What This Category's Data Looks Like
This category’s data originates from purchase orders, drawings and specification documents, supplier quality certificates, data collected by inspection equipment, historical quality ledgers, and supplier portals. Daily documents include inspection specifications, sampling plans, incoming material inspection reports, supplier audit reports, abnormality notification forms, 8D rectification reports, and concession acceptance approval forms. Data updates occur with each batch of incoming materials, monthly or quarterly supplier evaluations, and ad-hoc special audits. Core fields include batch number, inspection item, qualified judgment result, sampling quantity, and supplier code. Units include pieces, batches, workdays, and similar units.
Constraints for Workflow Orchestration
Multiple scattered data sources require workflows to include multi-node data pulling and integration steps, adapting to the call logic of different data sources. Irregular business update cycles require workflows to support event triggering, matching timelines such as per-batch incoming materials and monthly evaluations. Diverse document formats require workflows to integrate multiple document parsing nodes, ensuring all types of business documents can be read correctly. The interrelated nature of core fields requires workflows to bind fields like batch and supplier in variables to maintain cross-node data consistency. The need for exception handling requires workflows to include branch judgment nodes, separating qualified and unqualified processes and triggering corresponding processing steps. Quality control objects come from external parties and must balance production supply guarantees and risk. Unlike process inspection, where internal equipment parameters can be adjusted directly, workflows for this category must focus on integrating external supplier data and managing exception flows.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Mode | Trigger by business event (each batch of incoming materials, monthly evaluation nodes) | Adapt to the irregular business update rhythm of this category and avoid unnecessary execution |
variables Binding | Bind batch number and supplier code fields | Match the core associated fields of this category's data and ensure cross-node data consistency |
| Branch Judgment Node | Branch by qualified judgment result | Distinguish between qualified and unqualified processes and trigger corresponding exception handling or release links |
| Node Timeout | 600 seconds | Cover the conventional time consumption of multi-source data pulling and document parsing, adjust based on actual measurement |
| HTTP Request Output Configuration | Enable context output | Ensure that the return results of HTTP requests can be viewed in workflow nodes to facilitate exception troubleshooting |
nodeId Binding | Bind nodeId to the specified inspection equipment node | Accurately call inspection data of corresponding equipment and avoid cross-node confusion |
| Python Code Node | Set based on actual measurement | Used for custom data cleaning and format conversion to adapt to the diverse document processing needs of this category |
Three Common Misconfigurations
- Phenomenon: The output results of HTTP request nodes are not displayed in the workflow conversation. Cause: The context output configuration is not enabled, causing the node return data to not be included in the workflow visible scope.
- Phenomenon: The workflow loop cannot iterate through incoming material data by batch. Cause: The batch list field in
variablesis not correctly bound, causing the loop trigger condition to not match. - Phenomenon: Workflow node execution times out and reports an error. Cause: The node timeout configuration is not adjusted according to the actual business scenario, exceeding the preset threshold.
How to Verify a Correct Configuration
- Trigger a simulated incoming material inspection event, and verify that the workflow runs the preset qualified or unqualified process.
- View the context output of the HTTP request node, and confirm that the returned inspection data is displayed correctly.
- Check that the fields bound to
variablesmatch the core fields of the current business data. - Run the Python code node, and confirm that the custom processing logic functions as expected.
The values provided are common starting points and should be measured against the reader's own samples.