What This Category’s Data Looks Like
Data for this category originates from hot metal and scrap data, L2 process control systems, laboratories, 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. These documents carry fields such as heat numbers, process parameters, test results, and equipment status. Units include temperature, length, component proportion, casting speed, and additional units. Data update cycles follow three patterns: per heat, real-time, and per shift. The update frequency of each document aligns with its associated production stage.
What Constraints These Characteristics Impose on the Workflow Orchestration Link
Multi-source data integration requires workflows to configure multiple data pull and validation nodes, to avoid inconsistent data across different systems. Data sources with varying update rhythms need distinct trigger timing. Real-time data requires immediate trigger nodes, while batch heat data needs scheduled trigger nodes. Diverse document formats require workflows to adapt to different file parsing nodes, to ensure all production records can be imported normally. Special field and unit requirements need workflows to include parameter validation links, to prevent non-compliant process data from entering subsequent processing stages. The high-temperature continuous process nature of this category also requires workflows to avoid overly long serial node chains, to prevent disruption to production rhythm.
Configuration Settings
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Trigger Method | Trigger based on heat plan generation time; batch tasks trigger at the end of each shift | Matches the per-heat and per-shift data update rhythms, to avoid duplicate or missed triggers |
| Timeout Setting | 600 seconds | Adapts to the single-heat processing cycle of iron and steel smelting processes, to avoid mid-run interruptions |
variables Binding | Bind core fields such as heat numbers, hot metal composition, continuous casting pulling speed | Matches the core fields of documents in this category, to ensure workflows call correct data |
| File Processing Node | Support txt and csv format imports; single-processing limit of 800–1200 characters | Adapts to the size of individual daily document records, to avoid memory overflow |
nodeId Configuration | Assign unique identifiers based on node functions | Facilitates workflow debugging and error location |
| Rearrangement Rule | Set based on actual measurement calibration | Adapts to priority sorting for different process stages |
Three Common Misconfigurations
- Phenomenon: Offset 17 related errors appear in workflow run logs. Cause: The process parameter fields bound in
variablesare not mapped correctly, causing abnormal data positioning when the node reads data. - Phenomenon: No valid summary result after uploading a smelting record TXT document. Cause: No character processing rules configured for the file processing node, content exceeding the default length limit is truncated.
- Phenomenon: Workflow automatically terminates after running for more than 120 seconds. Cause: The timeout setting was not adjusted, using the default shorter timeout threshold that cannot adapt to the processing cycle of the iron and steel smelting process.
How to Confirm Proper Configuration
- Trigger a simulated heat task once, check whether the fields bound in
variablesare correctly loaded into workflow nodes. - Upload a standard converter smelting record TXT document, verify that the file processing node parses content normally.
- Run a test workflow, confirm that the running duration does not exceed the preset timeout threshold, and there are no abnormal errors.
- Check the inter-node transfer rules, confirm that data is passed in sequence according to the smelting process order, with no nodes omitted.
The values given are common starting points and should be measured against the reader's own samples.