This document is written based on FastGPT v4.17.0.
What this category of data looks like
This data comes from ERP orders and inventory, BOMs and process routes, equipment capacity, staff shifts, work-in-progress, and supplier arrivals. Daily documents include sales orders, master production schedules, material requirement plans, weekly and daily scheduling tables, work orders, material shortage lists, and delivery change notices. Fields include order numbers, process numbers, material codes, capacity load, delivery requirements, and more. Data update frequency is daily, weekly, or triggered by orders or abnormal events.
What constraints these characteristics impose on workflow orchestration
Data sources are scattered and update rhythms are inconsistent. Workflows must support pulling from multiple data sources and dynamic triggering. Field differences across multiple document types require passing standardized parameters via variables to link data across different stages. Scheduling logic across processes and resources requires workflows to connect scheduling, quality inspection, and traceability nodes, and support rescheduling after order insertions. High update frequency from abnormal triggers requires avoiding invalid workflow executions to reduce resource usage.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Use work order ID, process number, and quality inspection result as passed parameters | The category data includes work order and process fields, which are used to link associated data for quality traceability |
nodeId | Bind the IDs of the quality inspection image recognition node and scheduling rescheduling node | Connect the execution order of scheduling, quality inspection, and traceability workflows |
| Scheduled trigger interval | 300–600 seconds | Matches the update frequency of category data during daily and abnormal updates, balancing real-time performance and resource usage |
| Knowledge base selection scope | Bind the knowledge bases for corresponding product BOMs and process routes | Core category data comes from BOMs and process routes, which are used as the basis for defect analysis |
| Concurrent request limit | Top 20 requests | Adapts to the carrying capacity of a single 4c16g node, avoiding resource exhaustion |
| Trigger condition | Trigger when an abnormal work order is generated or delivery is changed | Matches the update rhythm when category abnormalities occur, reducing invalid executions |
Three Common Misconfiguration Issues
- Symptom: A 504 timeout error is returned when calling a workflow via API, and logs show node execution timed out. Cause: No reasonable scheduled trigger interval is set, causing the workflow to fail to complete in time when processing large volumes of scheduling data.
- Symptom: After configuring multiple knowledge base selections, the retrieved defect analysis data does not match the current work order. Cause: The product code corresponding to the current work order is not passed via
variables, leading to incorrect cross-knowledge base matching. - Symptom: The process becomes unresponsive when the 50th concurrent request runs on a single node. Cause: No concurrent request limit is set, exceeding the carrying capacity of the single 4c16g node.
How to Verify Correct Configuration
- Manually trigger the workflow, pass a test work order ID, and check if
variablesparameters are correctly passed to subsequent nodes. - View the workflow node execution logs to confirm that the quality inspection and scheduling nodes bound via
nodeIdexecute in the correct order. - Simulate the generation of an abnormal work order, and check if the workflow starts automatically per the set trigger conditions.
- Use a stress testing tool to adjust the number of concurrent requests, and verify the stable request carrying range of the single node.
The values provided here are common starting points, and should be evaluated against samples corresponding to the actual deployment scenario.