What the data for this category looks like
Data for spraying and coating scenarios covers end-to-end process workflows. Sources include TDS/SDS documents from coating suppliers, real-time sensor data from spray booths and curing ovens, test results from colorimeters and film thickness gauges, data from workshop environmental monitoring equipment, and production records from factory MES systems. Daily documentation includes coating process cards, coating batch lists, paint mixing logs, film thickness and color difference reports, oven temperature curves, defect repair orders, and VOC emission ledgers. Data is synchronized once per coating batch and per production shift. Equipment data is collected continuously. Fields include process parameters, test values, and batch identifiers, with category-specific units and formats.
What constraints these characteristics impose on HTTP interfaces and external systems
Multiple heterogeneous data sources require interfaces to support multiple formats: structured batch lists, CSV test data, and binary test reports. High-frequency data updates require interfaces to support batch submission and low-latency responses, to align with continuously collected equipment data and shift-based batch update requirements. Category-specific fields and units require interfaces to support custom metadata transmission, ensuring business parameters correctly match process cards and test reports in the knowledge base. External supplier documents must be synchronized via interfaces, so interfaces must support cross-system authorization. Configurations must also guarantee data transmission security and integrity to meet the strict requirements of coating scenario quality audits.
Configuration Setup
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| Authentication Method | Bearer {FastGPT Platform API Key} | Complies with the interface standard authentication format for FastGPT v4.17.0, ensuring security for external system calls |
| Variable Passing | {"batchNo":"{{batch_id}}", "filmThickness":{{film_data}}, "vocLevel":{{voc_value}}, "kbId":"{{target_kb}}"} | Transmits dedicated business variables including coating batch number, film thickness test value, VOC concentration, and target knowledge base ID. Matches process cards and test data in the knowledge base to enable specified knowledge base calls |
| Return Data Parsing | Parse according to the {"status":"code", "message":"msg", "result":"data"} format | Standardizes processing of results returned by FastGPT interfaces, adapting to the batch data verification logic for spraying scenarios |
| Request Timeout | 600 seconds | Accommodates transmission delays for continuously collected coating equipment data, preventing interruptions from high-frequency requests |
| Batch Submission Limit | First 20 entries | Matches the single submission scale of per-shift coating batches, reducing the probability of interface call failures |
Three Common Mistakes
- Symptom: The metadata field is empty when calling the
/api/core/dataset/collection/create/localFileinterface. Cause: Thevariablesparameter is not correctly included in the request body, and business metadata such as coating batch and film thickness parameters are not transmitted. - Symptom: The interface returns a 401 unauthorized error. Cause: The
Authorizationparameter format is incorrect, and the Bearer prefix is not used or the key is invalid. - Symptom: Unable to initiate requests normally when connecting using Java. Cause: The request header and request body are not constructed according to FastGPT interface requirements, and the
variablesandAuthorizationparameters are not correctly transmitted.
How to Confirm Configuration Completion
- Call the authentication interface to verify the validity of the
Authorizationparameter, confirm that the returned status code is 200. - Submit a simulated coating batch data entry, check whether the parsed
responseDataresult includes expected business fields and knowledge base ID. - Write a test script using Java to initiate a complete interface call, confirm that the request succeeds and the result meets expectations.
- Compare documents synchronized to the knowledge base with coating batch lists submitted by the external system, confirm that metadata matches consistently.
The values provided are common starting points and should be measured against the reader's own samples.