What the data for this category looks like
Production ledger data for complete home appliance manufacturing comes from ERP/MES systems, component batch collection equipment, tightening and charging process equipment, safety compliance testers, performance laboratory equipment, and after-sales fault databases. Daily documents include configured BOM, assembly work instructions, refrigerant charging records, electrical safety test reports, random performance inspection reports, and rework records. Data is updated per single product, per shift production batch, and per random inspection plan. Fields include component batch number, charging volume, tightening torque, test voltage, rework reason, and more, with units such as mL, N·m, V.
What constraints these characteristics impose on deployment and upgrade
Multi-source heterogeneous production data requires configuring connection parameters adapted to different data source types during deployment, to avoid incompatible access formats. High-frequency, small-batch single-product or shift data updates require adjusting the maximum connection count of the database connection pool to prevent service interruptions caused by resource exhaustion. Sensitive process data such as refrigerant charging and safety compliance testing requires configuring encrypted storage parameters to ensure data compliance. During cross-version upgrades, a full backup of all production ledgers and associated documents must be performed first to avoid data loss caused by field structure changes between versions. Upgrade scripts must also be executed in version order to prevent skipping structure adjustment steps from intermediate versions.
How to set the configurations
The following configurations are based on FastGPT v4.17.0, adapted for the complete home appliance manufacturing production ledger scenario:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 | Adapted to the high-frequency data update scenario for single products or shifts, prevents database connection pool overflow |
MONGODB_URI | mongodb://[account]:[password]@[mongodb host]:27017/fastgpt?authSource=admin | Stores unstructured documents such as rework records and random performance inspection reports |
PG_URL | postgresql://[account]:[password]@[pg host]:5432/fastgpt | Stores structured data such as configured BOM and assembly work instructions |
REDIS_URL | redis://:[password]@[redis host]:6379/0 | Caches frequently accessed assembly work instructions and component batch information |
STORAGE_S3_ENDPOINT | Configure according to the corresponding cloud service provider or local object storage address | Stores large-capacity data such as safety compliance test videos and original performance test data |
ROOT_KEY | Configure according to enterprise internal key generation rules | Manages system backend permission verification, prevents unauthorized access to ledger data |
Three Common Mistakes
- Symptom: After cross-version upgrade, some process fields in assembly work instructions are empty. Cause: Upgrade scripts were not executed in version order, and structure adjustment steps from intermediate versions were skipped, resulting in mismatched new and old data field formats.
- Symptom: Unable to install ffmpeg dependencies inside a Docker-deployed container, and voice input function reports an error. Cause: The container image does not preinstall basic compilation tools, and the container was not started with specified root permissions, so system permissions cannot be obtained to install dependencies.
- Symptom: Database connection timeout occurs after deployment on the Kylin V10 system with Kunpeng 920 architecture. Cause: Database images adapted to the arm64 architecture were not used, resulting in failure of binary executable files to run properly.
How to Confirm the Configuration is Correct
- Run the built-in database connection test tool to verify that normal connections can be established for data sources corresponding to
MONGODB_URI,PG_URL, andREDIS_URL. - Upload a standard configured BOM document to confirm that the system can successfully parse and extract core fields such as component batch numbers and assembly processes.
- Trigger a production ledger summary task to confirm that the generated report includes business data corresponding to this category, such as safety compliance test results, random performance inspection reports, and rework records.
- View the connection pool monitoring panel in the system backend to confirm that the actual usage count of
DB_MAX_LINKdoes not reach the configured upper limit.
The values provided here are common starting points and should be measured against the reader's own samples.