What data does this category include
Data sources for this category include ERP material master data, melting furnaces and thermometers, spectrometers, MES heat record systems, mold management systems, customer drawings and technical specifications. Update frequency is per heat, per batch, or daily. Daily documents cover casting process cards, batching lists, heat records, mold histories, first-piece inspection records, material test reports, and nonconforming product review forms. Fields include alloy element content values, furnace temperature, pouring temperature, mold cycle number, return charge ratio, defect type, and more. Units include degrees Celsius, kilograms, and piece counts.
What constraints do these characteristics impose on workflow orchestration
Multiple scattered data sources require the workflow to be configured with multiple cross-system connection nodes to ensure accurate data extraction across all links. The high-frequency update frequency requires the workflow trigger mechanism to match the heat, batch, or daily update cycle to avoid data lag or duplicate processing. The specificity of professional fields requires variable mapping to strictly correspond to casting-specific parameters such as alloy composition, furnace temperature, and pouring parameters to avoid summary errors caused by field mismatches. The complex verification and traceability links require the workflow to connect the full process of data extraction, verification, summary, and traceability, covering abnormal data interception logic to ensure the accuracy of ledger data.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Data Source Node Configuration | Create one independent node for ERP, melting equipment, spectrometer, and MES respectively | Covers all data sources for this category, avoids full process disruption from single node failure |
variables Mapping Rules | Map heat ID, alloy composition, furnace temperature, and mold cycle number to fixed variables | Matches core fields of foundry ledgers, ensures accurate parameter association during summary |
nodeId Binding Rules | Bind node IDs in the order of data extraction, verification, summary, and report generation | Clears workflow execution order, avoids confusion in node execution logic |
| Timeout Threshold | 300–600 seconds | Matches actual time consumption of data extraction from melting and spectral testing equipment |
| Abnormal Trigger Node | Trigger an alert node when batching ratio is abnormal or temperature exceeds range | Intercepts nonconforming data to avoid subsequent summary errors |
| Output Format Configuration | Generate reports in structured document format, including heat detail pages | Matches conventional archiving and viewing requirements of foundry quality departments |
Three common mistakes
- Variable mapping is lost after importing workflows across versions. The symptom is that some
variablesmapping fields are empty after importing to FastGPT v4.17.0. The cause is failure to retain mapping relationships for casting-specific fields, and failure to verify consistency of variable bindings between nodes. - Basic charts cannot correctly reference ledger variables. The symptom is that charts display no data or have mismatched field names. The cause is failure to correctly bind core fields such as heat ID and furnace temperature to chart axis variables, and failure to match field naming rules for foundry ledgers.
- HTTP request node output does not appear in execution results. The symptom is that no corresponding output content is displayed after running. The cause is failure to configure an output node in the workflow to associate with HTTP request return data, or failure to enable the variable output switch.
How to verify proper configuration
- Manually trigger the workflow once, and check whether the extracted data sources include corresponding documents such as batching lists and furnace temperature records for the current heat.
- Check whether the fields mapped by
variablesmatch the core fields of the foundry ledger, with no mismatched field names. - Simulate abnormal data such as batching ratio exceeding conventional range, and confirm whether the abnormal trigger node normally starts the alert process.
- View the workflow execution log, confirm that the
nodeIdexecution order of each node matches the configuration, and there are no timeout or error messages.
The values provided here are common starting points and should be evaluated against the reader's own samples.