What the 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, retained samples and microbial testing reports. Data sources cover milk source testing, production MES systems, homogenization and heat treatment equipment, online temperature and flow collection systems, CIP systems and LIMS laboratory management systems. Data updates are generated per milk tank, per production batch, or in real time, including fields such as raw milk tank number, antibiotic test items, heat treatment temperature and duration, CIP cleaning parameters, filling aseptic environment parameters, etc., to meet full-link traceability requirements for continuous production of liquid biological raw materials.
What constraints these characteristics impose on the "workflow orchestration" link
Since data is updated per milk tank or production batch, the workflow must support triggering execution per batch to avoid invalid full scans. Multi-source heterogeneous data sources require the workflow to be configured with multiple nodes to pull fields from different systems in parallel. Data from each link must also be aligned by timestamp to ensure accurate traceability logic. Liquid raw materials are perishable, and errors have serious consequences, so the workflow must embed real-time verification nodes to intercept abnormalities in key indicators such as raw milk antibiotics and heat treatment parameters. This prevents unqualified batches from entering subsequent links. The continuous production feature requires the workflow to have no overly long waiting nodes, ensuring overall traceability efficiency.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Trigger Timing | Triggered at the production batch completion node | Matches the update rhythm of raw milk tanks or production batches to avoid invalid execution |
variables | Bind batch number, raw milk acceptance test values, heat treatment timestamp | Accurately associate full-link data for a single batch, preventing cross-batch data confusion |
| Single data pull timeout | 200-300 seconds | Adapts to the average upload delay of multi-source device data |
| Exception interception threshold | Calibrated based on actual measurements | Adapts to quality standards for different products, avoiding false interception or missed interception |
| Result output configuration | Only retain the output of the last AI conversation node | Simplifies the display logic of traceability reports, aligns with business viewing habits |
nodeId | Bind the node ID of the retained sample retest node | Automatically trigger subsequent retest processes when abnormalities occur, ensuring a closed traceability loop |
Three Common Configuration Mistakes
- Calling a bound API to access the workflow returns a 400 status code. The cause is that the
variablesbinding for batch parameters was not configured in the workflow, resulting in an empty batch identifier carried in the API request. - Unable to select workflow variables in the JSON input box. The cause is that the variable mapping switch for the workflow was not enabled, and the output of the upstream node was not bound to the global variable pool.
- Multiple segments of AI conversation results are displayed after the workflow runs. The cause is that the result output node was not configured to only retain the output of the last node, resulting in replies from all AI nodes being displayed.
How to Confirm Configuration Correctness
This document is written based on FastGPT v4.17.0. Configuration correctness can be verified via the following actions:
- A test batch can be manually triggered to confirm that the workflow's
variablescorrectly bind the batch number and test data for the corresponding link. - Non-compliant abnormal data can be simulated as input to confirm that the exception interception node triggers normally and blocks subsequent processes.
- After the workflow runs, the output panel can be checked to confirm that only the results of the last AI conversation node are displayed.
- The bound API interface can be called to confirm that the returned parameters match the
variablesfields configured in the workflow.
The values provided are common starting points and should be measured against the reader's own samples.