Data Overview and Update Rhythm
Data for power industry intelligent due diligence reports primarily comes from real-time operational data of grid dispatching platforms, unit operation logs of power generation enterprises, on-site collected data from transmission line inspection systems, filing documents from local energy regulatory authorities, and energy consumption statistical reports from electricity users.
Data update rhythms fall into three categories: real-time operational data is synchronized every 15 minutes, inspection-collected data is uploaded immediately after inspection, and filing and statistical data is updated quarterly or monthly.
The overall document structure is divided into two parts: structured fields and unstructured attachments. Structured fields include unit number, generation power, transmission voltage, inspection point, and filing document number, with corresponding units of none, megawatt (MW), kilovolt (kV), point number, and document number respectively. Unstructured attachments include inspection photos, PDF fault troubleshooting reports, scanned grid connection agreements, and similar content.
Constraints Imposed on Deployment and Upgrade
The multi-source heterogeneous nature of power due diligence data requires configuring multiple data source connectors during deployment to adapt to different interface formats of dispatching platforms, inspection systems, and other systems.
The high-frequency synchronization requirement of real-time operational data restricts the write throughput parameters of the vector database, requiring sufficient concurrent processing capacity to be reserved.
Unstructured attachments contain large numbers of images and PDF documents, requiring adjustment of file parsing chunk length and timeout thresholds during deployment to avoid parsing failures for large attachments.
The characteristic that fields come with dedicated units requires configuring unit standardization rules during the embedding stage to prevent vector matching deviations caused by inconsistent units.
Quarterly updated filing documents and monthly energy consumption reports require reserving dynamic adjustment interfaces for field mapping during the upgrade process to adapt to periodic changes in document formats.
Additionally, the sensitivity of power data restricts encryption storage and access permission configuration items during deployment.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Power due diligence reports include large-volume PDF inspection reports and high-definition images. Standard timeout durations are insufficient to complete full parsing |
UPLOAD_FILE_MAX_SIZE | 1000 MB | A single transmission line inspection archive package may contain dozens of high-definition photos, requiring support for large-volume attachment uploads |
chunk_size | 800–1200 characters | Power data contains a large number of technical terms and long sentence descriptions. Excessively long chunks will destroy the contextual association of professional expressions, while excessively short chunks will lose key information |
similarity_threshold | 0.75–0.85 | Matching of power equipment parameters requires strict correspondence between units and numerical ranges. A threshold that is too low will introduce a large number of irrelevant matching results |
DB_CONNECTION_POOL_SIZE | 20–30 | High-frequency synchronization of real-time operational data requires stable database connections. An excessively large connection pool will occupy too many server resources |
AUTO_SYNC_INTERVAL | 900 seconds | The update frequency of real-time operational data is 15 minutes. Reserved redundant time ensures that synchronization covers all update nodes |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Symptom: After container restart, all power due diligence data stored in the vector database and application configuration items are lost. Cause: No data volume persistent mounting was configured during deployment, and the vector database and application configuration storage were placed in the writable layer of the container. The contents of the writable layer are reset after the container restarts.
- Symptom: Units of power equipment parameter fields returned during database queries are inconsistent. Cause: No unit standardization rules were configured during the embedding stage, resulting in generation power and transmission voltage data from different sources not being unified to standard units, leading to matching result deviations.
- Symptom: Parsing results for inspection photos are empty, and no corresponding fault analysis content can be generated. Cause: No base64 encoding conversion node was configured in the workflow, causing the visual model to fail to read the uploaded image files.
How to Verify Correct Configuration
- A test file containing a large-volume PDF inspection report is submitted. The parsing task status code is checked for no timeout errors, confirming that the
PARSE_FILE_TIMEOUT_SECONDSconfiguration meets requirements. - A real-time data synchronization is manually triggered. The number of new records in the vector database is checked to match the number of data entries from the synchronization source, confirming that the
DB_CONNECTION_POOL_SIZEandAUTO_SYNC_INTERVALconfigurations are reasonable. - A test document containing power data with different units is uploaded. The embedded vector matching results are checked to only include target data compliant with business rules, confirming that the
similarity_thresholdand unit standardization rules are configured correctly. - The deployment container is restarted. Application configuration items and data in the vector database are checked to not be lost, confirming that the data volume persistent mounting configuration is effective.
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.