What the data for this category looks like
Dairy processing covers end-to-end production data for categories including liquid milk, yogurt, milk powder, dairy ingredients, and more. Core documents include raw milk acceptance records, standardized ingredient lists, pasteurization or UHT sterilization records, CIP cleaning records, filling records, sample retention and microbial test reports. Data sources include dairy source testing equipment, production MES systems, homogenization and heat treatment equipment, online temperature and flow collection devices, CIP systems, and LIMS laboratory management systems. Data updates in real time per single raw milk tank and production batch. Fields include raw milk batch number, antibiotic test values, heat treatment time and temperature, CIP cleaning cycle count, filling sterility status, total microbial test colony count, and additional relevant fields. Units include milligrams per liter, seconds, degrees Celsius, cycle count, CFU per milliliter, and other standard units.
Constraints for HTTP Interfaces and External Systems
Real-time, high-frequency production data updates for dairy processing require HTTP interfaces to support low-latency batch requests. Single requests must carry synchronized content for multiple documents per production batch. Multi-source heterogeneous data sources require interfaces to support varying request and response body formats. Unified access permission verification for all external systems must be implemented via the Authorization parameter. Data includes biosafety indicators and equipment operating parameters. Interface return fields must strictly match field names and units of connected systems to avoid data parsing errors. High-risk events such as full raw milk tank rejection or sterile filling failure recall require interfaces to include failure retry mechanisms and complete request-response logs. These logs support tracing of data synchronization anomalies.
Configuration Settings
The following configurations are formulated for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {custom API key} | Uniformly verify request legitimacy across multiple connected external systems, and cover access permissions for full-process dairy processing data |
variables | {"batchType": "dairy", "updateCycle": "real-time"} | Pass batch type and update frequency parameters unique to dairy processing, to help downstream systems identify data ownership and synchronization cadence |
responseData | includeBatchId, includeMicroTestResult, includeCIPCycle | Only return core fields required for production ledger summary and report generation, reduce invalid data transmission, and align with multi-dimensional data requirements of dairy processing |
| Request timeout | 60–120 seconds, calibrated via actual testing | Cross-system delays may occur for data such as raw milk acceptance and microbial test reports. This interval covers response durations for most devices and systems |
| Number of failure retries | 2–3 times | Mitigate network fluctuations or temporary external system unavailability, and prevent ledger data synchronization interruptions caused by single request failures |
Three Common Configuration Errors
- Symptom: The interface returns a
401 Unauthorizedstatus code, and ledger data fails to synchronize to external systems. Cause: TheAuthorizationparameter is not configured correctly, or the key’s permissions do not cover the access scope of full-process dairy processing data. - Symptom: Workflow template imports show success but contain no content, and ledger synchronization cannot be triggered. Cause: Unique dairy processing batch variables (such as raw milk tank number) are not bound in the template, preventing the workflow from reading corresponding production data.
- Symptom: SQL Server connection tests fail, but configuration information is correct. Cause: The corresponding port for the dairy processing ledger database is not open, or the database account lacks read and write permissions for production data.
How to Verify Successful Configuration
- Initiate an interface request for single-batch raw milk acceptance data, and check if
responseDataincludes preset core fields, with field units matching the local system. - Review interface logs to confirm the
Authorizationparameter is correctly parsed, with no unauthorized error messages. - Simulate an external system receiving synchronized data, and verify that the received ledger data fully matches production records stored in FastGPT.
- Disconnect the external system network and retry the request, confirm the interface triggers the preset retry mechanism, and verify normal data synchronization after network recovery.
The values provided are common starting points and should be evaluated against the reader's own samples.