What data for this category includes
The data covers end-to-end production records for slaughterhouses, cooked meat and frozen prepared food enterprises. This includes animal quarantine certificates, slaughter and cutting ledgers, ingredient marination records, thermal processing parameters, cold chain temperature logs, microbial test reports, and more. Data sources include animal quarantine information, supplier raw material batches, production traceability systems, temperature collection equipment, metal detection results, and laboratory test data. Updates trigger per production batch and each shift. Continuous temperature control data is uploaded in real time. Fields include batch number, quarantine qualification mark, raw material weight, processing temperature, test values, and more. Some fields use statutory units of measurement.
What constraints these characteristics impose on database and operations work
Since data sources include real-time temperature control and batch-level quarantine records, the database must support high-concurrency writes and fast batch queries. Real-time uploads of continuous temperature control data generate high-frequency small-data writes. Connection pools must be optimized to avoid overload. Batch association requirements mean database table structures must explicitly bind raw material batches and quarantine information. This prevents data misalignment. In scenarios with large differences in update frequencies, storage strategies for real-time data and batch ledgers must be separated. Mixed storage will reduce query efficiency. Ledgers related to food safety must ensure data cannot be tampered with. The database must be configured with audit log functions to retain all data modification records.
How to set configurations
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | Configure as a replica set address, enable read-write separation | Adapt to write demands for high-frequency real-time temperature control data and query demands for multi-batch ledgers, balance cluster load |
DB_MAX_LINK | 20–30 connections | Handle concurrent connections from shift-based batch production ledger imports and real-time temperature control writes, avoid connection exhaustion |
REDIS_URL | Configure as a local cache instance with 1000 MB memory, store batch indexes and temporary session data | Accelerate batch association queries, reduce repeated database reads of frequently accessed batch information |
PG_URL | Use for storing structured compliance records such as quarantine certificates and test reports | Adapt to strong consistency requirements for compliance data storage, ensure data cannot be tampered with |
Three common misconfigurations
- Slow operation logged during knowledge base upload, database response timeout. Cause: Database read-write separation not configured, high-frequency real-time temperature control data writes block ledger query requests.
- Exported dataset.csv only contains the index field, no content field. Cause: Database fields and knowledge base index mapping not correctly established, text content of batch ledgers not properly extracted.
- Only partial production batch data captured. Cause:
DB_MAX_LINKconfigured too low, cannot support concurrent connections for batch imports and real-time writes, some requests are rejected.
How to confirm configurations are correct
- View the database monitoring panel, confirm that write latency of real-time temperature control data meets business requirements, and read/write request load is balanced.
- Export a test dataset for a single batch production ledger, verify that the exported file includes the
contentfield and corresponding complete ledger content. - Simulate importing data from multiple consecutive production batches, check that database connection count does not exceed the configured
DB_MAX_LINKlimit. - View the database audit log, confirm that all data modifications and import operations have complete records, meeting compliance storage requirements.
The values provided are common starting points and should be measured against the reader's own samples.