What Data for This Category Looks Like
This category of data comes from design documents, standard device and environmental monitoring data, calibration software, laboratory information systems, and customer range requirements. The data update frequency is once per calibration for each instrument. Daily documents include assembly and commissioning sheets, calibration procedures, original calibration records, error curves, metrology certificates, and software version records. Data fields include instrument identifiers, calibration parameters, error values, environmental conditions, metrology certificate numbers, and other items. Units correspond to the legal measurement units of the measured parameters. A single piece of data contains two types of content: structured parameters and unstructured documents.
What Constraints These Characteristics Impose on Database and Operations Maintenance
The data for this category is updated once per calibration for each instrument, and a single piece of data contains structured parameters and multiple unstructured documents. This creates demand for high-frequency, small-batch writes and large-field storage. Multi-source data access requires the database to support mixed storage formats to avoid format conflicts across data from different sources. Calibration records serve as key deliverables, so full audit logs must be enabled to meet traceability requirements for metrology audits. The need for associated traceability chains requires the database to support cross-table associated queries, while also ensuring consistency between data writes and queries to prevent misalignment of traceability information.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://fastgpt:your_password@mongo-host:27017/fastgpt_db?authSource=admin | Adapts to mixed storage of structured calibration records and unstructured error curves, metrology certificate files |
PG_URL | postgresql://fastgpt:your_password@pg-host:5432/lis_sync?sslmode=require | Stores structured fields associated with traceability chains, ensures strongly consistent queries |
DB_MAX_LINK | 200–300 | Matches concurrent demand from high-frequency calibration data writes, prevents connection pool exhaustion |
REDIS_URL | redis://:your_password@redis-host:6379/0 | Caches commonly used calibration parameters and basic instrument information, reduces database query pressure |
This configuration is based on FastGPT v4.17.0.
Three Common Misconfigurations
- Symptom: After starting the FastGPT service, the 3000 port interface cannot be logged into, and the logs show a MongoDB connection failure. Manually logging into MongoDB using the configured account password also fails. Cause: The
authSourceparameter configured inMONGODB_URIis incorrect, or the MongoDB database version is incompatible with the FastGPT driver version. - Symptom: When batch importing calibration records for multiple instruments, a connection timeout error occurs, returning status code 504. Cause: The
DB_MAX_LINKsetting value is too low, and concurrent write requests exceed the connection pool limit, causing new connections to fail to be established. - Symptom: When querying the traceability chain information of an instrument, the returned result is empty or has missing fields. Cause: The database table association relationship corresponding to
PG_URLis not correctly configured, causing traceability information to not be stored correctly or cannot be associated and queried.
How to Verify Proper Configuration
- A single instrument calibration record write request is submitted, and the corresponding database is checked to confirm whether a complete data entry is generated, including structured parameters and associated unstructured document information.
- The database connection pool monitoring panel is viewed, and the current active connection count is confirmed to not exceed the
DB_MAX_LINKsetting value, with no connection exhaustion occurring. - An instrument traceability chain query operation is manually triggered, and the returned result is verified to contain complete calibration procedures, environmental conditions and certificate information.
- The FastGPT service is restarted, and the startup logs are checked for the absence of error messages such as database connection failures or authentication errors.
The values provided are common starting points and should be measured against the reader's own samples.