What Data Looks Like for This Category
Data for this category originates from sources including animal quarantine information, supplier batches, production traceability, temperature collection, metal detection, and laboratory channels. Daily documents include quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature records, and microbial test reports. Data updates occur per batch and per shift. Cold chain temperature data is collected and updated continuously. Data fields include exclusive identifiers and units such as batch number, quarantine qualification mark, temperature reading (℃), and total viable count (CFU/g).
Constraints on Workflow Orchestration From This Category’s Data Characteristics
Four key constraints apply to workflow orchestration based on the category’s data traits:
- Batched and continuously updated data requires workflows to bind real-time batch-specific
variablesto avoid cross-batch data confusion. - Field validation for compliance documents such as quarantine certificates and microbial test reports requires workflows to include fixed field validation nodes to ensure tender document compliance.
- Business requirements for raw and cooked product separation and full-process temperature control require distinguishing processing data associations between raw meat and cooked products in workflows, to avoid information errors related to cross-contamination.
- High-frequency real-time temperature data collection requires workflow node trigger rhythms to match production beats, to avoid data omission or timeout.
Configuration Settings
These configurations are based on FastGPT v4.17.0, as detailed below:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Bind batch number, quarantine qualification mark, cold chain temperature threshold, total viable count qualification standard | Ensure tender documents and quotation preparation are associated with compliant production data for the corresponding batch, avoid information misalignment |
nodeId | Assign unique identifiers to quarantine validation nodes, data splicing nodes, and Feishu notification nodes | Avoid node resource preemption during concurrent calls, ensure correct workflow logic execution |
| Maximum workflow run count | 1000 times | Match the upper limit of document preparation processes for a single batch tender project, align with industry conventional configuration references |
| Concurrent call limit | 1 time/second | Avoid triggering external interface current limiting due to high-frequency calls, resolve the common issue of empty values returned by high-frequency calls |
| Node timeout setting | 600 seconds | Match the continuous collection cycle of cold chain temperature data, ensure complete acquisition of temperature control data for batch production |
| Failure retry count | 3 times | Address temporary exceptions in single data collection or interface calls, ensure automatic process recovery |
Three Common Misconfigurations
- Phenomenon: When concurrent workflow calls reach 2-3 times per second,
variablesreturn empty values. Cause: No concurrent call limit is set, leading to node resource preemption and failure to retrieve bound batch-specific data normally. - Phenomenon: Workflow node connections are normal, but the process remains stuck at the text splicing step after the loop node. Cause: No iteration termination condition is configured for the loop node, and loop-generated batch data is not correctly passed to subsequent splicing nodes.
- Phenomenon: Testing the Feishu Webhook separately sends messages normally, but no response occurs when called via the workflow. Cause: Workflow concurrency exceeds the preset limit, triggering external interface current limiting and blocking the Hook request.
How to Confirm Proper Configuration
- Review the fields bound to workflow
variablesto confirm they include category-specific data such as batch number and quarantine qualification mark. - Simulate a concurrent call scenario of 1 time per second, and verify that workflow logs contain no node errors or missing data.
- Trigger the workflow to run 900 times, and confirm that the process terminates normally and generates a complete document.
- Start loop node iteration, and check that text splicing results include production record data for all batches.
The values provided are common starting points and should be validated against site-specific operational samples.