What Data for This Category Looks Like
The data sources for this category include slabs or billet batches, heating furnaces, automatic mill control systems, online thickness and width measuring equipment, surface inspection devices, and MES systems. Data is generated in real time for each coil or individual steel roll. Daily documents include rolling plans, heating schedules, rolling specifications, flatness and thickness records, surface quality reports, and quality certificates. Document fields include batch numbers, heating temperature ranges, rolling reduction parameters, flatness crown, thickness deviation, surface defect levels, and more. Units include degrees Celsius, meters per second, millimeters, micrometers, and other standard industrial units.
Constraints Imposed on HTTP Interfaces and External Systems by These Characteristics
Real-time per-coil data updates require interfaces to support low-latency requests, preventing process documents from falling out of sync with actual production due to data lag. Multi-source heterogeneous document structures require interfaces to support mapping rules for different fields, adapting to format differences between document types such as rolling plans and surface quality reports. High-frequency single-coil data requests require interfaces to handle high concurrency, preventing request congestion in high-volume production scenarios. Business requirements for coil-level traceability require interfaces to return data bound to a unique batch number, ensuring accurate association between exception reports and corresponding steel coils. Data sources from production systems require interfaces to support identity authentication, ensuring secure transmission of core production data.
How to Configure Settings
This page applies to FastGPT v4.17.0. The following configurations are designed based on the interface functions of this version.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {pre-configured production system API key} | Follow identity authentication standards for industrial production scenarios, ensuring secure communication between external systems and FastGPT |
variables | {"steelBatch": "{{target_batch_id}}", "detectTimestamp": "{{current_ts}}"} | Bind steel coil batch and detection time fields, matching business requirements for coil-level traceability |
| Maximum single request data volume | First 8 entries | Adapt to the scale of inspection data corresponding to each steel coil, avoiding excessive request load |
| Request timeout period | 30–60 seconds | Cover the typical time required for real-time data collection and document generation, preventing interruptions to interface calls |
responseData | Only include rolling specification drafts, exception report snippets, and batch numbers | Streamline returned content, reduce transmission bandwidth usage, and meet low-latency requirements for production scenarios |
Three Common Configuration Mistakes
- Interface calls return a 401 Unauthorized error. The cause is incorrect configuration of the
Authorizationparameter, with no valid API key included. - Generated process documents lack flatness and thickness data. The cause is failure to bind the target steel coil's batch ID in
variables, preventing the interface from associating with the correct data source. - Interface calls time out. The cause is an excessively high single request data volume, exceeding FastGPT's processing threshold and preventing the request from completing within the timeout period.
How to Verify Successful Configuration
- Invoke the test interface to confirm whether the returned
responseDataincludes the target steel coil's rolling specifications and exception determination fields. - Review FastGPT's interface call logs to confirm that the
Authorizationparameter is correctly carried and has not expired. - Simulate the request process for a single steel coil to verify that the update time of the interface returned data matches real-time data from the production system.
- Review the
variablesparameter to confirm that it is bound to the correct batch number, ensuring accurate data association.
The values provided here are common starting points, and should be validated against available operational samples.