This document is written based on FastGPT v4.17.0.
Data Profile for This Product Category
Data for this product category comes from daily documents and production systems of motor factory winding, insulation, final assembly, and testing departments. This includes winding process cards, insulation treatment records, dynamic balance reports, dielectric strength test reports, and type and factory acceptance test reports.
Data sources cover electromagnetic design data, copper wire and insulation material batch information, winding equipment, balancing machines, electrical test benches, and ERP/MES systems. Update frequency is once per unit or batch produced, with sync on every design change.
Core fields include winding turns, wiring method, impregnation and drying parameters, vibration values, temperature rise data, dielectric strength indicators, and more. These fields require association with material batches and equipment operation logs.
Constraints for Database and Operations Maintenance
Data for this product category is multi-source and scattered. It requires linking electromagnetic design data, material batches, equipment operation logs, and test reports. Databases must support multi-table join queries and cross-system data synchronization.
Data updates occur frequently, and single changes link multiple linked documents. This creates clear requirements for database write performance and transaction consistency.
Core fields cover three professional parameter categories: electromagnetic, insulation, and mechanical balance. Unified field naming and unit standards are required to avoid data format chaos during operations.
Quality problem traceability requires full-link data alignment. Databases must support immutable historical version storage. Regular verification of data association integrity is required during operations to prevent traceability failure caused by misaligned batch or device information.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PG_URL | postgresql://motor_user:secure_pass@db-host:5432/motor_quality?sslmode=require | Supports multi-table join queries, adapts to cross-link association requirements for winding, material, and test data |
DB_MAX_LINK | 20–30 concurrent connections | Matches concurrent write requests per production batch, prevents write failures from exhausted connection pools |
REDIS_URL | redis://redis-user:redis-pass@redis-host:6379/0 | Caches frequently accessed dynamic balance reports and dielectric strength test data to reduce database read load |
MONGODB_URI | mongodb://mongo-user:mongo-pass@mongo-host:27017/motor_docs?authSource=admin | Stores unstructured type test reports and process documents, adapts to large field storage requirements |
Three Common Configuration Mistakes
- Phenomenon: After entering the pgvector container environment, SQL queries cannot be executed, and the error "Failed to resolve connection address" is returned. Cause: The database port was not correctly mapped when starting the container, or the
PG_URLparameter was not configured with the container intranet address, leading to connection failure. - Phenomenon: Batch upload of test reports returns "Request timed out" with a 504 status code. Cause: The
DB_MAX_LINKparameter was not properly set. Concurrent write requests exceed the connection pool limit, causing request queuing and timeout. - Phenomenon: SQL queries return "Field does not exist" or incomplete data. Cause: Professional fields such as winding turns and dielectric strength indicators were not unified in naming and units, leading to failure to match corresponding datasets during queries.
How to Verify Successful Configuration
- Run a test SQL query that links winding batch data with corresponding insulating material batch data. Confirm the returned results include expected professional fields.
- Initiate a batch write request to simulate synchronous upload of 10 production batches. Check that the database connection pool does not throw exhaustion errors.
- Access cached dynamic balance report data. Confirm that read latency meets preset business requirements.
- Verify the execution log of the database backup script. Confirm that the daily full backup task completes normally.
The values provided are common starting points and should be evaluated against the specific operational samples in use.