What This Category of Data Looks Like
Production ledger data for photovoltaic cell manufacturing comes from multiple sources: silicon wafer incoming materials, MES systems, production line terminals such as diffusion furnaces, coating equipment, and printing presses, EL and IV testing equipment, and laboratory test data. Data update cycles include per-batch production synchronization, real-time upload of per-product test results, and daily aggregate statistics. Document types include process cards, diffusion and coating parameter records, printing and sintering process ledgers, EL test images, IV grading data, and yield reports. Fields include batch numbers, process temperature and time parameters, equipment operating status, conversion efficiency metrics, crack determination results, and more. Units include degrees Celsius, seconds, watts per square centimeter, and others.
Constraints on Database and Operations Workflows
Multi-source heterogeneous data sources require the database to support mixed storage of structured parameters, unstructured images, and numerical reports. High-frequency real-time update cycles require the database to support high-concurrency writes and low-latency queries to avoid delayed ledger data. Fine-grained wafer-level data will lead to rapid growth in storage volume, requiring reasonable planning of data sharding and archiving strategies. The correlation between process parameters and test data requires the database to support complex associated queries to support efficiency loss tracing and grading statistics. At the same time, the confidentiality of production line data requires strict access permission control during database operations to prevent leakage of core process parameters.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://prod_user:secure_pass@prod-db:27017/photovoltaic_ledger?replicaSet=rs0 | Adapts to storage of unstructured and semi-structured data such as EL test images and process parameters, and supports sharding and scaling to handle wafer-level data growth |
PG_URL | postgresql://prod_user:secure_pass@prod-db:5432/prod_ledger?sslmode=require | Stores structured batch ledgers, yield reports and IV grading data, and supports complex associated queries and statistical analysis |
DB_MAX_LINK | 20–50 | Matches the concurrency of thousands of test data writes per batch, avoiding write failures caused by database connection pool exhaustion |
REDIS_URL | redis://:secure_pass@prod-cache:6379/0 | Caches frequently accessed process parameters and batch metadata to reduce query pressure on the primary database |
Three Common Configuration Mistakes
- The database connection plugin in the workflow shows a
connect ETIMEDOUTerror, and the database can be accessed normally locally. Cause: The internal network IP segment of the photovoltaic production line database is not added to the network whitelist of the FastGPT v4.17.0 container, or the configured database port is not open to the production network. - For database connection operations in the same workflow, the time taken by FastGPT v4.17.0 is significantly higher than that of older versions. Cause: The
DB_MAX_LINKconfiguration value is too low, causing the connection pool to fail to meet concurrent write requirements, and new connections must wait for a timeout before retrying. - It is impossible to distinguish ledger data from different production line devices in logs. Cause: The device ID is not carried as a metadata field during data writing, or the database query filter conditions are not correctly bound to the source identifier.
How to Verify Successful Configuration
- Run the built-in database connection test tool in FastGPT, enter the corresponding
MONGODB_URIandPG_URL, and verify that the connection status returns normal. - Simulate a write operation of thousands of pieces of data in a single batch, check the database connection number monitoring, and confirm that the actual connection number does not exceed the
DB_MAX_LINKconfiguration value. - Write test data from different production line devices, and confirm that each piece of data carries the correct source identifier through database queries or log retrieval.
- Check the Redis cache monitoring, and confirm that the cache hit rate of frequently accessed process parameters meets the standards calibrated through actual business testing.
The values provided here are common starting points and should be measured against the reader's own samples.