What Data Looks Like for This Category
Grid equipment data primarily comes from equipment factory quality inspection archives, grid operation and maintenance inspection ledgers, online monitoring systems, and industry equipment archive repositories. Static archives such as factory parameters are updated once when the equipment is put into operation. Operation and maintenance inspection data is synchronized on a daily or weekly basis. Online monitoring data is pushed in real time. The structure of a single due diligence document includes four modules: basic equipment information, operating parameters, defect records, and maintenance history. Fields include rated voltage (unit: kV), operating duration (unit: hours), insulation resistance (unit: MΩ), defect level, and others.
Constraints Imposed on Workflow Orchestration
The mixed static and dynamic nature of grid equipment data, combined with its multi-source heterogeneous characteristics, creates three core constraints for workflow orchestration. First, it is necessary to distinguish the trigger timing of static archive calls and real-time monitoring data pulling to avoid timeouts or data lag. Second, many fields have dedicated units, so a unit standardization conversion node must be configured in the workflow to ensure that fields from different sources can be aligned. Third, a single due diligence document has a large number of fields, so the per-round context length must be limited to prevent the model from generating redundant output or exceeding the context window.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Grid equipment due diligence documents have many fields and a large single data volume, so a sufficient context window is required to accommodate multi-module data |
recall count | Top 6–8 entries | A single equipment archive includes multiple types of parameters. Too many recalls will cause context overflow, while too few will result in missing key maintenance records |
similarity threshold | 0.75–0.85 | It is necessary to distinguish different batch archives of the same model equipment. A threshold that is too low will introduce irrelevant equipment data, while a threshold that is too high will fail to match operation records of the same model |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Factory quality inspection reports are mostly in PDF format with many pages, so sufficient parsing time must be reserved |
tool call timeout | 120 seconds | Online monitoring data needs to be pulled in real time. A timeout will cause the due diligence report to miss the latest operating parameters |
variable mapping rule | Align by field units | Rated voltage fields from different sources may be labeled as kV or V. Variable mapping must be used to unify units to kV to ensure data consistency |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- The phenomenon is that the tool call module in the workflow shows a status of not executed, and no tool call logs are generated. The cause is that the trigger condition for tool calls is not configured in the workflow, or the binding permissions of the tool are not associated with the current workflow.
- The phenomenon is that the knowledge base retrieval node returns an empty array, or the retrieved content includes archives of non-target grid equipment. The cause is that the unique equipment identifier is not passed as a retrieval keyword to the knowledge base module, or a retrieval filtering rule for grid equipment archives is not configured.
- The phenomenon is that the code execution module throws a ModuleNotFoundError, or the variable value received by the downstream node is empty. The cause is that the third-party library required for code execution is not installed in the global Python environment of the FastGPT container, or the variable mapping and transfer rules are not configured in the workflow.
How to Verify Correct Configuration
- Manually trigger the workflow, check the logs of the tool call module, and confirm that all bound tools are called normally, with no timeout or unexecuted records.
- Retrieve the knowledge base module, enter the model of a single grid equipment, and verify that the retrieved archive content includes the basic parameters and operation records of the equipment, and the quantity meets the configured recall count requirement.
- Run the code execution module, verify that the dependent libraries can be imported normally, and that variable transfer conforms to the preset mapping rules.
- Generate a single due diligence report, and verify that the field units in the report are unified and that all configured required modules are included.
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.