What the data for this category looks like
Daily documents for this category include batching lists, smelting records, electrolytic cell condition records, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Data sources include concentrate and auxiliary material analysis results, DCS system data, cell voltage and current monitoring data, laboratory test results, and energy and environmental protection monitoring data. Update frequency is adjusted in real time for each batch of raw materials, each smelting furnace run, or each electrolytic cell operation. Some ledgers are updated on a fixed cycle. Document fields include batching addition amounts, furnace temperature, cell voltage, current values, component test values, and more. Corresponding units are kilograms, degrees Celsius, volts, kiloamps, grams per liter, and others.
What constraints these characteristics impose on workflow orchestration
Data for this category comes from multiple dispersed systems with significantly different update frequencies. Workflow orchestration must support series and parallel calls of multi-source data nodes, while adapting to mixed trigger rules based on real-time events, batches, and scheduled tasks. The diversity of document fields and units requires workflows to include built-in unified format conversion nodes to prevent data validation failures. Errors are linked to production and environmental protection indicators, so workflows must add pre-check links to block processes immediately when abnormal values occur. When processing data for multiple electrolytic cells in batches, loop logic must be properly configured to avoid empty results caused by subscript exceptions. Workflows must also support calling custom environment variables to store system access credentials, simplifying permission configuration across multiple nodes.
How to set up configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Store custom values such as system access keys and unit conversion coefficients | Adapt to permission configuration requirements for multi-source systems, avoid hardcoding sensitive information |
nodeId | Bind unique identifiers for multi-source data nodes | Enable series calls across multiple nodes, ensure correct data flow order |
| Loop subscript step size | 1 | Match sequential processing logic for single data entries, avoid empty results caused by subscript jumps |
| SQL query timeout | 300 seconds | Adapt to batch query scenarios for smelting data, prevent query failures due to large data volumes |
| Single loop execution limit | First 20 entries | Match the processing scale of a single batch of electrolytic cells or smelting furnaces, avoid node overload |
| Data format conversion rules | Uniformly convert all document fields to standard numerical units | Adapt to the diverse document field characteristics of this category, eliminate unit mismatch issues |
Three common configuration mistakes
- Symptom: Workflow calls to custom environment variables return empty values, and system keys configured during deployment cannot be read. Cause: The mapping relationship for environment variables is not correctly declared in the workflow's
variablesconfiguration item, causing variables to fail to load. - Symptom: After the loop body executes a subscript increment operation, the output result is null, and the next electrolytic cell data cannot be retrieved. Cause: The loop subscript initial value is not set to a valid starting value, or the step size configuration is incorrect, causing the subscript to exceed the data array boundary.
- Symptom: After configuring an SQL query node, smelting-related records cannot be retrieved from the database, and logs show no matching data. Cause: Document field names do not match database table fields, or data is not filtered by batch, resulting in an overly large query scope.
How to confirm the configuration is complete
- Manually trigger the workflow. Verify that environment variables configured in
variablesare correctly loaded. Variable values can be viewed via logs or debug nodes. - Run the loop body node. Verify that the subscript step size and execution count match the preset processing scale. Confirm that the number of output results meets expectations.
- Execute the SQL query node. Compare the query results with smelting data in the actual database to confirm that fields and filter conditions match.
- Simulate abnormal data input. Check that the data format conversion node correctly handles unit mismatch situations and triggers preset verification logic.
The values provided are common starting points and should be evaluated against local operational samples.