What data for this category looks like
Data for this category primarily comes from ERP material master data, melting furnaces and thermometers, spectrometers, MES heat records, mold management systems, and customer technical drawings. Daily documents include foundry process cards, batching lists, heat records, mold histories, first article inspection records, material reports, and nonconforming product review forms.
Data updates are triggered per heat, batch, or daily. Core fields include alloy batching ratio, melting temperature, pouring parameters, heat number, mold cycle number, and defect type. Field units include degrees Celsius, seconds, and similar units.
Constraints for HTTP Interfaces and External Systems
The high-frequency updates and multi-source heterogeneous nature of this category’s data requires HTTP interfaces to support response times from milliseconds to seconds. This adapts to different input parameter formats of equipment such as melting furnaces and spectrometers, and management systems such as ERP and MES.
Strong correlations between core fields (for example, heat number corresponding to batching records, mold cycle number linked to defect data) require interfaces to support associated parameter passing. Identity authentication must be completed via the Authorization field to prevent unauthorized tampering or leakage of quality data.
Additionally, quality issues have serious consequences. Interfaces must include built-in timeout circuit breaking and failure retry logic to avoid interrupting audit workflows due to interface exceptions.
How to Configure
This configuration logic applies to FastGPT v4.17.0.
| Config Item | Recommended Value | Rationale |
|---|---|---|
Authorization | Bearer {foundry workshop-specific authentication token} | Adapts to standard authentication logic for industrial equipment and management systems, ensuring secure interface access |
variables | {"filter heat": "recent 3-day heats", "focus defects": ["shrinkage porosity", "gas porosity", "slag inclusion"]} | Matches core business requirements for filtering data by time and defect type during foundry supplier audits |
responseData | Return heat ID, alloy composition report, nonconforming product review form number | Returns streamlined fields only to reduce interface transmission load, adapting to scenarios with limited workshop network bandwidth |
| Interface timeout period | 30–60 seconds | Adapts to data upload delays from melting furnaces, spectrometers, and other equipment, avoiding call failures due to slow device responses |
| Failure retry count | 2 times | Addresses temporary network fluctuations or system lag, reducing audit workflow interruptions caused by single interface exceptions |
Three Common Mistakes
- Interface calls return the
401 Unauthorizedstatus code, indicating authentication failure. The cause is incorrect configuration of the token format in theAuthorizationfield, missing theBearerprefix, or using an expired authentication token. - The
responseDatafield returned by the interface is empty, and no quality data for the corresponding heat is obtained. The cause is failure to pass correct filtering parameters invariables, so the interface does not match qualifying heat records. - Interface calls time out, and data cannot be pulled. The cause is that the configured timeout period is shorter than the data upload duration of melting equipment, failing to adapt to device response characteristics of foundry scenarios.
How to Confirm Configuration is Complete
- Call the configured HTTP interface, check if
responseDataincludes preset core fields such as heat ID and alloy composition report. - Pass preset
variablesfiltering parameters, verify if the interface returns quality data matching the filtering conditions. - Simulate temporary network fluctuations, verify if the interface completes failure retries according to the configured retry count.
- Check interface authentication logs, confirm that the token in the
Authorizationfield is correctly identified, with no unauthorized access records.
The values provided here are common starting points, and should be evaluated against the reader’s own samples.