What the data for this category looks like
Inspection and process record data for photovoltaic cell manufacturing typically comes from silicon wafer incoming material ledgers, factory MES systems, real-time operating parameters of diffusion furnaces, coating equipment, and printing presses, test results from EL and IV testing equipment, and aggregated laboratory data. Daily documents include process cards, diffusion and coating parameter records, printing and sintering process ledgers, EL inspection images, IV grading data, and yield summary reports. Data updates occur per batch and per inspected wafer, while daily documents are aggregated each day. Common fields include batch number, measured process parameter values, appearance defect level, and electrical performance test results, with units aligned to general requirements for corresponding industrial parameters.
What constraints these characteristics impose on HTTP interfaces and external systems
Inspection data for photovoltaic cell manufacturing comes from multiple sources and updates frequently. This requires HTTP interfaces to support multi-source data pulling and unified field mapping, to adapt to differences in data formats across various devices and systems. High-frequency updates at both wafer and batch levels require interfaces to have low latency and high reliability, to avoid data transmission delays disrupting production workflows. Data includes both structured parameters and unstructured images, requiring interfaces to support both structured field transmission and file-associated uploads. Additionally, deviations between process parameters and test results directly affect product quality, requiring interfaces to have strict field verification capabilities to ensure the legality of incoming and returned data. This configuration is formulated based on the interface rules of FastGPT v4.17.0.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Fixed format: Bearer + application-specific secret key | Implements interface authentication to prevent unauthorized external systems from accessing inspection data |
variables | Preset production batch ID and inspection type as required incoming variables | Adapts to the batch-based traceability business logic of photovoltaic cells, simplifying parameter passing for external systems |
responseData | Configured to return structured data including batch number, EL defect flag, and IV grading results | Meets the structured storage requirements for inspection data in industrial systems |
| Request timeout | 60 seconds | Adapts to the transmission and processing duration of photovoltaic inspection data, preventing request failures due to delays |
| Batch request limit | 100 items per request | Adapts to the scale of inspection data for single-batch photovoltaic cells, preventing single requests from exceeding system processing capacity |
| Response format | JSON format | Compatible with data parsing standards of most production systems, facilitating direct storage in MES databases |
Three common mistakes
- A
401 Unauthorizedstatus code is returned when calling the interface, caused by incorrect configuration of theAuthorizationparameter and failure to carry the specific secret key generated by the FastGPT application. - The interface returns empty results, caused by failure to correctly pass the preset production batch ID variable and failure to match the inspection and process records for the corresponding photovoltaic cells.
- The interface triggers a timeout error, caused by unreasonable setting of the request timeout duration and failure to adapt to the delay in pulling device data.
How to confirm the configuration is correct
- Call the interface with the preset test batch ID, and check whether the returned response status code meets expectations.
- Verify that the returned fields match the configured
responseDatarules, confirming that only the photovoltaic inspection data required for the business is included. - Simulate multiple repeated calls to the same batch of data, check whether the interface can correctly return the same results, confirming that the idempotency configuration is in effect.
- View the call logs of the FastGPT application, confirm that the interface requests are correctly recorded and there are no abnormal error messages.
The values provided are common starting points and should be measured against the reader's own samples.