What This Category's Data Looks Like
Data sources include animal quarantine information, supplier batches, production traceability records, temperature collection data, metal detection results, and laboratory test reports. Daily documents include quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature records, and microbial test reports. Data is updated per batch and per shift, with continuous temperature control data synced in real time. Fields include batch number, quarantine number, temperature value (unit: ℃), detection time, microbial indicator values, and more. Strict matching must be maintained between raw material batches and corresponding quarantine information.
What Constraints These Characteristics Impose on Workflow Orchestration
Category data must be strictly bound to batches and quarantine information. Therefore, workflows must use variables to bind core variables such as batch number and quarantine number, ensuring each operation step is associated with the corresponding production batch. Continuous temperature control data is updated in real time, requiring workflows to be configured with scheduled trigger nodes or real-time data pull sources, and static cached data must not be used. Raw and cooked product separation and biological risk requirements mandate that workflows set permission checks and exception branches between nodes. If temperature exceeds thresholds or cross-contamination occurs, an alert must be triggered and the process terminated. High data update frequency requires limiting workflow execution cycles and data pull volumes to avoid resource overload. This document is written based on FastGPT v4.17.0.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind batch number, quarantine number, temperature control threshold (0-4℃) | Match the category's batch association and temperature control requirements, ensure each operation step is linked to the corresponding production batch |
| Node trigger condition | Trigger according to production rhythm (per shift/per batch) | Adapt to the data update rhythm of per batch and per shift, avoid invalid execution |
nodeId | Associate node IDs of temperature collection and metal detection plugins | Accurately call category-specific equipment data nodes, ensure accurate inspection information is obtained |
| Log retention configuration | Record execution results and parameters of each node | Facilitate troubleshooting of abnormal scenarios such as spoiled raw materials and microbial overstandard levels |
| Timeout setting | 600 seconds | Adapt to the response cycle requirements of temperature control data collection, avoid process blocking caused by long waits |
Three Common Mistakes
- Phenomenon: No abnormal alert logs for equipment inspection are generated after workflow execution. Cause: The node execution log retention switch is not enabled, or the exception identification field is not configured in
variables. - Phenomenon: No return data records are available after calling the temperature collection plugin. Cause:
nodeIdpoints to a non-target plugin node, or the parameter verification rule for plugin calls is not configured. - Phenomenon: Original text processing workflows cannot run normally after an upgrade. Cause: Original text processing nodes were removed in the new version iteration, and custom nodes were not used to adapt to the category's document processing requirements.
How to Confirm Proper Configuration
- Manually trigger the workflow once, check whether the batch number parameters bound in
variablesare correctly passed to the equipment inspection node. - View the node execution log, confirm that the plugin node corresponding to
nodeIdhas normally called equipment data such as temperature collection and metal detection. - Simulate an abnormal scenario where the temperature exceeds the threshold, check whether the workflow triggers the preset alert and process termination branch.
- Generate a workflow sharing link, test whether preset global variable parameters can be passed in via URL parameters.
The values provided here are common starting points and should be measured against the reader's own samples.