DEV Community: TongWu

Open Source for a Digital Water Ecosystem: Qiantong Tech's Smart Water Projects Are Now Live on Gitee

TongWu — Thu, 20 Aug 2026 02:52:24 +0000

As smart water conservancy initiatives deepen, the industry is shifting from traditional information management to a model of digital, intelligent collaboration.

This transition has created a growing demand for reusable technical components, standardized business capabilities, and an open, collaborative ecosystem.

Building on years of technical expertise and project experience in the smart water sector, Qiantong Technology is officially open-sourcing key digital water application on the Gitee platform.

Our goal is to collaborate with developers and water conservancy experts to explore a more open and efficient model for building smart water solutions.

The two open-source projects now available are:

Modern Reservoir Operation and Management Matrix Platform
Water Conservancy "One Map" Platform

These projects focus on two core areas—reservoir operation management and spatial business applications—and encapsulate reusable software architectures and business practices to serve as a reference for digital water system development.

Why Open Source?

Digital transformation in the water industry is complex, with high professional requirements and diverse application scenarios.

While specific needs vary by region and organization, there is significant commonality in foundational capabilities, such as:

Engineering operation status management
Spatial visualization of water objects
Multi-source data fusion
Business information querying
Comprehensive situation analysis

By open-sourcing these proven foundational capabilities, we aim to help developers and industry partners reduce redundant development efforts and focus their energy on business innovation and application deepening.

Qiantong Technology hopes that through open source, we can make technology more transparent, share practical experience, and foster greater collaboration in digital water construction.

Project 1: Modern Reservoir Operation and Management Matrix Platform

This platform is designed for reservoir operation scenarios, building digital management capabilities for engineering operations.

As a critical part of the water conservancy system, reservoirs play vital roles in flood control, water supply, irrigation, and ecology.

Traditional management methods relying on manual inspections and scattered records are increasingly unable to meet the needs of refined operation and management.

The Modern Reservoir Operation and Management Matrix Platform unifies engineering basic information, operation monitoring data, and management business processes to help build a more systematic digital management system for reservoirs.

Key capabilities include:

Reservoir Basic Information Management: Establishes a unified data management system for reservoir engineering objects, supporting the centralized management of basic reservoir information, engineering facility data, management unit details, and related business materials.

Comprehensive Operation Status Display: Aggregates reservoir operation data for a unified view of the engineering status, including water level information, storage capacity changes, operational status, and monitoring data.

Matrix-based Business Management Model: Aligning with modern reservoir management requirements, the platform organizes different management objects, business tasks, and operational indicators.

This matrix approach enables traceable engineering objects, manageable business items, and analyzable operational status.

Project 2: Water Conservancy "One Map" Platform

Built on spatial information, this platform creates a unified visual entry point for water conservancy operations. Water conservancy work is inherently spatial.

Rivers, reservoirs, dikes, sluices, and monitoring stations are distributed across different regions, and traditional systems often suffer from fragmented data and disjointed displays.

The Water Conservancy "One Map" Platform uses GIS capabilities to map various water objects and business data onto a unified map, enabling visual management of water resources.

The platform provides:

Spatial Display of Water Objects: Supports map-based visualization of different water objects, including rivers, reservoirs, dikes, sluices, pumping stations, and monitoring stations, helping users quickly grasp the distribution of regional water resources.

Multi-source Data Fusion and Display: The platform integrates business data with spatial information. For example, clicking on a water intake monitoring point on the map reveals corresponding engineering information, real-time monitoring data, management details, and historical records.

Extensibility for Business Applications: More than just a display tool, the "One Map" serves as a foundational capability for business applications. By offering open map services and component capabilities, it can further support flood control situation analysis, engineering inspection management, water resource supervision, and comprehensive business displays.

Open Source Project Positioning: Opening Foundational Capabilities to Serve Industry Practice

These two projects are not simple copies of a complete commercial system but an opening of technical capabilities refined through project practice. We hope developers can use these projects to:

Learn about digital water system construction approaches
Understand the digital implementation of water conservancy business
Explore more industry application scenarios

We also welcome water conservancy experts and the developer community to participate in project construction, offering suggestions for technical optimization and business improvement.

About the Projects

The Modern Reservoir Operation and Management Matrix Platform and the Water Conservancy "One Map" Platform are now live on Gitee. Developers and water industry partners are welcome to follow the projects and exchange ideas on digital water technology practices.

Gitee Project Address: https://gitee.com/qiantong-smartwater

Open source is not an end point, but a way of continuous communication and co-construction.

We hope to work with industry partners to promote the accumulation and innovation of digital water capabilities through open technology practices, enabling more reliable and reusable technical capabilities to serve smart water conservancy construction.

qData Professional Edition: Full-Link Data Lineage Now Connects Database Sync, Data Development, and API Nodes

TongWu — Thu, 20 Aug 2026 02:52:18 +0000

As enterprise data scales and pipelines grow more complex, data flows are becoming increasingly difficult to track.

Business data often originates from multiple systems, undergoes synchronization, integration, and processing to become data assets, and is ultimately consumed via API services. However, as these pipelines extend, enterprises face significant management challenges:

When anomalies occur in a data table, quickly locating the data source becomes difficult.
When data logic is adjusted, it is hard to accurately determine which downstream tasks and business applications will be affected.
After passing through multiple synchronization and processing stages, relying solely on table relationships makes it difficult to reconstruct the complete processing history.
Once data is consumed by business systems via API services, tracking these consumption relationships becomes a challenge.

Traditional lineage management focuses merely on "relationships between data tables," but enterprises need answers to: Where does the data come from? How was it processed? Who ultimately uses it?

This drives the evolution of data lineage from simple relationship management to comprehensive lifecycle tracking covering production, processing, assetization, and service delivery.

From Source to Service: qData Builds a Full-Link Lineage Management System

In an enterprise data platform, data rarely moves directly from the source to business use. It typically goes through multiple stages:

Raw data is generated by business systems and enters the platform via data connections, completing ingestion through full-database synchronization and data integration.

After entering the ODS layer, data development tasks perform cleaning, transformation, and processing to form DWD, DWS, and ADS data assets. Finally, data is provided to business systems via API services.

Therefore, lineage must go beyond simple "Source Table → Target Table" relationships.

qData integrates data tables, full-database sync tasks, data integration tasks, data development tasks, and API services into a unified lineage system, forming a complete chain: Business System → Data Connection → Full-Database Sync / Data Integration Task → ODS Table → Data Development Task → DWD/DWS/ADS Table → API Service.

Four Core Capabilities of qData Lineage

Lineage Map: Transforms invisible pipelines into visual flows. It covers four key nodes: Full-Database Sync Lineage (Source Table → Sync Task → Target Table), Data Integration Lineage (Input Table → Integration Task → Output Table), Data Development Lineage (Upstream Table → Development Task → Downstream Table, automatically parsing complex SQL), and API Service Lineage (Data Table/Asset → API Service), tracking data all the way to the consumption end.

Lineage Maintenance: Combines automatic generation with manual supplementation to adapt to complex enterprise environments (e.g., historical or external data), ensuring completeness while reducing maintenance costs.

Source Analysis: Traces data upstream (Source → Sync → Integration → Development → Current Data) to quickly locate the origin of anomalies.

Impact Analysis: Evaluates downstream impacts before data changes, including associated tasks, downstream tables, assets, and API services, reducing uncertainty.

Key Upgrade: Completing the Full-Link Lineage

This upgrade focuses on adding two critical relationships: Full-Database Sync Lineage and API Service Lineage. This is not merely adding nodes to a graph but formally integrating these elements into the unified lineage system.

Full-Database Sync Lineage Upgrade:
Previously, the sync process lacked intuitive display. Now, full-database sync tasks act as independent nodes (Source Table → Sync Task → Target Table).

Users can view task details (name, type, status, scheduling, project, results, I/O tables) directly in the lineage map. This integrates with maintenance, source analysis, and impact analysis, making task-level tracking transparent.

API Service Lineage Upgrade:
As data becomes service-oriented, lineage must track "who uses the data." API services are now formally integrated (Data Table/Asset → API Service). Users can view API details (name, version, URL, method, status, parameters, return fields) and trace upstream dependencies.

While API services are the downstream endpoint, upstream impact analysis will show which APIs depend on a specific table, allowing teams to assess risks before altering ADS table structures.

Upgrade Value

This upgrade completes the tracking from data generation to service consumption. Full-database sync lineage clarifies task-level data flow, while API service lineage extends tracking to the business service side.

The expanded coverage (Business System → Data Connection → Sync/Integration → ODS → Development → DWD/DWS/ADS → API Service) provides a complete data flow view. When anomalies occur, users can trace upstream; before changes, they can assess downstream impacts.

qData Data Platform: Covering the Entire Data Lifecycle

qData Professional Edition covers the entire lifecycle: Data Ingestion → Modeling → Development → Governance → Assets → Services → Applications.

Lineage acts as the connective tissue throughout this lifecycle, linking ingestion sources, processing tasks, asset relationships, and API service consumption.

This full-link upgrade helps enterprises build a clearer, more transparent, and manageable data system, turning data into truly understandable, governable, and sustainable enterprise assets.

qModel Open Source Closes the Loop: Algorithm Models Finally Move from "Integration" to "Active Use"

TongWu — Thu, 20 Aug 2026 02:52:12 +0000

In enterprise and research projects, businesses rely on numerous traditional algorithm models, industry-specific models, and lightweight computational models scattered across different teams.

These may be Python scripts or HTTP APIs used for predictive analysis, risk assessment, classification, and simulation. However, as model numbers increase, enterprises face new management challenges: models are scattered across personal computers and business systems, input parameters rely on manual documentation, and execution tracking is incomplete.

Models exist but haven't formed manageable, reusable algorithm assets. While past platforms solved "model integration," configuring, publishing, and tracking still required heavy manual effort.

Enterprises need a complete engineering workflow. qModel Open Source Edition achieves exactly this, extending from model integration to active utilization.

What is qModel?

qModel is an open-source platform centered on full lifecycle model management.

It targets traditional, industry-specific, and lightweight computational models for unified archiving, integration, publishing, computation, and tracing. "Models" here do not specifically refer to LLMs, but rather to the massive number of algorithms with clear business goals.

Examples include prediction algorithms, hydrological calculation logic, data interpolation programs, risk assessment models, and independently deployed API services.

The core problem qModel solves is not simply "storing models," but transforming scattered algorithms into manageable, iterable, callable, and governable assets.

Currently supporting Python scripts and API interfaces, qModel connects the engineering chain: Model Archiving → Integration → Parameter Definition → Publishing Governance → Computation Execution → Result & Tracing. Regardless of their initial technical form, models entering qModel enter a unified governance and computation system, transitioning from technical files to platform assets.

What Does a Complete Model Loop Entail?

The complete engineering loop can be summarized into four actions: Integrate, Manage, Utilize, and Trace.

Integrate: Resolve model identity, integration methods, and input rules.
Manage: Models must undergo publishing applications, approvals, and governance before entering formal business.
Utilize: Users select published models, bind input parameters, and create computation tasks executed by corresponding engines.
Trace: The platform retains task status, parameters, outputs, execution time, error messages, and logs for post-execution analysis.

What Do the Six Stages Do?

01 Model Archiving:

Establishes a unified model profile before execution. It supports hierarchical classification (by industry, business domain, or task type) and maintains basic info (name, unique code, version, author, tags).

Crucially, it determines whether the model is an API or Python script, defining the subsequent technical path.

02 Model Integration:

Solves how the model runs.

For API Models, integration is achieved via configuration and testing. Users configure the endpoint, request method, timeout, and authentication (fixed token or dynamic interface). A real request test validates connectivity before saving.

For Python Models, integration is achieved via upload, validation, and dependency building. The platform checks for main.py, requirements.txt, and a standard predict function.

It extracts the package, parses dependencies, and attempts installation. Build logs are retained for troubleshooting. Both paths convert algorithms into manageable platform assets.

03 Parameter Definition:

Clarifies how the model is called. Using JSON Schema, users define parameter names, data types, required status, and default values.

This establishes a unified input contract between the model and users, replacing informal documentation with structured, verifiable rules.

04 Publishing Governance:

Determines if the model can be officially used. "Integrated" (technically runnable) is distinct from "Published" (formally approved).

Users submit publishing requests, and admins approve or reject them, creating a clear lifecycle (Integrated → Under Review → Published/Rejected → Offline → Offline). Additionally, a Key Management module provides API Key authentication for third-party system calls.

05 Computation Execution:

Turns models into results. Users create tasks configuring names, parameters, priority, timeouts, and retries. Tasks enter a Redis priority queue and are processed by execution engines.

API models trigger HTTP requests, while Python models launch independent processes. Both share a unified task, status, and result system. Tasks follow a clear state flow (Queued → Running → Success/Failure/Terminated) with timeout control, failure retries, and dead-letter queues ensuring reliability.

06 Results and Tracing:

Answers what happened during a run. Post-execution, the platform records status, outputs, timing, errors, and logs. Results can be viewed as raw JSON, structured trees, or visualized via Base64 images and line charts.

Every run creates an independent execution record and call history (including client IP and resource usage for Python models), enabling precise troubleshooting without rerunning the model.

The Relationship Between the Six Stages

These stages form a continuous data relationship centered on the model. They answer: Who is the model? How does it run? How is it called? Can it be used? How are results generated? What happened during this run?

In short: Classification organizes, configuration runs, parameters define, approval governs, tasks compute, and records trace. This forms a continuous engineering loop: Integration → Governance → Usage → Result → Trace → Adjustment → Re-use.

What Does a Complete Loop Mean for Enterprise Algorithm Platforms?

For Algorithm Assets: Transitions from scattered files to unified management with consistent identities.
For Engineering Implementation: Moves from experimental outcomes to controlled usage, utilizing connectivity tests, dependency builds, and asynchronous execution with reliability mechanisms.
For Business Users: Unifies the upper-layer usage experience regardless of underlying technical differences (HTTP vs. Python), reducing repeated integration costs.
For Platform Governance: Provides clear status transitions, auditable approval records, and detailed execution logs for easier problem localization.
For qModel Open Source: Delivers a "Minimum Viable Engineering Loop." It focuses on the foundational ability to integrate, manage, use, and trace models. Advanced features like auto-containerization, visual workflow orchestration, and model marketplaces are reserved for the commercial edition, aligning with practical platform construction paths.

Conclusion: From "Model Files" to "Sustainable Algorithm Assets"

The true difficulty for enterprise algorithm platforms is ensuring models can be uniformly integrated, clearly defined, formally governed, stably executed, and quickly traced.

qModel Open Source connects model identity, runtime, input rules, publishing qualification, computation, and results into a continuous engineering chain.

When an algorithm model can be uniformly managed, stably called, continuously reused, and thoroughly recorded, it evolves from a Python file or API address into a sustainable enterprise algorithm asset.

Moving from "integration" to true "active use" is the core significance of qModel Open Source's completed engineering loop.

qKnow Professional Edition v3.1.2 Released: Seamless HDFS/OSS Integration for Instant Massive Data Ingestion

TongWu — Thu, 20 Aug 2026 02:52:06 +0000

The qKnow Agent Building Platform Professional Edition v3.1.2 is now live, featuring significant optimizations to knowledge file data synchronization.

This release introduces one-click import capabilities for third-party storage solutions, including HDFS, OSS, and FTP, further streamlining the data ingestion process for both knowledge bases and knowledge graphs.

By enhancing data access efficiency, this update ensures that files scattered across various storage systems can be seamlessly integrated into qKnow’s processing workflows.

The Value of Third-Party Storage Synchronization

In real-world enterprise scenarios, knowledge files are rarely centralized.

Large-scale business documents often reside in distributed file systems, cloud-based materials are stored in object storage platforms, and historical files are typically managed via FTP.

Traditionally, integrating these into a knowledge base required a cumbersome process: downloading from third-party storage, organizing locally, and manually uploading for parsing. This approach increased manual labor and delayed data updates.

Version 3.1.2 eliminates this friction by establishing direct connections between external storage and the knowledge base, significantly reducing operational complexity.

Core Capability Upgrades

Knowledge Base Third-Party Storage Sync:
The new release introduces an OSS synchronization feature for knowledge documents. Users can directly import files from third-party storage into the knowledge base via the document management page.

The process involves two simple steps:

Configure Data Connection: Select the storage type (HDFS for large-scale distributed storage, FTP for batch file reading, or Alibaba Cloud OSS for cloud data) and enter the connection details.

Select Files for Sync: Once connected, the system reads the external file list, allowing users to select specific files for direct import, eliminating redundant data transfer.

External File Sync for Knowledge Graph Unstructured Extraction:
Beyond the knowledge base, v3.1.2 enhances the unstructured extraction workflow for knowledge graphs.

A new OSS sync button has been added to the extraction module. Users can connect to third-party storage and select files for knowledge extraction using the same streamlined process, laying a solid data foundation for knowledge graph construction.

Optimization & Bug Fixes

Alongside new synchronization features, this release includes several experience enhancements:

Fixed an issue where the model dropdown in relationship configuration displayed incomplete data, improving selection usability.
Enhanced knowledge graph model management by allowing concepts with identical names to be created across different graph models, increasing design flexibility.
Improved the display of model classification titles on the large model configuration page for clearer structural organization.
Increased the stability of unstructured extraction tasks; isolated task failures no longer interrupt the execution of other tasks.
Optimized blank paragraph handling during extraction, skipping empty segments to reduce invalid processing and improve overall efficiency.

Release Value

qKnow Professional Edition v3.1.2 focuses on strengthening data ingestion for knowledge bases and graphs. By supporting synchronization with HDFS, OSS, and FTP, it enables seamless integration of distributed knowledge files into the agent building process.

This upgrade delivers value across three main areas: reducing knowledge data ingestion costs, completing the knowledge construction data pipeline, and enhancing overall platform stability.

Moving forward, qKnow will continue to optimize capabilities around knowledge enhancement, agent building, and enterprise-level knowledge management, driving greater efficiency across the entire lifecycle from data ingestion and comprehension to practical application.

Battling Typhoon "White Dolphin": Real-World Applications of Water AI and Digital Twins Behind the Smart Defense Line

TongWu — Thu, 20 Aug 2026 02:51:58 +0000

Every typhoon landfall serves as a comprehensive test for a river basin's flood control system.

Heavy rainfall brings not only short-term precipitation pressure but also a chain of impacts, including rising river levels, increased reservoir dispatch pressure, heightened flash flood risks, and urban waterlogging hazards.

Faced with the complex meteorological processes of Typhoon "White Dolphin," water management departments must quickly answer a series of critical questions within a limited timeframe:

What is the current overall status of the river basin?
Which areas might be affected?
How will water levels change in the coming hours?
Can existing engineering dispatches meet flood prevention needs?
How can risk prevention be proactively implemented using limited information?

Behind these questions lies a comprehensive test of water sensing, analysis, and decision-making capabilities.

As the construction of digital twin river basins advances, a new smart water system centered on data fusion, model computation, and artificial intelligence is helping traditional flood prevention transition from "real-time monitoring" to "predictive simulation and decision support."

The Data Challenge Behind a Typhoon: The Need for a Comprehensive "Basin Perspective"

In traditional flood prevention, staff must synthesize information from multiple sources during typhoon weather, including meteorological rainfall forecasts, real-time hydrological data, engineering operation statuses, historical flood data, and geographic spatial information.

However, in practice, these datasets are often scattered across different systems with varying formats and update frequencies.

When a typhoon rapidly impacts an area, relying solely on manual information aggregation can lead to delayed data retrieval, difficulties in multi-source correlation analysis, risk assessments heavily dependent on experience, and a lack of simulation capabilities for future changes.

Therefore, modern flood prevention requires not just "seeing the present," but the ability to analyze the future, which is exactly why digital twin river basins have emerged as a crucial technological direction.

From "Seeing Risks" to "Simulating Risks": Digital Twins Enable Dynamic Basin Analysis

A digital twin river basin is not merely displaying water data on a screen; it builds a digital space corresponding to the real basin, enabling data interaction between the physical and digital models.

During Typhoon "White Dolphin," the digital twin system integrates multi-dimensional data (meteorological, hydrological, engineering, and spatial) to form a complete picture of the basin's status.

For example, if the meteorological system predicts sustained heavy rainfall, the system can combine current river levels, basin topography, reservoir storage, river channel flow capacity, and historical flood data to analyze potential water conditions.

Managers can move beyond asking "Where has the risk already occurred?" to "Which areas might face risks?", "How might the risk develop?", and "What proactive measures should be taken?"

This represents the core value of digital twin river basins over traditional IT systems.

Digital Twin Practices in Typhoon Defense: Letting Data Drive Dispatch Decisions

During extreme weather, the core of water management is not displaying more data, but using data to support more scientific business judgments. The digital twin river basin plays three main roles in flood prevention:

*Real-time Fusion of Rain and Water Conditions: *

Typhoon impacts feature rapid rainfall changes and uneven spatial distribution. Single monitoring points cannot reflect the entire basin.

By integrating meteorological, hydrological, and engineering data, the system forms a dynamic sensing network that continuously updates rainfall distribution, river levels, reservoir statuses, and key risk areas, helping managers quickly grasp the overall situation.

Model Simulation Analysis:

Future water conditions are critical for decision-making. By combining hydrological and hydrodynamic models, the digital twin simulates water flow changes under different rainfall conditions.

It helps analyze river level trends, potential impact areas, and engineering dispatch impacts, providing a reference for flood control scheduling. It is important to note that model analysis does not replace manual decision-making but provides comprehensive data support.

*Coordinated Engineering Operation: *

Reservoirs, sluices, and pump stations play vital regulatory roles.

The digital twin system integrates their operational status into a unified management framework, allowing managers to understand current operations, available dispatch resources, and the potential impacts of different dispatch plans, shifting from individual engineering management to basin-wide coordinated dispatch.

How AI Further Elevates Smart Water Flood Prevention

As AI technology develops, it is becoming a crucial supplement to digital twin river basins, enhancing data analysis efficiency in typhoon defense:

*Intelligent Anomaly Recognition: *

By analyzing historical and real-time data, AI helps identify abnormal water level rises, rainfall changes, and engineering operation anomalies, helping staff locate areas of concern faster.

*Intelligent Judgment Support: *

Facing massive monitoring data, AI extracts key indicators, quickly summarizes the current basin status, analyzes similar historical weather events, and assists in generating risk assessment information, reducing manual workload and improving response efficiency.

Smart Water Knowledge Services:

Combining knowledge bases with intelligent Q&A, staff can conveniently query historical flood cases, engineering operation rules, and business materials, making water experience and knowledge more efficiently utilized.

Future Directions of Smart Water: Lessons from Typhoon Response

Extreme weather events like Typhoon "White Dolphin" remind us that water safety management faces a constantly changing complex system.

Future smart water development requires not only more monitoring equipment but also enhanced capabilities in:
Data Fusion: Unifying scattered data resources into a cohesive understanding.
Model Analysis: Equipping systems with predictive and simulation capabilities.
Artificial Intelligence: Making data analysis more efficient and intelligent.
Business Collaboration: Ensuring technology truly serves flood reduction, water resource management, and engineering operations.

The value of a digital twin river basin does not lie in building a "virtual world," but in integrating data, models, and algorithms within real-world water business.

This empowers managers to discover problems earlier, analyze trends more accurately, and formulate measures more scientifically.

Facing increasingly complex future climate environments, digital technology is becoming a vital support for enhancing risk prevention in the water industry, building a more reliable smart defense line for basin safety.

qModel OSS v1.4.0: One-Click API Publishing & Full-Link Computing Tasks Bring Models to Life

TongWu — Thu, 13 Aug 2026 06:57:52 +0000

In traditional algorithm development workflows, models often get stuck in the internal debugging phase. They are "runnable," but not quite ready for production.

Common bottlenecks include:

Deployed models can only be called within the platform, making external business integration difficult.
Computing task management is fragmented, lacking centralized control over execution and results.
Missing execution records make troubleshooting and task tracking costly.
Model outputs are displayed as raw data, lacking intuitive visualization for complex results.

To bridge the gap from development to practical application, qModel Algorithm Model Platform Open Source v1.4.0 is officially released!

This update focuses on model service invocation, computing task management, execution tracking, and result visualization, completing the workflow from model configuration to result analysis.

Here is a technical breakdown of the core capability upgrades in v1.4.0.

One-Click API Service Publishing

qmodelv1.4.0 introduces the ability to publish model API services.

For already deployed models, users can quickly expose capabilities to third-party systems via remote API calls.

The platform provides comprehensive API invocation information, including Base URL configuration, interface path definitions, authentication method descriptions, and parameter details.

Users can combine the Base URL with specific interface paths to generate complete call addresses.

Additionally, API keys can be centrally viewed and managed through the key management module, enhancing security and maintainability during invocation.

Brand-New Model Computing Task Management

To improve the manageability of the model running process, qModel v1.4.0 adds a dedicated model computing task list. The platform uniformly displays all computing task information, including running status, execution duration, basic task details, and search/management capabilities. Through centralized task management, users can gain a more intuitive understanding of current model computing operations, significantly reducing management complexity.

Visual Task Creation with Flexible Strategy Configuration

During the computing task creation process, v1.4.0 supports visual task configuration. Users can associate existing models, configure task execution strategies, and set computing parameters for different business scenarios. This ensures that the model invocation process is no longer dependent on fixed workflows but can be flexibly adjusted according to actual computing needs.

Unified Input Parameter Management

On the computing task details page, the platform now displays input parameters (Tags). Input information configured during task creation is presented uniformly, helping users quickly confirm the data parameters used, the model invocation context, and the task execution configuration. This further enhances task comprehensibility and troubleshooting efficiency.

Complete Execution Records for Traceability

qModel v1.4.0 fully records the model invocation process. The platform saves every execution history, displaying call time, execution status, and task running information in the execution record list. This allows users to quickly trace historical tasks, forming a complete model computing tracking chain.

Execution Details Combined with Resource Monitoring

After a computing task is completed, the platform displays the model's output results in a structured format, such as JSON. Simultaneously, qModel's built-in resource monitoring probes collect hardware resource usage during execution. Currently, it supports displaying average CPU usage, peak memory consumption, and execution duration. These resource metrics help developers further analyze model running status, providing references for performance optimization and resource scheduling.

Visual Display for Model Computing Results

Addressing the issue of monotonous output display, v1.4.0 enhances result visualization. On the result display page, the left side shows the JSON data structure returned by the model, while the right side visually renders the parsed JSON data.

Currently supported visualization components include Base64 image parsing and line charts. Users can add corresponding display components based on the output data structure and configure the component name, type, and bound field keys for intuitive result presentation. Configured components also support subsequent adjustments, including modification, deletion, and downloading.

The Bottom Line

qModel OSS v1.4.0 focuses on perfecting the application chain from "runnable" to "callable, manageable, and analyzable." This upgrade reduces external integration costs through API publishing, refines the running process via computing task management, enhances traceability with execution records and resource monitoring, and improves output comprehension through result visualization. Moving forward, qModel will continue to iterate around algorithm model engineering management and intelligent computing process optimization, helping developers manage and apply algorithm model capabilities more efficiently.

MLOps #MachineLearning #DataScience #OpenSource #qModel #API #Algorithm

What Data Sources Does qData Support? A Complete Guide to Database Integration

TongWu — Thu, 13 Aug 2026 06:57:46 +0000

When building a data platform, the first step isn't usually data modeling—it's getting your databases connected.

If a database can't connect, the connection type is mismatched, or account permissions are incomplete, it will directly block downstream data integration, development, metadata harvesting, and querying.

qData Open Source Data Platform provides a unified data connection management entry under the "Data Development" menu. Administrators can add, test, enable, or disable connections here.

This guide will walk you through qData's data connection capabilities, focusing on three key questions:

What databases are currently supported?
How to configure connection parameters for different databases?
How to test connections and troubleshoot issues after saving?

1. What Data Sources Does qData Currently Support?

qData Open Source currently supports direct configuration for 8 database connections: MySQL, DM8, Oracle, Oracle11, SQL Server2008, SQL Server, Kingbase8, and Doris.

These types cover common open-source relational databases, commercial databases, domestic databases, and analytical databases.

Important Note: qData provides separate connection types for specific database versions (Oracle vs. Oracle11, and SQL Server vs. SQL Server2008).

Always select the exact version matching your source database to avoid driver or protocol mismatches.

2. What Parameters Are Required to Add a Database Connection?

Navigate to "Data Development > Data Connections" and click "Add" at the top left.

While form fields vary slightly, you will need to provide the following basic information:

Connection Name: Used to identify the connection. We recommend a naming convention like "System Name - Environment - Database" (e.g., Order System - Prod - MySQL) to aid future search and maintenance.
Connection Type: Select the exact database and version. Do not use a similar version as a substitute.
IP: The database server address. Crucially, ensure the qData deployment server can access this address, not just your local machine.
Port: The actual port the database service is listening on. Use the real port if it has been customized; do not rely solely on defaults.
Username & Password: Create a dedicated database account following the principle of least privilege.
Database Name: Specifies the target database to access.
Description: Optional. Record the data source, connection purpose, and responsible department.
Status: Defaults to "Disabled." We recommend saving the connection, testing it, and only enabling it after confirming the configuration is correct.
Remarks: Optional. Record network zones or maintenance personnel, but never store plaintext passwords here.

3. How Do Database Fields Differ?

While MySQL and DM8 primarily require the database name, Oracle and SQL Server require an additional mandatory field: Schema Name.

Oracle / Oracle11:

The Schema Name is not the server IP or database name; it is the Schema used to organize database objects (tables, views). It is typically the same as the object owner's username.

For example, if business tables belong to the QDATA_APP user, the Schema Name should be QDATA_APP. If the connection account differs from the object owner, you must enter the actual Schema holding the tables and ensure the connection account has access permissions.

Misconfiguring this usually results in a failed connection test or an inability to find target tables during metadata sync. Before connecting, confirm the database name, login account, target Schema, and authorization scope with your DBA.

SQL Server / SQL Server2008:

The Schema Name is also mandatory. While dbo is the common default, business systems often use custom schemas like sales, ods, or report.

Always use the actual Schema to which the target table belongs. A successful login does not guarantee access to objects within the target Schema.

Confirm the database name, Schema name, login account, and authorization scope before configuration.

4. Reference Guide for Common Database Parameters

Below is a reference for configuring common databases. Note that ports listed are typical defaults; always use the actual port in your environment.

MySQL: Default port is 3306. Key field: Database Name. Ensure the account allows remote login from the qData server and has read/sync permissions.
DM8: Default port is 5236. Key field: Database Name. Verify the instance listening address, port, and account permissions.
Oracle / Oracle11: Default port is 1521. Key fields: Database Name, Schema Name (Mandatory). Select the type based on the source version; Schema usually matches the user Schema.
SQL Server / SQL Server2008: Default port is 1433. Key fields: Database Name, Schema Name (Mandatory). Ensure TCP/IP is enabled and the instance port is accessible. Common Schema is dbo.
Kingbase8: Common port is 54321. Key fields: Refer to the page after selecting the type. Verify network policies, driver versions, and actual listening ports.
Doris: Common port is 9030 (MySQL protocol). Key fields: Refer to the page after selecting the type.

Usually connects to the FE query port; do not mistakenly use the HTTP management port.

Disclaimer: This reference is for pre-configuration checks. Production ports, network policies, and permissions should always be verified by your DBA.

5. Understanding Connection Parameters (MySQL Example)

When connecting to a MySQL business database, select MySQL as the type, enter the accessible IP/domain, the actual listening port, the target database name, and a valid account.

Container Environments: If qData and MySQL communicate within a container network, use the internal port (usually 3306).

If accessing via the host machine, use the externally mapped port. Never use 127.0.0.1 or localhost, as these typically point to the qData container itself.

6. How to Test a Connection After Saving

After saving, locate the connection in the list and click "Test Connection" on the right.

Only enable the connection after seeing the "Database connection successful" prompt.

Note: A successful test only proves qData can access the database with the provided credentials. It does not guarantee the account has permissions for all downstream tasks (metadata sync, data querying, etc.).

Can I skip manual testing? Manual testing is not strictly mandatory. Togg the status switch to "Enable" will trigger an automatic validation.

However, from an operational perspective, we highly recommend manually testing first to quickly identify parameter, network, or account issues.

7. Troubleshooting Failed Connection Tests

If the test fails, follow this troubleshooting sequence:

Verify Parameters: Double-check the IP, port, database name, Schema name, username, and password.
Check Service Status: Ensure the database service is running and listening on the specified network interface and port.
Check Network Policies: Verify that firewalls, security groups, or ACLs allow the qData server to access the database.
Check Account Status: Confirm the password is valid, the account isn't locked, remote login is allowed, and the account has the necessary permissions.
Verify Database Type/Version: Ensure you haven't selected Oracle for an Oracle11 database, or SQL Server for SQL Server2008. Mismatches cause driver/protocol issues.
Check Doris Ports: For Doris, ensure you are using the FE query protocol port, not the Web management port.

Special Reminder: Connection requests originate from the qData server. Just because your local machine can connect does not mean the qData server has the same network access.

8. Where Are Data Connections Applied?

Once saved and validated, a data connection serves as a unified entry point for multiple platform scenarios:

Data Integration: Used to specify the read/write endpoints. Centralized maintenance eliminates the need to repeatedly enter database addresses and credentials across different tasks, reducing management overhead when passwords or ports change.

Data Development: SQL tasks rely on connections to access target databases. The configured Database and Schema names dictate which tables and views developers can discover. Incorrect Schema configurations for Oracle/SQL Server will hide target tables even if the connection is active.

Metadata Harvesting: Reads structural information (databases, schemas, tables, columns). The connection account must have permissions to read system catalogs; otherwise, metadata harvesting will be incomplete.

Data Querying: Used to view table structures, validate data, and execute authorized queries. If tables are missing or results are incomplete, verify both the SQL and the connection's Schema/account permissions.

Conclusion

qData Open Source supports 8 database connection types.

While form fields vary, the integration logic remains consistent: select the matching version, fill in the server/port/credentials, provide the Schema name where applicable, save, and test before enabling.

Connection issues rarely stem from the form itself; they usually involve database versions, network reachability, actual listening ports, Schemas, and account permissions.

Confirming these details beforehand prevents downstream failures.

Remember: a successful connection only means the address and credentials are valid. Full access to target objects depends entirely on the database name, Schema, and account permissions.

The Ultimate Beginner's Guide: Get qKnow Agent Platform Running in 10 Minutes

TongWu — Thu, 13 Aug 2026 06:57:39 +0000

After deploying the qKnow Agent Platform, new users often encounter a confusing scenario: the system is accessible, but they aren't sure where to start.

Models might be connected, yet knowledge base parsing or knowledge graph extraction still throws errors.

Or, after entering the Bot Management page, the difference between built-in Bots and regular Bots remains unclear.

These seemingly scattered issues usually boil down to two foundational configurations:

Is the underlying Large Language Model (LLM) connected correctly?
Is the built-in Bot linked to an actually available model in your current environment?

For first-time qKnow users, follow this "Golden Three-Step Guide" to complete your basic setup: Configure Model → Check Bot → Verify Usage.

Step 1: Configure the Underlying LLM (Give the Platform a "Brain")

The LLM is the foundation of any agent. Before creating or using a Bot, navigate to System Management → Model Market to configure your LLM platform.

qKnow supports four common model integrations: DeepSeek, Ollama, Qwen, and OpenAI. Configuration varies by platform, so ensure you fill in the correct connection details.

Configuring API Keys:

For platforms like Qwen, you will need to generate and enter an API Key in the "Key Settings" section. You can add multiple keys for the same platform.

We highly recommend distinguishing keys based on environments, business scopes, and maintenance responsibilities to make troubleshooting easier.

Security Note:

API Keys are sensitive. Never share them in plaintext via regular documents, chat logs, or screenshots.

Verifying Model Details:

After saving the key, qKnow will automatically sync the available models for that platform.

If the model list fails to display, check the following:

Is the API Key correct?
Is the key still valid?
Does the selected model platform match the key's source?
Can your deployment environment access the model service?
Are there extra access restrictions on the account or key?

The success criteria for this step isn't just saving the key; it's confirming that the Model Details page successfully syncs and displays available models.

Step 2: Check Built-in Bots and Link Available Models

Once your model platform is configured, you must verify the Bot's model settings. Navigate to Bot Management to view your applications.

Understanding Built-in vs. Non-Built-in Bots:

Built-in Bots are pre-configured by the system to power specific platform functions, such as knowledge graph extraction and document parsing. They are not just demo apps.

While Super Admins can customize them, proceed with caution. Do not delete nodes or drastically alter workflows unless you fully understand the system's calling relationships.

Non-built-in Bots are user-created agents designed for custom business needs. You have full freedom to configure, modify, or delete them as required.

Why Do Features Fail Even After Configuring a Model?

This is the most common pitfall for new users. The default LLM node in qKnow's built-in Bots is set to qwen-max.

If qwen-max is not configured in your Model Market or is unavailable in your environment, the built-in Bots will fail to call the model.

Having an available model in the Market does not mean the built-in Bot automatically switches to it.

How to Modify the Built-in Bot's Model:

If qwen-max is unavailable, change it to a conversational model that is fully configured and functional in your Model Market.

Before selecting a model, confirm it is synced, valid, suitable for the task, and accessible.

Crucially, you must save the Bot configuration after making changes. Unless you clearly understand the business logic, stick to adjusting only the unavailable model configurations.

Step 3: Perform Basic Verification

Do not assume the system is ready immediately after configuration. You must verify that the setup forms a complete chain.

Check the Model List: Go to "Model Details" and ensure the target model is synced and available.
Check the Built-in Bot's LLM Node: Verify that the node is using an available model and that the default qwen-max has been adjusted for your environment.
Confirm Configuration is Saved: Ensure your changes are actually saved to prevent the system from reverting to old configurations during runtime.
Verify Business Functions: Test the actual features. Can knowledge graph extraction execute normally? Can documents be parsed? Can the Bot call the configured model?

If errors persist, troubleshoot in this order: API Key validity → Model sync success → Built-in Bot LLM selection → Configuration saved status → Network accessibility → Task-model compatibility.

The Bottom Line

For new qKnow users, deployment is only the beginning.

To get knowledge graph extraction and document parsing working, you must complete the model integration and built-in Bot configuration.

The most easily overlooked step is Step 2.

If the built-in Bot's LLM node still points to an unavailable qwen-max, platform features will fail regardless of what else you've configured.

By confirming these three links, you can eliminate repetitive troubleshooting and get qKnow into a fully operational state much faster.

Unlocking the "One Map" for Water Resources: Mastering the Complete Water Management Network

TongWu — Thu, 13 Aug 2026 06:57:32 +0000

As smart water management initiatives advance, the industry is shifting from traditional, fragmented IT applications to a collaborative, digital, and intelligent management model.

Historically, water information systems have struggled with diverse data sources, isolated business systems, and scattered spatial information.

This lack of data sharing and collaboration across domains makes it difficult to support refined supervision and comprehensive decision-making.

To address this, the "One Map" platform leverages water census data and GIS spatial technology. It aggregates multi-source data—rivers, lakes, water conservancy projects, monitoring stations, and water resource management—onto a unified spatial base map.

This enables full-domain visualization, traceable business associations, and real-time operational awareness, providing digital support for water resource management, disaster prevention, and project supervision.

Overall Construction Concept

The "One Map" solution uses a GIS platform as its core, forming an integrated architecture of "Spatial Base — Data Resources — Business Applications."

By spatializing water objects, the platform maps reservoirs, sluice gates, pump stations, hydropower plants, and hydrological stations into a unified map system.

This shifts mnagement from fragmented records to spatial, visual oversight.

Furthermore, by linking project archives, real-time monitoring, historical data, and video feeds, the platform extends beyond "viewing a map" to "viewing business status," offering multi-dimensional analytical capabilities.

1. System Technical Architecture

GIS Spatial Foundation Layer:

The platform uses GIS as the unified spatial base for modeling and expressing water resources.

By loading foundational geographic information (basins, river systems, administrative regions), it maps all water projects and monitoring facilities onto a single map environment.

It supports standard map operations like zooming, regional positioning, and layer switching to meet diverse browsing needs.

Water Resource Data Layer:

The platform establishes a unified data management system that merges static archival data with dynamic operational data.

Resources include project basics, river/basin spatial info, hydrological and water withdrawal monitoring data, video surveillance, and management unit associations.

This standardized organization ensures data accuracy for map displays, business queries, and statistical analysis.

Data Fusion & Business Association Layer:

This layer fuses spatial and business data, expanding from simple location displays to comprehensive business information.

Clicking a target object on the map reveals its basic profile (name, code, basin, coordinates) and dynamic metrics (real-time water levels, flow rates, withdrawal volumes).

Through knowledge graph tags, the platform links upstream/downstream water systems, related projects, and withdrawal permits, building a comprehensive relational network for analytical support.

Business Application Layer:

Tailored to actual management needs, the platform constructs specialized applications for different scenarios:

Water Resource Management & Allocation:

Unifies the display of withdrawal outlets, monitoring data, and business ledgers to support dynamic supervision and regional water replenishment.

Flood & Drought Disaster Defense:

Combines hydrological monitoring, project info, and spatial distribution to quickly locate key areas and projects, aiding in flood dispatch and risk assessment.

Video Inspection:

Integrates live video feeds linked to spatial locations, allowing remote viewing of reservoirs, rivers, and stations to improve inspection efficiency.

2. Core Functional Capabilities

Full-Domain Visual Management:

Displays the spatial distribution of water resources, supporting combined queries by river system, management unit, administrative region, or project type to quickly locate targets.

Multi-Dimensional Information Fusion:

Associates project archives, real-time monitoring, historical trends, and live video. Managers can view project statuses from a unified spatial entry point without switching between multiple systems.

Statistical Analysis:

Provides comprehensive statistical panels displaying the quantity and distribution of various water objects using pie charts, bar charts, and trend graphs for quick overview and categorized viewing.

Unified Resource Directory Management:

Supports establishing a resource directory for map objects, including names, codes, river/basin affiliations, and update times. Directories can be exported to facilitate resource management and business queries.

3. Application Value

Enhances Unified Management: Integrates scattered resources onto a GIS base, helping managers quickly grasp regional water resource layouts.
Supports Business Collaboration: Fuses static project info with dynamic operational states, providing intuitive data support for water dispatch, project supervision, and flood management.
Improves Refined Supervision: Drives the transition from fragmented management to digital, collaborative operations through unified data maintenance and specialized application development.

4. Future Outlook

Looking ahead, the "One Map" platform will continuously expand its business topics and data resources.

By connecting water resources, business applications, and management processes through a unified spatial base, it will provide a stable data foundation and scalable business support for the ongoing digital transformation of the water conservancy industry.

qData Open Source ETL Orchestration: Build Data Pipelines with Drag-and-Drop

TongWu — Thu, 13 Aug 2026 06:57:25 +0000

In enterprise digital transformation, data is rarely the bottleneck. Instead, the real challenge lies in building stable, clear, and maintainable data processing paths.

A complete data pipeline typically involves three stages: Data Ingestion (extracting raw data), Data Transformation (cleaning and structuring based on business needs), and Data Output (writing to target systems).

When source structures change or output targets shift, maintaining these processes purely through code can quickly become a nightmare as task complexity scales.

qData Open Source Data Platform solves this with visual ETL orchestration. By transforming hidden code logic into a clear data flow diagram on a visual canvas, users can break down data processing into discrete nodes.

Here is a technical deep dive into how qData’s visual ETL orchestration empowers data engineers.

ETL Orchestration: From Code to Visual Logic

At its core, ETL is about extracting, processing, and delivering data. In qData, this is achieved through a simple workflow: drag components onto the canvas, configure parameters, connect nodes to define execution order, save, and execute.

This approach doesn't eliminate technical configuration; it reorganizes it. Database connections, field mappings, and transformation rules are encapsulated within individual nodes, each with a clear responsibility.

This makes step-by-step validation and future maintenance significantly easier.

1. Managing Pipelines via the Task List

The Data Integration Task List is the entry point. Each task represents a distinct data processing pipeline.

We highly recommend naming tasks based on business semantics (e.g., "User Info Cleansing Task") rather than generic terms.

Important: When a task is in an "Enabled" state, the configuration entry is locked. To modify ingestion, transformation, or output nodes, you must first disable the task.

This prevents runtime configuration conflicts and ensures execution consistency.

2. Building the Pipeline: Input → Transform → Output

When designing a pipeline on the canvas, always follow this golden rule: Define the entry point first, design the processing logic, and configure the output last.

Clear boundaries prevent overly complex flow designs.

3. Input Components: Defining Data Sources

The input component is the starting point. You must configure the source database connection, the target table, the read mode, and the specific attribute fields.

Naming conventions matter here.

Using a format like "SourceDB - TableName" helps maintainers instantly understand the node's purpose. The fields selected at this stage form the foundation of the entire pipeline; if the input configuration is flawed, no amount of downstream transformation will fix it.

Drag-and-drop doesn't mean "no configuration"—it means configuring parameters within explicit, dedicated components rather than hunting through code.

4. Transformation Components: Shaping the Data

Transformation components sit between input and output, defining how data is processed. qData provides a rich set of transformation capabilities:

Remove Duplicate Records: Identifies duplicates based on specified business fields. For example, you can set water_level_m as the unique identifier. You can also configure case sensitivity and add multiple fields for composite checks.

Field Deriver: Generates new fields based on existing ones. Using the "Concatenation" method, you can combine station_code and station_id with a hyphen and a prefix (e.g., "Monitoring Point: ST001-1") to create a new station_display_name field.

Field Selection & Modification: Controls which fields are passed downstream. You can retain, rename, change types, or remove fields. For instance, you might keep station_code but drop quality_code, while renaming fields to match target table structures.

This makes structural adjustments highly transparent and much easier to maintain than inline script logic.

5. Output Components: Controlling the Destination

The output component writes the processed data to the target system. Key configurations include the target data source, target object, synchronized fields, and field mappings.

Why is Sync Field Configuration Critical?

The field list in the output component acts as a gatekeeper.

Only checked fields will be written to the target. Even if a field was ingested and transformed upstream, it will be dropped if it isn't explicitly checked here.

This allows precise control over the output scope. Always verify that source and target fields map correctly and that data types are compatible.

6. Write Modes: Defining Target Behavior

The "Write Mode" dictates how data enters the target table:

Append: Retains existing data and adds new records. Ideal for continuous data ingestion.
Full Load: Replaces the entire target dataset. Best for complete refreshes.
Incremental Update: Updates only new or changed records based on a primary key.

When using Incremental Update, selecting the correct "Update Primary Key" is vital. It must uniquely identify a record (e.g., ID or a composite key like Station + Observation Time).

Choosing a non-unique field can lead to data corruption or failed matches. Remember: Write Mode and Sync Fields are independent configurations; one does not replace the other.

Additionally, you can configure the batch write size (default is 1000 rows) and execute Pre-SQL or Post-SQL scripts for advanced pipeline control.

7. Ensuring Maintainability from Config to Execution

A robust data task isn't just executable; it must be understandable.

When results are unexpected, you can trace the issue node by node: verify input connections, check transformation rules, and validate output mappings.

This modular debugging is vastly superior to parsing a monolithic script.

The True Value Behind Drag-and-Drop

Visual orchestration doesn't oversimplify technical work; it changes how data processing logic is expressed.

For beginners, it provides a clear, guided path. For engineers, it centralizes nodes, parameters, and dependencies for rapid adjustments.
For teams, a visual flowchart builds consensus far better than verbal descriptions or raw scripts.

Ultimately, qData’s ETL orchestration turns fragmented configurations into reusable, readable, and reviewable data pipelines.

When every node has a clear responsibility and every field's journey is transparent, data integration tasks become sustainable data assets.

NDRC Sets AI Growth Over 30%: Why Data Platforms Are the First Bottleneck for Enterprises

TongWu — Thu, 13 Aug 2026 06:57:18 +0000

On July 31, the National Development and Reform Commission (NDRC) disclosed that AI-related industries in China maintained high growth of over 30% in the first half of the year.

By the end of June, national intelligent computing power had reached 2.8 times that of the same period last year, with high-quality datasets exceeding 120,000.

As computing power scales and large models advance, the AI industry is accelerating.

However, shifting from macro-level industry trends to internal enterprise realities raises a practical question: With abundant computing power and mature models, why do many enterprise AI applications remain stuck in demonstrations, pilots, and localized validations?

The answer usually lies not in the models themselves, but in the underlying data systems.

Enterprises often lack not another model API, but a data infrastructure capable of continuously providing high-quality, traceable, and reusable data.

1. Over 30% AI Growth Signals a Shift in Competitive Focus

High-speed AI growth signals a transition from technical capability building to large-scale application.

Early on, computing resources and model availability were core prerequisites. Now, as infrastructure improves and model services standardize, the barrier to accessing AI capabilities is lowering.

The intelligence gap between enterprises will no longer depend solely on having models, but on whether they possess clearly structured, consistently defined data, can continuously fix quality issues, trace data origins, and securely provide data to business and AI systems.

In short, as computing power and models become public capabilities, the key competitive variable is shifting from the model side to the data side. Computing power solves "can we calculate," models solve "how to calculate," and data governance solves "what to calculate with, whether the basis is reliable, and if results can be trusted."

Before entering production, enterprises must systematically reconstruct their data infrastructure.

2. The True Bottleneck Is Data Usability, Not Volume

Most enterprises have abundant data across various systems, but "having data" and "using data" are entirely different.

Data formed across different departments and systems over time often results in isolated structures and metrics. These issues surface when attempting cross-system analysis or AI applications:

Inconsistent Standards: The same "customer" might be identified by phone in sales, company name in finance, and contract entity in projects. Without unified data elements and metrics, models face conflicting expressions rather than clear business facts.

Uncontrollable Quality: Nulls, duplicates, and anomalies are common. In AI scenarios, these are amplified. Models don't inherently understand anomalies, leading to plausible but untrustworthy outputs.

Opaque Data Pipelines: Without unified metadata and lineage tracking, troubleshooting requires checking systems and SQLs individually, making change assessments difficult.
Data Trapped in Systems: Even with data warehouses, data often remains as tables or files. Without standardized services, it cannot be easily reused by AI applications.

3. Data Platforms Are Enterprise Data Production Systems

Data platforms become bottlenecks because they connect the entire data lifecycle.

A sustainable system requires four continuous stages: Data Ingestion → Data Governance → Data Assetization → Data Servicing. Missing any stage disrupts applications.

A data platform should not just be centralized storage or a collection of tools; it is an enterprise-level data production system that continuously ingests heterogeneous data, translates business definitions into standards, embeds quality rules, manages governance, and packages results into standard services.

Bridging the "last mile" means ensuring data has clear definitions, controllable quality, traceable origins, clear permissions, and stable calls before reaching models.

4. Achieving the Transition from "Data Aggregation" to "Data Usability"

These challenges point to a lack of data governance capabilities. qData Data Platform is designed to bridge this gap by integrating connections, standards, development, quality, assets, and services into a unified architecture:

Data Can Enter: qData uses standardized connectors to ingest diverse sources (relational, domestic, big data, messaging, files) via single-table, full-database, SQL, file, or real-time sync, managed through scheduling and monitoring to form sustainable data channels.

Data Is Regulated: Through a "Standard Data Elements—Logical Models—Standard Documents" framework, qData unifies definitions. This translates scattered business definitions into executable governance rules, shifting governance from post-hoc correction to the design phase.

Data Is Governed: qData supports single and cross-table validation rules across accuracy, completeness, consistency, uniqueness, and validity. It provides quality scores and problem lists, enabling automated cleaning (e.g., null filling, format unification) and manual correction workflows, embedding continuous governance into pipelines.

Data Is Clarified: Through unified metadata management and asset maps, qData integrates tables, files, and APIs with structures, quality scores, and lineage. Field-level lineage helps assess the impact of upstream changes, upgrading asset management to understanding and tracking data.

Data Is Utilized: qData publishes tables and queries as API services with authentication, authorization, and monitoring. This isolates applications from underlying source changes and decouples them from databases, making the platform a unified entry point for data capability output.

Conclusion: The Second Half of AI Competition Is About Data Engineering

The 30%+ AI growth means the industry is entering a new phase, but purchasing computing power and models is only the starting point for enterprises.

Converting scattered, heterogeneous data into unified, trusted, and reusable assets determines whether models accurately understand business and whether AI enters production.

Data platforms are not separate projects but critical infrastructure connecting business systems, data, and AI. qData helps enterprises transition from data dispersion to aggregation, from aggregation to trust, and from trust to usability.

Future intelligence gaps will depend on continuously providing real, accurate, and governable data to models.

Computing power determines speed, models determine reach, but data governance determines whether AI ultimately enters real business.

Enhancing Groundwater Refined Management Through Digital and Smart Solutions

TongWu — Thu, 06 Aug 2026 01:52:26 +0000

Focusing on key business areas such as groundwater extraction plans, actual water consumption, over‑quota alerts, and water level changes, the Groundwater Full‑Process Supervision Platform leverages GIS, IoT, and big data technologies.

It promotes the unified aggregation of multi‑source data, continuous tracking of business processes, and closed‑loop handling of anomalies, providing digital support for refined groundwater resource supervision and comprehensive over‑extraction governance.

Groundwater Supervision Cannot Stop at Just "Looking at Data"

Groundwater is characterised by wide distribution, numerous monitoring points, and relatively hidden changes. In actual supervision, managers need to know not only "how much water is extracted" but also answer deeper questions:

How are annual extraction plans allocated across different regions and months?
Does actual water consumption exceed planned targets?
Have over‑quota issues been resolved?
How have regional groundwater levels changed compared to the same period last year?

These questions involve plan management, water extraction monitoring, statistical analysis, anomaly alerting, and water level assessment.

If related data is scattered across different systems, ledgers, and reports, managers often need to repeatedly aggregate, compare, and verify information — making it difficult to form a continuous and complete supervision chain.

Traditional manual inspection and ledger‑based management models struggle to coordinate groundwater extraction and water level monitoring, hindering efficient total volume control and illegal extraction investigations.

To address this gap, the Groundwater Full‑Process Supervision Platform aggregates multi‑source data from extraction stations, water level monitoring, and business ledgers, connecting the business chain of monitoring perception → data aggregation → analysis alerting → closed‑loop handling.

In smart water conservancy construction, data ingestion is merely the foundation.

A system that truly supports groundwater supervision must link monitoring data with planned targets, administrative divisions, alert records, and handling results — allowing managers to see the current state, understand the reasons for changes, and continuously track anomalies.

Therefore, the platform does not just solve a single issue of water volume display; rather, it establishes a relatively complete business management mechanism around the groundwater development and utilisation process:

Plans are evidence‑based, execution is comparable, anomalies are detectable, handling is traceable, and water level changes are analyzable.

Guided by the main line of "Extraction → Monitoring → Alerting → Handling", the platform integrates extraction plans, actual water use, over‑quota information, and water level changes into a single business system, reducing information breakpoints between plan management, statistical analysis, and anomaly handling.

Groundwater Extraction Plan Management: Clarifying "How Much Can Be Extracted"

Total volume control requires clarifying extraction targets for different regions, years, and months. The platform uniformly manages plan data across municipal and autonomous region levels, as well as various administrative divisions.

Managers can query plans by year and administrative division, viewing:

Annual plans
Monthly original targets
Available targets
Consumed targets

…step by step, and export results as needed.

Unlike static annual ledgers, the platform focuses on dynamic changes during actual execution:

Annual plans can be calculated on a rolling monthly basis.
Unused available targets for the current month can be carried over to the next month according to business rules.
When targets are adjusted, the system retains records for traceability.

This mechanism transforms extraction plans from static tables into management references that continuously update alongside actual water use.

For management departments, this module solves three key issues:

Unified Calibre – Integrating targets across different years, levels, and regions.
Monthly Granularity – Breaking down annual plans into monthly targets for process control.
Adjustment Traceability – Recording target changes to prevent missing adjustment reasons during future audits.

Water Plan Execution Statistics: Seeing the Extent of Plan Execution

After setting plans, it is necessary to continuously judge whether actual water use aligns with them. The execution statistics module compares actual water consumption with planned targets across administrative regions, displaying monthly execution progress, actual monthly water use, and completion rates through charts and detailed lists.

Managers can:

Query execution by month
Visually compare actual use with targets via bar charts
Drill down through administrative divisions to analyse regional execution differences
Support both monthly and annual comparisons, with exportable results

At the business level, this helps managers quickly identify:

Which regions are approaching limits
Which have fast execution progress
Whether deviations are short‑term fluctuations or continuous trends

Information previously requiring manual aggregation from multiple reports is now centrally presented under unified statistical standards, providing a data foundation for subsequent audits and management adjustments.

Over‑Quota Extraction Alerts: Moving Anomalies from "Discovery" to "Handling"

When actual groundwater extraction exceeds red‑line targets, merely displaying excess data in statistical reports is insufficient. The platform transforms anomaly data into queryable, filterable, and actionable business records.

Managers can filter records by:

Administrative division
Alert time, level, and type
Judgment type and clearance status

Each record shows:

Alert month
Water target and actual use
Over‑quota volume
Alert level
Clearance status and handling reasons (for verified cases)

The key value here is transforming data anomalies into pending tasks.

From a system logic perspective, an over‑quota alert should not end upon generation — it must go through:

Discovery
Verification
Handling
Feedback
Archiving

Through status and reason recording, the platform creates a queryable trajectory from anomaly generation to handling, preventing alerts from lingering indefinitely without confirmed resolution.

For cross‑level supervision, this mechanism clarifies problem areas, handling progress, and results, enhancing the continuity of anomaly management.

Groundwater Water Level Fluctuation Analysis: Observing Resource Changes Beyond Extraction Results

Groundwater supervision must not only focus on extraction volume but also combine water level changes to assess regional resource status. The platform compares the current month's average groundwater level with the same period last year, calculates water level fluctuations, and generates corresponding indicators.

Managers can:

Switch between different statistical standards
Query data by month
View regional water level changes through comparison charts and hierarchical statistical lists

In regional management, short‑term fluctuations at single monitoring points often fail to directly explain the overall situation.

The platform generates hierarchical statistics by administrative division, supporting further drilling down into lower‑level regional data. This enables managers to understand water level changes at different spatial levels — from overall trends to specific areas.

Water level year‑on‑year analysis can also be combined with plan execution, actual water use, and over‑quota alerts.

For example, when a region consistently approaches or exceeds targets, managers can further check its concurrent groundwater level changes to determine if deeper business verification is needed.

⚠️ Note: The platform provides unified data, change analysis, and auxiliary assessment capabilities. For complex risks such as land subsidence and ground fissures, comprehensive judgments still require combining geological conditions, long‑term monitoring data, and professional analysis.

Forming a Supervision Business Closed Loop Through Four Functional Modules

The four modules — extraction plan management, execution statistics, over‑quota alerts, and water level fluctuation analysis — are not independent pages. Together, they form a continuous supervision chain:

Plan Management → Execution Comparison → Over‑Quota Alerts → Water Level Analysis

First, manage extraction plans by year and administrative division to clarify monthly available targets.
Second, statistically compare actual water use with planned targets to grasp completion rates and regional execution.
When actual use exceeds red‑line targets, the system generates alert records and continuously tracks clearance status and handling reasons.
Finally, combine year‑on‑year average water level data to analyse regional water level fluctuations, providing more information for managers to verify groundwater development and utilisation.

The significance of this closed loop lies in enabling different business segments to use the same data foundation and management calibre. Managers can:

View plan targets and execution results level‑by‑level through administrative divisions
Identify water use deviations through monthly and annual statistics
Conduct verifications combining alert records and water level changes

This ensures continuous querying and tracing of plan execution and anomaly handling.

For Smart Water Conservancy Projects, the Platform Brings More Than Just Visualization

While the platform enhances data presentation efficiency through maps, charts, and lists, its core value is not merely "displaying data."

Unifying the Groundwater Supervision Data Foundation – Integrating extraction stations, water level monitoring, extraction plans, actual water use, and alert handling into a unified system reduces multi‑system queries and manual splicing, establishing a consistent data foundation for statistical analysis and business collaboration.
Shifting Management from Result Statistics to Process Control – Through monthly plans, execution progress, completion rates, and over‑quota alerts, managers can grasp execution before year‑end and promptly identify deviations, rather than waiting for centralised accounting at year‑end.
Improving Anomaly Traceability – From over‑quota volumes and alert levels to handling status and clearance reasons, the platform retains business records, forming a continuous chain between anomaly discovery and subsequent handling.
Supporting Hierarchical and Regional Refined Management – Displaying plans, execution results, and water level changes level‑by‑level through administrative divisions allows viewing overall situations while drilling down to specific areas, better suiting multi‑level groundwater management.
Providing a Data Basis for Over‑Extraction Governance and Resource Assessment – Long‑term accumulated plan execution, water level changes, and anomaly records provide basic information for analysing groundwater development, optimising regional management measures, and conducting comprehensive over‑extraction governance.

In Conclusion

Groundwater supervision is a long‑term, continuous endeavour. It requires stable monitoring perception capabilities, as well as clear indicator systems, unified statistical standards, and sustainably traceable handling mechanisms.

Centred on a business closed loop, the Groundwater Full‑Process Supervision Platform connects extraction plans, actual water use, over‑quota alerts, and water level changes, gradually shifting groundwater management from scattered ledgers and post‑event statistics to data‑driven process supervision.

For smart water conservancy projects, the platform's focus is not adding more isolated functional modules, but enabling:

Data to enter business processes
Anomalies to drive handling
Management processes to be queryable and traceable

Through continuously improving monitoring perception, data aggregation, analysis alerting, and closed‑loop handling capabilities, groundwater supervision can establish a clearer, more standardised, and more actionable digital support system.

This article is part of our series on smart water management and digital transformation. For more insights on IoT, GIS, and big data applications in environmental supervision, stay tuned.