What this category's data looks like
Core data sources for this category include concentrate and auxiliary material analysis reports, equipment operation data collected by DCS systems, electrolyzer voltage and current monitoring data, laboratory test results, and energy and environmental monitoring systems. Data updates occur in real time for each batch of raw materials, each furnace, or each electrolyzer. Daily business documents include batching lists, smelting records, electrolyzer condition records, test reports, anode and cathode quality records, and environmental protection ledgers. These documents contain dedicated fields such as raw material composition parameters, equipment operation indicators, and test values, using units specific to the corresponding business scenarios.
What constraints these characteristics impose on workflow orchestration
The data sources for this category are scattered, and their update rhythms vary widely. This requires the workflow to support series invocation of multiple data sources, with reasonable configuration of node trigger order and intervals. Business documents contain a large number of dedicated fields, so workflow variable extraction nodes must accurately match the dedicated field paths. Data abnormalities directly affect metal recovery rates and product quality. The workflow must therefore include failure retry and exception alert links. This category also has high requirements for electrolyzer group management and impurity control. The workflow must add targeted verification and calculation nodes to ensure the process conforms to business logic.
How to set configurations
These configurations are developed based on FastGPT v4.17.0:
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind business fields such as concentrate grade, cell voltage, and anode quality | Match the core audit and monitoring parameters of this category |
nodeId | Name according to business links, such as "smelting record extraction" or "electrolyzer condition verification" | Facilitate workflow troubleshooting and maintenance |
| Database connection timeout | 600 seconds | Queries for this category's data may involve multiple furnace batch historical data, requiring a longer timeout period |
| JSONPath variable configuration | Match business field paths based on actual measurement calibration | Adapt to the semi-structured format and dedicated fields of this category's documents |
| Question classification node background knowledge | Fill in typical fields and audit logic of batching lists and smelting records | Improve the matching accuracy of classification nodes for dedicated scenarios |
| Workflow export settings | Select JSON format and enable the permission switch | Facilitate workflow backup and cross-environment deployment |
Three common errors
- Symptom: The downstream node receives empty variables after the HTTP request node runs. Cause: The JSONPath extraction rule is not configured correctly, and the dedicated field path of this category's business data is not matched.
- Symptom: A database connection failure error pops up during workflow runtime, with a prompt similar to "Failed to connect to". Cause: The correct database instance address and port are not configured, or network policies restrict access to the target database.
- Symptom: The question classification node cannot accurately match the audit scenario type. Cause: The background knowledge does not include audit document examples and field descriptions specific to this category.
How to confirm the configuration is complete
- Run the single-step test node to check whether the business parameters extracted in
variablesare consistent with the source data. - Initiate a simulated database connection request to confirm that the connection status is normal.
- Submit a batch of simulated audit documents to check whether the matching results of the question classification node meet expectations.
- Try to export the workflow configuration to confirm that the export operation can be completed normally and generate valid files.
The values provided are common starting points and should be measured against the reader's own samples.