What the data for this category looks like
Core data for dairy processing covers raw milk acceptance, full production workflows and quality inspection links. Data sources include raw milk testing equipment, production MES systems, homogenization and heat treatment equipment, online temperature and flow sensors, CIP cleaning systems, and LIMS laboratory management systems. Data is updated via real-time reporting per milk tank batch and synchronized archiving per production batch. Daily documents include raw milk acceptance records, standardized ingredient lists, pasteurization or UHT sterilization records, CIP cleaning records, filling records, retained sample and microbial test reports. Fields include raw milk antibiotic and physicochemical index values, sterilization temperature and duration, cleaning parameters, microbial test results, and more. Corresponding units include mg/kg, ℃, seconds, mL, and others.
What constraints these characteristics impose on deployment and upgrade
The high real-time performance and multi-source heterogeneous nature of data in this category imposes multiple constraints on FastGPT deployment and upgrade. First, real-time reported equipment data requires a properly configured database connection pool to avoid production audit interruptions caused by exhausted connections. Second, multi-source data needs to adapt to different access protocols, so deploy data source drivers compatible with industrial equipment in advance. Third, audit timeliness requirements for perishable liquid raw materials are high, so deploy core services on the production intranet during deployment to reduce network latency. Finally, maintain compatibility with the interface specifications of the enterprise’s existing MES and LIMS systems to avoid modifying existing production workflows.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 | Adapts to the high-concurrency connection demand of real-time reporting per milk tank, avoiding database connection exhaustion that disrupts real-time audits |
MONGODB_URI | Fill in according to the address of the enterprise’s existing industrial document storage cluster | Stores unstructured archived documents such as raw milk acceptance records and retained sample microbial test reports |
PG_URL | Point to the PostgreSQL instance storing real-time time-series data such as temperature and flow | Supports real-time parameter verification and quick queries for heat treatment and CIP links |
REDIS_URL | Configure as the enterprise’s internal cache cluster address | Caches frequently queried basic information of raw milk batches to reduce the load on the core database |
FE_DOMAIN | Fill in the exclusive domain name of the enterprise’s internal audit system | Enables unified domain access for internal audit personnel |
STORAGE_S3_ENDPOINT | Point to the address of the enterprise’s self-built compliant object storage service | Stores production data requiring long-term archiving such as filling records and CIP cleaning records |
Three common mistakes
- Phenomenon: A timeout error is returned when auditing heat treatment parameters, and the logs show that the database connection pool is exhausted. Cause: Failing to adjust the
DB_MAX_LINKconfiguration based on the concurrency of real-time production data leads to insufficient connections to read sterilization temperature and duration data. - Phenomenon: Retained sample microbial test reports cannot be loaded and viewed normally after submission. Cause: The
STORAGE_S3_ENDPOINTconfiguration points to an external public storage service instead of the enterprise’s internal compliant object storage address, which does not meet the data security management requirements of food processing. - Phenomenon: The judgment result for raw milk antibiotic indicators is empty. Cause: Failing to correctly configure the
PG_URLto point to the time-series data storage instance makes it impossible to obtain real-time detected antibiotic values to complete the audit judgment.
How to confirm the configuration is correct
- Verify the currently deployed FastGPT version is v4.17.0, which meets the scenario adaptation requirements.
- Log in to the FastGPT management backend, check the database connection pool status, and confirm the current number of connections does not exceed the threshold set by the
DB_MAX_LINKconfiguration. - Submit a simulated raw milk acceptance record, and check whether it can be properly stored in the database cluster specified by
MONGODB_URI. - Initiate a heat treatment parameter verification task, and confirm real-time temperature and duration data can be read from the instance specified by
PG_URLto complete the judgment. - Access the configured
FE_DOMAIN, and confirm the internal audit interface can be loaded normally and related business interfaces can be called.
The values provided are common starting points and should be measured against the reader's own samples.