What this category's data consists of
The production ledger data for metal cutting machining includes documents such as process routes, operation cards, tool lists, CNC programs, first-piece inspection records, patrol inspection forms, and measuring tool calibration records. Data sources include product drawings and 3D models, CAPP/CAM systems, machine tool collection data, tool management systems, MES, and measuring and inspection tool data. Updates are triggered per work order, per machined workpiece, during sampling inspections, and when tools are replaced. Fields include operation number, tool model, cutting speed, feed rate, machining dimensional deviation, and tool wear amount, with units of piece, minute, revolutions per minute, mm/min, millimeter, and micrometer respectively.
Constraints imposed on database and operations maintenance
The ledger data for metal cutting machining is multi-source and heterogeneous. It requires the database to support hybrid storage of structured process parameters and unstructured CNC programs and drawing files. The high update frequency causes frequent concurrent writes. Database connection pool configurations must match the business concurrency volume. The need for associated storage of multiple document types demands clear data mapping relationships to avoid field confusion. Data anomalies directly cause workpiece scrap or machine tool downtime. Database backup and verification mechanisms must meet the business’s high reliability requirements.
How to set configurations
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://metalcutting:pass123@db-host:27017/fastgpt_metal?authSource=admin | Adapts to multi-source heterogeneous data storage, supports real-time machining data collected by machine tools and unstructured files such as CNC programs |
PG_URL | postgresql://metaluser:pass456@db-host:5432/metal_workflow?schema=cutting | Stores structured ledger data such as operation cards and patrol inspection forms, ensures efficient relational query performance |
DB_MAX_LINK | 20–30 | Matches concurrent access requirements for multiple work orders in metal cutting machining, prevents connection exhaustion |
REDIS_URL | redis://:redispass@cache-host:6379/0 | Caches frequently accessed tool lists and process route data, reduces read and write pressure on the primary database |
Three common errors
- Issue: Connection to the FastGPT local MongoDB database via MongoDB Compass fails, with prompts for authentication failure or connection timeout. Cause: The correct username, password, and authentication database are not configured in
MONGODB_URI, or the local firewall blocks access to port 27017. - Issue: FastGPT fails to start after Docker deployment, with database connection errors in the logs. Cause: The
MONGODB_URIorPG_URLparameters are not correctly modified in the deployment environment variables, pointing to an incorrect database address or missing authentication information. - Issue: After importing an Excel-format patrol inspection form, some dimensional deviation fields are empty or have abnormal values. Cause: The import mapping is not configured according to the fixed field structure of the metal cutting machining patrol inspection form, and the unit consistency of numeric fields is not verified.
How to confirm configurations are correct
- Run the built-in database connection test tool to verify that
MONGODB_URIandPG_URLcan establish normal connections, with no authentication or network errors. - Submit a simulated machining work order data entry, check that the data can be correctly written to the database and associated with corresponding tool lists, operation cards, and other related documents.
- View Redis cache monitoring metrics, confirm that the cache hit rate of frequently accessed process route data meets the preset threshold for the business scenario.
- Trigger a production ledger summary task, check that the generated report includes all required fields, with no missing or format disorder issues.
The values provided are common starting points and should be measured against the reader's own samples.