Data Overview for This Category
This category covers enterprises producing hot-rolled, cold-rolled, section steel, and steel pipe products. Daily documentation includes rolling plans, heating schedules, rolling specifications, plate shape and thickness records, surface quality reports, and quality certificates. Data sources include slab or billet batches, heating furnaces, automatic mill control systems, online thickness and width measuring equipment, surface inspection systems, and MES systems. Fields include batch identifiers, process parameters, test results, and quality ratings. Data updates in real time per coil or per piece. Full coil-level end-to-end traceability is supported.
Constraints for HTTP Interfaces and External Systems
The combination of real-time coil-level data and multiple process document types requires HTTP interfaces to support high-concurrency, low-latency batch requests, and adapt to parameter formats from multiple data sources. Variations in fields across document types require interfaces to support dynamic matching of document structures, and avoid hard-coded field mappings. The real-time update feature requires interface timeout settings to accommodate millisecond-level response requirements. It also requires verification of batch consistency in returned data, to prevent incorrect data from triggering cross-review workflows. The coil-level traceability requirement means associated data returned by the interface must carry unique batch identifiers, to ensure review results can be traced.
This page is explained based on the configuration specifications of FastGPT v4.17.0.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {internal enterprise API key} | Complies with identity authentication standards for external systems, and prevents unauthorized access |
variables | {"batchId": "{{target_batch}}", "prodLine": "{{current_line}}"} | Adapts dynamic parameter transfer for coil-level data, and matches review requirements for different rolling batches |
responseData | filter_by_field: ["batch_no", "process_temp", "surface_defect"] | Only returns core fields required for cross-review, and reduces data transfer volume |
| Maximum single request count | First 10 entries | Adapts single-batch data volume per coil or per piece, and avoids interface overload |
| Timeout period | 500–800 milliseconds | Meets low-latency requirements for real-time data, and prevents review interruptions caused by timeout waits |
| Data verification switch | Enabled | Ensures batch consistency of returned data, and meets coil-level traceability requirements |
Three Common Configuration Mistakes
- Scenario: An external interface returns a 400 status code, and the console prompts that the SQL statement contains invalid multi-line formatting. Cause: Attempting to pass multi-line query statements in a single interface request, and not following the external system’s single-statement, single-request specification.
- Scenario: Cross-review results do not return reference sources for corresponding documents, and the
sourcefield is empty. Cause: Not configuring transfer parameters for reference sources invariables, and not requiringresponseDatato return document paths and batch identifiers. - Scenario: Process parameters returned by the interface do not match the actual rolling batch, leading to incorrect review results. Cause: Not enabling the data verification configuration, and not verifying that the batch identifier of returned data matches the request parameters.
How to Verify Proper Configuration
- Send a test request with a valid
Authorizationheader, and confirm the interface returns a 200 status code. - Check if fields returned in
responseDatainclude batch identifiers, core process parameters, and document reference information. - Simulate a single request to send a single query statement, and confirm no syntax error prompts appear.
- Check if the interface timeout period falls within the configured range, and verify low-latency response performance.
The values provided are common starting points, and should be evaluated against the reader's own samples.