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HarmonyOS Next Vehicle Network Real-time System—Dance with Lightweight Threading and Compilation Period Optimization

This article aims to deeply explore the technical details of Huawei HarmonyOS Next system and summarize them based on actual development practices.
Mainly used as a carrier of technology sharing and communication, it is inevitable to miss mistakes. All colleagues are welcome to put forward valuable opinions and questions in order to make common progress.
This article is original content, and any form of reprinting must indicate the source and original author.

When developing the HarmonyOS Next smart cockpit system, we face strict real-time requirements: the closed-loop control of sensor data to execution instructions is completed within 5ms.Through the lightweight threading and compilation period optimization technology of Cangjie language, a deadline satisfaction rate of 99.999% was finally achieved.

1. Real-time scheduling architecture

1.1 Vehicle signal processing assembly line 

@Pipeline(priority: .realtime)
func processSensorData() {
    let raw = CANBus.read()  // 1.2μs
    let filtered = kalmanFilter(raw)  // 4.7μs
    let decision = makeDecision(filtered)  // 8.3μs
    Actuator.execute(decision)  // 1.8μs
}
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Key Optimization Techniques:

  • Pre-allocated memory on stack
  • Lockless ring buffer
  • SIMD instruction optimization
  • Priority Inheritance Agreement

1.2 Hybrid critical stage scheduler 

graph TB
A[event trigger] --> B{key level?}
B -->|Safety Critical|C[Take it now]
B -->|Functional Safety|D[Time Fragment Rotation]
B -->|Ordinary Tasks|E[Idle Scheduling]
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Scheduling performance indicators:
| Task Type | Response Delay | Jitter Range |
|---------------|----------|----------|
| Safety Key | ≤50μs | ±2μs |
| Functional Safety | ≤200μs | ±15μs |
| Normal task | ≤1ms | ±100μs |

2. Cross-ECU communication optimization

2.1 Zero copy shared memory 

@SharedRegion(name: "sensor_data", policy: .lockFree)
struct SensorPack {
    @Atomic var timestamp: UInt64
    @Volatile var values: (Float32, Float32, Float32)
}

// Producer ECU
sensorRegion.values = (accel.x, accel.y, accel.z)
memoryFence(.release)
sensorRegion.timestamp = getNanoseconds()

// Consumer ECU
while true {
    let ts = sensorRegion.timestamp
    memoryFence(.acquire)
    let (x, y, z) = sensorRegion.values
    if ts != lastTs { process(x, y, z) }
}
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Performance comparison:
| Communication method | Delay | CPU occupation |
|---------------|---------|---------|
| Traditional CAN | 1.2ms | 18% |
| SOME/IP | 800μs | 12% |
| This solution | 35μs | 3% |

2.2 Time triggers Ethernet 

@TimeTriggered(schedule: """
// TSN scheduling configuration
    [Schedule]
    Cycle=1ms
    [Frame1]
    Offset=0μs
    Duration=200μs
    Priority=6
""")
func transmitControlSignal() {
// Ensure that it is sent in the specified time window
}
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Deterministic guarantee:

  • Clock synchronization accuracy ±500ns
  • Transmission jitter <1μs
  • Bandwidth utilization rate 92%

3. Optimization effect of the entire system

3.1 Worst execution time analysis 

@WCET(deadline: 2ms, 
     min: 1.2ms, 
     avg: 1.5ms, 
     max: 1.9ms)
func brakeControl() {
// Brake control algorithm
}
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Analysis Toolchain:

  1. Static code analysis determines the theoretical boundary
  2. Hardware performance monitoring obtains measured data
  3. Machine learning models predict boundaries

3.2 End-to-end latency optimization

Critical Path Decomposition:
| Stage | Before Optimization | After Optimization |
|-----------------|--------|--------|
| Sensor Acquisition | 250μs | 80μs |
| Data Processing | 1.8ms | 650μs |
| Decision Calculation | 3.5ms | 1.2ms |
| Actuator Response | 1.2ms | 400μs |
| Total | 6.75ms | 2.33ms |

Methods of implementation:

  • Critical path function inline
  • Interrupt context optimization
  • Cache prefetch policy
  • Locality improvement of memory access

Project Implications: When developing the autonomous driving module, we reduced the control loop delay from 15ms to 2.3ms through the combination of "compilation period scheduling strategy generation + runtime lightweight monitoring".Huawei Car BU experts pointed out: "Real-time system is not fast enough, but it is necessary to complete the determined things within a certain time."

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