What the Data for This Category Looks Like
The data sources for coal chemical industry ledger summary primarily include on-site PLC control systems, manual shift handover ledgers, and daily quality inspection reports. Data update rhythms fall into two categories: real-time collected process parameters such as furnace temperature and system pressure, and daily updated quality inspection data. A single ledger document includes fields like workshop section name, collection time, core process parameters, and material consumption data. Most parameter units are degrees Celsius, megapascals, cubic meters per hour, tons, and similar units. The material consumption field is marked with batch number and material type.
Constraints for Workflow Orchestration
The multi-source, heterogeneous nature of coal chemical industry ledgers requires workflows to use multiple parallel pull nodes to connect to the PLC system, shift handover ledger system, and quality inspection database separately. High-frequency updates to real-time process parameters require trigger nodes to poll hourly or per shift. Daily quality inspection data requires scheduled trigger tasks. Differences in fields and units between ledgers from different workshop sections require inserting standardized conversion nodes in the workflow to unify formats. Summarizing large volumes of discrete ledgers requires configuring grouping and aggregation nodes to organize data by batch or date, preventing data overload during single processing runs.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Frequency | 15-60 minutes per run | Adapts to the update rhythms of real-time process parameters and daily quality inspection data |
| Single Pull Data Limit | 1000 entries per run | Prevents data overload during single processing runs |
| Variable Mapping | Configure based on the correspondence between ledger fields and report templates | Aligns with the multi-field, multi-unit characteristics of coal chemical industry ledgers |
| Knowledge Base Selection | Bind the dedicated knowledge base for coal chemical industry process ledgers | Ensures search nodes call the correct corresponding data source |
variables | Configure as workshop section name, collection time, material batch | Provides grouping criteria for subsequent aggregation nodes |
nodeId | Assign a unique identifier to each workshop section pull node | Simplifies workflow debugging and failed node localization |
Three Common Misconfiguration Issues
- Symptom: The variable reference dropdown has no selectable values when selecting a knowledge base for a knowledge base search node in the workflow. Cause: No variable parameters associated with the knowledge base are defined in the
variablesconfiguration, so the node cannot read available variables. - Symptom: The model selection dropdown for the workflow AI chat component is empty. Cause: No dedicated AI model adapted for coal chemical industry scenarios is bound in the platform backend, so the node has no available model list.
- Symptom: Subsequent nodes do not execute after configuring a stream reply to trigger them. Cause: The trigger condition for the subsequent node is not correctly set to wait for AI reply completion, with the trigger timing mistakenly set during the streaming reply process.
How to Confirm Successful Configuration
- Manually trigger the workflow, check the logs for each pull node, and confirm that data sources are correct and no fields are missing.
- Review the dataset after variable mapping, and confirm that all ledger fields have completed format and unit conversion per the report template.
- Trigger the AI chat component, confirm that the model list displays normally, and that knowledge base data can be called normally to generate reports.
- Test the scheduled trigger task, confirm that the workflow runs automatically at the set frequency and generates summary reports.
The values provided here are common starting points and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.