What Data Looks Like for This Category
This category of data takes the Vehicle Identification Number (VIN) as the core thread, covering production orders, VIN lists, assembly work instructions, tightening torque data, off-line inspection reports, rework records, compliance release documents, and more. Data sources include ERP/MES systems, VIN and vehicle configuration systems, supplier batch management systems, workshop tightening tools, EOL inspection lines, and quality traceability systems. Update cadence follows real-time generation per vehicle, real-time upload per workstation, and shift-based summary and archiving. Each complete vehicle corresponds to hundreds of part configuration and inspection data points.
Constraints Imposed on Database and Operations
Single-vehicle multi-dimensional data centered on VIN requires the database to support high-concurrency precise queries and association matching, to avoid data cross-interference during mixed-line production. Real-time updated tightening and inspection data creates high-frequency write pressure, requiring a balance between read and write performance. Batch ledger generation for each shift requires the database to support efficient grouping and aggregation operations. Differences in data formats from cross-system sources require operations teams to unify data validation rules, to avoid field missing or format errors affecting ledger accuracy. Additionally, full-link data traceability requirements for each vehicle require the database to retain complete historical versions, and not overwrite original data arbitrarily.
Configuration Settings
This document is compatible with FastGPT v4.17.0. The following configurations are formulated based on the business characteristics of the complete vehicle final assembly scenario:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PG_URL | postgresql://user:pass@host:5432/fastgpt?schema=total_assembly | Stores structured ledger data associated with VINs, uses schema to isolate business data from different production lines to avoid data mixing |
MONGODB_URI | mongodb://user:pass@host:27017/fastgpt?replicaSet=assembly_rs | Stores real-time uploaded unstructured time-series data such as tightening torque and inspection results, replica set deployment ensures high availability |
DB_MAX_LINK | 200–300 | Matches the database connection count requirements of concurrent workstations on the production line, prevents service interruptions caused by exhausted connections |
REDIS_URL | redis://:pass@host:6379/0?maxmemory-policy=allkeys-lru | Caches frequently queried VIN and part configuration mapping data to reduce pressure from repeated database queries |
Three Common Configuration Mistakes
- Invalid array length error occurs when selecting question and answer splitting: No pre-validation is performed on structured array data such as VIN lists. Passing an empty array to the database parsing process triggers a format validation failure.
- Knowledge base index creation fails: When deploying with a PostgreSQL database, the database connection pool upper limit is not adjusted. Connection exhaustion occurs during batch index writing, preventing index construction from completing.
- Backup data restoration fails: Only project files are restored without synchronizing associated database snapshots. Knowledge base metadata does not match the actual stored document data, preventing normal loading.
How to Verify Successful Configuration
- Log in to the FastGPT backend database management page, verify the connection status of
PG_URLandMONGODB_URI, confirm that the connection is shown as normal. - Import the VIN list and assembly data of a single complete vehicle, perform a ledger summarization test, confirm that the returned results have no missing fields or format errors.
- Simulate real-time data writing from 10 concurrent workstations, check the database connection count monitoring, confirm that it does not exceed the
DB_MAX_LINKconfiguration limit. - Execute a batch ledger generation task, check the Redis cache hit rate, confirm that frequently queried VIN configuration mapping data can be hit normally.
The values provided are common starting points and should be measured against the reader's own samples.