What the Data for This Category Looks Like
Core business data for photovoltaic solar cell manufacturing comes from wafer incoming material ledgers, manufacturing execution systems (MES), diffusion furnaces/coating equipment, printing and sintering equipment, EL and IV testing equipment, and laboratories. Data updates follow three cadences: production batches, single-wafer test results, and daily summaries. Daily documents include process cards, diffusion and coating parameter records, printing and sintering ledgers, EL test images, IV grading data, and yield reports. Core fields include batch identifiers, process parameters, test defect results, and performance grading results. There are no fixed statistical percentages or monetary standards.
What Constraints Do These Characteristics Impose on HTTP Interfaces and External Systems
Multi-source heterogeneous data sources require interfaces to support mixed transmission of structured parameters and binary files, to adapt to the pulling of different types of business documents. The high-frequency update cadence requires interfaces to have low-latency batch query capabilities, to avoid affecting the timeliness of tender quotations due to data synchronization delays. The fine-grained batch and single-wafer data requirements require interfaces to support precise filtering based on conditions such as batch numbers and production times, to ensure returned data matches tender needs. Large-volume EL test images and IV data documents require interfaces to have large file transfer and parsing capabilities, to prevent call failures due to file size limits.
How to Set Up Configurations
The following configurations are based on FastGPT v4.17.0, and involve core interface parameters such as Authorization, variables, and responseData, to adapt to the business data characteristics of photovoltaic solar cell manufacturing.
| Configuration Item | Suggested Setting | Rationale |
|---|---|---|
| Authentication Configuration | Enable Bearer Token authentication, configure the secret key according to enterprise security policies | Ensure transmission security of production data and tender data |
| Request Timeout | 60–120 seconds | Adapt to the pull and parsing time of large-volume EL test images and IV data |
| Response Data Filtering | Only retain fields including batch numbers, process parameters, grading results, and defect records | Streamline returned content and improve interface call efficiency |
| Batch Query Limit | Calibrate based on actual measurements | Adapt to data volumes of different production batches and avoid interface overload |
| File Upload Size Limit | 1000 MB | Support transfer of complete large documents such as EL test images and sintering records |
| Retry Mechanism | Retry 2 times after failure, with a 5-second interval | Respond to temporary network fluctuations or delays in equipment data synchronization |
Three Common Misconfigurations
- Phenomenon: Calling the file parsing interface returns a 504 timeout error, or parsing progress stagnates for a long time. Cause: The interface timeout period is not adjusted, and the parsing requirements for large-volume files such as EL test images and IV data are not adapted.
- Phenomenon: Testing the call fails after configuring the local model proxy, returning authentication failure or connection timeout prompts. Cause: The
Authorizationparameter is not correctly configured, or the network access permission of the local model is not opened. - Phenomenon: Reusing a previously configured proxy service is not possible when calling a custom AI interface, requiring repeated entry of proxy parameters. Cause: The current proxy service is embedded in the FastGPT platform, and does not support binding to separate call links.
How to Confirm Configurations Are Correct
- Execute a batch production data pull request, and check whether the returned fields include the process parameters, grading results, and defect records required for the tender.
- Upload an EL test image, and verify that the interface can receive and parse the file normally, with no timeout or format error prompts.
- Initiate a test call after configuring the local model proxy, and confirm that the request is normally forwarded to the target model and returns valid results.
- Simulate a request without a valid token, and check that the interface returns the correct authentication failure prompt.
The values provided here are common starting points, and should be evaluated against the reader's own samples.