What data looks like for this category
Thermal power sector financial report data mainly comes from enterprise ERP financial modules, collection data from energy metering systems, and public regulatory reports. The update cadence matches the quarterly and annual financial report disclosure cycles, and also supports weekly updates for operational-level data. Most documents are structured reports, including fields such as business segment revenue, heat supply volume, unit heating cost, fuel procurement cost, and capacity utilization rate. Heat supply volume is measured in gigajoules (GJ), heating area in ten thousand square meters, revenue in ten thousand RMB, and unit heating coal consumption in kgce/GJ.
Constraints on workflow orchestration
Thermal power sector financial report data includes both structured financial fields and unstructured metering reports, and indicator units follow industry-specific specifications. This imposes three constraints on workflow orchestration:
- Configure multi-source data access nodes to adapt to heterogeneous data formats.
- Add built-in unit verification links to unify measurement standards for indicators such as heat supply volume and energy consumption, and avoid unit deviations across data sources.
- Configure scheduled triggers based on fixed quarterly and annual cycles to match financial report disclosure rhythms, while supporting weekly trigger requirements for operational-level data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
workflowTriggerMode | Scheduled by fiscal quarter + Manual trigger | Matches the quarterly disclosure cadence of thermal power financial reports, also supports manual execution for temporary data verification |
retrievalTopK | Top 8 entries | Energy consumption and revenue indicators in thermal power financial reports have high correlation. Retrieving too many will introduce irrelevant data, while retrieving too few will fail to cover business-related dimensions |
similarityThreshold | 0.72–0.78 | Industry indicator terminology in the thermal power sector is highly specialized. This range balances retrieval precision and coverage to avoid missing key business fields |
variableScope | User-level | Heating area and baseline energy consumption indicators vary across different thermal power enterprises. User-level variables enable multi-tenant adaptation under the same configuration |
parseFileTimeoutSeconds | 600 seconds | Thermal power financial report metering sheets may contain multi-page historical data, requiring sufficient timeout time for parsing |
maxContext | 800–1200 characters | Business indicator descriptions in thermal power financial reports are concise. This length range retains core business information |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis, and testing on in-house samples is recommended before finalizing settings.
Three common configuration errors
- Issue: Knowledge base retrieval nodes cannot reference the configured global variable
datasetId, and the interface displays "Variable not found". Cause: The global access permission for this variable was not enabled in the workflow global variable settings, or the variable name was spelled inconsistently with the configuration. - Issue: When testing a workflow based on question classification, only the first classification branch returns knowledge base reference results, and subsequent branches have no matching content. Cause: Subsequent classification branches are not correctly bound to the knowledge base retrieval node, or the classified query parameters are not passed to the retrieval node.
- Issue: The output content of the AI chat node configured in the workflow cannot be hidden, and all node results are displayed on the final interaction interface. Cause: The "Result Output" switch was not turned off in the advanced configuration of the AI chat node, or the node hiding container function was not used.
How to verify correct configuration
- Trigger the scheduled task of the workflow, check the field verification results of the data access node in the scheduling log to confirm that the units match the business indicators.
- Switch different test user IDs, run the workflow, and check whether the variable values are isolated by user, with no cross-user data misuse.
- Configure a test thermal power financial report sample, run the workflow, and check whether the AI-generated analysis report includes core indicators such as heat supply volume and unit coal consumption.
- View the node operation logs of the workflow to confirm that no node triggers timeout errors, and the number of retrieved entries matches the configured
Top 8 entries.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.