What Data Looks Like for This Category
Data sources for this category include animal quarantine information, supplier batch records, production traceability ledgers, temperature collection data, metal detection results, and laboratory test reports. Daily documents include quarantine certificates, slaughter and cutting records, ingredient marination logs, thermal processing parameter records, cold chain temperature ledgers, and microbial test reports. Data updates are performed per production batch and work shift. Cold chain temperature data must be collected and recorded continuously. Fields include batch number, quarantine number, temperature value, detection indicators, and more. Units are adjusted according to the collection item, with no unified fixed format.
Constraints for Deployment and Upgrade Posed by These Characteristics
Because data updates are per batch and shift and include real-time streaming data for continuous temperature control, deployment must adapt storage configurations for high-frequency, small-batch data writes. Multiple types of heterogeneous documents (paper scans, structured ledgers, temperature logs) require deployment to support multi-format parsing and field mapping. Strong compliance requirements for quarantine certificates and microbial test reports mean upgrade processes must retain the original data traceability chain, and field mapping rules cannot be modified arbitrarily. Highly correlated data for raw/cooked product zoning and cold chain temperature control requires configuring cross-node data synchronization mechanisms during deployment to avoid batch data misalignment.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–200 connections | Adapts to concurrent demands of high-frequency batch data writes, prevents connection pool exhaustion |
REDIS_URL | Local Redis instance address, port 6379 | Stores temporary cache for real-time temperature collection, reduces database read/write pressure |
STORAGE_S3_ENDPOINT | Configure per local object storage service address | Stores unstructured documents such as quarantine certificates and test reports, supports multi-format upload requirements |
FILE_TOKEN_KEY | 32-character random key string | Encrypts document access tokens, ensures access security for sensitive quality inspection data |
ROOT_KEY | Custom complex key string | Manages system highest privilege configuration, restricts unauthorized modification of deployment parameters |
Three Common Configuration Mistakes
- Symptom: Querying quarantine certificate data returns empty fields after deployment. Cause:
PG_URLis not correctly configured to point to the structured database containing slaughter and quarantine ledgers, so batch data cannot be associated. - Symptom: Model responses append rule prompt symbols at the end after upgrading FastGPT to v4.17.0. Cause: The built-in trace display switch is not disabled. The default enabled state appends rule prompts to the end of results.
- Symptom: Uploaded cold chain temperature logs cannot be parsed correctly. Cause: Multi-format document parsing adaptation is not configured. Only structured table formats are supported, and cannot recognize continuously collected temperature time-series data.
How to Verify Proper Configuration
- Initiate simulated batch data writes, check via the database monitoring panel that connection count does not exceed the
DB_MAX_LINKconfiguration limit. - Upload two types of documents: quarantine certificates and cold chain temperature logs, verify that
STORAGE_S3_ENDPOINTis configured correctly, and documents can be successfully stored and associated with corresponding production batches. - Run one defect analysis query, confirm that no extra rule prompt symbols appear in the returned results, and verify that the trace display switch is disabled.
- If multi-modal video recognition support is needed, confirm that FastGPT has been upgraded to v4.17.0 or later, and test the video upload and parsing process normally.
The configuration values provided here are common starting points, and should be evaluated against the reader’s own data samples.