What the Data for This Category Looks Like
Data for this category includes batching batch ledgers, coating and rolling parameter records, winding or lamination operation logs, liquid injection ledgers, formation and grading curves, and OCV and safety test reports. Data sources cover raw material batch ledgers, production MES systems, slurry and coating testing equipment, workshop dew point monitoring devices, formation and grading cabinets, and laboratory testing instruments. Update frequencies cover single batch materials and single cell dimensions. Some parameters, such as workshop dew point and real-time formation and grading curves, update at millisecond intervals. Each single record includes fields such as batch number, timestamp, equipment number, test value, and operator number. Units include milliampere hours, pascals, degrees Celsius, and other electrochemical and industrial testing standard units.
Constraints Imposed on Database and Operations Maintenance by These Characteristics
The data characteristics of this category impose multiple constraints on the database and operations maintenance stage. Multi-source heterogeneous data access requires adaptation to production equipment and test systems with different protocols. Real-time data must support low-latency writing capabilities. Full-link traceability for a single cell requires storing data associated by batch and individual, which places high requirements on the relational query performance of the database. High update frequency creates continuous write pressure, so connection resources must be allocated reasonably to avoid service blocking. High-precision values from electrochemical testing require retention of sufficient precision fields, and excessive compression that causes data distortion must be avoided. At the same time, data must retain the full life cycle link, and historical records cannot be deleted or modified arbitrarily. An immutable storage strategy must be configured, which differs from the lightweight data storage requirements of ordinary electronic assembly.
How to Set Configurations
This configuration is written based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | Format: mongodb://[username]:[password]@[host]:[port]/[database_name]?authSource=admin | Adapts to multi-source heterogeneous production equipment and test data storage, supports high-concurrency writing and cross-batch relational queries |
PG_URL | Format: postgresql://[username]:[password]@[host]:[port]/[database_name] | Used to store structured batch ledgers and test report metadata, supports traceability queries with complex conditions |
DB_MAX_LINK | 200–300 | Matches concurrent write requests for single-batch cell production, avoids service interruptions caused by exhausted connections, calibrated based on actual measurements |
REDIS_URL | Format: redis://[password]@[host]:[port]/[database_index] | Used to cache temporary data of real-time dew point and formation and grading, reduces direct query pressure on the primary database |
Three Common Misconfigurations
- Authentication failure errors occur when connecting to the MongoDB database, returning status code 401. The cause is that the authSource parameter in
MONGODB_URIis not configured correctly, and does not match the authentication source of the production database. - Timeouts occur when requesting external service interfaces during local deployment, returning status code 504. The cause is that a reasonable request timeout threshold is not configured, and the network latency of external devices and systems is not adapted.
- The database connection pool is exhausted, and a
too many connectionserror occurs. The cause is that a reasonableDB_MAX_LINKparameter is not set, and the concurrent write request volume of the production environment is not matched.
How to Confirm the Configuration Is Correct
- Execute a database connection test script to verify whether
MONGODB_URIandPG_URLcan establish normal connections, and check whether the returned connection status is normal. - Simulate real-time data write requests for a single batch of cells, observe the connection usage of
DB_MAX_LINK, and confirm that the number of connections does not reach the upper limit threshold. - Initiate an external service interface request, verify that the request can return data normally, and confirm that the timeout threshold adapts to the response speed of the external service.
- View the real-time monitoring data cached in Redis, and confirm that the cache write and read processes are running normally.
The values provided are common starting points and should be measured against the reader's own samples.