Cross-Review of Technical Documentation for Iron and Steel Smelting and Continuous Casting: Workflow Orchestration

The data sources for technical documentation for iron and steel smelting and continuous casting include hot metal and scrap data, L2 process control

What the data for this category looks like

The data sources for technical documentation for iron and steel smelting and continuous casting include hot metal and scrap data, L2 process control system, laboratory test data, MES system, energy medium system, and equipment condition monitoring system. Daily documents include heat plans, batching lists, converter smelting records, composition reports, continuous casting process records, and slab judgment sheets. Core fields include carbon content (%), tapping temperature (℃), casting speed (m/min), and others. Data update frequency is per heat, real-time, or shift-based. Single-heat document data volume fluctuates with process complexity.

Constraints on Workflow Orchestration from These Characteristics

Real-time data per heat requires workflow node trigger frequency to match business rhythm. This prevents review failure caused by data lag. Multi-source heterogeneous data sources require workflows to bind fields from different systems via the variables parameter. This enables cross-document data linkage. The high-temperature continuous process and ladle casting coupling characteristics require strict timing between workflow nodes. Arbitrary interruptions are not allowed, so fault tolerance mechanisms and timeout retry strategies must be configured. Field differences across multiple document types require workflows to configure unified field mapping rules. This ensures accurate information matching during cross-review.

Configuration Settings

Configuration ItemRecommended PracticeRationale
variablesBind heat number, target carbon content, and casting speed threshold as the variables parameterMatch core business fields for iron and steel smelting and continuous casting, enable cross-document field linkage
Form InputSet as heat number and anomaly type input boxes, support variable passingAdapt to entry requirements for unique identifiers per heat, reduce manual input errors
User SelectionPre-set defect type options such as inclusions, segregation, surface defects, and link to corresponding knowledge base documentsQuickly match corresponding process documents, improve review efficiency
API Access ConfigurationSet timeout to 300 secondsMatch real-time response requirements of the smelting process, avoid process interruptions
Workflow Import/ExportEnable version compatibility conversion switchAddress workflow migration requirements between versions v4.6.7 and v4.8.10
nodeIdAssign a unique nodeId to each document review nodeEnable precise calling and status tracking of workflow nodes

Three Common Misconfigurations

  • Symptom: Workflow node configuration is lost after importing a v4.6.7 workflow into FastGPT v4.17.0. Cause: The version compatibility conversion switch was not enabled, so the old version's parameter format cannot be recognized by the new version.
  • Symptom: Workflows published via API calls do not display user selection or form input prompts. Cause: The switch to enable interactive components for API triggering was not turned on in the workflow configuration. The default setting only supports background calls.
  • Symptom: An error occurs when passing a knowledge base ID variable. Cause: The knowledge base ID was not bound to the variables parameter, or the variable format does not meet system requirements.

How to Verify Correct Configuration

  • Manually trigger the workflow for a test heat, check if the form input and user selection components pop up normally and receive input.
  • Call the API to pass a preset heat number variable, confirm that the variables parameter is correctly parsed and linked to the corresponding documents.
  • Export the current workflow configuration, import it into another FastGPT instance to verify format compatibility.
  • View the workflow node logs, confirm that the execution status and data return for each nodeId are normal.

The values provided here are common starting points, and should be evaluated against the reader's own samples.