What the data for this category looks like
Daily documents for spraying and coating scenarios include coating process cards, coating batch sheets, paint mixing records, film thickness and color difference reports, oven curves, defect repair orders, and VOCs ledgers. Data sources include TDS/SDS documents from coating suppliers, real-time collected data from spray booths and ovens, detection data from colorimeters and film thickness gauges, environmental monitoring records, and factory MES systems. Core data focuses on coating appearance and curing parameters, which differs from tank solution data for categories such as electroplating. Data updates follow per-coating-batch, per-shift production, and continuous collection rhythms. Document fields include coating batch numbers, film thickness values, color difference parameters, oven temperature curves, VOC emission records, and some fields have fixed units.
Constraints imposed on deployment and upgrade
Multi-source heterogeneous data sources and fixed field format requirements require configuring access rules adapted to different data sources during deployment. High-frequency data updates require adjusting the trigger frequency of synchronization tasks to avoid data backlog or duplicate synchronization. Documents include compliance-related VOCs ledger fields, so corresponding verification rules must be configured to ensure data accuracy. Matching the business logic of pre-treatment, spraying, and curing windows requires associating multi-batch process and detection data. Data association query logic must be optimized during deployment to ensure retrieval efficiency.
Configuration Settings
| Configuration Item | Recommended Setup | Rationale |
|---|---|---|
MONGODB_URI | Enter the MongoDB connection address containing the username, password, and port, for example mongodb://user:pass@10.0.0.1:27017/fastgpt | FastGPT core metadata storage relies on MongoDB. Ensure network connectivity and correct permissions. This applies to FastGPT v4.17.0 |
REDIS_URL | Configure as redis://:custom password@redis service address:6379 | Used to cache embedding vectors and session data to ensure performance in high-concurrency retrieval scenarios |
DB_MAX_LINK | 100–150 | There are many multi-source data synchronization and retrieval requests in spraying scenarios. Adapt to concurrent connection requirements, calibrated based on actual testing |
FE_DOMAIN | Enter the frontend access address for deployment, for example http://192.168.2.10:3000 | Used to generate correct access paths for frontend resources and avoid resource loading exceptions |
ROOT_KEY | Set to a random string of no less than 32 characters | Used for root account identity verification to prevent security risks caused by default configurations |
STORAGE_S3_ENDPOINT | Leave blank for local deployment, fill in the corresponding endpoint address when using object storage | Used to store uploaded coating-related documents and detection data files |
Three Common Mistakes
- Accessing the frontend page returns a 500 error with the prompt
URI malformed. The cause is that theFE_DOMAINconfiguration contains invalid characters or does not add thehttp:///https://protocol prefix, resulting in abnormal route generation. - The root account password is automatically reset to 123456 every day. The cause is that the
ROOT_KEYconfiguration was not modified, and the default key triggers the built-in system reset mechanism. - Retrieval results are missing coating data for the corresponding batch. The cause is that the
DB_MAX_LINKconfiguration value is too low, causing some data synchronization requests to be rejected and preventing complete loading of historical detection records.
How to Verify Successful Configuration
- Access the configured
FE_DOMAINaddress, confirm that the frontend page of FastGPT v4.17.0 loads normally, and there are no 500 or 404 errors. - Log in to the root account to change the password, restart the FastGPT service, and log in again to verify that the password has not been automatically reset.
- Upload a coating process card document, confirm that the system can correctly parse and extract core fields such as coating batch number and film thickness requirements.
- View the data synchronization log, confirm that multi-source detection data from colorimeters, ovens, and other devices is normally synchronized to the system at the set frequency.
The values provided are common starting points and should be measured against the reader's own samples.