Data Profile for Dairy Processing
Data for dairy processing covers the entire production workflow for liquid milk, yogurt, milk powder, and dairy ingredients. Daily documents include raw milk acceptance records, standardized batching sheets, pasteurization/UHT sterilization records, CIP cleaning records, filling records, sample retention reports, and microbiological test reports. Data sources include milk source detection systems, MES production management systems, homogenization and heat treatment equipment, online temperature and flow monitoring devices, CIP systems, and LIMS laboratory management systems. Data updates are performed per milk tank and per batch. Some temperature and flow data is synchronized in real time. The overall data chain is continuous and has high timeliness requirements.
Constraints for Deployment and Upgrade
Continuous production and real-time data characteristics of dairy processing require deployment workflows to adapt to high-frequency, small-batch data writes, and prevent storage overflow caused by backlogged single-batch data. Multi-source heterogeneous data — including directly connected device data and system-exported documents — must be accessed with unified formatting. Data mapping rules must be planned in advance. Risk scenarios such as raw milk rejection and batch recall require that rollback windows be reserved during upgrades, and that critical data collection not be interrupted during upgrades. The perishable nature of liquid raw materials requires that traceability link response delays match production cycles. Core data node stability must be prioritized during deployment.
Configuration Guidelines
This configuration is based on FastGPT v4.17.0, and supports deployment and upgrade needs for dairy processing production scenarios.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 connections | Adapts to high-frequency data write requirements per milk tank and per batch, prevents connection pool exhaustion |
MONGODB_URI | Points to a dedicated replica set address storing production records and micro-test reports | Isolates business data from data from other scenarios, ensures stability of traceability queries |
REDIS_URL | 192.168.1.100:6379 (calibrated via on-site testing) | Reduces query latency for real-time temperature and flow data, matches production cycles |
STORAGE_S3_ENDPOINT | Points to an enterprise-built object storage node | Adapts to storage needs for large-volume sample retention reports in non-public network environments |
FE_DOMAIN | http://fastgpt-intranet.example.com | Adapts to deployment scenarios accessed via FRP penetration |
FILE_TOKEN_KEY | Configured as the enterprise-wide unified file access key | Ensures file access permission verification for intranet deployments |
Three Common Misconfigurations
- Symptom: Markdown first and second level headings in the knowledge base fail to render correctly, and become invalid after being added in local deployment scenarios. Cause: Unified text parsing rules are not configured for multi-source formatted dairy processing documents, such as batching sheets and microbiological test reports. This leads to differences in format parsing logic between this deployment and the public version.
- Symptom: The
Reached the max retries per request limiterror occurs when calling the vector database. Cause: High-frequency writes of real-time temperature and flow data from dairy processing are not adapted, leading to request concurrency exceeding database quotas. Reasonable retry intervals and concurrency limits are not configured. - Symptom: After deploying via NAS, FastGPT services cannot be called through intranet penetration access. Cause: The mapping relationship between
FILE_TOKEN_KEYand intranet access paths is not configured, leading to failed file access token verification and inability to complete service calls.
How to Verify Successful Configuration
- Run an import test for a single batch of raw milk acceptance records, verify field integrity after document parsing, and confirm that the configured storage path matches the actual storage node.
- Initiate a real-time temperature and flow data synchronization test, check if database connection counts are within the preset range, and confirm that the
DB_MAX_LINKconfiguration is active. - Initiate a vector retrieval test, verify that the number of retrieved results matches the configured retrieval rules, and confirm that vector database connections and concurrency settings meet production requirements.
- Trigger a service rollback test, confirm that the upgrade rollback script can execute normally, ensuring stability of the production workflow.
The values provided here are common starting points, and should be evaluated against the reader's own samples.