What Data for This Category Looks Like
Data sources for this category include material grades, customer technical specifications, temperature control instruments, furnace temperature uniformity testing equipment, hardness testers, metallographic equipment, and MES systems. Data is updated per furnace load or per batch. It includes structured heat treatment process cards, furnace loading diagrams, equipment inspection forms, exception handling documents, as well as unstructured furnace temperature curve images, hardness and metallographic reports. Core fields include furnace number, process temperature, holding duration, hardness value (unit HRC), and metallographic structure type. Data formats cover both structured tables and binary files.
Constraints for HTTP Interfaces and External Systems
Data is updated per furnace load or batch and linked to multi-source equipment and documents. HTTP interfaces must support furnace number and batch number as unique query parameters to enable accurate traceability of quality data for a single furnace load. Multi-format data requires interfaces to support both structured parameters and binary file uploads, to meet transmission needs for unstructured data like furnace temperature curves. Access to multi-source equipment requires interfaces to be compatible with different authentication and data formats, to avoid docking failures caused by system differences. Additionally, high requirements for quality problem traceability mean interfaces must include exception reporting and retry mechanisms, to prevent loss of critical data from failed single calls.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Interface authentication configuration | Use Bearer token authentication, token length 32–64 characters | Matches the authentication specifications of external equipment and MES systems, ensures valid transmission of the Authorization parameter |
| Request parameter template | Fixed fields for furnace number, batch number, and detection time; populate field values with original collected data | Adapts to the storage logic of heat treatment data updated per furnace load or batch, facilitates passing core identifiers via the variables parameter |
| Response field filtering | Only return two core fields: exception judgment result and associated document ID | Uses the responseData parameter to streamline returned content and reduce invalid data transmission |
| Timeout threshold setting | Set to 10–30 seconds | Adapts to the data return rhythm of equipment such as temperature control instruments and hardness testers, avoids process blocking caused by excessive waiting time |
| Retry mechanism configuration | Retry 2 times when a 503 status code is encountered, with a 5-second interval | Addresses scenarios where external equipment has temporary high load, ensures stability of data collection |
| Data format conversion | Convert collected furnace temperature curve images to base64 encoding before transmission | Meets the transmission requirements of unstructured data for external systems |
This page configuration references FastGPT v4.17.0.
Three Common Misconfigurations
- A 503 status code error occurs, with a prompt indicating the interface or equipment is unavailable. The root cause is that no retry mechanism for external equipment is configured, so no automatic retry is triggered when equipment has temporary high load.
- No fields are returned after interface calls, and corresponding furnace load data cannot be matched. The root cause is that required parameters such as furnace number and batch number are not passed per the preset template, so the external system cannot locate the target data.
- External equipment interface calls fail after the deployment environment upgrades the HTTPS protocol. The root cause is that the external system access address protocol has not been updated in FastGPT configuration, and SSL certificate verification rules have not been adapted, resulting in request interception by security policies.
How to Confirm Configuration Is Correct
- Manually pass request parameters for a known abnormal furnace load, and check if the exception judgment result returned by the interface matches the actual inspection report.
- View FastGPT's interface call logs, confirm that the
Authorizationparameter is correctly carried and not truncated or tampered with. - Simulate a 503 status code returned by external equipment, check if the preset retry mechanism is triggered and data collection is completed.
- Check the request data received by the external system, confirm that required fields such as furnace number and batch number are included and the format meets requirements.
The values provided are common starting points and should be evaluated against the reader's own samples.