What the data for this category looks like
This category covers the full production process of complete units for air conditioners, refrigerators, washing machines, and small home appliances. Daily documents include configuration BOM, assembly work instructions, refrigerant charging records, electrical safety test reports, performance spot check reports, and rework records. Data sources include ERP/MES systems, component batch ledgers, tightening and charging equipment, safety compliance testers, performance lab data, and after-sales fault feedback. Update cadence follows individual product units, shift production batches, or spot check schedules. Fields include component batch numbers, process parameter values, test results, rework reasons, and after-sales fault codes. Units include process and testing-specific units such as pressure values, torque values, and energy efficiency grades.
What constraints these data characteristics impose on database and operations
These data characteristics impose multiple constraints on the database and operations phase. Mixed production of multiple models creates large differences in batch data fields. This requires the database to support dynamic field adaptation and multi-table join query capabilities. The high-frequency update cadence, per individual product unit or shift production batch, requires database connection pool configuration to adapt to real-time write throughput and avoid write blocking. Cross-source production, testing, and after-sales data association requirements require the operations phase to configure cross-system data synchronization and verification mechanisms to ensure data consistency. Storage requirements for high-precision process parameters and test results require matching dedicated data types and storage strategies to prevent precision loss.
How to set configurations
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://<user>:<pass>@<host>:27017/homeapp_trace?authSource=admin | Stores unstructured process records, after-sales fault documents, and other data; specifies a dedicated business database to isolate data |
PG_URL | postgresql://<user>:<pass>@<host>:5432/homeapp_meta?sslmode=require | Stores structured metadata such as BOM and safety compliance test results; SSL encryption ensures secure transmission of sensitive data |
DB_MAX_LINK | 150–250 | Adapts to concurrent connection requirements for single-shift production; prevents database connection exhaustion during production peaks |
REDIS_URL | redis://<pass>@<host>:6379/1?maxmemory=1000 MB | Caches frequently accessed data such as component batches and real-time process parameters; limits memory to match deployment resources |
| Data synchronization frequency | Every 5 minutes | Synchronizes batch data from ERP/MES systems; balances real-time performance and system load |
Three common mistakes
- Symptom: A
connection refusederror appears after deployment, with logs showing database connection failure. Cause: Authentication information forMONGODB_URIis not configured correctly, or the Docker container network is not properly connected, preventing access to the database instance. - Symptom: Empty field return results appear when batch uploading assembly work instructions. Cause: Dedicated fields for complete home appliance data are not adapted, and indexes for required fields such as component batch numbers and refrigerant charging volume are not created in the database.
- Symptom: Request timeouts occur during production peaks, returning a
504 Gateway Timeouterror. Cause: TheDB_MAX_LINKconfiguration value is too low, causing the database connection pool to be exhausted and unable to handle concurrent write requests.
How to confirm the configuration is correct
- Run a database connection test script to verify connectivity for
MONGODB_URIandPG_URL, and confirm that authentication parameters and network configurations are correct. - Import test data for a single home appliance unit, check that all dedicated fields are properly written and read, and confirm that the data structure matches business requirements.
- Simulate concurrent write requests for a single shift’s production, observe the database connection pool status, and confirm that the
DB_MAX_LINKconfiguration can cover peak load. - Review Redis cache monitoring, confirm that frequently accessed component batch data is properly cached, and that the caching policy is active.
The values provided are common starting points and should be measured against the reader's own samples.