What this category's data looks like
Data sources for this category include composition test data of concentrates and auxiliary materials, real-time operating parameters from production DCS systems, voltage and current monitoring data from electrolytic cells, material quality reports issued by the laboratory, and energy and environmental monitoring data. Update rhythms follow per batch of raw material feeding, per furnace smelting cycle, and real-time per cell. Daily documents include batching lists, smelting operation records, electrolytic cell condition logs, test result reports, anode and cathode quality ledgers, and environmental operation ledgers. Fields include cell voltage (unit: millivolts), operating current (unit: amperes), feeding mass (unit: kilograms), flue gas emission concentration (unit: milligrams per cubic meter), and others.
What constraints do these characteristics impose on the "workflow orchestration" link
The multi-source data sources, differentiated update rhythms, and complex field characteristics of this category impose multiple constraints on workflow orchestration. First, support node configuration for parallel pulling of multi-source data to avoid process delays caused by serial calls. Second, define three types of trigger rules — batch-based, real-time, and periodic — through variables to match the update rhythms of different data. Third, configure parameter verification nodes to perform range checks on unit-bearing fields such as cell voltage and operating current, to prevent abnormal data from entering subsequent logic. At the same time, bind nodeId to associate specific production batches and electrolytic cell identifiers, to ensure the accurate correspondence between data and corresponding production links and prevent data confusion.
How to set the configurations
The following configurations are written based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Configure in three categories: batch-based, real-time, periodic. Set periodic trigger to 1–12 hours, real-time trigger to every 30 seconds | Match the update rhythm of this category's data by batch, furnace/cell, and real-time |
nodeId | Bind to electrolytic cell ID or production batch ID | Ensure queried data is accurately associated with the corresponding production link |
| Parallel conversation node | Start 3–5 AI conversation nodes simultaneously | Cover the parallel query needs of multi-source data and avoid process delays |
| Query merging module | Merge multi-source data by production batch ID | Ensure tank condition, test, and batching data correspond to the same production link |
| Failure handling node | Retry 2 times before triggering an alert, set timeout threshold to 600 seconds | Match the real-time requirements of this category's data and avoid process blocking |
| Custom knowledge base variable | Set to string type | Match the text retrieval and parameter matching needs of this category's documents |
Three common mistakes
- Phenomenon: Multiple AI conversation nodes in the workflow fail to start in parallel. Execution logs show serial execution. Cause: The trigger rules for parallel nodes are not configured correctly, and multi-node parallel scheduling is not enabled.
- Phenomenon: Conversation logs cannot be associated with specific production batches or electrolytic cells. Fields are empty or show generic identifiers. Cause:
nodeIdis not bound to a specific production link identifier, and batch or cell number parameters are not passed throughvariables. - Phenomenon: The number of output results from the query merging module does not match expectations, and some data fields are missing. Cause: Production batch ID is not used as the merge key, leading to incorrect matching of multi-source data and omission of tank condition or test data.
How to confirm the configuration is correct
- Check the workflow configuration to confirm that the trigger rules for
variablescorrespond to the batch, real-time, and periodic update rhythms of this category. - Run a test workflow, view node execution logs, and confirm that multiple AI conversation nodes start in parallel with no serial execution records.
- Enter parameters of a known production batch, verify that the query merging module can correctly integrate multi-source data and output corresponding fields.
- Simulate an abnormal trigger scenario, confirm that the failure handling node executes the preset retry and alert logic.
The values provided are common starting points and should be measured against the reader's own samples.