What this category of data looks like
Data for this category comes from ERP material master data, melting furnaces and thermometers, spectrometers, MES heat records, mold management systems, customer drawings and technical specifications. Update frequency is per heat, per batch, or daily. Daily documents include casting process cards, batching lists, heat records, mold history, first article inspection records, material reports, and nonconforming product review forms. Data fields include heat number, melting temperature (unit: degrees Celsius), pouring duration (unit: seconds), mold usage count, return charge addition amount (unit: kilograms), measured values of alloying elements, defect type and location.
What constraints these characteristics impose on workflow orchestration
This category has scattered data sources and high-frequency update cycles, so workflows must support multi-source data pulling and real-time synchronization. Data fields include specific physical quantities and business identifiers, so workflows require configured unified format validation rules to avoid analysis deviations caused by unit inconsistencies or missing fields. Single heat data is associated with multi-dimensional identifiers such as heat number and mold number, so workflows must bind association keys during orchestration to ensure complete traceability links. Manual verification links take a long time, so workflows need embedded automatic verification nodes to replace repeated verification operations. Data anomalies can trigger batch scrapping or claims, so workflows require configured failure retry and exception notification logic.
How to set configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
variables | Pre-set heat number and mold number as global variables | Adapt to the association and binding requirements of multi-source data |
nodeId | Bind the melting data node and verification node | Ensure accurate triggering of the process link |
| Data source configuration | Bind specified data interfaces of spectrometers and MES | Match the data source types of this category |
| Timeout setting | 600 seconds | Adapt to the conventional response duration of melting and testing links |
| Failure handling strategy | Trigger an alert after 2 retries | Avoid full process interruption caused by single node abnormality |
| User interaction node | Configure as the abnormal result confirmation link | Used for manual intervention in process triggering |
This configuration is developed based on the workflow function of FastGPT v4.17.0.
Three common configuration mistakes
- The phenomenon is that after calling a sub-workflow, the nodes after the user selection node in the sub-workflow have no execution records, and the interface shows the sub-workflow status as incomplete. The cause is that the automatic submission rule for the user selection node is not configured, causing the process to get stuck in the manual interaction link without triggering subsequent flow.
- The phenomenon is that the text extraction node returns empty results, and the log shows a model prompt error. The cause is that a dedicated prompt is not configured for the professional fields of foundry documents such as heat number and pouring temperature, causing the model to fail to accurately extract target data.
- The phenomenon is that the database query node returns a number of results that does not match expectations, and a timeout error is triggered. The cause is that the association key and time range are not limited, causing cross-heat data to be pulled in batches, exceeding the node response threshold.
How to confirm the configuration is complete
- Trigger simulated heat data, check whether multi-source nodes pull corresponding data according to preset rules.
- Run a test process that includes a user selection node, confirm that the subsequent process executes normally after the node is triggered.
- Simulate node timeout or abnormality, check whether the preset retry and alert logic is triggered.
- Call a sub-workflow, check whether the main workflow can correctly receive the return results of the sub-workflow.
The values given are common starting points and should be measured against the reader's own samples.