What the data for this category looks like
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 test reports. Daily documents include batching records, coating and rolling parameter ledgers, winding/lamination process records, liquid injection operation logs, formation and grading data reports, OCV and safety test reports. Data is updated in real time per batch and per individual cell. Fields include batch numbers, process parameter values, test results, environmental indicators, and more. Units include weight percentage, millimeters, millivolts, seconds, milliampere-hours, and others.
What constraints these characteristics impose on deployment and upgrade
Lithium-ion cell manufacturing data is multi-source and high real-time. It includes precise process parameters, environmental monitoring indicators, and test data for individual cells. Deployments must reserve resources to support multi-source data access. Data update frequencies cover batch, per-cell, and real-time scenarios. Parameters must be configured to support high-concurrency queries and bulk imports. Formation and grading process data has large volume. Reasonable database connection limits must be set. Electrochemical systems have strict data accuracy requirements. Configurations must match data fields and update rhythms to avoid synchronization errors. Production data involves process details. Deployments must meet local storage and access isolation requirements.
How to configure settings
This page is compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 connections | Meets concurrency requirements for multi-process data synchronization and real-time queries, matching data interaction scenarios across multiple cell manufacturing links |
MONGODB_URI | Points to a dedicated MongoDB instance storing process records and formation and grading data | Used to store unstructured process logs and real-time test data, supporting flexible field expansion |
REDIS_URL | Configured as a local cache instance, with memory quota set to 8 GB | Caches frequently queried dew point monitoring and batching parameter data, reducing database query pressure |
STORAGE_S3_ENDPOINT | Points to the intranet address of the enterprise’s internal object storage service | Stores large files such as OCV test reports and safety inspection documents, avoiding public network transmission delays |
FE_DOMAIN | Fill in the intranet access domain name or IP:port | Adapts to production workshop intranet access scenarios, avoiding public exposure of internal systems |
ROOT_KEY | A 32-bit randomly generated string from the deployment team | Used for signature verification of configuration files, ensuring deployment permission security |
Three common configuration errors
- Phenomenon: After Docker deployment, the 3000 port page cannot be accessed, while the 3001 port opens normally. Cause:
FE_DOMAINis not correctly configured to point to the intranet access address, or front-end container port mapping parameters are set incorrectly. - Phenomenon: After upgrading to a new version, the knowledge base can retrieve matching content, but the model cannot output the final result. Cause:
REDIS_URLin the configuration file is not updated correctly, causing context cache failure and preventing the model from obtaining complete retrieval results. - Phenomenon: Uncaught exceptions occur in the private deployment environment. Cause: Connection parameters for
MONGODB_URIorPG_URLin the configuration file contain invalid characters, or database permissions are not opened to the deployment server.
How to confirm configuration is complete
- Run the
docker pscommand to confirm that front-end, database, and cache-related containers are all running, and port mapping parameters match the configuration file. - Import a batch of test coating and rolling parameter data, and verify successful data synchronization and field matching with configured mapping rules via the system query interface.
- Access the configured
FE_DOMAINaddress, log in to the system, and initiate a traceability query for a single cell’s data, confirming that retrieval results match the original data. - Modify
ROOT_KEYin the configuration file and restart the service, verify that unauthorized access cannot enter the system backend, confirming that permission configuration takes effect.
The values provided are common starting points and should be measured against the reader's own samples.