, and other reference standards into the platform in a unified manner.

② Standard Search and Management：Define basic information such as standard name, category, implementation status, publication date, and implementation date.

③ Establish Standard Data Elements：Unify the Chinese name, English name, type, length, category, source standard, and business meaning of fields.

④ Establish Code Tables：Unify the code values allowed for enumerated fields and the business meaning corresponding to each value.

⑤ Associate Actual Fields with Standard Data Elements：After business confirmation, map different physical fields such as RSCD, river_code, and river_id across different systems to the same enterprise data definition.

⑥ Associate Enumerated Fields with Standard Code Tables：Enable actual values such as 0, 1, 2, and 3 to correspond to unified standard business meanings.

⑦ Associate Cleansing and Inspection Rules：Based on standards such as field type, length, and value domain, continue checking and governing the actual data.

The entire process can be further summarized as：Unified Standard Sources → Unified Field Definitions → Unified Code Values → v → Data Governance Rules Implemented

Through this process, data standards are no longer an independent rule management function.

They gradually become connected to real data assets and the data governance process.

Conclusion

Inconsistent enterprise data definitions do not necessarily begin at the final stage of report calculation.

They may originate much earlier—from inconsistent field definitions, code values, and data meanings.

qData Professional Edition’s data standard management uses:

Standard Registration → Data Element Definition → Code Table Management → Data Asset Association → Governance Rule Implementation

to transform requirements that once existed only in standard documents into a rule system that can establish relationships with real data and participate in governance execution.

Data standards cannot replace metric definitions or statistical logic.

But they can first address a more fundamental question:

Can data from different systems be understood through the same language?

This is also the foundation for gradually unifying enterprise data definitions and improving consistency in data governance.

Handling Task Dependencies: How to Orchestrate Workflows in qData Data Middle Platform Pro

TongWu — Thu, 17 Sep 2026 05:49:00 +0000

In the early stages of building a data middle platform, the number of data tasks is usually limited. When there aren't many tasks, developers can configure and run them individually without much trouble.

However, as data processing becomes more complex, a final data result is rarely produced by a single task.

For example, generating a water level statistical report might require several steps:

Ingesting historical water level data.
Ingesting basic water station data.
Processing details.
Handling dimensions.
Performing summary statistics.

Some tasks must wait for upstream data to be ready, while certain downstream results might depend on multiple preceding tasks simultaneously. At this point, simply knowing "what tasks exist in the system" is no longer enough. You need to solve several problems:

Which task should run first?
Which tasks depend on each other?
How do you express a relationship where one result depends on two upstream tasks?
If an upstream task fails, which subsequent processes are affected?
After multiple tasks run, how do you view the progress of the entire pipeline?

These questions mark the transition from single task management to multi-task collaborative management.

qData Data Middle Platform Pro's Job Management addresses this by organizing multiple tasks into a data production pipeline with clear upstream and downstream dependencies, sitting on top of existing data integration and development tasks.

Why Do We Need "Jobs" When We Already Have Tasks?

Let's look at a typical data processing scenario.

Suppose you need to generate a daily river water level statistical report. After ingesting raw data from a third-party system, you cannot get the final result immediately. It requires multiple processing stages:

Source Data Ingestion → Detail/Dimension Processing → Summary Statistics

This might involve:

Syncing historical water level data from the source DB to ODS.
Syncing water station basic data to ODS.
Processing ODS historical water levels to form DWD details.
Processing ODS water station data to form DIM dimensions.
Using DWD and DIM data to form the DWS daily water level statistics.

If you look at these tasks separately, each can independently complete a specific data processing job. However, from the perspective of the final result, they are collectively doing the same thing: gradually processing raw water data into results usable for analytics.

Therefore, as steps increase, what needs to be managed is no longer just individual tasks, but how these tasks should run together in order.

1. First, Clarify: What is the Difference Between a Task and a Job?

In qData, tasks and jobs solve problems at two different levels.

Task: "How to complete this specific data processing work"

A task is a specific execution unit. For example, a task named River_Water_Historical_to_ODS_Incremental_Hourly is responsible for reading data from the source and syncing it to the ODS table. It handles:

Where to read data.
Where to write data.
Field mapping.
Execution configuration.

Data synchronization, cleaning, detail processing, and summary statistics can all be built as independent tasks.

Job: "How to organize multiple tasks to achieve the final result"

A job focuses on how multiple existing tasks should be organized, not on the internal logic of a single task.

For example, a job named River_Water_Monthly_Stats_Daily_Job might include historical ingestion, dimension processing, detail processing, and daily summarization. The job doesn't redefine the logic; it organizes existing tasks based on their dependencies.

Summary:

Task: How to do this specific work.
Job: How to run multiple tasks together to get the result.

2. How Does qData Chain Multiple Tasks into a Job?

Job orchestration isn't just putting tasks on one page. It involves reorganizing tasks according to real data relationships.

Step 1: Group Tasks into a Job

In the job configuration page, you can see existing integration and development tasks. You can group them by processing stage. For the water level scenario, you would add:

Historical data sync.
Station data sync.
ODS to DWD processing.
ODS to DIM processing.
DWD/DIM to DWS aggregation.

Step 2: Establish Upstream/Downstream Dependencies

Adding tasks to a job only defines membership. The critical step is defining the relationships.

In our scenario, there are two parallel preparation chains:

Historical Chain: Source → ODS → DWD.
Station Chain: Source → ODS → DIM.

Once both are ready, they feed into the final step: DWD / DIM → DWS.

The final result depends on both inputs. The job canvas makes this explicit, transforming a list of tasks into a flow: Ingestion → Processing → Statistics.

Step 3: Maintain Task Configurations

Once inside a job, you can still view and maintain specific configurations for each node (e.g., owner, connection, source/target tables, priority). The job organizes the flow without detaching you from the specific task details.

Step 4: Check Before Running

Before running a complex pipeline, you should verify the configuration. qData provides a task check capability. This acts as a pre-flight check to ensure the orchestration meets execution requirements, preventing configuration errors from propagating down the chain.

The configuration process:
Select Tasks → Organize into Job → Define Dependencies → Refine Config → Run Check.

3. Why Can't We Just Look at One Task When Things Go Wrong?

When tasks are independent, troubleshooting focuses on that specific task (SQL errors, connection issues, etc.).

However, when tasks are linked in a job, your perspective must shift.

If the "Historical Data Ingestion" fails, you shouldn't just ask "Why did this fail?" You must also ask:

Where does this sit in the pipeline?
What downstream tasks depend on it?
Will the final daily report be affected?

Job orchestration changes the focus from "Did this task run?" to "What is the impact of this task on the whole chain?"

4. How Do You Track the Progress of the Entire Pipeline?

Once a job is running, you need to know the status of the whole chain.

qData allows you to view Job Instances. At this level, you can see:

Job Name.
Execution Status.
Start/End Times.

For complex jobs, you can drill down into specific tasks (e.g., checking if the ODS-to-DWD step is finished while the DIM step is still running). This tells you exactly where the data pipeline stands.

Drilling Down to Logs

If a specific node fails, you can access the Run Logs directly from the instance view. Logs include submission time, duration, status, and downloadable log files.

This creates a hierarchical troubleshooting path:
Job Overview → Specific Task Status → Run Logs

5. Summary: The Complete qData Job Management Lifecycle

To recap, a complete job management process involves:

Build Independent Tasks: Create sync and processing tasks in integration/development modules.
Create Job: Group relevant tasks around a final data product.
Define Dependencies: Map out who runs first and who waits for whom.
Review Config: Check owners, connections, and priorities.
Pre-Run Check: Validate the orchestration logic.
Unified Execution: Run the pipeline based on the defined strategy.
Monitor Instance: View the overall status of the run.
Log Analysis: Drill down to fix specific errors.

The Value of Job Management in qData

In a data middle platform:

Data Integration solves how to ingest.
Data Development solves how to process.
Job Management solves how to organize.

It bridges the gap between scattered tasks and a cohesive data production line. It addresses three layers:

Task Layer: Execution details.
Relationship Layer: Dependencies and order.
Operation Layer: Pipeline status and impact analysis.

Job orchestration transforms isolated tasks into a clear, dependent data production pipeline, shifting the focus from managing individual units to managing the flow toward the final result.

qKnow Open Source v2.4.3 Released: Expanded Data Ingestion and Customizable Knowledge Construction

TongWu — Thu, 17 Sep 2026 05:48:09 +0000

The qKnow Agent Construction Platform Open Source Edition v2.4.3 focuses on upgrades regarding unstructured knowledge ingestion, knowledge asset export, and customizable document parsing. Key updates include:

New Import Capabilities: Support for importing JSON/JSONL files into Knowledge Bases and Knowledge Graphs.
Data Export: One-click export of Q&A data from Knowledge Bases to JSON/JSONL formats.
Customization: Opened capabilities for customizing file parsing models and prompts.

These updates further refine the processing pipeline for unstructured documents, covering ingestion, parsing, and reuse.

From "Uploading Documents" to Enabling Data Ingestion, Processing, and Reuse

When enterprises build knowledge bases and agents, raw knowledge isn't limited to standard documents like Word or PDF files. Real-world projects often involve document parsing results processed by other systems, semi-structured data, and pre-organized Q&A datasets.

This means the enterprise knowledge processing workflow is not simply:
Upload File → Parse → Build Knowledge Base

It is closer to:
External Data Preparation → Data Import → Document Parsing → Knowledge Generation → Data Export → Secondary Processing & Reuse

When a platform's data entry/exit methods and parsing logic are too rigid, three issues often arise as scenarios increase:

Lack of Import Entry: Parsed or organized JSON/JSONL data lacks a standard way to enter the platform.
Difficulty Exporting: Q&A knowledge generated in the knowledge base cannot be easily exported for external processing or dataset accumulation.
Rigid Parsing: Different document types require different parsing logic, but fixed models and prompts make it hard for users to adjust to specific business scenarios.

Therefore, the focus of qKnow Open Source v2.4.3 is not just adding file formats, but completing the full loop:
Data enters platform → Parsed per business needs → Knowledge formed → Results output for reuse

1. New JSON/JSONL Import for Knowledge Bases & Graphs

Previously, qKnow relied primarily on standard file uploads. However, in enterprise data processing, some unstructured documents may have already been parsed or converted into more structured formats like JSON or JSONL by other systems. If the platform forced users to start from raw files again, it would lead to redundant work.

This version adds JSON/JSONL import capabilities to provide a standard entry point for this data.

Direct Upload Support

The update supports uploading JSON and JSONL files. This means data that has already been organized doesn't need to be reverted to raw documents; it can be imported directly based on its actual form.

The workflow now supports:
Raw Document → External Parsing/Processing → JSON/JSONL → qKnow Knowledge Base/Graph

This is particularly suitable for enterprises with existing document processing pipelines or those needing to ingest data generated by other systems.

Distinguishing Data Sources

The update distinguishes between unstructured and semi-structured data to adapt to different ingestion scenarios. While JSON/JSONL has structure, the content may originate from unstructured document parsing or be pre-organized semi-structured knowledge. This allows different forms of knowledge data to enter the processing flow.

Bridging the Gap for Re-ingestion

A key application is re-ingesting data that has already been parsed. For example, if an enterprise uses external tools to parse files and saves the results as JSON, qKnow can now import this directly, avoiding the cycle of converting results back to files just to re-parse them.

Note: The JSON/JSONL import solves the ingestion problem. How this data flows into knowledge parsing and construction depends on the specific data format and the enterprise's configuration.

2. Export Q&A Data to JSON/JSONL for Reuse

A knowledge platform must address not only how data enters but also how it leaves.

In enterprise knowledge base construction, parsing a document often generates a large amount of Q&A knowledge. This data is valuable for:

External system processing.
Dataset organization.
Model evaluation.
Data annotation.
Cross-system reuse.

If parsing results are trapped inside the platform, knowledge assets cannot flow freely.

One-Click Export

qKnow Open Source v2.4.3 adds the ability to export Q&A data. After parsing documents and building the knowledge base, users can export the Q&A data into standard JSON or JSONL files.

The workflow becomes:
Upload → Parse → Generate Q&A → Build KB → Export JSON/JSONL → External Processing

Standardized Output for Dataset Accumulation

For teams building enterprise agents long-term, Q&A data is a sustainable asset. It can be used for verification, test set construction, training data preparation, or manual review. Standardized export formats facilitate batch processing better than manual copying.

From "KB Results" to "Tradable Assets"

This shifts the paradigm from:
File → Parse → Internal Use
To:
File → Parse → Knowledge → Internal Use → Export → External Reuse

This increases the flexibility of document-based knowledge assets across different tools. However, exported data may still require quality checks or format adjustments depending on the target use case (e.g., model training).

3. Customizable Parsing Models and Prompts

Enterprise documents vary significantly—policy documents differ from technical manuals, contracts, or maintenance logs.

Technical docs might focus on: Equipment, parameters, steps, exceptions.
Policy docs might focus on: Scope, rules, responsibilities, execution requirements.

A fixed parsing logic often fails to accommodate these differences. Previously, qKnow used fixed system logic for models and prompts.

Open Model Selection

Users can now adjust the model used for document parsing based on business needs. Parsing is no longer solely dependent on a preset platform model but can leverage the enterprise's own model resources.

The logic flows as:
Identify Doc Type → Select Parsing Model → Configure Prompt → Execute Parsing → Review Results

Configurable Prompts

The update also allows for custom parsing prompts. Prompts directly influence:

What content to extract.
How to understand the document.
How to organize the generated results.
Which business information to prioritize.

This allows the same model to adjust its extraction targets based on the scenario (e.g., focusing on main content vs. specific business entities).

Adapting to Scenarios

This shifts the workflow from a rigid process to a flexible one:
Upload → Select Model → Configure Prompt → Parse → Use Results

This is ideal for enterprises with diverse document sources. However, customization solves the adjustability of logic; actual results still depend on document quality, model capability, and prompt design.

4. A More Open Knowledge Processing Pipeline

Combining these three features, qKnow v2.4.3 establishes a more complete knowledge data pipeline.

Previous Flow:
Standard File Upload → Fixed Parsing → Knowledge Base → Internal Use

New Flow:
Raw File / JSON / JSONL → Import → Custom Model/Prompt Parsing → KB Q&A Data → JSON/JSONL Export → External Reuse

JSON/JSONL Import: Solves how different data forms enter.
Custom Parsing: Solves how knowledge is processed according to business logic.
JSON/JSONL Export: Solves how processed knowledge flows out for reuse.

Version Value Summary

This release makes unstructured knowledge handling more flexible from ingestion to reuse:

Open Ingestion: JSON/JSONL import reduces redundant conversion for pre-processed data.
Asset Circulation: Export capabilities allow knowledge to be used for external processing and dataset accumulation.
Controllable Logic: Custom models and prompts allow parsing to adapt to specific document types and business goals.

Overall, qKnow Open Source v2.4.3 connects the "Import — Parse — Export" loop, giving enterprises more autonomy in building and reusing non-structured knowledge.

From a Database Table to an API: How qData Open Source Delivers Data as a Service

TongWu — Thu, 17 Sep 2026 05:46:46 +0000

Data sync is done. Data processing is complete. The final table has landed in the database.

But for the business systems that actually need this data, that's usually not the end of the story.

Dashboards, business applications, or third-party systems can't each directly connect to the database, understand the underlying table structure, and write their own SQL queries. For a data middle platform, beyond solving how to bring data in and how to process it, there's one more question:

How do you deliver processed data in a standardized, controlled way?

qData Open Source Edition provides API management capabilities within its data service module. It lets you configure database tables, SQL queries, or forwardable data assets into callable APIs — complete with parameter configuration, interface testing, application authorization, and call record management.

In this article, we'll walk through the most straightforward scenario: how to turn a ready-to-use database table into a callable data API step by step in qData.

Before You Start: Prepare the Data You Want to Serve

Let's say your database already has a hydrological monitoring data table containing fields like:

Station code
Station name
Monitoring time
Water level

Now you want to expose an interface to external business systems so callers can query water level data by station code.

Before creating the API, confirm two prerequisites:

The target data source and database table are accessible and working properly.
You've already set up a service category under Data Service > Service Categories for this API.

qData's service categories support a parent-child hierarchy for organizing different data services. Enterprises can pre-build categories based on business domain, data type, or system purpose. Once APIs are created, they can be found and maintained under a unified directory.

Once data preparation is done, you're ready to enter the API creation flow.

Create the API: Configure a Data Table as a Data Service

Go to Data Service > API Management and click "New".

qData uses a three-step flow to create APIs:

Property Configuration → Parameter Configuration → Testing

These three steps essentially answer three questions:

What is this API?
Where does the data come from, and what parameters are passed when calling it?
Does the API return results correctly after configuration?

Step 1: Configure Basic API Properties

The first step is Property Configuration. Here you define the API's core information:

API category
API name
API endpoint path
API version
Request method
Response format
API description

You can also configure management controls as needed:

IP blacklist
Request rate limiting
API online/offline status

For example, in the hydrological monitoring scenario, you might define this as a "Water Level Daily Report Query Service," specifying the API path, version, POST method, and a business description.

These properties serve both internal platform management and communicate to future callers what this API does and when to use it.

If there are call frequency limits, you can set rate limiting. If certain IPs should be blocked, you can control that via the IP blacklist.

Once basic properties are confirmed, move on to parameter configuration.

Step 2: Select the Data Table and Define "How to Query, What to Return"

In Parameter Configuration, you first determine where the API's data comes from.

qData currently offers three configuration modes:

Single-table wizard
SQL script mode
Third-party forwarding

For our single-table scenario, select the corresponding data source, then choose the table you prepared earlier. This tells the platform: "This API's data comes from this table."

But selecting a table doesn't mean exposing the entire database table directly to business systems.

When serving data externally, you need to define two things:

01 — What conditions can callers use to query?

For example, if you want business systems to query water level data by station code, add a request parameter station_code in the Request Parameters section, and configure:

Whether it can be empty
Parameter type
Parameter description
Example value
Default value

This way, callers only need to pass in a station code to query the corresponding station's data.

02 — What fields can callers actually receive?

After defining request conditions, configure the response fields. For example, you might select:

Station code
Station name
River name
Water level

So turning a database table into an API isn't about exposing the raw table structure. The platform lets you precisely define what callers can query by and what data they ultimately receive.

Step 3: Test the API Online Before Delivery

After parameter configuration, it's not recommended to hand the API over to business systems immediately. Run it once in qData first.

In the Testing step, the page displays:

API name
Version
Request method
Response format
Call endpoint

It also provides a request data input area. Fill in the request parameters you defined, then click "Call API".

If the request succeeds, the platform shows a successful call and displays the actual query results in the response area. If a required parameter is missing, it will prompt that the parameter cannot be empty.

During this phase, focus on confirming three things:

Do the query conditions actually take effect?
Are the returned fields correct?
Does the actual returned data match expectations?

After testing, the API enters the API management list. You can re-verify later by going to the API details and using "Test Info" to trigger another call.

At this point, the database table has essentially completed its transformation from "table" to "API." But to let other business systems actually use it, you still need to handle access identity and authorization.

Create a "Caller Identity" for Business Systems

Once the API is created, not just any system can access it by grabbing the endpoint URL.

In qData, business systems that actually call the API need to first establish an application identity through Application Management.

Go to Data Service > Application Management and create a new application.

For example, if a water resource monitoring system needs to call the API you just created, register it as an application in qData and maintain:

Application name
Application type
Application description

This answers the question: Who is using this API?

For enterprise data services, this matters because the API itself is a data capability, but the actual callers are usually frontend apps, business systems, upstream platforms, or third-party integrations. Only by identifying the caller first can you then control which data services each application is allowed to access.

Authorize the Application to Access the API

After creating the application, it doesn't automatically gain access to all APIs.

Go to API Authorization in the application details, click "New", select the API you created earlier, and set the authorization validity period.

qData supports:

Permanent validity
Specified start and end dates

After submission, the API appears in the application's authorization list.

This creates a clear mapping in the platform: which application can call which API.

When your enterprise grows to dozens of business applications and hundreds of data APIs, this relationship becomes critical. Different business systems can each maintain their own allowed data services, rather than opening all APIs indiscriminately to every caller.

Get Access Credentials and Let Business Systems Call the API

Once the application identity and API authorization are in place, you're ready for actual system integration.

Business systems need to obtain an access token according to the authentication method configured in your qData deployment environment, and include the valid token when calling specific business APIs.

From the business system integration perspective, the complete flow is:

Create Application → Obtain Application Identity → Authorize API → Get Token → Call API with Token

At this point, the way business systems access data has fundamentally changed.

Before: Direct database access.

Now: Call APIs through the data service layer to retrieve data.

The database tables still serve as the underlying data store, but business systems no longer need to understand the database structure directly. They query through the platform's data service layer.

Track API Calls After They Happen

Once APIs go live in production, the management work doesn't end. You'll quickly face practical questions:

Has the API been called?
Who called it?
How much data was returned?
Why did a particular call fail?

qData provides unified call tracking under Data Service > Call Records, where you can view:

API service name
Caller IP
Called endpoint
Data volume returned
Response time

Drilling into details, you can further inspect:

Call parameters
Request method
Call status
Error information

For example, when a business system reports "this API didn't work just now," operations or development staff can locate the corresponding request by service name and call time, then review the parameters, status, and error details.

This means the API isn't just a configured interface handed off and forgotten — it has a complete lifecycle from creation, authorization, and invocation to ongoing tracking.

From Table to API: The Full Flow at a Glance

Looking back, the entire process from a prepared database table to a live, callable API can be summarized as:

Prepare Data Table → Create API → Configure Properties → Select Data Table → Configure Request Parameters and Response Fields → Test Online → Create Calling Application → Authorize API → Obtain Access Credentials → Business System Calls API → Review Call Records

This pipeline reflects a fundamental challenge in data middle platforms: syncing data in and processing it only covers the first half of data construction.

The real business usage phase requires answering: How do you deliver processed data securely and clearly to other systems?

qData Open Source Edition's data service module adds a data service capability layer between database tables and business applications.

The underlying storage remains the database tables you've already built, but when serving externally, you can further define interfaces, query parameters, response fields, application identities, and authorization relationships.

This transforms a table that could previously only be queried inside the database into a data API callable by business systems.

Wrapping Up

For enterprise data middle platforms, data doesn't create real business value just by being "synced" or "processed."

What matters more is how you deliver that prepared data to dashboards, business systems, and third-party applications.

qData Open Source Edition achieves this through:

API Creation → Parameter Definition → Online Verification → Application Identity → API Authorization → Token-based Calls → Call Records

This progressively converts a raw database table into a data service usable by business systems.

It's not simply wrapping an interface around a table. It's establishing a clear service boundary between data and business applications — defining where data comes from, how it can be queried, what can be returned, who can call it, and how calls are tracked afterward.

Extending from "bringing data in and managing it" to "delivering data out and putting it to real use" — that's the practical significance of qData's data service within the broader data middle platform pipeline.

From Chaos to Order: Deep Dive into qModel Open Source Edition's Model Version Management and Multi-Version Comparison

TongWu — Thu, 17 Sep 2026 05:46:01 +0000

Algorithm models are rarely "done" after initial development.

Once a model enters production, it continuously evolves — driven by data drift, parameter tuning, algorithm optimization, and shifting business requirements.

A single optimization pass may produce a new version. After multiple rounds of experimentation, you end up with test versions, production versions, and historically stable versions. When the version count is small, teams can still get by with file naming conventions and manual records.

But as iterations accumulate, this approach quickly breaks down:

Files like model_v2_final_0315, final_final_v3 keep piling up, making it hard to confirm which version is actually live.
Online models and offline experiment versions can't be accurately mapped, making it difficult to reproduce performance fluctuations.
When multiple people iterate on models simultaneously, different versions easily overwrite each other or create collaboration conflicts.
After parameters or configurations change, it's hard to quickly answer "what exactly changed this time," making it difficult to attribute model performance changes.

For algorithm model platforms that need to run continuously, model management can't stop at "can the model be uploaded and executed." It also needs to manage version relationships, configuration differences, and current active states throughout the entire iteration lifecycle.

To address this, qModel Algorithm Model Platform consolidates version management, version comparison, new version creation, and multi-version switching into the model management workflow — transforming models from a series of isolated modifications into a version evolution history that can be viewed, compared, switched, and rolled back.

Version Management: Centralize Scattered Model Versions

The first step in model version governance is answering: "What versions exist, and which one is currently active?"

In the past, when models were managed through file directories, different versions might be scattered across different folders or held by different team members. Finding a specific historical iteration usually required cross-referencing file names, modification timestamps, and developers' memories.

As model iterations increase, this approach becomes increasingly unsustainable.

qModel adds a dedicated "Version Management" tab on the model detail page, centralizing all versions under the same model.

Once you enter the version management page, you can view:

All versions created for the current model
The currently active version
Total version count
Basic information for different historical versions

Version management is integrated directly into the model detail page, rather than splitting different versions into separate, disconnected model records. This design means teams are no longer managing a batch of similarly named but isolated model files — instead, they manage:

One model entity + multiple continuously evolving versions.

After multiple rounds of experimentation and optimization, historical versions remain preserved within a unified version lineage. Developers and operations staff can quickly confirm which version is currently active.

This lays the foundation for subsequent model comparison, switching, and historical rollback.

Version Comparison: Answer "What Exactly Changed in the New Version?"

Once you know a model has multiple versions, the next question is usually:

What actually changed between two versions?

In traditional model management, this often requires developers to open two versions separately and manually compare configurations and parameters. If the model adjustments are extensive, this is not only inefficient but also prone to missing subtle changes.

qModel supports selecting any two model versions for side-by-side comparison. The system systematically organizes differences across configurations, parameters, and other dimensions.

On the version comparison interface, the two versions being compared are displayed on the same page, allowing you to directly view version basic information and configuration differences side by side.

This transforms model version analysis from:

Open two versions separately → Manually hunt for differences

to:

Select two versions → View differences in one place

For example, when a new version goes live and performance shifts, algorithm engineers can first compare the old and new versions to confirm whether configurations and parameters were adjusted, then combine that with actual model performance to continue root cause analysis.

For models that have gone through multiple rounds of experimentation, you can also select two specific versions to compare, helping the team answer more concrete questions:

What was modified in this iteration compared to the previous version?
Which configurations remained unchanged?
Which parameters were adjusted?
What differences exist between the current version and a historically stable version?

It's worth noting that version comparison itself can't directly determine which parameter caused a model performance change. But it surfaces the differences between versions first, providing a clearer version-level basis for subsequent analysis combined with experiment results, data changes, and runtime performance.

New Version Creation: Continue Iterating on the Existing Model

During model iteration, there's another very practical question:

Does creating each new version require reconfiguring the model from scratch?

If every experiment round requires creating a new model and re-entering all configurations, the work is not only repetitive — it also risks parameter omissions that introduce unintended differences between versions.

qModel supports creating a new version directly based on the current model version.

After creation, the new version automatically inherits the original version's configuration and context. There's no need to rebuild from zero — you continue the next round of adjustments on top of the existing version.

New version creation still follows the original model configuration workflow. Users can continue completing subsequent configurations based on the inherited version information, which better aligns with actual model development practices.

This is because most model iterations don't completely scrap the existing model and start over. Instead:

Most of the previous version's configuration is retained, with adjustments made only to specific parameters, data, or model settings.

For example, when a stable model needs parameter optimization, you can create the next version based on the current one, making modifications while preserving the original configuration relationships.

This reduces repetitive configuration work and establishes a clearer evolution relationship between old and new versions.

The model iteration process shifts from:

Copy file → Modify → Copy again

to:

Current version → Create new version → Adjust configuration → Test and verify → Form next version

Multi-Version Switching: Let Test, Production, and Historical Stable Versions Coexist

Once a model has multiple versions, not every version immediately replaces the current production version.

In practice, the same model may simultaneously have:

A currently running production version
A new version being validated
A previously stable historical version

So version management needs to solve not just "how to create," but also "which version is currently active."

qModel supports multi-version coexistence and version switching under the same model. During testing and production use, you can switch between different model versions as needed. Once a new version meets requirements, you can switch to it. If the new version causes runtime or performance issues after going live, you can switch back to the previous historically stable version.

For actual algorithm model operations, this is important because:

A model upgrade doesn't mean the historical version loses its value.

Historical stable versions are not just part of the iteration history — they also serve as a recovery mechanism under abnormal conditions.

Through multi-version coexistence, models maintain a relatively clear state relationship across different stages:

Historical stable version → Currently active version → Next iteration version

Rather than directly overwriting the previous version every time a new model goes live.

This way, when production issues arise, the team still knows which version was previously in use and can restore to the corresponding historical version when needed — instead of searching through scattered model files.

From "Saving Model Files" to Managing Continuous Model Evolution

The significance of model version management isn't just about adding a version number to a model.

Once models enter enterprise algorithm applications, a single model typically goes through:

Model development → Parameter tuning → Multiple experiment rounds → Test validation → Production use → Continuous optimization → Exception rollback

If version relationships across these stages aren't uniformly recorded, as models and team members grow, many problems revert to manual management:

Which version is running in production?
Which historical version was this version adjusted from?
What configurations changed in the new version compared to the previous one?
After a model performance fluctuation, can you quickly locate the previously stable version?

These may seem like model development-stage concerns, but they continuously impact downstream model testing, release, and operations.

qModel addresses this through:

Version management — consolidating multiple historical versions under a single model
Version comparison — surfacing configuration and parameter differences in one place
New version creation based on current version — preserving model iteration lineage
Multi-version coexistence and switching — supporting testing, production use, and historical version recovery

Together, these capabilities form a foundational management pipeline from version creation to comparison, usage, and rollback.

For different roles, these version relationships also serve distinct practical purposes:

Algorithm developers can continue iterating on existing versions and use version comparison to confirm configuration changes between experiment versions.
Model operations and application staff can identify the currently active version and relocate historical stable versions when anomalies occur after version changes.
Project management and delivery staff can understand which stages a model has gone through via unified version records, reducing long-term reliance on individual file naming conventions and verbal explanations for model version relationships.

Wrapping Up

As algorithm models move from experimental environments into actual business use, the focus of model management gradually shifts.

Early on, the concern may be:

Can the model be created? Can it run?

But after a model goes through multiple optimization rounds, the team also needs to answer:

Which version is currently running?
What changed in the new version compared to the old one?
Which version should the next iteration be based on?
If the new version has issues, can the historical stable version be quickly restored?

These are the core problems model version management needs to solve.

qModel Algorithm Model Platform addresses this by organizing different versions produced during continuous model iteration through version management, version comparison, new version creation, and multi-version switching.

For algorithm models that require continuous development, validation, and operation, having model versions that are visible, comparable, and switchable is a critical foundation for transforming models from one-time R&D outputs into sustainably managed assets.

From Water Application to Dispatch Order: Streamlining the Full Irrigation District Water Scheduling Process

TongWu — Thu, 17 Sep 2026 05:43:44 +0000

In daily water management for irrigation districts, a water supply dispatch is never as simple as "submit a water usage request and issue a dispatch order."

From the moment an irrigation district submits a water demand, through management unit review and approval, to the creation of dispatch orders and their execution, the process typically involves multiple management levels, various business roles, and numerous water usage condition verifications.

Especially as the number of irrigation districts grows and application frequency increases, if applications, approvals, and dispatching still rely primarily on offline communication, spreadsheet circulation, and manual handoffs, problems quickly emerge: duplicated information entry, unclear processing progress, and loose connections between approval results and dispatch orders.

Therefore, the focus of digitizing irrigation district water scheduling isn't simply moving paper-based applications online. It's about further connecting:

Demand Declaration → Review & Approval → Dispatch Order Creation → Order Issuance → Order Execution

into a relatively complete business chain — enabling water demands, processing workflows, dispatch orders, and subsequent execution to be linked together.

1. From Water Application to Dispatch Execution: Building a Complete Irrigation District Water Scheduling Chain

The starting point of irrigation district water scheduling is accurately collecting actual water demands across different regions and time periods.

Water Applications Enter the System Through a Unified Entry Point

The system first provides a unified water application entry point.

Water users in irrigation districts can fill in details such as the water-using unit, water usage period, and requested water volume based on actual needs. Once submitted, this creates a standardized water application record, providing a unified data foundation for subsequent review, approval, and scheduling.

Management personnel can also view water demands by irrigation district, application period, and other conditions — gaining visibility into declaration volumes, water usage plans, and current processing statuses across different regions.

For irrigation district users, they can directly create water applications for their own district and submit them to the upper-level management unit, reducing the repeated circulation of offline spreadsheets and multiple rounds of communication.

This way, water demands first form a unified entry point in the system, and subsequent processing steps can continue flowing around the same application record — without needing to recreate business data at different stages.

Applications, Reviews, and Approvals Are Processed Step by Step According to Workflow

After a water application is submitted, the system routes it sequentially to the corresponding management units and approvers based on the established workflow.

Reviewers can view application details, verify application information, water usage quotas, and supply conditions, then provide review comments. Approvers then make approval decisions based on the irrigation district's supply capacity, water usage plans, and actual scheduling arrangements.

In terms of permission design, different roles correspond to different business operations. For example, irrigation district users acting as applicants don't see the review button — review and approval permissions are consolidated at the appropriate management levels, reducing role misuse.

Meanwhile, the system synchronously records processing time, processing personnel, and handling opinions, preserving process evidence for future queries and traceability.

Management personnel can also view the complete process of an application — from submission through review to approval — via process records, clearly identifying which processing stage it's currently at, what pending items remain, and the handling results at each previous node.

This means a water application is no longer just a static form — it continuously flows through the actual management process.

Supporting Tiered Collaboration with Continuous Tracking for Priority Applications

Irrigation district water scheduling typically involves different management levels, so application information also needs to be organized according to actual management relationships.

The system supports tiered viewing by irrigation district, management unit, and water user — enabling users to drill down from overall declaration status to specific applications. Management personnel at each level can prioritize pending review items within their area of responsibility while also staying informed about water demands and approval progress across different regions.

For key irrigation districts, priority water users, or large-volume water applications, the system can further retain application information, review comments, approval results, and related dispatch order records.

Business personnel can continuously track approval nodes and scheduling arrangements around the same priority application, conducting supplementary verifications, coordination, and follow-up tracking when needed — preventing priority applications from losing context as they flow through multiple management levels.

Approval Completion Flows Seamlessly into Dispatch Order Creation and Issuance

Approval is not the endpoint of the water scheduling process.

After review and approval are completed, the approval results still need to be converted into executable scheduling requirements.

The system manages dispatch orders in a unified manner, centrally displaying dispatch order numbers, execution units, approved users, preparers, and dispatched flow volumes — helping management personnel identify current scheduling tasks that need attention.

For dispatch orders generated through the normal application process, dispatch order information remains linked to the original application record and approval results, enabling duty personnel to quickly locate the application background and execution content corresponding to the current scheduling task.

At the same time, considering that special situations may arise during actual scheduling — such as the absence of a corresponding water application, or an approved application not fully matching actual scheduling conditions — management personnel can also manually create dispatch orders based on business needs, preserving necessary on-site scheduling flexibility.

Once a dispatch order is officially issued, relevant management units and execution personnel can promptly view specific scheduling requirements and organize water supply and usage accordingly, reducing omissions and delays that occur during manual handoffs.

At this point, a complete irrigation district water business cycle progresses from initial demand declaration, through review, approval, dispatch order creation, and order issuance, and finally enters the actual execution stage — forming a relatively complete online business chain.

2. Beyond "Digitizing Approvals" — Supporting Actual Water Scheduling Collaboration

Once water applications, review/approval, and dispatch order management are connected, the practical significance goes far beyond reducing a few paper forms. It enables scheduling information — originally scattered across different people and different stages — to remain continuously linked around the same business process.

The system further refines:

Demand Declaration → Review & Approval → Dispatch Order Creation → Order Issuance → Order Execution

connecting water applications, approval processing, scheduling orders, and subsequent execution status.

For daily management personnel, multiple operations can be completed within a relatively unified business interface: viewing declaration demands from different irrigation districts, filtering current pending items, verifying application and approval information, and generating dispatch order content after completing the corresponding approvals.

This reduces duplicate data entry for the same business process across different stages, and eliminates the need to repeatedly search across multiple pages to trace the origin of a dispatch order.

For the overall irrigation district management process, this produces several key changes:

Demands can be collected more promptly — Water applications submitted by different irrigation districts enter the system uniformly, enabling management personnel to grasp declaration demands within a given time frame at an aggregate level, providing a basis for subsequent coordinated planning.
Processing relationships between management levels become clearer — Who reviewed the application, which node it's currently at, and what opinions were given at the previous stage are all preserved through process records, reducing information gaps caused by interpersonal communication.
Approval results connect with scheduling execution — After approval is completed, the business doesn't stop at an "approved" status. It continues into dispatch order creation, order issuance, and execution stages, enabling management personnel to track the entire journey from demand submission to actual scheduling.

3. Sustaining Business Operations Requires Data and Process Maintenance as a Foundation

Whether a water scheduling process can operate stably over the long term depends not only on whether the front end has application, approval, and dispatch order capabilities, but also on whether the underlying business baseline information and process rules can be continuously maintained.

Therefore, beyond business processing, the system also provides corresponding data and process maintenance capabilities.

Around actual water scheduling, the following baseline information can be uniformly maintained:

Water users (irrigation districts), management units, water usage periods, application types, approval stages, and other baseline information — along with permission configurations to restrict approval authority for different roles.

This may seem like backend configuration, but it directly determines how front-end business flows.

For example, when management units are restructured, approval levels change, or different types of water applications require new approval workflows, there's no need to rebuild an entirely separate business logic. Instead, adjustments can be made around the corresponding baseline data and process rules.

After configuration changes, the system executes subsequent application routing, approval processing, and dispatch order management according to the latest rules — while ensuring that applications, approvals, dispatch orders, and detail records continue to use a consistent data source.

For a business like irrigation district water scheduling — which requires long-term operation and continuous adjustment — this baseline data and process maintenance capability is also critical infrastructure for ensuring stable business chain operation.

Summary

Irrigation district water scheduling involves more than a single application, nor is it an isolated approval or dispatch order issuance.

From a practical business perspective, it's more like a continuously running management chain:

Irrigation district submits demand → Management unit reviews → Business approval → Dispatch order creation → Order issuance → Organized execution

Therefore, the core of digitizing irrigation district water scheduling shouldn't stop at digitizing any single stage. It needs to further address:

Can the business flow continuously?
Can data remain linked?
Can the process be queried and traced?
Can different levels collaborate on processing?

By integrating water applications, review/approval, tiered collaboration, priority application tracking, dispatch order creation and execution, as well as underlying data and process maintenance into a single business system, a water scheduling cycle can extend all the way from "submitting a demand" to "creating and executing a scheduling order."

For smart irrigation district construction, the greater value of this full-process business connectivity lies in transforming originally scattered management stages into a clear, traceable, and sustainably maintainable water scheduling process — laying the business foundation for more standardized and collaborative irrigation district water resource management.

Timely Warning for Water Use Over-Plan: Enhancing Refined Water Resource Management

TongWu — Thu, 10 Sep 2026 07:46:53 +0000

In the daily management of water resources, formulating annual, monthly, or phased water use plans is only the first step.

Once the execution phase truly begins, managers need to continuously answer several questions:

How much water has actually been used currently?
To what extent has the plan indicator been completed?
Which regions, units, or water use objects have a significantly fast pace of water usage?
For objects that have already exceeded the plan, has there been subsequent rectification or a return to the plan control range?

If these questions can only be discovered through phased summaries, manual verification, or post-event reports, then even if a water use plan has been formulated, it is difficult to timely grasp deviations that occur during the execution process.

Therefore, planned water use management cannot stop at "plan issuance"; it further needs to establish a management mechanism of:
Continuous comparison of plan indicators and actual water use, timely identification of anomalies, and continuous tracking of key objects.

Centered around this need, the River and Canal Diversion Over-Limit Warning Function links capabilities such as plan execution analysis, over-plan judgment, hierarchical management, key object tracking, map warning, and scrolling prompts.

This allows managers to gradually locate specific abnormal water use objects from the overall plan execution situation and continuously understand subsequent changes.

Why is Continuous Execution Analysis Needed When We Have Plan Indicators?

In the actual management process, the plan indicator itself is just a control target.
Only by comparing it with continuously generated actual water use data can the current execution status be judged.

The system summarizes planned water use indicators and actual water consumption according to the year, month, or specific management cycle, and further calculates:

Plan completion rate;
Usage amount exceeding the plan, etc.

These data first form a unified basis for subsequent warning judgments.

For example, a water use object's annual plan indicator might seem to have a large margin, but if the current time is just the middle of the year and the actual water consumption has already approached the annual plan, then from the perspective of execution progress, it is already worthy of attention.

Therefore, plan execution analysis focuses not just on:
"Whether the plan is ultimately exceeded."
But also on:
"Whether the current water use progress is deviating from plan control requirements."

The front-end warning dashboard will centrally display objects where actual water use has exceeded plan indicators. Managers can view the plan execution situation according to regions, water use units, and their own management scope, further finding specific objects with fast water use progress from the overall indicators.

1. How is the Over-Plan Warning Determined?

Displaying actual water consumption is not enough.
For the warning to be truly usable for business management, it is necessary to clarify:
Under what circumstances does it count as over-plan? Based on what rules are warnings generated?

The system will continuously compare and judge actual water use data in combination with the plan cycle, plan indicators, and warning thresholds.
When the actual water consumption within the statistical cycle exceeds the corresponding plan indicator, the system generates an over-plan water use warning and records:

The quantity exceeded;
The percentage exceeded.

In this way, the warning information is not just a simple "abnormal" status but further explains:

How much has actually been used currently;
How much it exceeds the plan;
How large the degree of excess is.

For managers, this information also provides a more specific basis for subsequent judgments on the severity of the problem and determining the priority of attention.

Why the Warning was Generated: The Rules Themselves Need to be Viewable

If the warning system only tells staff: "This object is over-plan,"
but cannot explain the judgment basis, doubts are still likely to arise in actual business use.

Therefore, managers can enter specific rule details through "Rule View" to verify:

Plan cycle;
Warning threshold;
Judgment conditions;
Applicable objects.

In this way, every over-plan warning can be further traced back to the corresponding business judgment basis.
This is especially important for water resource management scenarios.
Because different regions, different objects, and different management cycles may adopt different plan indicators and management requirements.

A truly usable warning is not just "the system judging an anomaly" but also needs to be able to answer:
Why was it judged as abnormal?

2. After Discovering Over-Plan, Locate Specific Objects from the Overall Situation

When the management scope expands, there may be multiple regions, management units, and water use objects at one time.
If all warnings are concentrated and stacked in a single list, managers still need to spend a lot of time finding the objects they are responsible for.

Therefore, the system supports viewing hierarchically according to:
Administrative Region → Management Unit → Water Use Object

Managers can first grasp the overall plan execution situation, then gradually locate specific water use subjects, and prioritize viewing warning information within their own scope of responsibility.

In the specific management process, focus can be placed on:

Objects with a large quantity over-plan;
Objects with a high percentage over-plan;
Objects with continuous anomalies.

This transforms the warning from simple "abnormal information display" into a way of organizing information according to management hierarchy and problem severity, allowing limited regulatory energy to be prioritized for objects that are more worthy of attention.

3. One-Time Over-Plan and Continuous Over-Plan Need to be Treated Differently

Not all over-plan situations imply the same problem.
An object might have a one-time excess due to short-term business fluctuations in a certain stage; or it might maintain a high water use level for multiple consecutive cycles, forming a continuous over-plan.

The management methods corresponding to these two situations are usually not exactly the same.
Therefore, for objects that need continuous attention, the system will retain their plan indicators and actual water use situation on the detail page, forming a continuous tracking record for key objects.

By continuously viewing the water use trend of the same object, one can further distinguish between:

One-time fluctuation
Continuous over-plan

On this basis, managers can take measures such as reminders, verification, or rectification in combination with the actual business situation.
This is also the key to the warning capability moving from "discovering a single anomaly" to "observing changes over a period of time."

After Rectification, the Warning Cannot Simply Disappear
After an over-plan warning is generated, the subsequent status also needs to be managed.
If the water use object completes rectification through the business system, or returns to the plan control range with subsequent water use changes, the system will update the corresponding warning status.

However, the original warning record will not be directly deleted but will continue to be retained for subsequent queries.
In this way, the entire process can not only see:
"Which objects are currently over-plan."
But can further retain:
"Which objects had over-plan previously, and how they changed later."

For key water use objects that need continuous tracking, historical warning traces can also provide more complete process information for subsequent analysis.

4. Where are the Water Use Problems? Further View Regional Distribution Through Maps

For water use objects that have been associated with spatial locations, simply viewing data tables cannot fully reflect the spatial distribution of problems.
Therefore, the system can further mark the plan execution status of water use objects on the map.

Through different colors or icons, it can distinguish between:

Objects approaching the limit;
Objects that have already exceeded the plan.

Managers can thus view from a spatial dimension:

In which regions are over-plan objects relatively concentrated;
Which locations have already shown anomalies;
Which regions are currently in a state of approaching the limit.

For businesses with obvious spatial attributes, such as river and canal diversion and water resource scheduling, combining indicator status with geographical location can allow abnormal objects originally scattered in lists to further form a regionalized understanding.

5. The Dashboard Not Only Displays Statistical Results but Also Timely Prompts New Anomalies

Smart water conservancy dashboards usually undertake the role of duty monitoring and viewing the overall situation.
If anomalies only exist in a certain list or detail page, duty personnel still need to actively enter the corresponding module to discover them.

Therefore, a water use warning prompt bar is set at the bottom of the dashboard.
The system generates brief prompt information based on:

Warning objects;
Plan cycles;
Over-plan situations; And displays them via scrolling.

More importantly, the scrolling prompt is not an independent information display area but remains synchronized with:

Statistical data;
Object lists;
Map status.

In this way, while viewing the overall water use situation, duty personnel can simultaneously pay attention to newly emerging over-plan problems, as well as key objects that have not yet completed disposal.

From One Warning to a Complete Planned Water Use Management Process

Linking these capabilities together again, one over-plan water use warning actually goes through such a process:

Establish Plan Indicators → Continuously Summarize Actual Water Use → Continuously Compare According to Rules → Generate Over-Plan Warning → Hierarchical Location → Continuous Tracking of Key Objects → Synchronized Display on Map and Dashboard → Update Status After Rectification or Recovery

In this way, the warning is no longer just a red prompt in the system but further enters the process of plan execution analysis, anomaly judgment, object location, and subsequent tracking.

Summary: Moving Further from "Plan Issuance" to "Traceable Execution Process"

The focus of water resource plan management is not just to formulate a water use indicator at the beginning of the year or the start of a cycle.
More importantly, during the plan execution process, one must continuously know:

How much was actually used;
To what extent the plan is being executed;
Which objects are approaching or have exceeded control requirements;
Where the problem occurred;
Whether the anomaly is a one-time fluctuation or persists;
Whether the status has changed after rectification.

The River and Canal Diversion Over-Limit Warning Function revolves around this process.
It links plan execution analysis, over-plan rule judgment, hierarchical management, key object tracking, map warning, scrolling prompts, and warning status updates with historical traces, extending planned water use management from static indicators to the continuous execution process.

For smart water conservancy construction, the value of such capabilities does not lie in simply adding a form of alarm.
Instead, it allows the originally scattered plans, actual water use, judgment rules, abnormal objects, and disposal statuses to form a relatively continuous management chain.

Moving from "discovering how much water has been used" to "timely discovering which objects deviate from the plan and continuously tracking subsequent changes" is also a basic capability for the extension of water resource management towards refined, process-oriented control.

qModel Open Source v1.4.2 Launches Version Management and Comparison Features: Making Every Model Iteration Traceable

TongWu — Thu, 10 Sep 2026 07:46:42 +0000

The qModel Algorithm Model Platform Open Source Edition v1.4.2 introduces new model version management and version comparison capabilities.

An independent version management entry has been added to the model details page, supporting rapid creation of new versions based on existing ones, horizontal comparison between any two versions, as well as multi-version coexistence, switching, and historical version rollback.

This further improves the management chain from continuous model iteration to version governance.

From "Constantly Copying Model Files" to Establishing Clear Version Evolution Relationships

Models are usually not developed once and left unchanged for a long time.
Once they enter actual business use, a single model may generate new versions continuously due to:

Parameter adjustments;
Data changes;
Business rule changes;
Algorithm optimization;
Feedback on effects after going live.

Therefore, the long-term usage process of a model is closer to:
Initial Model → Adjust Configuration → Form New Version → Test & Validate → Official Use → Optimize Again → New Version

If there is a lack of unified version management, model iteration can easily degenerate into simple file management.
Teams might distinguish different model files using names like:
model_v2_final_0315
or:
final_final_v3

When the number of versions is small, this method might be temporarily usable; however, as the model continues to iterate, files will gradually be scattered across different directories, different members, and different environments, making the relationships between versions increasingly difficult to confirm.

The real problem to be solved is not "giving files a more standardized name," but rather:

What versions currently exist?
Which version is currently in use?
From which version did the new version evolve?
What exactly changed between two versions?
If an online anomaly occurs, can we switch back to a historically stable version?

Therefore, starting with qModel Open Source v1.4.2, model versions are treated as independent management objects to further refine version relationships during the continuous iteration process.

01. New Version Management Tab: Making Historical Model Versions Centrally Visible

When a model is continuously iterated, the most basic question is:
How many versions does this model actually have right now?

If versions are scattered across different files or pages, the first thing administrators need to solve is not analyzing version changes, but finding the historical versions first.

qModel Open Source v1.4.2 adds an independent "Version Management" tab to the model details page, placing all model-related versions into the same entry point for viewing.
Users can centrally understand:

Existing versions of the current model;
The currently effective version;
The total number of model versions.

In this way, the model details no longer just display a single current state but further add a perspective on the model's historical evolution.

Seeing the Model's Version Lifecycle Beyond Single Model Information

In the past, when viewing a model, it was easier to focus on: What is the current state of this model?
With the addition of version management, one can also continue to focus on: How did this model iterate step-by-step to its current state?

For long-running enterprise models, these two questions are not the same.
For example, the same business prediction model might go through:
V1.0 → V1.1 → V1.2 → V2.0
Different versions may correspond to data, configurations, or parameter adjustments at different stages.
Through a unified version list, these originally scattered model states can form clearer version relationships.

Reducing Reliance on File Names for Version Identification

In actual business scenarios, teams often rely on manual management of model files via names like:

model_v2_final_0315
final_final_v3

The problem is not just non-standard naming. As the number of members and iterations increases, issues may arise such as:

Uncertainty about which file is the current version;
Difficulty in quickly finding historical versions;
Lack of clear archiving relationships between versions;
Need for manual re-confirmation when restoring old models.

Therefore, the significance of version management lies first in:
Turning model versions from a "file naming habit" into explicit management objects within the platform.

02. Support for Rapidly Creating New Versions Based on Current Versions, Retaining Original Configuration Context

Model iteration usually does not start from scratch.
More often, algorithm engineers continue to adjust on the basis of existing models:

Parameters;
Configurations;
Data;
Business adaptation methods.

If creating a new version requires rebuilding the entire model configuration every time, it not only increases repetitive operations but also容易 causes unexpected differences between old and new versions due to omitted parameters.

qModel Open Source v1.4.2 supports rapidly creating new versions based on the current model version.
The new version can automatically inherit the original version's configuration and context, without needing to rebuild from scratch.

Model Iteration Can Continue to Evolve from Existing Versions

The creation logic for new model versions can be summarized as:
Select Current Version → Create New Version → Inherit Original Configuration & Context → Continue Adjusting on This Basis → Form New Model Version

This approach aligns better with the real model iteration process.
Because model upgrades are usually not:
Creating a completely independent new model
But rather: Modifying parts of the content on an existing stable basis to form a new version.

Reducing Repetitive Configuration While Retaining Relationships Between Versions

For example, if a deployed prediction model needs to adjust some parameters.
If the model is recreated, one needs to reprocess:

Basic configuration;
Parameters;
Runtime context;
Related model information.

However, by creating a new version through the existing version, the original content can be directly inherited, and adjustments can be made only to the parts that need to change.
This reduces repetitive configuration and establishes a clearer evolutionary relationship between the old and new versions.

It is important to note that inheriting configuration only reduces repetitive operations; it does not mean the new version can enter production use without verification.
Before the official model switch, verification must still be completed in conjunction with the enterprise's own model testing, effect evaluation, approval, and launch specifications.

03. New Version Comparison: Making it Easier to Confirm "What Exactly Changed in This Version"

The real difficulty in multi-version management is not "having many versions."
It is: What exactly is the difference between versions?

When multiple model versions exist simultaneously, online services and offline experiments may not accurately correspond to specific versions.
If there is a need to reproduce experimental results or trace back historical states, one often has to rely on file comparison and manual confirmation.

At the same time, when model parameters or data are adjusted, if the model's performance fluctuates, the team also needs to further determine: Where did this change come from?

Therefore, qModel Open Source v1.4.2 adds version comparison capabilities.

Select Any Two Versions for Horizontal Comparison

The platform supports selecting any two versions from existing model versions for comparison.
After comparison, it can automatically sort out differences across multiple dimensions, including:

Configuration;
Parameters; Making the changes between different versions more intuitive.

In this way, version comparison can shift from:
Manually opening two models to find differences item by item
To:
Select Version A + Version B → View Differences

Providing Clearer Version Context for Model Problem Tracing

For example, after a model upgrades from V1.3 to V1.4, business results change.
At this point, the team first needs to determine:

Which parameters changed?
Which configurations were adjusted?
What are the explicit differences between the two versions?

Through version comparison, these version-level changes can be sorted out first, and then further analysis can be conducted in conjunction with actual operational data and model effects.

Therefore, version comparison is more suited to undertaking:
Clarifying "what changed between versions."

As for:
"Why did these changes cause the model effect to improve or decline?"
This still requires further judgment based on actual evaluation metrics, test data, operational results, and business analysis.
Version comparison can provide context for problem analysis but cannot directly replace model effect evaluation.

04. Support for Multi-Version Coexistence and One-Click Switching, Reserving Space for Testing, Release, and Rollback

Model version management is not just for saving more historical records.
Ultimately, it still needs to answer a practical question: Which version should the current business actually run?

qModel Open Source v1.4.2 supports multi-version coexistence and provides version switching capabilities.
For different usage stages, the corresponding version can be selected as needed.
For example:

Testing Phase → Use the new version for validation.
Official Operation → Use the confirmed stable version.

This approach extends version management further from "viewing history" to "actual use."

Test Versions and Official Versions Can Be Switched as Needed

During the model development process, new versions usually need to be tested before entering official use.
If the platform only allowed retaining one version, every upgrade might mean overwriting the original model.
Once the new version has problems, restoring it would require finding historical files again or redeploying.

After supporting multi-version coexistence, the following can be retained simultaneously:

Current stable version;
New test version;
Historical versions.

Different stages can be switched according to actual needs.

Quickly Return to Historical Stable Versions When Online Fluctuations Occur

After a model goes officially live, it cannot be guaranteed that the new version will always meet expectations in the long term.
Changes in data distribution, parameter adjustments, or changes in the business environment may all cause fluctuations in the model's actual performance.

Therefore, model upgrades require not only:
"Being able to switch to the new version"
But also:
"Being able to roll back when problems occur."

qModel v1.4.2 supports rapidly rolling back to a historical stable version when online fluctuations occur.
From the perspective of version operation logic, the following can be formed:
Stable Version → Create New Version → Complete Adjustments → Test & Validate → Switch to New Version → Observe Actual Operation → If Anomalies Occur, Switch Back to Historical Stable Version

This gives the model upgrade process a more complete space for version selection.

However, version rollback cannot replace the enterprise's formal production release system.
For core production models, it is still necessary to use mechanisms such as test verification, launch approval, operational monitoring, and business impact assessment together.

Easier to Establish Unified Version Cognition During Multi-Person Collaboration

In situations where multiple people participate in model development, different members may adjust the model simultaneously.
If there is a lack of unified version management and permission control, problems such as different versions overwriting each other, branch conflicts, and increased collaboration costs are prone to occur.

This qModel Open Source v1.4.2 explicitly adds version management, version creation, version comparison, and version switching capabilities.
From the existing capabilities in this release, the more direct change lies in:
The team can discuss models based on unified platform version information, rather than relying entirely on personal file naming to determine which version is currently in use.

05. From Version Chaos to Version Traceability: What Changes Occurred in the Model Governance Chain?

If we look at these several features together, qModel v1.4.2 actually supplements a link in the model lifecycle that was previously easily overlooked: Model Version Governance.

In the past, model iteration might have manifested as:
Existing Model → Export/Copy Model File → Modify File Name → Adjust Parameters → Redeploy → Manually Record Which Version is in Use

As versions increase, the following issues become prone to occur:
More and more files → Increasingly difficult to confirm version relationships → Need for manual comparison → Difficulty in restoring historical states

After adding version management, the process can be further adjusted to:
Current Model → Create New Version Based on Current Version → Inherit Existing Configuration → Complete Model Adjustment → Compare with Historical Version → Test & Validate → Switch to Effective Version → Rollback if Necessary

Wherein:

Version Management is responsible for organizing historical versions;
New Version Creation is responsible for undertaking model iteration;
Version Comparison is responsible for clarifying changes;
Version Switching and Rollback are responsible for controlling the actual version in use.

These capabilities together further transform the model's "iteration process" into a manageable version chain.

06. Version Value: Making the Evolution Process of Model Assets More Manageable

For enterprise algorithm model platforms, model management cannot only focus on "whether the current model can run."
When models are continuously iterated, the governance issues between versions need to be further resolved.

More Centralized Version Status
Through an independent version management tab, the model version list, currently effective version, and version count can be viewed uniformly, reducing the situation where model versions are scattered in different files and environments.
More Continuous Model Iteration
New versions can be created directly based on the current version and inherit the original configuration and context, making model upgrades better align with the actual development method of "continuing to evolve on a stable version."
Easier Confirmation of Version Changes
Through horizontal comparison between two versions, differences in configuration, parameters, etc., can be viewed more intuitively, providing a version basis for model change confirmation, problem tracing, and experiment reproduction.
Model Upgrades Have Rollback Space
Multi-version coexistence, switching, and historical stable version rollback ensure that testing, official release, and anomaly recovery no longer rely entirely on finding and deploying historical model files again.

Overall, the value of qModel v1.4.2 is not simply adding a "version list," but further establishing a complete management relationship for model versions from creation, comparison, to switching and rollback.

Conclusion

For enterprise algorithm models, after truly entering production use, original documents list version chaos, difficulty in corresponding online and experimental versions, version conflicts in multi-person collaboration, difficulty in tracking reasons for model changes, and difficulty in comparing old and new versions as the main problems in current model iteration.

This is also the key problem that qModel Open Source v1.4.2 focuses on solving this time.

This version further supplements:

On the version organization side: Added a version management tab to model details to centrally view all versions and current effective status;
On the version iteration side: Supports rapidly creating new versions based on existing versions and inheriting original configurations;
On the version analysis side: Supports horizontal difference comparison between any two versions;
On the version operation side: Supports multi-version coexistence, flexible switching, and historical stable version rollback.

These capabilities cannot replace the enterprise's own model effect evaluation, testing processes, approval mechanisms, operational monitoring, and production release systems.

But it can solve a more fundamental problem in the continuous model iteration process:
Ensuring every model upgrade has a clear version identity, and allowing one to know where it came from, what changed, whether it is currently effective, and which historical state it can return to if problems occur.

For algorithm model platforms, it is not enough for the model itself to be manageable; the model's evolution process also needs to be managed.

The addition of version management and comparison capabilities in qModel Open Source v1.4.2 is precisely further improving the complete chain from model access, operation management, to continuous iteration governance.

How to Use qKnow Agent Platform for "Small-Scale Validation, Large-Scale Expansion"?

TongWu — Thu, 10 Sep 2026 07:46:35 +0000

When promoting AI agents and knowledge applications, a common issue is the desire to integrate all knowledge files, business systems, departmental permissions, and application requirements right from the start.

From a final goal perspective, this direction is not wrong. However, for large enterprises with complex data sources, diverse knowledge types, and long business processes, if the initial scope is too broad, it becomes difficult to quickly determine whether unsatisfactory Q&A results stem from the model, file quality, knowledge parsing, retrieval parameters, knowledge graphs, or the application process itself.

Therefore, enterprise AI agent construction is better suited to first select a small number of high-frequency scenarios for validation, and then gradually expand the capability boundaries.

Taking equipment knowledge Q&A as an example, you can start by selecting one type of equipment, one maintenance team, and a batch of real questions to run through the complete process: model integration, knowledge processing, knowledge retrieval, Q&A validation, and feedback optimization.

Once validation is effective, the methods and platform capabilities formed can be replicated to more equipment, personnel, and business processes.

This is also a landing method for the qKnow Agent Platform in enterprise AI agent construction:
Keep the initial business scope small, but unify the platform architecture first.

I. Enterprise Product Design Thinking: Small-Scale Validation, Platform-Based Expansion

01. Why are enterprise AI projects not suitable for a full rollout in the first phase?

The larger the enterprise, the more complex the knowledge and data environment usually is.
The same type of equipment may have different codes, technical materials may be scattered across different systems, and different departments may have different rules regarding file versions, approval processes, and data permissions. If all content is integrated in the first phase, the project will quickly face a massive number of variables.

At this point, the hardest questions to answer are often the three most basic ones:

Is the model truly suitable for the current business?
Can the knowledge materials be correctly parsed and retrieved?
Does the final application actually solve the user's problem?

If these three questions cannot be independently verified, even if the final result is unsatisfactory, it is difficult to determine which link needs adjustment. Therefore, the first phase is better suited to actively narrowing the scope, allowing every problem to be located and every adjustment to be re-verified.

02. Establish a "Minimum Viable Loop" first

Taking equipment knowledge Q&A as an example, a complete minimum loop is not just "uploading a few files and letting the large model answer questions."
At least the following must be completed:
Model Integration → Knowledge Base/Graph Construction → File Processing → Retrieval Configuration → Real Question Recall Test → Knowledge Q&A → User Validation → Problem Feedback → Readjustment

Only when these links can connect with each other can we judge whether the enterprise has truly established an AI application foundation that can continue to expand.
Completing model interface configuration, knowledge file uploading, or a single demo Q&A independently cannot be equated to completing a business loop.

First-phase scenarios should not pursue "bigness," but "ease of validation"

Scenarios suitable for first-phase validation usually share several common characteristics: high usage frequency, basically available existing materials, clear target users, and easily judgeable effects.

Scenario Scope: Select 2-3 high-frequency scenarios (Avoid covering all departmental needs simultaneously).
User Scope: One team or business group (Avoid facing all employees upon initial launch).
Equipment Scope: One type of equipment or one equipment family (Avoid including all equipment at once).
Question Scope: 20-50 real questions (Avoid using demo questions without business context).
Data Scope: Materials directly related to target questions (Avoid moving all historical files from the entire database).

"Small incision" does not mean the underlying architecture should also be small

The business scope can start from one scenario, but the underlying design should ideally consider future reuse from the very beginning.

In the actual construction of qKnow, the following can be considered separately:

Model Layer: Decouple model integration from application calls, allowing for future replacement or addition of other models.
Data Layer: Unify file naming, equipment coding, versions, and permission rules in advance.
Knowledge Layer: Build knowledge bases in a reusable way, keeping knowledge graph concepts and primary keys unified.
Application Layer: Q&A, retrieval, agents, and business workflows share the same knowledge base.
Operation Layer: Continuously retain user questions, audit responsibilities, and knowledge update mechanisms.

In this way, the first phase validates a small scenario, but subsequent expansions reuse the same set of platform architecture and governance methods, rather than rebuilding a new system for every added scenario.

Once the first-phase loop is validated, the scope can be gradually expanded according to:
Similar Objects → Adjacent Users → Adjacent Processes

For example, after the main unit knowledge Q&A is stable, add valves, sensors, electrical cabinets, and other equipment; subsequently expand to other maintenance teams; when users begin to need to query the relationships between equipment, components, fault phenomena, causes, and maintenance measures, then add knowledge graphs and entity relationship retrieval.

Each expansion still forms a new "small loop," rather than integrating all remaining content at once.

II. Operational Process: Using qKnow to Complete the Equipment Knowledge Q&A Loop

Below, taking "Pump Station Equipment Fault Knowledge Q&A" as an example, let's specifically look at how to use qKnow to complete knowledge processing, retrieval, and final Q&A validation step-by-step starting from the model.

Note: In actual projects, model names, business parameters, knowledge scope, and user permissions need to be configured according to the enterprise's own situation.

Step 1: Integrate the model and replace the model used in the actual application

First, you need to complete the target large model configuration in the qKnow Model Market.
After filling in the key and related necessary parameters, conduct a connectivity test first.

However, for knowledge Q&A, interface connectivity is just the first step.
After successful model integration, you also need to enter the actual Q&A workflow, Bot, or agent configuration to replace the original model node with the target model.

At this point, focus on confirming:

Can the dialogue model return results normally?
Can the context length meet the needs of knowledge Q&A?
Is the prompt configuration correct?
Is the knowledge retrieval node correctly connected to the model node?
Can the output node return results normally?

After the model replacement is complete, a full dialogue needs to be re-executed, rather than just confirming that the model interface "call was successful."

If the enterprise plans to integrate multiple models simultaneously, it is recommended to fix one main model for the first phase.
The reason is simple: if the model changes frequently, then when the answer effect changes, it is difficult to determine whether it is caused by model differences or knowledge retrieval.

Step 2: Create a knowledge base or knowledge graph around the first-phase scenario

Once the model can be called normally, the next step is to build the knowledge base.
Depending on the form of business knowledge, you can:

Create only a knowledge base;
Create only a knowledge graph;
Use both knowledge base and knowledge graph simultaneously.

The problems solved by the two are not exactly the same.

Knowledge Base: More suitable for carrying document-type knowledge such as technical manuals, maintenance records, fault cases, and operating procedures.

Knowledge Graph: Suitable for expressing clear entities and relationships such as: Equipment → Component → Fault Phenomenon → Fault Cause → Maintenance Measure.

For example: In the first phase, you can create a "Pump Station Main Unit Fault Case Knowledge Base"; if subsequent equipment relationship analysis is needed, you can simultaneously build a "Pump Station Equipment Fault Knowledge Graph."

After creation, it is recommended to keep it in an unpublished state until file processing, relationship checking, and retrieval testing are completed before officially opening it.
At the same time, the knowledge base owner, applicable departments, material scope, and subsequent update responsibilities need to be clarified.

Compared to generalized names like "Comprehensive Knowledge Base" or "Temporary Knowledge Base," the knowledge base name should directly explain the business scope, so that subsequent users can quickly judge what content it contains.

Step 3: Establish knowledge classification that fits business users' habits

Enter the target knowledge base and establish a knowledge classification system via: Knowledge Base Settings → Knowledge Classification.

For the pump station equipment fault scenario, you can first establish classifications such as:
Fault Phenomenon, Fault Cause, Equipment Component, Maintenance & Disposal, Operating Conditions.

Note here that knowledge classification is not to make the backend directory "look more complete," but to help with subsequent file governance and business user understanding.
Therefore, classification names should use expressions familiar to business users as much as possible.
The same level should also maintain a consistent division standard.
If a certain category has no knowledge files or query needs in the first phase, there is no need to create it in advance for the sake of system completeness.

Step 4: Upload, parse, and check knowledge files

Enter "Knowledge Files," select the corresponding classification first, and then upload knowledge materials such as Word, PDF, TXT, etc.

Before formal import, it is recommended to perform basic governance on the files first.
Focus on cleaning up: duplicate files, expired versions, and materials with poor scanning quality that may affect parsing results.

After the file upload is complete, the parsing results need to be further checked. For example:

Is the document title correct?
Are the body text and tables parsed completely?
Are chapter numbers preserved?
Does the segmentation structure conform to the original logic?
Does the maximum segment length cause context truncation?
Can the overlap length preserve cross-segment explanations?

These settings will directly affect subsequent knowledge recall.
For example, if a piece of equipment fault description originally consisted of "phenomenon, cause, disposal steps," and segmentation恰好 splits the cause and disposal steps, even if the model capability is sufficient, it may not be able to obtain the complete context.

If the business also needs to query relationships between equipment, components, fault causes, and maintenance measures, you can further establish unstructured extraction tasks after file import to extract relevant entities and relationships into the knowledge graph.
However, if the first-phase goal is only to complete document Q&A, you can temporarily not introduce the graph and keep the first-phase scope within knowledge base Q&A.

Step 5: Configure knowledge retrieval methods

After file parsing is complete, enter: Knowledge Base Settings → Retrieval Settings to start configuring the knowledge recall method.

For enterprise knowledge scenarios where technical manuals, maintenance cases, and maintenance records coexist, hybrid retrieval can be adopted first.

On one hand, use full-text keywords to match professional terms, equipment models, and fault names;
On the other hand, use vector similarity to handle users' natural language expressions.

When the data volume increases, or there are many similar knowledge segments, the Rerank model can also be used to further rerank results.

Top K and score thresholds are also not recommended to be set too tightly at the beginning.
A more suitable approach is to first use relatively loose conditions to observe recall results, and then gradually adjust based on real tests.

At the same time, try to modify only one parameter per round of testing.
For example, adjust only Top K in this round, and adjust the score threshold in the next round.
Otherwise, if multiple parameters are changed at once, even if the final effect improves, it is difficult to determine which factor played a role.

Step 6: Use real business questions for recall testing

When knowledge Q&A effects have problems, often the issue is not in the "large model answer," but in the earlier knowledge recall stage.
Therefore, before formally entering the Q&A application, you can first enter "Recall Testing."

Input the 20-50 real business questions organized in advance into the platform one by one.
For example: "Which parts should be checked first when the main unit has abnormal vibration?"

At this time, do not focus on whether the final answer is fluent enough, but focus on confirming:

Was the correct file found?
Did the truly relevant text segments enter the recall results?

Different phenomena correspond to different processing paths.

If the wrong file is hit, check file naming, question expression, and retrieval weights;
If the file is correct but the returned segment is incomplete, focus on checking segment length and overlap length;
If the correct segment exists but is ranked too low, further adjust Top K, Rerank, or check if there is a large amount of duplicate content in the knowledge base;
If recalled materials are old versions, it indicates the problem is no longer just retrieval parameters, but that knowledge file version management and content responsibility need further governance.

Only after this step is basically stable should you proceed to the large model answer generation stage.

Step 7: Associate knowledge base and knowledge graph in knowledge Q&A

After the recall test basically passes, you can enter: Application Center → Horizontal General Applications → Knowledge Q&A to create a new conversation.

Depending on the specific business, you can select only the target knowledge base, or simultaneously select the knowledge graph as the Q&A basis.
For example, select: "Pump Station Main Unit Fault Case Knowledge Base."
If the current question involves relationships between equipment, components, faults, and maintenance measures, continue to associate the corresponding equipment fault knowledge graph.

Subsequently, input the real question: "Which parts should be checked first when the main unit has abnormal vibration?"

What truly needs to be verified here is not just what answer the model finally generated.
You should also simultaneously check:

Is the answer content correct?
Do the citation sources come from the correct knowledge files?
Do the relevant knowledge segments match the current equipment?
When continuing to ask about applicable conditions, operation sequences, or original text basis, can context consistency be maintained?

It is recommended to record every abnormal answer.
Including: no answer, wrong citation, answer not matching current equipment, and answers ultimately not adopted by the user.
This type of information is often more valuable than simply recording "answer accuracy rate," because it can directly guide subsequent model, knowledge, and retrieval adjustments.

It must also be emphasized that important disposal suggestions in enterprise equipment maintenance scenarios should still be confirmed by corresponding business personnel.
Intelligent Q&A can help users find materials and organize basis faster, but cannot replace the enterprise's original professional review and safety management processes.

Step 8: Feed user questions back into the model and knowledge processing flow

After the equipment knowledge Q&A is truly online, the new work has just begun.
It is recommended to regularly summarize actual Q&A questions and return to the corresponding links according to different causes.

For example:

Unstable model performance: Return to the model, prompts, or workflow for adjustment;
Missing or expired files: Supplement knowledge materials and clarify file versions and update responsibilities;
Answer exists in the file but was not recalled: Return to segmentation methods and retrieval parameter adjustments;
Multiple names exist for the same equipment: Supplement aliases, tags, or entity normalization rules;
Users begin to need to analyze relationships between equipment, components, faults, and maintenance measures: Gradually increase graph models, knowledge extraction, and entity relationship retrieval capabilities.

After each adjustment, it is recommended to continue using the original question set for re-testing.
Only then can the results before and after adjustment be compared to determine whether this modification truly brought improvement, rather than judging based on individual Q&A experiences.

Step 9: Determine if the first-phase minimum loop has truly passed

After the first phase is completed, the acceptance criteria should not just be "the agent is online" or "users can open the page normally."
It can be judged from at least four dimensions:

Model Usable: Q&A and workflows can stably call the target model;
Knowledge Usable: High-frequency business questions can recall correct knowledge files and corresponding segments;
Answer Usable: Key conclusions have clear sources, are understandable by business personnel, and are actually adopted in appropriate scenarios;
Process Usable: Once an error occurs, the team can further determine whether the problem comes from the model, knowledge files, retrieval mechanism, or knowledge governance link.

If these four aspects are basically stable, it indicates that the first business loop has a foundation for replication.
The next phase can continue to expand to the second type of equipment, the second team, or the second business scenario.

During the expansion process, there is no need to rebuild a whole set of systems, but to continue reusing the existing:
Model integration methods, data standards, knowledge classification systems, permission rules, and testing methods.
What is added is only the data and knowledge capabilities truly needed by the new business.

Conclusion

Enterprise AI agent construction does not necessarily need to start with "big and all-inclusive."
Compared to integrating all data, knowledge, and business processes at once, a more controllable way is:
First complete a small-scale loop around real users and real questions: the model can be stably called, knowledge can be accurately recalled, answers can find basis, and problems can continue to be located and adjusted after they occur.

qKnow places model integration, knowledge files, knowledge extraction, knowledge bases, knowledge graphs, and Q&A applications in the same platform link, enabling enterprises to first validate a specific scenario, and then gradually reuse existing data, knowledge, and application architectures to expand to more business.

First make one real scenario work, then replicate the validated method to more scenarios.

For enterprise AI agent platforms, this "small-step validation, platform-based expansion" method is more important not for how big the first phase is, but for ensuring that every subsequent expansion has a validated foundation to reuse.

AI #EnterpriseAI #KnowledgeManagement #RAG #qKnow #TechStrategy

qKnow Professional Edition v3.1.3 Released: New Extraction Task Notifications and Multi-Dimensional Refinements

TongWu — Thu, 10 Sep 2026 07:46:29 +0000

qKnow Professional Edition v3.1.3 focuses on optimizing task status awareness, file previewing, and homepage notification configurations within the knowledge governance process.

Key updates include adding message notifications for structured and unstructured extraction tasks, introducing kkFileView deployment configurations for file previews, and enabling homepage notification visibility control via parameter management.

These updates aim to further reduce repetitive operations during task execution and platform configuration.

From "Task Submission" to Real-Time Status Awareness

In the construction of enterprise knowledge bases and knowledge graphs, file uploading is just the beginning. Once the knowledge governance workflow begins, a piece of knowledge material typically undergoes the following process:

File Preparation → Structured/Unstructured Extraction → Task Execution → Status Check → Exception Handling → Utilization of Extraction Results

Knowledge extraction itself often requires a certain amount of execution time. If users have to constantly return to the task page and manually refresh to check progress after submitting a task, several practical issues arise:

Where is the task currently executing?
Is it complete?
Has an exception occurred?
If processing multiple tasks simultaneously, has a task finished without being noticed?

Beyond task status, internal enterprise deployment environments also impact daily usage. For instance, file preview functions may face different access conditions in public versus private networks. Additionally, if page notifications can only be controlled by modifying front-end configuration files, it increases daily maintenance costs.

Therefore, the focus of qKnow Professional Edition v3.1.3 is not simply adding more pages, but continuing to refine the connection between these high-frequency operations:
Task Execution → Status Feedback → File Viewing → Platform Configuration

01. New Message Notifications for Extraction Tasks

Knowledge extraction is a critical part of the qKnow governance workflow. Whether it is structured data extraction or unstructured document extraction, tasks require actual execution time after submission.

Previously, after submitting these tasks, users had to manually refresh the page to check progress. This meant the workflow was often:
Submit Task → Wait → Refresh Page → Check Status → Wait Again → Refresh Again

When the number of extraction tasks is high, this method can easily lead to missed tasks or delayed processing.

System Notifications in the Top Right Corner

qKnow Professional Edition v3.1.3 introduces message notification data for both structured and unstructured extraction processes. Once a task is executed, the system continuously updates task progress information in the top right corner.

Unlike the past, where users had to enter the task page to check status, users can now perceive the execution status of extraction tasks while using other platform features. This shifts the task viewing method from:
Users actively seeking status
to:
The system actively providing status feedback based on task changes.

Covering Execution, Completion, and Exception States

Notifications do not only appear when a task ends. According to the release notes, the system automatically pushes messages for the following states:

Task Executing
Task Completed
Task Exception

This allows users to more timely distinguish whether a task is still running, or if it has finished normally or encountered an exception requiring further handling. This feedback is particularly important for enterprise users who need to submit multiple knowledge extraction tasks at once.

From "Watching Tasks" to "Waiting for Status Changes"

From an operational logic perspective, the update can be understood as:

Past: Submit task → Enter task page → Manual refresh → Check status → Refresh again → Wait for completion.
Now: Submit task → Use other platform features → System message updates task status → Decide next steps based on completion or exception messages.

This adjustment does not change the execution logic of the extraction tasks themselves but reduces the need for users to repeatedly refresh pages to confirm status.

Value for Batch Knowledge Processing

Enterprise knowledge governance often involves processing more than one task at a time. For example, during centralized knowledge base construction, there might be:

Multiple unstructured document extraction tasks.
Multiple batches of structured data extraction.
Content processing occurring simultaneously in different knowledge bases.

As the number of tasks increases, relying solely on users to actively check can lead to missed completions or unnoticed exceptions until the data is actually used. Unified message notifications help shorten the time gap between a task status change and user awareness.

Note: While message notifications improve status awareness, they do not replace task exception diagnosis. If a task fails, specific causes must still be located using task details, logs, and actual file or data content.

02. New kkFileView Configuration for File Preview

In a knowledge platform, file management usually involves more than just uploading and parsing. Users often need to view file content directly. During knowledge governance, it is frequently necessary to confirm:

If the uploaded file is correct.
If the file content is complete.
Which material is currently being processed.
If the file content matches the knowledge base classification.

Therefore, although file preview is a basic function, it directly impacts the efficiency of knowledge material management.

Limitations of Original Preview Addresses

Previously, qKnow file preview used the address provided by the platform. However, in actual deployment, network environments vary significantly across enterprises. For example, some clients use:

Enterprise intranet deployment.
Isolated network deployment.
Custom network access environments.

In these scenarios, the original preview address might fail to display files correctly due to different network access conditions. The core issue is not that the file itself cannot be read, but that the file preview capability needs to match the enterprise's actual deployment environment.

System Deployment Configuration Adds kkFileView

To address this, qKnow Professional Edition v3.1.3 adds kkFileView configuration to the system deployment settings and synchronizes the file preview address. Users can complete the configuration for file preview capabilities by enabling the corresponding configuration when deploying the platform.

From a deployment logic perspective, this can be understood as:
Deploy qKnow → Configure and Start kkFileView → Platform Uses Corresponding Preview Service → View Knowledge Files

This provides a clearer configuration entry for file preview methods to align with the actual deployment environment.

Adapting to Intranet Deployment Scenarios

For enterprise clients, the actual environment is often not a uniform public network deployment. Especially in sectors like government, manufacturing, water conservancy, and energy, platforms are frequently deployed on enterprise servers or internal networks. Therefore, whether the preview service can be configured alongside the deployment environment directly affects the usability of knowledge file management.

The value of this update lies in incorporating the previously relatively fixed file preview method into the platform's deployment configuration system.

Note: Adding the kkFileView configuration does not mean all preview issues will automatically disappear. Actual deployment still requires ensuring the corresponding service starts correctly and meets basic conditions for servers, network access, and permissions.

03. Homepage Notifications Moved to Parameter Management

In addition to knowledge extraction and file preview, this version also adjusts the control method for homepage notifications.

Previously, the display logic for homepage notifications was configured in the system's front-end configuration file. If an enterprise wished to hide homepage notifications, they had to:

Locate Front-end Config File → Modify Configuration → Restart Project

The issue with this approach is that a page display requirement, which is essentially a simple switch, required entering the technical configuration layer. This is inconvenient for general platform administrators.

Homepage Notification Switch Enters Parameter Management

qKnow Professional Edition v3.1.3 migrates homepage notification-related parameters to Parameter Management. Users can now directly control the homepage notification via parameter data:

Show
Hide

There is no longer a need to modify the front-end configuration file and restart the project every time.

From Code Configuration to Platform Parameter Configuration

Before: Modify front-end config file → Save config → Restart project → View page changes.
After: Enter Parameter Management → Adjust corresponding parameter → Control homepage notification visibility.

This effectively transforms an action偏向 "system development
configuration" into "platform runtime parameter management," reducing the maintenance cost for simple display adjustments.

While this change does not involve knowledge extraction algorithms or core knowledge base capabilities, it reduces frequent but unnecessary technical maintenance operations for long-running platforms.

04. What Problem Do These Three Optimizations Solve?

Viewing these three features together, they address different points in the usage of the knowledge platform:

Extraction Task Message Notifications solve the issue of not knowing where a submitted task is executing. It shifts task status from "requires active refresh to view" to "system actively reminds."
kkFileView Preview Configuration solves the issue of files being in the platform but unable to be previewed in certain enterprise deployment environments. By adding a deployable preview service configuration, file viewing becomes more adaptable to the enterprise's actual operating environment.
Homepage Notification Parameter Management solves the issue of a simple page notification switch requiring code configuration changes and project restarts. Through parameter management, this technical configuration operation is further transformed into platform configuration.

Therefore, while these three adjustments belong to tasks, files, and system parameters respectively, they point in the same direction: reducing the need for extra manual operations, repeated confirmations, and technical intervention in the knowledge governance process.

Conclusion

Once knowledge governance capabilities enter the stage of continuous use, users care about:

Where is the task execution?
Can I know immediately if an exception occurs?
Can files be previewed normally in our specific deployment environment?
Do page display requirements require finding a technician to modify configuration every time?

qKnow Professional Edition v3.1.3 makes adjustments primarily around these questions:

On the knowledge extraction side: Structured and unstructured extraction tasks add message notifications, allowing the system to automatically update task information based on executing, completed, or exception states.
On the file management side: System deployment configuration adds kkFileView and adjusts the file preview address, making file preview capabilities further adaptable to different enterprise deployment environments.
On the platform configuration side: Homepage notification control is migrated from front-end configuration files to parameter management, allowing users to directly control visibility via parameters, reducing maintenance costs associated with modifying configuration files and restarting projects.

These adjustments do not change the core business logic of enterprise knowledge governance, nor do they replace task log analysis, network environment configuration, or system operation and maintenance management. However, for an enterprise agent platform that needs to run for the long term, what truly affects the user experience is often not just whether core functions exist, but also:

Whether tasks can provide timely feedback.
Whether files can be viewed normally.
Whether configurations can be adjusted conveniently.

The qKnow Professional Edition v3.1.3 upgrade continues to supplement these basic but high-frequency links, making the usage chain from extraction and viewing to daily management of enterprise knowledge smoother.