What Data for This Category Looks Like
The data for this category comes from raw material batch ledgers, workshop MES systems, slurry and coating testing equipment, workshop dew point monitoring devices, formation and grading cabinets, and laboratory testing equipment. Daily generated documents include batching records, coating and rolling parameter ledgers, winding/stacking process records, liquid injection operation records, formation and grading data, and OCV and safety test reports. Data update cycles cover single battery batches and single cell dimensions. Some testing data is synchronized in real time. Document fields include batch numbers, parameter thresholds, testing values, and timestamps. Some entries require associated indicators such as ambient humidity and cleanliness. Compared with standard electronic assembly categories, requirements for associated fields related to moisture and cleanliness are more stringent.
Constraints Imposed by These Characteristics During Deployment and Upgrade
Multi-source real-time data synchronization requires a stable cross-system data pull link during deployment. This avoids delays in process document generation caused by data lags. Fine-grained data for single batches or single cells requires support for document splitting and storage by batch dimension. Sufficient database storage space must be reserved. Associated fields including humidity and cleanliness need special validation during document parsing. Corresponding field mapping rules must be configured during deployment. The real-time requirements for formation and grading data require adjusting data pull timeout thresholds during upgrades. This prevents loss of critical exception report data due to timeouts. Different field requirements from standard electronic assembly need custom field templates configured before deployment. This ensures generated documents comply with production specifications.
How to Configure These Settings
The following configurations are written for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 items/second | Matches the real-time pull frequency of cell manufacturing data, avoids database connection overload |
MONGODB_URI | Cluster connection string with read-write separation identifier | Stores multi-batch process documents and exception reports, cluster deployment ensures data read-write stability |
REDIS_URL | Local cluster address with password verification and 8 GB memory specification | Caches real-time testing data and temporary sessions, adapts to real-time update requirements of cell data |
STORAGE_S3_ENDPOINT | Access address of enterprise self-built object storage, reserve more than 1000 GB storage per bucket | Stores large-capacity formation and grading data and test reports, adapts to data storage needs of this category |
FILE_TOKEN_KEY | 32-character random string | Encrypts sharing links for process documents, prevents unauthorized access |
ROOT_KEY | 64-character strong random key used only during deployment initialization | Ensures initial permission security for the system backend, avoids default key leaks |
Three Common Misconfigurations
- Symptom: After deploying FastGPT with Docker on a Windows system, process document sharing links become inaccessible after restarting the computer. Cause: The local storage directory mounted by the Docker container was not set to automatically mount on boot, resulting in lost file token cache.
- Symptom: When configuring multiple model combinations, API requests return a 401 status code. Cause: Associated permissions for multiple API_KEYs were not configured correctly, resulting in the system blocking invalid requests.
- Symptom: Field parsing fails when importing liquid injection record documents. Cause: Corresponding field mapping rules were not configured before deployment, and dew point monitoring data was not bound to process documents.
How to Confirm Proper Configuration
- Run
docker logs fastgptto view database connection logs, and confirm there are noconnection refusedortimeouterror messages. - Upload a coating and rolling parameter document, verify that batch numbers, rolling thickness, vehicle speed and other fields can be extracted after parsing, with no missing fields.
- Generate a draft exception report, verify that it can be accessed via a sharing link and requires a valid token.
- Restart the Docker container, then revisit the previously generated sharing link, confirm it still loads normally.
The values provided here are common starting points, and should be evaluated against local operational samples.