What this type of data looks like
Photovoltaic investment research data comes from several sources: public technical documents from component manufacturers, regular financial reports of listed photovoltaic enterprises, supply and demand and policy data released by industry associations, real-time operational time-series data from ground-mounted power stations, and irradiance and sunshine duration data from meteorological stations.
Update cadence differs by data source:
- Technical parameter documents are updated irregularly alongside product iterations
- Financial reports are released quarterly and annually
- Supply and demand data is updated monthly
- Real-time operational and meteorological data is refreshed hourly
Document structure falls into three categories: structured parameter tables, unstructured research report text, and time-series datasets. Unique fields include:
- Peak power (unit: Wp)
- Module efficiency (unit: %)
- Installed capacity (unit: MWp)
- Irradiance (unit: W/㎡)
What constraints do these characteristics impose on database and operations workflows?
The multi-source heterogeneous nature and varied update cadences of photovoltaic investment research data create multiple constraints for database and operations work.
Structured parameter tables include multiple fields with specialized units. Strict unit validation rules must be configured to prevent numerical errors during queries. Real-time operational time-series data requires high-frequency writes. Adjust connection pool parameters and write buffer sizes to maintain throughput. Unstructured research report text has wide variation in length. Implement chunked storage and dynamic word segmentation strategies to handle this. Multi-source data uses inconsistent update schedules. Design a scheduling process that combines incremental synchronization and full updates. Cross-source data association requires unified unit conversion logic to ensure accurate associated queries. Data volume shifts with industry trends. Reserve storage resources that can scale elastically.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DATABASE_POOL_SIZE | 15–25 | Adapts to concurrent read and write demands of photovoltaic multi-source data, and avoids connection pool exhaustion |
TIMESERIES_BATCH_SIZE | 500 records per batch | Matches hourly batch write cadence of photovoltaic real-time operational data, and improves data ingestion efficiency |
UPLOAD_MAX_FILE_SIZE | 800 MB | Supports uploads of large photovoltaic power station annual operational logs and industry research report collections |
SQL_PARSE_TIMEOUT | 90 seconds | Covers processing time for cross-source associated queries, and prevents timeout interruptions |
VECTOR_RECALL_TOP_N | Top 8 results | Core information for photovoltaic investment research is highly concentrated, so 8 recall results meet context requirements |
DATA_CLEAN_CRON | 0 2 * * * | Runs old non-core data cleanup daily at 2 AM, and aligns with daily updated auxiliary data |
The parameter values provided on this page are common starting points 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 mistakes
- Phenomenon: Generated SQL query statements cannot be parsed by the database due to included punctuation, returning empty results or throwing errors. Cause: No punctuation filtering rules were configured in the SQL generation link, leading to invalid punctuation mixed into natural language-generated query statements.
- Phenomenon: Database connection timeout errors occur during concurrent queries, and some requests fail to complete. Cause: Database connection pool parameters were not adjusted to match the concurrent read and write demands of photovoltaic multi-source data, and the number of available connections is insufficient to support concurrent requests.
- Phenomenon: Fields return empty values during time-series data queries, and cannot match the unique unit parameters of photovoltaic projects. Cause: No field unit verification and conversion rules were configured, leading to inconsistent units across different data sources, which prevents successful associated queries.
How to confirm configurations are properly implemented
- Execute a single structured parameter query, verify that the units of returned fields match preset rules, and confirm that field verification configurations are active.
- Upload a small photovoltaic operational log file, wait for data ingestion to complete, then query time-series data. Confirm that write delay aligns with business expectations, and verify that connection pool and buffer configurations are active.
- Initiate concurrent query requests from multiple users, monitor database connection counts and query success rates, and confirm that connection pool parameters support concurrent demands.
- Generate an SQL query that includes unit conversion, verify that returned numerical results match actual business logic, and confirm that unit conversion rules are active.
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.