What This Category’s Data Includes
This category includes data across the full coating workflow. Data comes from TDS and SDS documents provided by coating suppliers, real-time operating data from spray booths and ovens, test results from colorimeters and film thickness gauges, and production records from MES systems. Daily output documents include coating process cards, coating batch sheets, paint mixing records, film thickness and color difference reports, oven curves, defect repair sheets, and VOCs ledgers. Data updates follow batch updates per coating batch, shift updates per shift, and continuous collection during production. Document fields include film thickness, color difference ΔE, oven temperature, VOCs concentration, and other metrics, using standard industry-specific units.
Constraints Imposed on HTTP Interfaces and External Systems
The multi-source data, varied update rhythms, and multiple document types create multiple constraints for the HTTP interfaces and external systems link. Multi-source data from instruments, MES systems, and supplier documents means interfaces must support parsing both structured production data and unstructured document formats. Batch-based and continuous collection update rhythms require interfaces to pull data precisely by batch identifier or timestamp, to avoid resource waste from full-volume pulls. Differences in fields and units across documents means interfaces must unify format mapping when receiving or sending data, to prevent information errors from field mismatches. Additionally, transmission of compliance-related data such as VOCs ledgers requires stable linkages, to prevent data loss that could lead to compliance issues.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {custom authentication key} | Complies with general HTTP interface authentication specifications, adapts to authentication requirements of most external coating-related systems, and aligns with the authentication configuration logic of FastGPT v4.17.0 |
| Timeout Period | 30–60 seconds | Adapts to batch pulling and real-time collection rhythms of coating data, avoids interrupting production data synchronization due to request timeouts |
variables | {"batchCode": "{{auto_fill_batch}}", "testDevice": "film_thickness"} | Dynamically binds business variables such as coating batches and test equipment, adapts to data pulling requirements for different scenarios |
| Response Data Extraction Rule | Extract business content by specified fields | Unifies processing of response formats from different external interfaces, simplifies subsequent call logic |
responseData | Extract the content of the data field under the root node | Most external services encapsulate business data in the standard data field, facilitating quick parsing of production and test data related to coating |
| Retry Attempts | 2–3 times | Addresses temporary network fluctuations, avoids missing coating quotation data due to a single failed request |
Three Common Misconfigurations
- Phenomenon: Syntax errors occur when calling the fetch method in the HTTP module, or requests cannot be sent normally. Cause: FastGPT’s HTTP module encapsulates request logic by default. Manual writing of fetch code is not required; use the built-in configuration to complete requests. Manual fetch code will trigger syntax verification failures.
- Phenomenon: Configured external domain interfaces cannot be accessed, returning connection timeout or domain name resolution failure. Cause: The deployment environment is not configured with the correct network proxy, or the external domain has not opened public network access permissions for the corresponding port, resulting in failed link connectivity.
- Phenomenon: The
responseDatafield returned after calling the interface is empty, or expected coating data is not extracted. Cause: The response data extraction rule is not configured correctly, or the business data returned by the external interface is not encapsulated in the standarddatafield, resulting in parsing failure.
How to Verify Successful Configuration
- Send a test request, check the response content returned in the FastGPT interface, confirm whether it includes expected coating process cards, coating batch data, and other content.
- Check the interface authentication configuration, use a third-party tool to simulate the same
Authorizationrequest header, confirm that the external interface can respond normally. - Simulate requests for different batches of coating data, verify whether the dynamic variables in
variablesare correctly bound, and whether the pulled data matches the corresponding batch. - Disconnect the network and send the request again, confirm that the retry mechanism is triggered normally, to avoid data synchronization interruption caused by a single failure.
The values provided here are common starting points and should be evaluated against the reader’s own samples.