What Data for This Category Looks Like
This category of data originates from hot metal and scrap steel datasets, L2 process control systems, laboratories, MES, energy medium systems, and equipment status monitoring. Daily documents include heat plans, batching lists, converter smelting records, composition reports, continuous casting process records, and slab judgment sheets. Data is generated per individual heat. Process parameters are synchronized in real time. Exception reports are summarized per shift. Fields include heat number, process temperature range, composition parameters, equipment operating status, and more. Some parameters use units of degrees Celsius and revolutions per minute. The data structure is fixed based on the production flow, with no additional custom fields.
Constraints for HTTP Interfaces and External Systems
The real-time and strongly coupled nature of iron and steel smelting and continuous casting requires HTTP interfaces to adapt to high-frequency, low-latency request rhythms. The trigger timing for each heat must align with the production beat. Multiple data sources and fixed document structures require interfaces to support structured parameter transfer, avoiding parsing errors from unstructured text. Frequently updated production data requires interfaces to have concurrent processing capabilities, while limiting the per-request data volume to prevent excessive system resource usage. Exception reports need to associate data from multiple systems, so interfaces must support batch pulling of fields from different modules to ensure report content integrity. Additionally, production flows cannot be stopped arbitrarily, so interfaces must include fault tolerance mechanisms to handle temporary system response anomalies.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {AIPROXY_API_TOKEN} | Follow FastGPT API authentication standards, match the permission verification logic of production systems |
variables | Structured data containing heat number, target temperature range, and equipment status identification | Match the field structure of heat plans and composition reports, accurately transmit business context |
responseData | Field set containing only process records, exception reports, and slab judgment results | Reduce invalid data transmission, adapt to high-frequency real-time request scenarios |
| Request timeout | 300 seconds | Align with the process processing cycle of each heat, avoid blocking production flows |
| Retry count | 2 times | Address temporary network fluctuations or external system response delays, ensure interface call stability |
Three Common Misconfigurations
- Symptom: Interface calls return
401 Unauthorized, and system logs promptIPROXY_API_ENDPOINT or AIPROXY_API_TOKEN is not set. Cause: The API proxy endpoint and token are not correctly filled in the FastGPT configuration. Identity verification cannot be completed, so process and exception data from production systems cannot be pulled. - Symptom: After calling the interface, the returned result only contains partial fields, or lacks key content such as slab judgment and continuous casting process records. Cause: All required business fields are not specified in the
responseDataconfiguration. The interface automatically filters non-predefined data. - Symptom: Timeouts or slow system query logs appear during consecutive interface calls. Cause: Reasonable timeout and retry counts are not set, and request frequency is not limited. This exceeds the concurrent processing limit of the production system, leading to excessive system resource occupation.
How to Confirm Successful Configuration
- Call the configured interface, check whether the returned
responseDataincludes preset business fields such as heat plans and composition reports. - Simulate an interface call triggered by a single heat, confirm that identity verification passes, and no
401or403errors occur. - Initiate 3 consecutive simulated requests, check whether the interface response time falls within the preset timeout range, with no obvious delays.
- Check the FastGPT v4.17.0 system log to confirm that there are no uncaught API call exceptions or data parsing errors.
The values provided are common starting points and should be measured against the reader's own samples.