What data for this category looks like
Data for this category comes from hot metal and scrap steel collection systems, L2 process control modules, laboratory testing systems, manufacturing execution systems (MES), energy medium monitoring platforms, and equipment status monitoring devices. Daily circulating documents include heat plans, batching lists, converter smelting records, composition test reports, continuous casting process records, and slab quality judgment sheets. Data update cycles cover per-heat generation, real-time synchronization, and shift-level summarization. Fields include hot metal temperature, smelting duration, slab thickness, and casting speed, with corresponding units: degrees Celsius, minutes, millimeters, and meters per minute.
Constraints on HTTP interfaces and external systems
This category’s data has high update frequency and strong scenario coupling. It requires HTTP interfaces to support high-frequency real-time requests and batch synchronization of heat or shift data. Multi-source heterogeneous document formats and field requirements mean interfaces must support varying request parameter structures. Because data links smelting and continuous casting processes, interface interruptions directly disrupt production schedules. A stable authentication mechanism and timeout retry strategy must be configured. Additionally, data fields include physical quantity parameters. Interfaces must clearly define units and data formats to avoid cross-system parsing discrepancies.
How to configure settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer <platform-generated access token> | Complies with general authentication specifications for industrial external systems to secure interface access |
variables | Pass heat IDs, cast sequence numbers, up to 10 custom parameters | Matches single heat or cast sequence data association logic for this category, reduces invalid parameter transmission |
responseData | Only return quality judgment results and exception marker fields | Adapts to lightweight data processing needs of production systems, reduces network load |
| Retry count | 3 times | Balances data reliability and system load, supports automatic recovery after process interruptions |
| Request timeout | 600 seconds | Matches average single heat data processing time for this category, avoids interrupting production processes due to timeout |
| Request frequency limit | 1 request per heat | Avoids process conflicts caused by repeated triggers, adapts to production schedules |
Three common configuration mistakes
- Symptom: Interface returns 401 unauthorized error. No authentication information is present in the request header, or the token is invalid. Cause: The
Authorizationparameter is not configured correctly, the token has expired, or the token format does not meet requirements. - Symptom: Interface returns results missing slab judgment-related fields, with incomplete data parsing. Cause: Required fields are not filtered via the
responseDataconfiguration. Redundant data is returned that production systems cannot recognize. - Symptom: Interface call fails, and production data for the corresponding heat cannot be associated. Cause: Core parameters such as heat ID and cast sequence number defined in
variablesare not passed during the call. Production process association logic cannot be matched.
How to confirm configurations are correct
- Call the test interface, check if the
Authorizationparameter is correctly included in the request header, and confirm the authentication logic is working. - Pass preset
variablesparameters, verify that the interface can correctly associate document data for the corresponding heat, with no parameter missing errors. - Review the fields returned by
responseData, confirm only pre-configured necessary content such as quality judgments and exception markers is included. - Simulate production scenario call frequencies, verify interface stability under varying request pressures, and adjust configuration items to match actual production schedules.
The values provided are common starting points and should be measured against the reader's own samples.