What data for this category looks like
Data sources for this category include online composition analysis equipment, DCS control systems, laboratory testing systems, kiln and grinding production equipment, raw and fuel ledgers, and energy metering devices. Data updates follow three frequencies: real-time, per-shift, and daily. Real-time data comes from online monitoring and equipment operating parameters. Per-shift data corresponds to patrol records and team operation ledgers. Daily data includes test reports and quality control documents. Daily documents cover raw material batching plans, kiln operation records, test reports, quality control notices, equipment inspection forms, and energy reports. Fields include raw and fuel composition, production process parameters, quality test results, and equipment operating status. There is no unified fixed format, and adjustments are made based on differences in production stages.
What constraints do these characteristics impose on workflow orchestration
The data characteristics of this category create multiple constraints for workflow orchestration. Multi-source heterogeneous data sources require workflows to include multi-node data pulling capabilities. These capabilities adapt to different interface formats across online equipment, laboratory systems, and ledger systems. Data with different update frequencies need matching mixed trigger rules. Use high-frequency polling for real-time production parameters. Schedule per-shift triggers for team patrol records and energy reports. Run daily scheduling for daily quality reports. Non-standard document structures require workflow parameter parsing nodes to support custom field mapping. This adapts to field differences across production stages. Production errors have clear consequences. Configure exception branches and retry logic to ensure process integrity.
How to set configurations
The following configurations are based on FastGPT v4.17.0:
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind core fields such as raw and fuel composition, kiln cylinder temperature, clinker strength test values | Cover core data dimensions for quality traceability and defect analysis of this category |
nodeId | Specify unique identifiers for the laboratory report parsing node and DCS data pulling node | Precisely call workflow nodes for each link, avoid confusion across nodes |
| Scheduled trigger configuration | Set real-time data to 15-second polling, per-shift data to 8-hour triggers, daily data to 24-hour triggers | Match the update frequencies of different data in this category |
| Exception branch configuration | Configure two types of branches: data empty and interface timeout, enable automatic retry | Address instability of multi-source data, ensure process integrity |
| Sub-workflow call configuration | Set timeout threshold to 300 seconds, enable failure retry | Adapt to complex quality analysis calculation processes, avoid timeout disconnection of non-streaming requests |
Three common errors
- Phenomenon: After a workflow is called, the conversation log shows empty running data. Cause: Core fields for the corresponding link are not bound in
variables, so pulled data is not injected into the workflow context. - Phenomenon: After the main workflow calls a sub-workflow, the sub-workflow cannot complete its full execution. Cause: The correct sub-workflow node is not specified via
nodeId, or sub-workflow trigger parameters are not configured. - Phenomenon: Non-streaming requests disconnect automatically after 180 seconds, preventing complex quality analysis processes from finishing. Cause: No reasonable timeout threshold is set, and sub-workflow failure retry is not enabled.
How to confirm the configuration is correct
- Check workflow data source nodes. Confirm core data fields for the category are bound. Verify field mapping matches actual data formats.
- Verify trigger rules. Match scheduling cycles to actual data update frequencies for each update type.
- Test sub-workflow calls. Confirm the main workflow triggers the sub-workflow normally. Confirm sub-workflow execution results return to the main workflow context.
- Simulate exception scenarios. Verify exception branch trigger logic works. Confirm the process runs preset branches when data is empty or an interface times out.
The values provided here are common starting points and should be measured against the reader's own samples.