This page is compatible with FastGPT v4.17.0.
What this category of data looks like
Data for this category comes from material grade libraries, customer technical requirement documents, temperature control instruments, furnace temperature uniformity testing equipment, hardness testers, metallographic analyzers, and manufacturing execution systems. Data is updated per furnace charge or batch. Daily documents include heat treatment process cards, charging diagrams, furnace temperature curves, hardness and metallographic reports, equipment inspection forms, and exception handling sheets. Core fields include heating temperature (unit: degrees Celsius), holding duration (unit: hours), cooling rate, hardness value (unit: HV), metallographic structure type, and corresponding furnace number and batch number.
What constraints do these characteristics impose on workflow orchestration
The data updated per furnace charge or batch requires workflows to bind real-time trigger logic, matching the execution rhythm of individual furnace charges. The presence of multiple types of heterogeneous documents requires workflows to configure separate nodes to handle process parameter parsing, chart and curve reading, and test report text extraction. Data sources cover equipment and systems, requiring workflows to connect to multiple data source nodes to avoid manual entry errors. The most time-consuming links—determining process specifications, checking charging, and analyzing exceptions—require workflows to split into independent nodes and configure verification logic, to pre-block non-compliant process branches. Errors have serious consequences, so workflows must add exception branch handling to trigger early warning and exception sheet generation processes.
Configuration Recommendations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind material grade, furnace number, and test batch per furnace charge | Match the feature that heat treatment data updates per furnace charge, uniformly call corresponding fields from each data source |
| Trigger Node | Bind charging completion event by nodeId | Correspond to the trigger rhythm of each furnace charge, avoid invalid execution |
| Prompt Optimization | Use prompt templates calibrated by actual measurements | Adapt to the multi-document format recognition needs of process cards and furnace temperature curves |
| Timeout Setting | 1200 seconds | Cover the longest time required for furnace temperature curve parsing and hardness data reading |
| Context Window | 8000 token | Accommodate the full content of process cards and multiple test reports |
| Retry Mechanism | Retry 2 times after failure, 60-second interval | Address scenarios where device data reading fails temporarily |
Three Common Mistakes
- Workflow execution stalls at a node without an error report. The cause is that no default value for
variablesis configured, leading to empty furnace number and batch parameters relied on by subsequent nodes, which prevents triggering the next execution step. - The prompt optimization link cannot call environment variables. The cause is that environment variable call permission is not enabled in system configuration, or the placeholder format of environment variables is not correctly referenced in the prompt.
- The conversation model prompt cannot correctly recognize spaces. The cause is that full-width spaces are used in the prompt, or the configuration to retain half-width spaces for text preprocessing is not enabled.
How to Verify Proper Configuration
- Trigger a workflow with simulated furnace charge data, check whether parameters of each node are correctly bound to corresponding fields in
variables. - Simulate a scenario where device data reading fails, verify whether the retry mechanism triggers as configured.
- Edit the prompt and add keywords with half-width spaces, verify whether the prompt optimization link can recognize them correctly.
- Check the connection order of multiple code running components, confirm that the execution link matches the business process.
The values provided are common starting points and should be measured against the reader's own samples.