What the Data for This Category Looks Like
Power yield-related data is primarily sourced from regional power trading platforms and real-time grid dispatch monitoring systems. There are two update cadences: intraday trading data is pushed every 15 minutes, and daily yield summary data is generated after 22:00 each day. Data documents use structured JSON or CSV format. A single record contains the following fields: trade_date (date type, format YYYY-MM-DD), market_area (3-digit regional code string), generator_id (12-digit unique power plant identifier string), on_grid_price (unit: yuan per thousand kilowatt-hours), actual_output (unit: megawatts), daily_return (numeric base offset). Fields must strictly follow grid coding standards to prevent invalid data from entering the system.
What Constraints These Characteristics Impose on Database and Operations Workflows
High-frequency real-time updates of power data require database write throughput to support 15-minute batch writes. Avoid excessive single write volumes that cause database table locking. Standardized coding across multiple fields requires a pre-data validation step to filter dirty data that does not meet grid coding rules. Scheduled generation of daily summary data requires matching fixed time window task scheduling to ensure data timeliness. Cross-regional data sources have high access latency, so adjust connection timeout configurations to cover data pull time. Compliant storage of power data requires extended backup cycles while retaining full historical transaction records, which increases storage resource planning pressure.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
max_concurrent_tasks | 20 | Matches the CPU core count of a 4c16g single node, avoids service unresponsiveness caused by concurrent overload |
db_write_batch_size | 150 records per batch | Matches the single push volume of power intraday data, reduces database write pressure |
db_connection_timeout | 35 seconds | Covers data pull latency from cross-regional power trading platforms, prevents connection timeout errors |
daily_sync_cron | 0 22 * | Matches the generation cadence of daily yield data at 22:00 daily, triggers scheduled summary and data import |
data_validate_rules | Validate market_area and generator_id formats | Aligns with power data coding standards, filters invalid data |
backup_retention_days | 90 days | Meets compliance requirements for data storage in the power industry |
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. Tests should be conducted on local samples before finalizing settings.
Three Common Configuration Mistakes
- Database connection logs return
Failed to connect to 192.168.xx.xx.:1433 - 38FBA17error, unable to pull power trading data. Firewall whitelist configuration for the power trading platform is missing, causing the node to fail access to the data source service on the specified port. - When deployed on a single node, service becomes unresponsive after 50 concurrent tasks are triggered, with CPU usage remaining at 100%. The
max_concurrent_tasksparameter is not adjusted, and the default concurrent value exceeds the single node resource limit. - The data source plugin returns a database connection structure but fails to generate expected SQL execution results. Query result mapping configuration for the plugin is not enabled, so the plugin only returns connection metadata and not actual query data sets.
How to Verify Proper Configuration
- Execute a database connection test, confirm the connection status aligns with the configured timeout threshold, with no timeout errors.
- Submit simulated intraday power data for writing, verify the number of write batches matches the
db_write_batch_sizeconfiguration. - Review scheduled task logs, confirm the daily data sync task triggers and runs normally at the time specified in the
daily_sync_cronconfiguration. - Adjust concurrent task counts, verify service CPU and memory usage do not exceed single node capacity limits, with no response anomalies.
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.