FastGPT v4.17.0
What data for this category looks like
Data sources include iron melt and scrap data, L2 process control, laboratory systems, MES, energy medium systems, and equipment status. Daily documents include heat plans, batching lists, converter smelting records, composition reports, continuous casting process records, and slab judgment sheets. Update frequencies are per heat, real-time, and per shift. Fields include heat number, composition content, equipment operating parameters, defect type, and others. Units include temperature (℃), composition percentage, pressure (MPa), time (hours), and others.
What constraints do these characteristics impose on the workflow orchestration link
Multi-source data with varying update frequencies requires workflows to support parallel pulling of real-time data from different sources. This prevents a single node from blocking the overall process. The strongly coupled nature of heats and casting sequences requires strong temporal dependencies between workflow nodes. This ensures the rhythm of smelting and continuous casting processes matches. Frequently updated documents require workflows to pull the latest version each time they trigger. This avoids using expired data that affects inspection and maintenance judgments. High-temperature continuous processes cannot be stopped arbitrarily. Workflows must have breakpoint resumption capabilities. This means retaining execution status during exceptions, and continuing to complete the process after recovery.
How to set configurations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
variables | Bind heat number, equipment ID, current shift | Match the business logic of iron and steel smelting managed by heat, equipment, and shift, to accurately pull corresponding business data |
nodeId | Assign unique IDs in the order of smelting → continuous casting → quality inspection | Match the strong temporal dependencies of process industry, to ensure node execution order conforms to the production process |
| Single trigger node count | First 3 entries | Avoid overloading the system by calling multi-source data simultaneously, and adapt to the rhythm of real-time updated data |
| Timeout threshold | 600 seconds | Cover the typical processing duration from data pulling to defect judgment, and avoid interrupting high-temperature continuous processes |
| Context window | 800–1200 characters | Adapt to the average length of documents related to a single heat, to ensure coverage of key inspection and maintenance information |
| Data cache duration | 2 hours as measured empirically | Adapt to the update rhythm of documents per shift, and avoid increasing system burden from frequent pulling |
Three Common Misconfigurations
- Phenomenon: Node order is reversed during workflow execution, causing the continuous casting process to fail to match the heat plan. Cause:
nodeIdis not assigned in the smelting → continuous casting → quality inspection sequence, violating the strong temporal dependency constraint. - Phenomenon: Empty fields are returned when the workflow pulls data, making it impossible to generate maintenance suggestions. Cause: The heat number in
variablesis not bound, so smelting records for the corresponding heat are not pulled. - Phenomenon: Workflow execution times out and triggers system interruption. Cause: The timeout threshold is set too short, failing to cover the actual duration of data processing for high-temperature processes.
How to Confirm Proper Configuration
- Trigger the workflow once, view the returned node logs, and confirm that the node execution order conforms to the smelting → continuous casting → quality inspection process logic.
- Check the workflow's variable binding configuration, and confirm that it includes core business fields such as heat number, equipment ID, and current shift.
- Simulate a data pulling delay scenario, and verify the workflow's exception handling and breakpoint resumption capabilities.
- Check the workflow's timeout and cache settings, and ensure that the thresholds match the actual duration of business processing.
The values provided are common starting points and should be measured against the reader's own samples.