What This Category of Data Looks Like
Data sources include material grade archives, customer technical requirement documents, workshop temperature control instruments, furnace temperature uniformity test reports, hardness testers and metallographic testing equipment, and production MES systems. Update frequency is once per heat load or per batch. Daily generated documents include heat treatment process cards, furnace loading layout diagrams, real-time furnace temperature curves, hardness and metallographic test reports, equipment inspection records, and abnormal disposal forms. Data fields include material grade, heating temperature, holding time, cooling rate, part loading position, hardness value HRC, and others. Corresponding units include degrees Celsius, hours, millimeters, Rockwell hardness units, and others.
Constraints on the Workflow Orchestration Link
The per-heat-load and per-batch update frequency requires workflows to support triggers per heat load, with small per-process data volume but frequent triggers. Multi-source data access requires workflows to configure multiple data nodes to connect different devices and systems, to avoid inconsistent data formats. Professional fields and document types require workflows to include format verification nodes, to ensure professional data such as furnace temperature curves and hardness values comply with industry specifications. The most time-consuming manual verification process must be integrated via workflows, automatically pulling loading position and furnace temperature curve data for comparison, reducing manual operations.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables binding | Bind the lotId and hardnessValue fields of the current heat load | Associate real-time heat load data to avoid manual input errors |
| DB node query result mapping | Directly map to the process card template by field name | Heat treatment SQL query results are mostly structured JSON arrays, which can directly match document fields |
| MCP call concurrency threshold | 1 time/second | Avoid empty service responses caused by high concurrency |
| Loop node content splicing | Splice part information according to loading order | The part order in the loading diagram must match actual production |
| Feishu Webhook trigger timing | Trigger after all nodes complete | Ensure abnormal report content is complete before sending notifications |
nodeId binding | Bind the nodeId of the loop node | Retrieve the output result of the loop node for content splicing |
Three Common Configuration Mistakes
- Phenomenon: Default values of form input nodes are not automatically populated, and fields are empty. Cause: Batch variables within the
variablesscope are not correctly referenced, resulting in failed variable binding. - Phenomenon: SQL query results from DB nodes cannot directly populate the process card, and format errors occur. Cause: No result mapping node is configured, and raw JSON array data is used directly without matching the field requirements of the process card.
- Phenomenon: When MCP is called 2-3 times per second concurrently, the returned result is
none. Cause: Concurrency threshold exceeds the service's carrying limit, causing requests to not be processed normally.
How to Verify Successful Configuration
- Trigger a single heat load workflow, check if the default value of the form input node automatically populates the current heat load batch number.
- Run the DB node, check if the query results are correctly mapped to the corresponding fields of the process card with no format errors.
- Simulate 1 call per second concurrency, check if the MCP returned results are normal with no empty values.
- Test triggering Feishu Webhook after loop node splicing, check if the message content includes information for all loop items.
The values provided are common starting points and should be measured against the reader's own samples.