What Data for This Category Looks Like
Daily documents for dairy processing include raw milk acceptance records, standardized ingredient lists, pasteurization or UHT heat treatment records, CIP cleaning records, filling records, and retained samples and microbial test reports. Data sources cover milk source testing, production MES systems, homogenization and heat treatment equipment, online temperature and flow collection devices, CIP systems, and LIMS laboratory management systems. Data updates occur per milk tank, per batch, or in real time. Fields include raw milk antibiotic test values, heat treatment temperature (℃), holding duration (seconds), CIP cleaning pH value, filling sterility (CFU/mL), and exclusive parameters and units corresponding to different equipment and process links.
Constraints for Workflow Orchestration
Since data updates happen in real time per tank or batch, workflows must align with the single-trigger batch or tank dimension to prevent cross-batch data mixing. Multi-source heterogeneous data sources require workflows to map fields across different systems via variables to ensure accurate parameter references. Liquid raw materials are perishable, and production processes are continuous. Workflows must not include overly long waiting nodes, and must strictly arrange CIP cleaning, heat treatment, filling, and other links in production order. After an exception triggers, the workflow must quickly jump to the corresponding verification branch to avoid production delays or batch quality issues.
Configuration Setup
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Map data source fields per tank or batch | Match the batch/tank-based update data characteristics of dairy processing, ensuring accurate reference to raw milk acceptance and heat treatment parameters |
| Trigger Condition | Match by batch number or tank number | Adapt to real-time updated production data and avoid repeated workflow execution |
nodeId | Bind specified nodes for CIP cleaning and filling links | Ensure direct jumps to corresponding verification branches when exceptions are triggered, aligning with continuous production process logic |
| Timeout Duration | 300–600 seconds | Cover the reasonable duration for multi-device data pulling, adapting to the return cycles of online temperature and flow, and LIMS data |
| Global Variable Assignment | Synchronize from query parameters or API interfaces | Meet the delivery requirements for dynamic parameters such as batch numbers and tank numbers, adapting to multi-scenario calls |
| Branch Verification | Calibrate based on actual measurements | Adapt to the trigger logic of different exception types, ensuring accurate exception handling workflows |
Common Misconfigurations
- A
quote type erroroccurs. The cause is that the field format referenced byvariablesdoes not match the configuration requirements. For example, a numeric raw milk test indicator is referenced directly without format conversion. - The tool call module outputs both AI replies and executes the next-level branch. The cause is that the verification node for branch triggering is not configured, resulting in parallel execution of multiple workflow links.
- HTTP requests time out and the duration cannot be adjusted. The cause is that the timeout parameter is not modified in the corresponding configuration item, and HTTP requests added within nested loops do not have a reasonable waiting interval set, triggering chain timeouts.
Verify Configuration Success
- Trigger a raw milk acceptance workflow once, and check whether
variablescorrectly map the antibiotic test fields in the raw milk acceptance records. - Simulate an exception trigger, and confirm that only the verification process of the corresponding branch is executed, with no simultaneous AI reply output.
- Modify the HTTP request timeout parameter, initiate a CIP data pull, and confirm that the request duration falls within the preset 300–600 second range.
- Pass a batch number as a query parameter, and confirm that the global variable is successfully obtained and applied to the subsequent ingredient list generation link.
The values provided are common starting points and should be measured against the reader's own samples.