What Data for This Category Looks Like
Data sources for this category include real-time collected data of molten iron and scrap steel, L2 process control data, laboratory composition test reports, MES production scheduling data, energy medium system operation records, and equipment status monitoring data. Data update frequencies cover per heat, real-time, and shift cycles. Daily associated documents include heat plans, batching lists, converter smelting records, composition reports, continuous casting process records, and slab determination sheets. Each document contains fields such as heat number, cast number, composition parameters, process temperature, and equipment operating duration. Units include temperature, weight, time, composition proportion, and more. Some documents also include special data such as furnace temperature fluctuation curves and pulling speed parameters.
What Constraints These Characteristics Impose on Workflow Orchestration
The high-frequency real-time data and multi-source dispersed nature of this category require workflows to support per-heat triggering or real-time data pulling. This avoids fixed-period scheduling that does not align with production rhythms. The strongly coupled business logic between heats and casts requires binding core variables parameters within the workflow. This ensures accurate data association across all links and prevents cross-heat data confusion. The field differences across multiple professional documents require configuring data standardization nodes in the workflow. This unifies the formats of composition, temperature, and process parameters. The high risk of error consequences requires adding a key parameter verification link before quotation generation. This blocks abnormal data from entering the bid document preparation process and avoids losses such as heat downgrading or scrapping.
This document is written based on the workflow orchestration function of FastGPT v4.17.0.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Mode | Triggered by heat plan completion signal | Matches production rhythm advancing per heat, avoids invalid scheduling |
| Core Variable Binding | Heat number, cast number, molten iron composition, target steel grade | Bind variables parameters to ensure accurate data association across all links, aligns with heat-cast coupling characteristics |
| Data Pull Waiting Duration | 600 seconds | Adapts to collection delays of multi-source data, prevents process interruption |
| Parameter Verification Node Threshold | Calibrated based on actual measurements | Adapts to composition and temperature requirements of different steel grades, avoids poor adaptability of fixed thresholds |
| Result Output Configuration | Only output final bid documents and quotation sheets | Meets requirements for displaying only final results, hides intermediate processing steps |
| Debug Node Settings | Support manual termination and breakpoint resumption | Adapts to fault troubleshooting for complex processes, resolves issues where debugging cannot be stopped |
Three Common Misconfigurations
- Phenomenon: The debug node cannot be manually terminated after startup, and the process becomes stuck. Cause: Manual termination permission for the debug node is not configured, or the trigger mode is bound to an uninterruptible real-time signal.
- Phenomenon: Generated bid documents lack key composition data, with empty fields. Cause: The heat number or cast number in the
variablesparameters is not bound, leading to failure to match records for the corresponding heat during data pulling. - Phenomenon: The workflow only calls a single model to complete the entire process, and cannot process data in steps. Cause: Nodes are not split into multiple independent
nodeIdfor data pulling, parameter verification, and document generation, and the entire process is incorrectly bound to a single model node.
How to Confirm Proper Configuration
- Trigger test: Manually simulate the heat plan completion signal, observe whether the workflow starts as expected, and verify that the trigger logic matches production rhythms.
- Variable verification: Check whether the
variablesparameters are bound to core fields such as heat number and cast number, confirm that data across all links can correctly associate records for the corresponding heat. - Node split check: Confirm that the workflow has split independent nodes for data pulling, parameter verification, document generation, and other tasks, to support step-by-step debugging and retries.
- Output verification: After triggering the process, confirm that only the final bid document and quotation results are displayed, with details of intermediate processing steps hidden.
The values given are common starting points and should be measured against the reader's own samples.