What this type of data looks like
Multiple internal systems including real-time production monitoring systems, fuel management platforms, and safety supervision archives supply ledger data for power generation groups. The system updates this data per shift, with a single update covering unit operating parameters, fuel consumption records, and maintenance execution status for that shift. Each ledger document includes fields such as unit number, power generation load (unit MW), fuel consumption (unit tons), maintenance duration (unit hours), and number of safety incident records. It also includes metadata such as audit status and report time. Some historical ledgers use PDF scan format.
What constraints do these characteristics impose on workflow orchestration?
Multi-source heterogeneous data sources require multi-format data pull nodes in workflow orchestration, to adapt to three input types: structured database tables, CSV export files, and PDF scans. Shift-based update rhythms require trigger nodes with execution rules matched to the corresponding cycle. Fields with specific unit parameters require field validation nodes, to verify values and units for fields such as power generation load and fuel consumption. Cross-system merging of multiple ledgers requires field mapping nodes, to unify unit number naming rules across different systems. Pre-configure OCR processing nodes for historical ledgers in PDF scan format, to complete text extraction before entering the summary stage.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger frequency | Every 8 hours | Matches the shift-based update rhythm of power generation group ledgers |
| Single data pull limit | 1000 MB | Prevents workflow timeouts caused by excessively large single data pull volumes |
| Variable binding | Bind unit number and power generation load fields in variables, associate with the data source node with nodeId set to data_pull | Covers core ledger data fields, provides basic parameters for subsequent summarization |
| Node flow rule | Execute branching based on data type | Adapts to differentiated processing flows for structured data, CSV files, and PDF scans |
| Error retry configuration | 3 retries, 60-second interval | Addresses temporary network fluctuations or system response delays during single data pulls |
| OCR preprocessing switch | Enabled | Processes historical ledger data in PDF scan format, completes text extraction |
Reference only; verify against the target FastGPT release and deployment documentation.
Three common mistakes
- Phenomenon: The workflow-generated report does not include a flowchart link, and the error prompt indicates that the drawing node configuration is not found. Cause: No node for calling MCP Server is configured in the workflow, and ledger summary parameters required for drawing are not correctly passed.
- Phenomenon: The loop node executes an insufficient number of times, failing to complete full ledger summarization. Cause: The loop count upper limit is set too low, and does not match the actual number of ledgers to be summarized.
- Phenomenon: Existing workflows cannot run normally after an upgrade, and the text processing node reports an error indicating that the configuration does not exist. Cause: Configuration items of legacy text processing nodes have been adjusted in the new version, and the
nodeIdassociated configuration in the workflow has not been synchronized and updated.
How to confirm the configuration is correct
- View workflow execution logs to confirm that all configured data sources have been successfully pulled, with no field missing prompts.
- Verify the trigger node's execution rules to confirm they match the ledger update frequency.
- Check the generated report document to confirm it includes all preset ledger fields and their corresponding units.
- Simulate an exception scenario to trigger the workflow, confirm that the error retry mechanism activates normally and logs are recorded.
The values provided 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.