What data looks like for this product category
This product category covers the full production lifecycle of complete home appliances including air conditioners, refrigerators, washing machines, and similar items. Data sources include ERP/MES systems, component batch ledgers, tightening and refrigerant charging equipment, safety compliance testers, performance lab data, and after-sales fault records.
Daily documents include configuration BOMs, assembly work instructions, refrigerant charging records, electrical safety test reports, performance sampling inspection reports, and rework records. Data updates occur at three levels: individual product, shift team, and sampling plan. Fields include component batch numbers, charging volume (unit: grams), tightening torque (unit: newton-meters), test time, non-conformity judgment results, and other relevant fields.
This page configuration is based on FastGPT v4.17.0.
Constraints on database and operations
The high-frequency, multi-source data characteristics of this product category create multiple constraints for the database and operations phase. Single-product-level high-volume write requests require the database to support high-throughput concurrent writes to avoid data backlogs. Mixed-model assembly lines require the database to isolate data by product model, preventing ledger mixing across different product batches. Critical data related to safety compliance, energy efficiency, and other areas needs an immutable storage setup to ensure data traceability and credibility. Data access from multiple industrial devices and systems requires the operations phase to adapt to different data source connection formats, complete standardized cleaning, and import data into storage.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://user:pass@host:27017/fastgpt?authSource=admin | Adapts to storage requirements for multi-source ledger data in home appliance manufacturing, supports high-concurrency writes |
PG_URL | postgresql://user:pass@host:5432/fastgpt | Stores structured documents such as configuration BOMs and sampling inspection reports, ensures efficient associated queries |
DB_MAX_LINK | 200–300 | Matches concurrent connection counts for single-shift production, prevents service interruptions caused by exhausted connections |
REDIS_URL | redis://:pass@host:6379/0 | Caches frequently accessed component batch data, reduces database query pressure |
| Database connection timeout setting | 60 seconds | Adapts to latency characteristics of industrial equipment data access, prevents valid data loss caused by short timeouts |
Three common mistakes
- Issue: After upgrading from FastGPT v4.9.13 to v4.10.1, original production ledger knowledge base data no longer displays in the interface. Cause: The database version migration script was not executed, and the legacy data storage format was not adapted.
- Issue: FastGPT deployed via Docker fails to connect to MongoDB after running for several hours. Logs show
connection timed out. Cause: The MongoDB connection pool idle recycling policy was not configured. Long idle periods cause connections to be recycled by the system, preventing reuse. - Issue: When creating a new question classification node, running or saving and publishing using the initialized AI model returns the
model initialization failederror. Cause: The initialized model has not completed database association configuration, and the actual model file path was not loaded.
How to confirm configurations are correct
- Run a database connection test, check if the returned connection status is normal. Adjust the connection pool upper threshold based on production concurrent volume.
- Import simulated ledger data for a single home appliance product, check if the data is fully written to the storage partition for the corresponding model, with no mixed cross-model data.
- Run a scheduled summary task, check if the generated report includes the latest sampling inspection and rework records, and the time range matches the configured update cycle.
- Simulate high-concurrency write operations, check if database connection counts stay within the configured
DB_MAX_LINKrange, with no connection overflow errors.
The values provided are common starting points and should be evaluated against the reader's own samples.