What the data for this category looks like
Power grid equipment revenue and market data primarily comes from power grid SCADA systems and public datasets from provincial power trading platforms. Two update cadences apply: Real-time collected data updates every 15 minutes. Daily aggregate data completes full prior-day data integration by 02:00 each day. The document structure is a structured JSON array. Each entry includes fields such as device code, station attribution area, daily operating duration, daily revenue benchmark value, grid-connected current parameters, and more. The device code is a 12-character string identifier. The grid-connected current parameter uses ampere as its unit. The daily revenue benchmark value is a figure relative to industry benchmarks.
What constraints these characteristics impose on deployment and upgrade
The 15-minute real-time update cadence requires deploying high-frequency incremental sync tasks. This avoids insufficient data timeliness caused by overly long sync intervals. The daily full data integration time requires configuring fixed-time full validation tasks. This ensures data integrity. The device code as the unique identifier field requires configuring primary key deduplication rules during the data preprocessing stage. This prevents duplicate data imports. Unit consistency for parameters like grid-connected current requires configuring data validation logic. This filters entries with abnormal formats. Additionally, access permissions for power grid data require configuring dedicated authentication rules. This ensures data transmission security.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DATA_SYNC_INTERVAL | 15 minutes | Matches the collection and update cadence of real-time power grid equipment data to ensure timeliness of market trend reports |
VECTOR_DB_BATCH_SIZE | 200 | Adapts to single-batch data volume to avoid overloading the vector database during high-frequency synchronization |
PARSE_DATA_PRIMARY_KEY | device_code | Uses the device code as the unique primary key to enable deduplication matching during incremental synchronization |
DAILY_FULL_SYNC_TIME | 02:00 | Matches the summary update time of daily power grid data to complete validation and synchronization of full data |
MAX_DATA_VALIDATE_TIMEOUT | 600 seconds | Reserves sufficient time for format validation and unit conversion of power grid data |
EMBEDDING_MODEL_PATH | /path/to/local/m3e | Adapts to the M3E model deployed locally via Docker to meet vector encoding requirements for power grid equipment data |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules, and specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Symptom: Vector recall results are empty or have abnormal matching degrees. Cause: The
EMBEDDING_MODEL_PATHparameter is not configured correctly, causing the local M3E model to fail to load properly and unable to generate valid vectors. - Symptom: Code execution nodes in the SaaS version return a
400 Bad Requeststatus code. Cause: No format validation is performed on thegrid_trade_pricefield in power grid data, causing the incoming value type to not meet code logic requirements. - Symptom: Incremental synchronization tasks do not trigger updates. Cause: The used FastGPT 4.8 version does not fully support the incremental synchronization logic with primary key deduplication. Upgrade to a subsequent version or adjust configuration parameters.
How to confirm the configuration is complete
- View the running logs of scheduled synchronization tasks to confirm that the daily 02:00 full synchronization task and the 15-minute incremental synchronization task both start normally and complete data processing.
- Call the vector recall interface, pass in a known power grid equipment
device_code, and verify that the primary key field of the returned result matches the input parameter. - Check the running status of the local Docker container to confirm that the M3E model container is running normally and no ports are occupied.
- Run the test script for the code execution node, pass in simulated power grid equipment data, and confirm that there are no field parsing or type conversion errors.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.