What Data Looks Like for This Category
Data for this category comes from raw material batch ledgers, production MES systems, slurry and coating testing equipment, workshop dew point monitoring devices, formation and grading cabinets, and laboratory testing equipment. Daily documents include batching records, coating and rolling parameter ledgers, winding or lamination process records, liquid injection operation records, formation and grading data reports, and OCV and safety test reports. Data updates occur in real time per production batch and per individual cell. Some testing indicators are updated immediately upon completion of the corresponding process. Document fields include material batch numbers, process parameter values, test results, and timestamps. Some parameters have dedicated process units, such as coating areal density, internal resistance, and dew point temperature.
Constraints Imposed on Deployment and Upgrade
Data sources for this category are scattered, including multiple heterogeneous sources such as production equipment, testing devices, and management systems. Connection parameters adapted to different data protocols must be configured during deployment to ensure full-link data access. The coexistence of real-time and batch-level updates requires reasonable division of synchronization task priorities during deployment. This prevents high-frequency small-data synchronization from occupying excessive cluster resources. There are many document fields with dedicated process units. Original field mapping rules must be retained during upgrades to avoid parameter matching deviations during cross-review. Data abnormalities can directly cause safety issues such as internal short circuits, bulging, or thermal runaway of cells. Mandatory verification logic must be built into deployment and upgrade links to ensure the format and compliance of accessed data.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 100–200 connections | Adapt to concurrent connection requirements of multiple data sources, avoid data synchronization interruptions caused by exceeded connection limits |
MONGODB_URI | Cluster connection address with authentication information, default port 27017 | Store batch-generated process records, formation and grading historical data, and safety test reports; support high-concurrency read/write and massive data storage |
REDIS_URL | Connection address of a single-node or clustered instance with a password | Cache real-time testing data and temporary verification results to reduce repeated query overhead |
STORAGE_S3_ENDPOINT | Access endpoint of the enterprise internal object storage | Store large-volume test reports and original testing data, adapt to batch document storage requirements |
FILE_TOKEN_KEY | Randomly generated 32-bit string | Encrypt file access tokens to prevent unauthorized access to sensitive process documents |
ROOT_KEY | Key configured according to enterprise internal permission rules | Manage system-level permission configurations, ensure only authorized operation and maintenance personnel can modify review rules |
Three Common Configuration Mistakes
- Issue: Dependency errors occur when running the
docker run --gpus all -itd -p 7231:7231command to installmarker_images:v0.1. Cause: The CUDA runtime environment is not pre-configured in the deployment environment, causing the GPU image to fail to load properly. - Issue: After upgrading FastGPT to v4.9.0, an error persists when creating a knowledge base via URL. Background logs show
cannot fetch internal url. Cause: TheFE_DOMAINparameter is not configured as a valid access address for internal services, causing the system to fail to parse internal resource paths. - Issue: The file processing plugin cannot be found after deployment, and technical document parsing cannot be performed. Cause: The image volume of the corresponding plugin is not mounted in the startup command, or the plugin version does not match the current FastGPT version.
How to Verify Successful Configuration
- Use the background management interface of FastGPT v4.17.0, enter the system settings page, and check whether the configured connection parameters match the preset values.
- Run the
docker pscommand to confirm that themarker_imagescontainer is in running status and port 7231 is listening normally. - Log in to the FastGPT background, enter the knowledge base configuration page, enter the URL of an internal process document, and test whether it can be pulled and parsed normally.
- Check the background logs to confirm that there are no connection timeouts or permission errors in data synchronization tasks, and that field mapping rules are working properly.
- Upload a test batching record document to trigger cross-review verification, and confirm that the results match the preset rules.
The values provided are common starting points and should be measured against the reader's own samples.