What the data for this use case looks like
Data sources for inspection and process record scenarios in complete vehicle final assembly typically include ERP/MES systems, VIN and configuration systems, supplier batch management systems, tightening tools, EOL test lines, and quality traceability systems. Data is generated in real time for each vehicle and archived in shifts. Covered content includes production orders, VIN lists, assembly work instructions, tightening torque data, offline test reports, rework records, consistency and release documents, and other related documents. Each vehicle’s data includes hundreds of configuration and part matching fields. Field formats include 17-digit VIN codes, torque values (newton meters), test result identifiers, and more.
What constraints do these characteristics impose on deployment and upgrades
This scenario generates high-frequency real-time data with complex field structures. During deployment, this requires adapting to high-concurrency read and write demands, so highly available database and cache services must be configured. Mixed-line production leads to large fluctuations in data volume. Sufficient connection pool capacity must be reserved to handle peak traffic. Multi-source data access requires pre-configuring data format verification rules during deployment to prevent invalid data from being written. For intranet deployment scenarios where public images cannot be pulled, all dependent component image packages must be exported in advance. Rolling update strategies must be used during upgrades to avoid production line downtime affecting production tempo.
How to configure the settings
The following configurations are based on FastGPT v4.17.0, and are adapted to the deployment needs of complete vehicle final assembly scenarios.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | Intranet cluster address, maximum connection pool size set to 200 | Supports high-frequency writing of tightening and test data generated in real time for each vehicle |
REDIS_URL | Intranet cache instance with memory no less than 1000 MB | Caches VIN and configuration matching results to reduce duplicate calculation overhead |
DB_MAX_LINK | 150–250 | Adapts to concurrent connection fluctuations during mixed-line production |
STORAGE_S3_ENDPOINT | Access address of intranet object storage | Stores large-volume documents such as offline test reports and rework records |
FILE_TOKEN_KEY | Randomly generated 32-bit string | Encrypts file download links to prevent unauthorized access |
FE_DOMAIN | Domain name or IP address for intranet service access | Configures a unified access entry for front-end and back-end services |
Three common misconfigurations
- No corresponding entry appears in the front-end model list after deploying a rerank model: The model container port is not mapped to the host network segment in the docker-compose configuration, or the FastGPT service and model container are not on the same intranet network.
- Selecting a v2 version model but actually calling v1 version computing power: The FastGPT service is not restarted after modifying the configuration file, or the
FE_DOMAINparameter does not point to the deployment address of the v2 model. - Startup failure is logged after intranet deployment: The exported docker image does not include all dependent components, or the intranet environment cannot access necessary configuration resources.
How to confirm the configuration is correctly applied
- Access the address configured in
FE_DOMAIN, enter the model management page, and confirm that the corresponding entry exists in the list of deployed rerank models. - Initiate a simulated VIN matching query, check whether the database connection count in the service logs falls within the interval configured in
DB_MAX_LINK. - Upload an offline test report, confirm that the file can be stored normally and downloaded via an authorized link, and verify that the
STORAGE_S3_ENDPOINTconfiguration takes effect. - Restart the FastGPT service after modifying any configuration item, wait 10 minutes, then check the monitoring panel to confirm that the database and cache connection status is normal.
The values provided are common starting points and should be measured against the reader's own samples.