What Data for This Category Looks Like
Daily data sources for this category include concentrate and auxiliary material analysis reports, DCS control system data, electrolyzer voltage and current monitoring data, laboratory test results, and energy and environmental monitoring ledgers. Data update frequencies cover each batch of raw materials, each smelting furnace process, each electrolysis tank process, and real-time monitoring scenarios. Daily documents include batching lists, smelting records, electrolyzer condition records, test reports, anode and cathode quality records, and environmental protection ledgers. Fields include batch number, tank number, element content values, current and voltage parameters, and detection timestamps. Different data sources have inconsistent field units, and alignment must be completed separately by source.
What Constraints These Characteristics Impose on Workflow Orchestration
Multi-source heterogeneous data with greatly varying update rhythms requires workflows to split nodes based on trigger conditions. For example, real-time tank condition data is bound to real-time trigger nodes, and batch documents are bound to batch trigger nodes. The presence of multiple fields without unified units requires workflows to align data formats through field mapping rules to avoid verification failures. High requirements for metal recovery rate and product purity require workflows to embed abnormal verification links, and configure failure retry and alert logic. The batch management requirement for electrolyzer groups requires workflows to support concurrent execution of batch nodes to reduce overall time consumption.
How to Configure Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context Scope | Only current workflow node context | Non-ferrous metal smelting data is associated by batch, which avoids global context interfering with batch verification |
| Batch Concurrency Configuration | Enable concurrency, set the number of nodes per batch to 8-12, set the upper limit of single request to the first 10 entries | Adapt to the batch inspection requirements of electrolyzer groups, calibrated based on actual measurements |
| Node Timeout Threshold | 600 seconds | Matches the average time consumption of DCS data collection and test report parsing |
| Variable Mapping Rules | Bind batch number and tank number fields via variables | Aligns core identification fields of multi-source data, simplifies data verification |
| Failure Trigger Configuration | Bind retry and alert nodes | Addresses risks such as reduced recovery rate and environmental over-standard that may occur after errors |
Three Common Misconfigurations
- Calling global context in workflow nodes: The symptom is that node output includes historical data not from the current batch. The cause is that the context scope parameter was not configured correctly.
- Large model node returns 500 Gateway forwarding error: The symptom is that the node status shows failure and the log contains this error message. The cause is that the service connection for the workflow node was not properly established.
- Serial execution of batch running nodes: The symptom is that node execution time exceeds expectations. The cause is that concurrent execution configuration was not enabled.
How to Verify a Successful Configuration
- Trigger a single-batch workflow, check the node context panel, and confirm that only the batching list and tank condition data of the current batch are included.
- Submit a batch inspection task, check the execution log, and confirm that the mark for concurrent execution is present.
- Simulate a node error, trigger the retry process, and confirm that the alert notification is triggered normally.
- Check the file upload module, confirm that there are no network error prompts, and complete troubleshooting of common abnormal scenarios.
The values provided are common starting points and should be measured against the reader's own samples.