What data in this category looks like
Data sources include iron melt and scrap collection data, L2 process control data, laboratory composition reports, MES system data, energy medium system data, and equipment status data. Updates occur per heat, in real time, or on a shift basis. Daily associated documents include heat plans, batching lists, converter smelting records, composition reports, continuous casting process records, and slab judgment sheets. Document fields include heat identifiers, process parameters, composition data, and equipment status values, with units aligned with industrial metrology standards.
What constraints these characteristics impose on the HTTP Interfaces and External Systems link
Steel smelting and continuous casting production processes have no arbitrary shutdown windows. HTTP interfaces must adapt to high-frequency, low-latency call requirements to avoid long-term blocking of production rhythms. Multiple heterogeneous data sources require interfaces to support integration with multiple external systems. A unified authentication mechanism must be used to manage access permissions. The number of documents associated with each heat is fixed and limited. Interface request load must match the document scale of this scenario to avoid overload. Additionally, document cross-review results directly affect heat quality. Interfaces must have reliable failure handling and status feedback capabilities to ensure traceability of exceptions.
How to configure settings
| Configuration Item | Recommended Practice | Rationale |
|---|---|---|
Authorization | Bearer {Enterprise-specific API key} | Complies with industrial system API security specifications to prevent unauthorized access to external production data |
variables | Bind heat number, cast sequence number, and composition threshold variables | Matches core identifier fields of steel smelting documents to enable accurate cross-document matching |
responseData | Return review results, matched document IDs, and difference field lists | Meets the needs of scheduling departments to quickly locate abnormal documents and process deviations |
| Maximum number of documents per single request | Top 10 entries | Adapts to the average number of associated documents per heat to avoid request timeouts |
| Request timeout period | 30-60 seconds | Aligns with the collection and processing cycle of steel smelting data without blocking production processes |
| Number of failure retries | 2 times | Addresses short-term network fluctuations without impacting production rhythms |
Three common configuration mistakes
- Phenomenon: Documents uploaded via the file collection API have inconsistent segmentation compared to documents uploaded directly on the platform. Cause: The API upload does not adapt to the fixed segmentation rules of steel smelting documents, causing a deviation between the chunk division logic and the platform's default logic.
- Phenomenon: It is not possible to quickly interrupt the generation process after calling the API to initiate a review request. Cause: The real-time status return logic for
responseDatais not configured, causing the request to block until the complete generation result is returned. - Phenomenon: The API request returns a 400 status code with a prompt that required fields are missing. Cause: Core identifier fields such as heat number are not bound in
variables, causing the system to fail to match the corresponding document set.
How to confirm the configuration is correct
- Use the interface debugging tool of FastGPT v4.17.0 to initiate a test request, upload a single converter smelting record document, and verify that the document ID returned in
responseDatamatches the actually uploaded document. - Simulate sending multiple review requests at short intervals to verify that the interface can respond quickly and support real-time status feedback.
- Check the interface logs to confirm that
Authorizationverification passes, and there are no unauthorized related errors. - Compare the same document uploaded via API and uploaded directly on the platform to confirm that the segmentation results are completely consistent.
The values provided here are common starting points and should be measured against the reader's own samples.