What this category of data looks like
Data for this category typically comes from concentrate and auxiliary material analysis reports, production DCS systems, electrolyzer voltage and current monitoring, laboratory test results, energy and environmental monitoring equipment. Documents such as batching sheets, smelting records, electrolyzer condition records, test reports, anode and cathode quality records, and environmental protection ledgers are routinely retained. Data updates are triggered by production batches: corresponding records are generated for each batch of raw materials, each furnace, or each electrolyzer. Real-time data such as electrolyzer conditions, voltage, and current are updated synchronously. Fields include component proportions, furnace temperature, tank current, batch number, test time, and more. There is no unified fixed format.
What constraints do these characteristics impose on the "workflow orchestration" link
The multi-source and heterogeneous data characteristics require workflow orchestration to adapt to data sources of different formats, and configure cross-system data pulling and format conversion nodes. For data sources with different update frequencies, trigger logic must be distinguished: real-time electrolyzer condition data requires event-triggered or fixed-period polling, while batch-type smelting and test data are executed triggered by furnace heats or batches. At the same time, quality traceability requires linking data across multiple links, so the workflow must include a link for connecting abnormal data chains, aligning furnace conditions, tank parameters, test results, and environmental protection ledgers to ensure that defect analysis covers all production links.
How to set the configuration
The following configurations are formulated based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
variables | Bind three core variables: concentrate composition, tank current, and test batch number | Match the core traceability dimensions of this category, covering key data from batching, smelting, and testing links |
| Trigger node type | Combine event-triggered and batch-triggered | Adapt to different update frequencies of real-time electrolyzer condition data and batch-type production data |
| HTTP request node timeout | 30–60 seconds | Cover the average response time of cross-system data pulling, avoid single orchestration interruptions |
nodeId | Bind unique identifiers of smelting record nodes and test report nodes | Ensure the accuracy of cross-node data association, avoid data mixing |
| Result display node | Output the top 5 abnormal records | Focus on core abnormal data for defect analysis, avoid interference from redundant information |
| Custom tool call times | First 3 calls | Balance the completeness of data pulling and orchestration execution efficiency |
Three common mistakes
- Phenomenon: The output of the HTTP request node is not displayed in the conversation result, and the status code returns 200 but the fields are empty. Cause: The target field returned by the request is not bound in the
variablesconfiguration, causing the node output to not be captured by the context. - Phenomenon: No data is displayed when generating a chart using axis variables in the orchestration. Cause: The variable is not bound to the corresponding data source field, and the variable value does not match the actual data field.
- Phenomenon: The SQL generated by the text2sql link cannot be associated with the target smelting batch data. Cause: The unique identifier of the batch number node is not bound in the
nodeId, causing the SQL query scope to not cover the target production batch.
How to confirm the configuration is complete
- Trigger a test orchestration, check whether the core variables bound by
variablesare normally displayed in the node logs. - Run the HTTP request node, verify whether the returned fields match the output fields bound in the configuration.
- Execute the text2sql link, check whether the generated SQL contains filter conditions for the target production batch.
- View the node flow logs of the orchestration, confirm that the
nodeIdidentifiers of all associated nodes do not have duplicates or misplacements.
The values provided are common starting points and should be measured against the reader's own samples.