What Data in This Category Looks Like
Power investment research data sources include real-time power grid dispatching monitoring systems, SCADA collected data from power generation enterprises, power grid operation and maintenance logs, industry regulatory policy documents, and public financial reports of listed power companies. Update cycles cover real-time time-series data (minute-level to hour-level), monthly/quarterly industry statistical documents, and annual policy planning texts. Document structures include structured time-series fields such as unit number and output power, semi-structured operation and maintenance reports, and unstructured policy interpretation texts. Field units mostly use standard power industry units: megawatt (MW), megawatt-hour (MWh), and hour.
What Constraints Do These Characteristics Impose on Database and Operations Workflows
High-frequency writing of real-time time-series data requires databases to deliver high throughput, avoiding performance loss from single-item writes. Mixed storage of multiple data types requires unified management of structured, semi-structured, and unstructured data to prevent data silos. Batch updates of monthly/quarterly statistical documents require databases to support batch import and incremental synchronization, reducing resource usage from full updates. Standardized field units for the power industry require databases to automatically verify unit consistency during data access, avoiding unit errors in subsequent investment research analysis. Associated query requirements for equipment ledgers require databases to build multi-table association indexes, improving cross-field query efficiency.
How to Configure Parameters
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Meets the upload requirements for large documents such as power industry operation and maintenance reports and equipment ledgers, and prevents upload failures caused by oversized files |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Power industry documents usually contain a large number of equipment parameters and long text content. Extending the timeout period can prevent parsing interruptions |
mongo_write_concurrency | 20–30 | Matches the high-frequency writing requirements of real-time time-series data, balancing writing performance and cluster resource usage |
rag_recall_top_k | Top 8 entries | Power investment research needs to cover multi-dimensional equipment data and industry policies. Increasing the number of recalled entries ensures information completeness |
api_rate_limit | Calibrated based on the number of deployed cluster nodes | Adapts to the concurrency limits of external model interfaces, avoiding 429 status code errors |
chunk_size | 800–1200 characters | Adapts to the chunking requirements of long text documents in the power industry, ensuring complete semantics after chunking and facilitating recall |
The parameter values provided on this page are conventional recommendations that serve as a starting point for configuration. Actual values are influenced by material form, data volume, and business rules. Specific issues require specific analysis, and testing on local samples is recommended before finalizing.
Three Common Configuration Mistakes
- Symptom: External model interfaces frequently return 429 status codes, and requests are directly rejected. Cause: The
api_rate_limitparameter is not configured to limit concurrent requests, exceeding the preset concurrency limit of the external interface. - Symptom: Model response latency increases significantly in high-concurrency scenarios. Background logs show that Mongo query and write time accounts for a large proportion of total runtime. Cause: The
mongo_write_concurrencyparameter is not adjusted. The single-threaded write logic cannot adapt to the high-frequency time-series data writing requirements of the power industry, causing database blocking. - Symptom: After importing PPT format documents into a database connected to Feishu, equipment parameter fields are empty and cannot be recalled normally. Cause: The text parsing plugin for PPT format is not enabled, so structured parameters in power industry operation and maintenance PPTs are not correctly extracted and stored.
How to Verify Correct Configuration
- Upload a single large power operation and maintenance report. Confirm that the upload and parsing processes complete successfully, and that the
UPLOAD_FILE_MAX_SIZEandPARSE_FILE_TIMEOUT_SECONDSconfigurations adapt to current document processing requirements. - Send simulated requests multiple times exceeding the external model's concurrency limit. Check whether current limiting errors are triggered, and confirm that the concurrency limit configuration meets the external interface's requirements.
- Import multiple power equipment ledger documents. Check whether fields such as unit number and output power are fully extracted in the database, and confirm that the document parsing configuration is correct.
- View the background monitoring panel. Check whether the Mongo database's write concurrency remains stable within a reasonable range, and confirm that the write concurrency configuration adapts to business traffic.
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.