What data for this category looks like
Data for this category is generated and circulated around vehicle identification numbers (VINs). Data sources include ERP/MES systems, VIN and configuration management systems, tightening tools, EOL offline inspection lines, and quality traceability systems. Daily documents include production orders, VIN lists, assembly work instructions, tightening torque data, offline inspection reports, rework records, and consistency release documents. Data update frequencies cover real-time reporting per vehicle, real-time station synchronization, and shift-level aggregation. Core fields include VIN codes, part batch numbers, tightening torque values, inspection pass status, and more. Some data requires matching hundreds of vehicle configurations and corresponding part information.
What constraints do these characteristics impose on deployment and upgrade
Multi-source real-time data, high-frequency updates, and VIN-centric association logic for this category impose multiple constraints on deployment and upgrade processes. Access to multi-source heterogeneous data requires pre-configuring compatible storage and parsing rules to avoid aggregation failures caused by data format mismatches. High real-time torque and inspection data require database connection pools to maintain sufficient concurrent connections to prevent request backlogs. Shift-level batch aggregation scenarios require adjusting cache expiration policies to ensure VIN-associated configuration data remains accurate during aggregation cycles. During upgrades, priority must be given to ensuring write consistency of core ledger data to avoid interrupting production data collection links during version updates.
The configuration and troubleshooting guidance on this page applies to FastGPT v4.17.0.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 connections | Accommodates concurrent requests from real-time data reporting across multiple workstations, prevents connection exhaustion |
MONGODB_URI | Points to a dedicated MongoDB instance storing tightening data and rework records | Isolates production ledger data from other business data, ensures performance for complex queries |
PG_URL | Points to a PostgreSQL instance storing VIN lists and production orders | Stores structured ledger data, supports association queries between VINs and part configurations |
REDIS_URL | Configured as the address of a local cache cluster, with cache validity set to 30 minutes | Quickly retrieves vehicle configuration information corresponding to VINs, reduces database query pressure |
STORAGE_S3_ENDPOINT | Points to the enterprise’s internal object storage service address | Stores large-volume documents such as offline inspection reports, reduces local storage load |
Three common mistakes
- Phenomenon: A custom reranking model passes unit tests after deployment, but reranking results always return false during retrieval. Cause: The custom request address of the reranking model was not correctly filled in the FastGPT configuration, causing the service to fail to call the reranking model interface.
- Phenomenon: The platform fails to start after upgrading fastgpt-mcp-server. Cause: Database connection parameters in the original configuration file were not retained during the upgrade, causing the service to fail to connect to the ledger data storage instance.
- Phenomenon: Garbled Chinese characters appear after importing a CSV file of production orders. Cause: The character encoding format of the CSV file was not specified, or the configured encoding did not match the actual encoding of the file, causing parsing failure of Chinese fields.
How to confirm configurations are correct
- Run the platform’s built-in data source connectivity test, verify whether the storage instances corresponding to
MONGODB_URIandPG_URLcan read and write data normally. - Trigger a single-vehicle ledger aggregation task, check whether the generated report includes complete VIN-associated configuration, tightening torque data and inspection results.
- View system logs, confirm that the connection number configured by
DB_MAX_LINKdoes not show exhaustion errors, and that the update frequency of configuration data in the Redis cache meets expectations. - Upload a test CSV-format production order file, confirm that there is no garbled text after file parsing, and that fields can be correctly mapped to the ledger database.
The values provided here are common starting points and should be measured against the reader's own samples.