What the data for this category looks like
Data for this category primarily comes from raw fiber inspection, ERP systems, real-time data from spinning and looms, evenness testers, fabric inspection machines, and order specification documents. Daily documents include cotton blending or ingredient lists, process setup sheets, equipment operation records, yarn inspection reports, grey fabric inspection records, and defect ledgers. Data updates occur per batch of raw materials, per shift, and per piece of fabric. Each document includes fiber indicators, process parameters, equipment operating status, inspection results, and defect information. Some fields use specialized units: for example, twist is measured in twists per meter, and fabric length is measured in meters.
What constraints these characteristics impose on the HTTP Interfaces and External Systems workflow
Multi-source heterogeneous data sources require interfaces to support receiving different formats of structured and semi-structured data, and to adapt to the native output formats of devices such as evenness testers and fabric inspection machines. The high-frequency update rhythm requires interface calls to match the trigger timing of each batch, shift, and fabric piece, to avoid request backlogs or missed critical node data. Each document contains multiple specialized fields, so the interface request body needs to reserve sufficient field capacity while accommodating field differences across data sources. The time sensitivity of production links requires interface response times to be kept within a reasonable range, to avoid disrupting the connection between equipment operation and inspection processes.
How to configure the settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Authorization | Bearer custom secret | Complies with HTTP interface authentication standards to secure communication between external systems and FastGPT |
| Interface timeout period | 300–600 seconds | Matches the typical processing time for single-batch data in the spinning and weaving link, preventing request failures due to delayed equipment data collection |
variables | {"batchId": "{{batchId}}", "fabricLength": "{{fabricLength}}"} | Passes specialized business parameters for spinning and weaving such as batch ID and fabric length, to facilitate matching subsequent audit logic with corresponding data |
| Retry count | 2–3 times | Addresses scenarios such as network fluctuations or temporary device disconnections, reducing the risk of data loss |
responseData | Only return verification results and defect markers | Streamlines returned content, reduces parsing pressure on external systems, and adapts to the low-latency requirements of production links |
| Authentication method | Fixed secret authentication | Matches common authentication configurations for industrial systems such as spinning equipment and ERP systems, eliminating the need for complex OAuth processes |
Three common misconfigurations
- The FastGPT HTTP interface cannot be accessed by external systems after deployment. Cause: Container port mapping was not configured correctly, or firewall rules restricted access to the corresponding port.
- Calls to third-party large model interfaces return a 401 Unauthorized error. Cause: The
Authorizationparameter was not configured correctly, or the secret format or permission scope did not meet the authentication requirements of the third-party model. - Grey fabric inspection data passed in by external systems cannot be parsed correctly. Cause: Specialized business fields for spinning and weaving such as fabric length and defect type were not passed via the
variablesparameter, causing the audit logic to fail to match the corresponding data.
How to confirm the configuration is correct
- Send a test request for a simulated cotton blending list to the FastGPT HTTP interface, check that the returned status code is 200 and includes the expected verification results.
- View the interface logs to confirm that the
Authorizationparameter was correctly carried and passed authentication. - Simulate high-frequency interface calls, check that a reasonable retry mechanism and QPS limits are triggered, with no abnormal errors.
- Pass test data containing specialized fields, confirm that the
variablesparameter was correctly parsed and applied to the audit logic.
The values provided are common starting points and should be measured against the reader's own samples.