What the data for this category looks like
Data for this category comes from material grade archives, customer technical requirement documents, temperature control instruments, furnace temperature uniformity testing equipment, hardness testers, metallographic analyzers, and shop floor MES systems. Data is updated in real time per heat and per batch. Daily outputs include heat treatment process cards, furnace loading layout diagrams, furnace temperature curve records, hardness and metallographic inspection reports, equipment inspection sheets, exception handling documents, and other related materials. Data fields include furnace temperature set and measured values (unit: ℃), holding time (unit: hours), hardness test values (unit: HV), metallographic structure ratings, and more. Individual data entries are small in size, but differences between batches are significant.
What constraints these characteristics impose on HTTP interfaces and external systems
HTTP interfaces for this category must support precise query parameters using furnace number and batch number, to meet the real-time per-heat and per-batch data update requirements. This prevents full data pulls that create unnecessary redundancy. The requirement for multiple fields with specific units means interface requests must carry limiting parameters such as material grade and inspection item type. Returned results must strictly match preset unit formats. The variety of daily document types requires interfaces to support transmission formats for both structured time-series data (such as furnace temperature curves) and unstructured documents. The real-time synchronization need for exception handling documents requires interfaces to have low-latency callback capabilities. This prevents delays in quality audit workflows.
How to set up the configuration
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Authorization | Carry a Bearer token from the heat treatment shop floor MES system | Used for interface identity verification to prevent unauthorized access |
variables | Bind furnace number and batch number as query variables | Accurately match inspection data for a single heat |
responseData | Extract furnace temperature, hardness values, and metallographic rating fields | Only return core data required for quality audits |
| Timeout period | 600 seconds | Accommodates complete transmission of time-series data such as furnace temperature curves |
| Retry count | 3 times | Addresses interface call failures caused by shop floor network fluctuations |
Three common misconfigurations
- A 401 Unauthorized status code is returned by the interface, and an invalid token message is displayed. This occurs when the Bearer token from the heat treatment shop floor MES system is not correctly bound, or the token has expired and not been updated.
- Quality data fields returned by the interface lack unit information. For example, hardness values do not include the HV unit. This happens when unit mapping rules for fields are not specified in the
responseDataconfiguration. Parsed data formats will then fail to meet audit requirements. - Interface data pulls include redundant information from multiple heats, making accurate matching to the current audit batch impossible. This occurs when furnace number and batch number are not bound as query parameters in
variables. Data is pulled using only a time range instead.
How to confirm the configuration is properly set up
- Call the configured HTTP interface, pass a test furnace number and batch number, and verify that the returned data includes the preset core fields.
- Check the interface response headers to confirm that
Authorizationverification passes, with no 401-level errors. - Review interface call logs to confirm that only heat treatment data for the current batch is returned, with no redundant multi-heat information.
- Simulate an exception scenario such as an expired token, and confirm that the interface returns a corresponding error prompt that meets expectations.
The values provided here are common starting points, and should be evaluated against the reader's own samples.