What the data for this category looks like
Core data sources for this category include animal quarantine information, supplier batches, production traceability, temperature collection, metal detection, and laboratory testing. Daily retained audit-related documents include quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature records, and microbial test reports. Data update cadence follows one quarantine and batch binding per raw material batch, production records uploaded per shift, and cold chain temperature data updated via continuous collection. Data fields include batch number, quarantine number, temperature value (unit: ℃), test results, processing time, and others. Some fields must be bound and associated with supplier qualifications.
What Constraints These Characteristics Impose on Workflow Orchestration
Business characteristics of this category impose multiple constraints on workflow orchestration. The batch-by-batch and shift-by-shift update cadence requires workflows to bind a unique batch number to trigger, preventing cross-batch data confusion. Real-time requirements for continuous temperature control require configuring real-time collection nodes, with alert branches triggered when timeouts occur. The strong binding between quarantine certificates and raw material batches requires adding association field validation in workflows, ensuring diseased raw materials cannot enter production. Strict controls for raw-cooked zoning and sterilization temperature require setting branch judgment nodes in workflows, implementing differentiated audits for different processing links. High-risk error consequences require configuring failure retries and manual intervention nodes in workflows, ensuring exceptions can be traced.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Trigger Method | Trigger by binding batch number via nodeId | Matches the independent audit cadence for each raw material batch, ensuring one-to-one correspondence between workflow and batch |
Global variable variables assignment format | Array format: {"批次号":"XXX", "检疫编号":"YYY"} | Implements binding validation between batches and quarantine certificates, meeting production traceability requirements |
| Node timeout setting | 300-600 seconds | Matches the continuous monitoring duration of cold chain temperature collection, preventing the workflow from getting stuck waiting for real-time data |
| Failure handling branch | Trigger manual review node | Addresses high-risk error scenarios such as microbial overstandard and cross-contamination, ensuring exceptions can be traced |
| Context splicing switch | Turn off automatic splicing | Avoids content superposition in variable results from AI conversation replies, fixing common variable superposition issues |
| Export configuration | Export audit records by batch dimension | Matches the document retention requirements of production traceability, facilitating subsequent audits |
Three Common Mistakes
- Node connections appear normal but the status remains stuck in running, with no next-step log output. Cause: No trigger validation for the batch number in
variablesis configured. The workflow cannot match the quarantine certificate data for the current batch, so trigger conditions are not met. - Fields are empty after assigning values to global variable string arrays. Cause: Standard key-value pair array format is not used for assignment, leading to variable parsing failure.
- Variable results in AI conversation replies include historical content superposition. Cause: The automatic context splicing switch is not turned off, so results from previous conversations are carried over to the current variable.
How to Verify the Configuration Is Correct
- Manually trigger a test batch workflow. Check that nodes execute in the preset order without stalling.
- Enter a globally variable array in the correct format. Confirm that the workflow can normally read fields such as batch number and quarantine number.
- Simulate an AI conversation reply. Check that the returned variable results only include current session content, with no historical data superposition.
- Configure an export node and trigger it. Confirm that an audit record file for the corresponding batch can be generated.
The values provided here are common starting points, and should be evaluated against the reader's own samples.