What this category of data looks like
Supplier and process audit data for lithium-ion cell manufacturing comes from raw material batch records, MES systems, slurry and coating testing equipment, dew point monitoring devices, formation and grading cabinets, and laboratory test data. Daily documents include batching records, coating and rolling parameters, winding or lamination records, liquid injection records, formation and grading data, OCV and safety test reports.
Data is updated in real time per lithium-ion cell batch and single cell. Fields include batch number, parameter values, detection time, and qualification judgment. Units cover weight, thickness, voltage, humidity and other electrochemically related indicators.
What constraints do these characteristics impose during deployment and upgrade
The real-time nature and strong batch association of this category of data require storage configurations adapted to high-frequency, small-batch data writes. Multi-source heterogeneous data (direct device connections, laboratory reports, MES exports) requires unified format parsing rules. Upgrades must maintain compatibility with legacy device docking protocols. The high-precision requirements of lithium-ion cell data for sensitive indicators such as moisture and cleanliness require sufficient field storage precision and verification logic. Long-cycle formation and grading data must support batch-associated queries. Deployment requires planning a reasonable indexing strategy to avoid query delays after upgrades.
How to configure the settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://username:password@host:27017/fastgpt?authSource=admin | Adapts to multi-source heterogeneous batch lithium-ion cell data storage, supports high-concurrency writes |
REDIS_URL | redis://:password@host:6379/0 | Caches real-time detection data and batch indexes to reduce high-frequency query delays |
DB_MAX_LINK | 200–300 | Matches the concurrent write volume of per-batch lithium-ion cell detection data to avoid connection exhaustion |
STORAGE_S3_ENDPOINT | Fill in according to the actual address of the enterprise’s internal object storage | Stores large-capacity formation and grading historical data and test reports |
ROOT_KEY | Randomly generated 32-bit string | Ensures permission security for internal system interface calls |
FILE_TOKEN_KEY | Match the internal storage service secret key | Verifies the legitimacy of uploaded test report files |
Three common mistakes
- Containers fail to initiate normal conversations after startup, and the interface prompts that no valid secret key is configured. The cause is incorrect setup of
ROOT_KEYorFILE_TOKEN_KEY, leading to failed internal system permission verification. - An error occurs when executing
docker run --gpus all -itd -p 7231:7231to install marker_images:v0.1. The cause is missing corresponding mount parameters in the deployment script, or the host machine not having properly configured GPU driver compatibility. - After upgrading to FastGPT v4.17.0, an error occurs when creating a knowledge base using a URL. The backend log displays
cannot fetch internal url. The cause is not configuringFE_DOMAINas a valid internal access address, leading to the system being unable to parse internal resource paths.
How to confirm proper configuration
- Log in to the system backend, view the storage connection status panel, and confirm that the connection tests for
MONGODB_URIandREDIS_URLreturn successful results. - Upload a test document of coating and rolling parameters, verify that the file upload and parsing process has no errors, and that the extracted fields conform to the format specifications of lithium-ion cell data.
- Initiate an associated query for a single batch of lithium-ion cell data, confirm that the query process has no timeout errors, and that the returned results match the original data.
- Check the system backend logs for no internal URL fetch failure error messages, confirming that the
FE_DOMAINconfiguration complies with internal access rules.
The values provided are common starting points and should be measured against the reader's own samples.