What the data for this category looks like
Data for this category is generated and circulated per individual vehicle VIN. Daily documents include production orders, VIN lists, assembly work instructions, tightening data, offline inspection reports, rework records, conformity and release documents. Data sources include ERP/MES systems, VIN and vehicle configuration systems, supplier batch databases, workshop tightening tools, EOL inspection lines, and quality traceability systems. Data is generated in real time for each vehicle, and synchronized in batches per work shift. Core processing steps include matching hundreds of vehicle configurations and corresponding parts by VIN, controlling critical connection torque, completing offline diagnostics, and resolving cross-station defects.
What constraints these characteristics impose on the deployment and upgrade phase
The multi-source heterogeneous data and high real-time requirements of this category impose multiple constraints on the deployment and upgrade phase. Teams must interface with multiple systems to synchronize real-time information such as VIN lists and tightening data, and adapt to the dynamic data scale of mixed-line production. During upgrades, vehicle data flow must not be interrupted to avoid impacting assembly beat. The feature that individual vehicles are associated with hundreds of configurations requires reserving sufficient data processing and storage resources during deployment to prevent delays in the configuration matching phase. For intranet deployment scenarios, local images and dependency sources must be configured in advance to avoid deployment failures caused by external network restrictions.
How to configure the settings
This document provides configuration instructions based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 200–300 | Adapt to the concurrent data query requirements of hundreds of configurations associated with a single VIN, and avoid connection pool exhaustion |
MONGODB_URI | Intranet MongoDB cluster connection address, with read-only/write permission parameters | Store unstructured document data such as tightening data and rework records, and adapt to the high-frequency write frequency of multi-source data |
PG_URL | Enterprise-level PostgreSQL instance address, with connection timeout set to 60 seconds | Store structured business data such as VIN lists and production orders, and ensure the stability of configuration matching queries |
REDIS_URL | Intranet Redis cluster address, with memory limit set to 8 GB | Cache frequently accessed VIN configuration mapping data to reduce real-time query latency |
STORAGE_S3_ENDPOINT | Intranet object storage service endpoint address | Store large-volume documents such as assembly work instructions and offline inspection reports, and adapt to batch data synchronization requirements |
Three common configuration errors
- The symptom is an API request returning
404 - Resource Not Found, caused by incorrect configuration ofSTORAGE_S3_ENDPOINTor the associated storage path, which prevents the system from locating the storage address of process documents and exception reports. - The symptom is a Docker build error
ERROR: failed to solve: archive/tar: unknown file mod, caused by the deployment package containing files with special workshop intranet permissions, and unnecessary local configuration files not being excluded via.dockerignore. - The symptom is no follow-up contact after submitting the commercial version purchase form, caused by incomplete enterprise contact information or contact email being provided, which prevents the system from triggering subsequent notification processes.
How to confirm the configuration is complete
- Run a local interface test script to verify whether the return result of the VIN configuration matching interface includes the assembly work instruction link for the corresponding vehicle.
- Check the system monitoring dashboard to confirm that the database connection count does not exceed the configured threshold of
DB_MAX_LINK, and that the Redis cache hit rate is in a stable range. - Upload a simulated offline inspection report to verify whether the system can correctly store it and generate the corresponding exception report draft.
- Trigger a batch data synchronization task in an offline intranet environment to confirm that documents from all source systems can be properly imported into the system.
The values provided are common starting points and should be measured against the reader's own samples.