What Data for This Category Looks Like
Daily documents for this category include batching lists, smelting logs, electrolyzer status logs, test reports, anode and cathode quality records, and environmental protection ledgers. Data sources cover concentrate and auxiliary material analysis results, DCS system data, cell voltage and current monitoring values, laboratory test data, and energy and environmental monitoring data. Update frequency shifts with production batches. Each raw material batch, furnace run, or electrolyzer generates corresponding records. Key monitoring items update at real-time intervals. Document fields include raw material component proportions, furnace temperature parameters, cell voltage values, test element contents, environmental emission indicators, and more. Some records must be linked to batch numbers and cell position numbers.
What Constraints Do These Characteristics Impose on Workflow Orchestration?
Data sources are scattered and update at inconsistent rhythms. Workflows must support scheduled pulling from multiple data sources and real-time trigger nodes. Document fields are numerous and linked to batches and cell positions. Context parameters such as batch numbers and cell numbers stored in variables must be passed between nodes. Real-time monitoring data has strict latency requirements. The execution duration of a single workflow must be limited. Exception reports must be linked to full-chain data for the corresponding production batch. Workflows must support cross-node data aggregation and verification. Document formats vary across production links. Nodes must be configured to adapt to different field extraction rules.
Configuration Settings
This configuration is adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Global Variable Binding | Store batch numbers, cell numbers, and furnace numbers in variables | Full-chain data linked to production batches and cell positions is required, and context parameters must be passed between multiple nodes |
| Node Trigger Rules | Combine real-time threshold triggering and scheduled data pulling | Real-time monitoring data requires timely response, and batch documents require scheduled synchronization to the workflow |
| Single Execution Timeout | 600 seconds | Matches the latency requirements of real-time monitoring data, avoids blocking subsequent production links |
| Knowledge Base Recall Count | Top 8 entries | Covers multi-source process parameters and historical exception records, balances recall efficiency and completeness |
| Context Window Limit | 8000–12000 token | Accommodates multi-batch test data and cell status logs, avoids context overflow |
| Exception Report Generation Node | Calibrated based on actual measurements | Adapts to the exception judgment rules and document format requirements of different enterprises |
Three Common Misconfigurations
- Symptom: The selector cannot call a knowledge base selection-type global variable bound in
variablesas a judgment condition. Cause: The scope of the global variable is not configured as cross-workflow nodes. Variables bound only to a single node cannot be read by the selector. - Symptom: The workflow terminates directly after reaching the AI reply node, and subsequent data aggregation and report generation nodes are not triggered. Cause: The output parameter transfer of the AI reply node is not configured. By default, only text results are returned, and context is not written to
variables, causing downstream nodes to have no valid input. - Symptom: After the workflow is published to the Feishu bot, production data cannot be pulled normally. Cause: The data source access permissions of the Feishu bot are not configured, or the real-time data trigger rules are not adapted to external invocation scenarios.
How to Verify the Configuration Is Correct
- Trigger a simulated production batch workflow, check whether correct batch numbers, cell numbers, and furnace number parameters are written to
variables. - Configure an exception trigger threshold, simulate qualified real-time data input, confirm whether the workflow starts according to the rules and generates intermediate node logs.
- Pull historical production data and import it into the workflow, confirm whether the knowledge base recalled content covers the process records and exception cases corresponding to the batch.
- Publish the workflow to the test environment, execute the complete process, and check whether the fields of the exception report match the preset document structure.
Note: The configuration values listed above are common starting points. Adjust them based on your own specific samples and operational requirements.