What this category's data looks like
Core data for this category comes from dyeing, finishing, laboratory, and quality departments of fabric enterprises and dyeing and finishing factories. Daily documents include dyeing formulas, chemical weighing records, dye vat operation curves, sample confirmation sheets, color difference reports, setting process sheets, and sewage ledgers. Data sources cover customer color samples, internal formula libraries, dye chemical batch ledgers, dye vat control systems, colorimeters, setting equipment, and environmental monitoring devices. Data is updated at the per-vat, per-batch, or per-shift frequency. Fields include process parameters, batch identifiers, test values, and other related content.
What constraints these characteristics impose on the "HTTP Interfaces and External Systems" link
Batch-based, high-frequency data updates require interfaces to bind unique batch identifiers to associate process and test records for individual vats or batches. Multi-source and heterogeneous data types — structured ledgers, device-reported data, and report files — require interfaces to support mixed-format requests and responses. The per-vat, per-batch, or per-shift update rhythm requires interface single-request payloads to adapt to small-batch data bulk uploads to avoid timeouts. Because errors such as color differences or excessive emissions have serious consequences, interfaces must include clear permission verification mechanisms to ensure compliant data access.
How to set up configurations
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Request timeout | 30-60 seconds | Adapts to the transmission duration of small-batch vat-level data, avoids request blocking caused by device network fluctuations |
Authorization | Use a fixed secret key as the verification header | Ensures compliant interface access permissions, prevents unauthorized tampering of process and test data |
variables binding | Bind dye vat numbers and batch numbers as custom variables | Enables automatic association between batch data and workflow processes, matches vat-level data processing requirements |
| Request body format | Include batch number, process type, and test value fields | Meets the associated storage requirements of this category's data, facilitates subsequent traceability for quality audits |
responseData processing | Extract batch verification results and upload status | Quickly confirm the validity of data uploads, supports real-time quality monitoring |
| Retry strategy | Retry 3 times when 400 or 500 status codes are triggered | Adapts to the unstable network environment of device reports, reduces the probability of data upload failures |
Three common errors
- Phenomenon: The interface returns 400 Bad Request, and the POST request fails. Cause: The
Authorizationparameter is not configured correctly, or required fields such as batch numbers are missing from the request body, which does not comply with interface verification rules. - Phenomenon: The third-party caller shows no permission. Cause: The caller's secret key is not included in the
Authorizationverification configuration, or the permission scope does not cover the corresponding quality audit workflow. - Phenomenon: No corresponding audit result is generated after calling the specified workflow. Cause: The process parameters of the corresponding dye vat are not bound in
variables, causing the workflow to fail to match the corresponding batch data.
How to confirm the configuration is correct
- Initiate a single test request carrying a valid batch number and
Authorizationparameter, check that the returnedresponseDatacontains the expected verification results. - Simulate multi-batch data uploads, confirm that the interface completes data classification according to the batch identifiers bound in
variables, with no data mixing. - Initiate a request with an unauthorized secret key, confirm that the interface returns a clear permission error prompt.
- Disconnect the network before initiating a request, confirm that the interface triggers the preset retry mechanism with no risk of data loss.
The values provided are common starting points and should be measured against the reader's own samples.