What data looks like for this category
Daily operational data from professional heat treatment plants, heat treatment workshops of automotive and bearing component manufacturers, including heat treatment process cards, charging charts, furnace temperature curves, hardness and metallographic reports, equipment inspection forms, and exception handling documents. Data originates from material grades, customer technical requirements, temperature control instruments, furnace temperature uniformity testing equipment, hardness testers and metallographic equipment, and MES systems. Update frequency is per heat batch and per lot. Fields include heat batch number, batch number, material grade, furnace temperature parameters, hardness values, metallographic structure ratings, and more. Some parameters have fixed units: furnace temperature in degrees Celsius, hardness in HV or HRC.
What constraints these characteristics impose on HTTP interfaces and external systems
Since data is updated dynamically per heat batch and per lot, HTTP interfaces must support passing exclusive business parameters for the current heat batch to avoid mixing data across batches. Cross-validation of multiple document types and multi-source data requires interfaces to return structured validation results to quickly locate abnormal fields. The consequences of abnormal heat treatment data are severe, so interfaces must use reasonable timeout and retry policies to prevent validation interruptions caused by network fluctuations. At the same time, interfaces must support data in different formats from equipment such as temperature control instruments and hardness testers, and adapt to the authentication rules of internal enterprise systems.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Authorization | Bearer {internal enterprise API authentication key} | Adapts to the authentication rules of heat treatment equipment and MES systems to prevent unauthorized access |
variables | {"炉次号": "{{batchId}}", "材料牌号": "{{material}}", "炉温曲线ID": "{{curveId}}"} | Passes core business parameters for the current heat batch, matching the update frequency per heat batch |
responseData | {validation result: boolean, abnormal field list: array, exception description: string} | Returns cross-review results in a standardized format to quickly locate process or data abnormalities |
| Request timeout setting | 240–300 seconds | Covers the actual time required to pull furnace temperature data and perform multi-document validation |
| Retry trigger condition | Trigger only on connection timeout or 5xx status codes | Prevents duplicate processing of heat batch data caused by repeated submissions |
| Parameter validation switch | Enable full-field validation | Intercepts abnormal data that does not meet material grades or customer technical requirements |
Three common configuration mistakes
- Symptom: The interface returns a
504 Gateway Timeoutstatus code, or the interface displays "Request timed out". Cause: The timeout configuration was not adjusted based on the actual time required to pull heat treatment data, and the default short timeout setting was used. - Symptom: The interface returns an
Invalid URLerror, or theresponseDatafield is empty. Cause: The interface address of the external system was not configured correctly, or the request path does not match the actual route of the external service. - Symptom: The cross-review result does not include abnormal values from the hardness report, or the fields do not match the actual data collected by the equipment. Cause: The SQL execution results of the data source plugin were not mapped to the
variablesparameter, causing the external system to fail to obtain complete validation data sources.
How to verify the configuration is complete
- Call the configured HTTP interface, pass test heat batch numbers and material grades, and check if the returned
responseDatacontains the expected validation result fields. - Send a request with simulated abnormal parameters (such as furnace temperature values beyond a reasonable range) to confirm that the interface returns a clear exception description.
- Check the FastGPT v4.17.0 system logs to confirm that the
Authorizationauthentication parameter was correctly carried, and there are no authentication failure errors. - Adjust the test request duration to verify that the timeout configuration triggers a retry according to the set threshold.
The values provided are common starting points and should be measured against the reader's own samples.