What does the data for this category look like
Data related to power industry yield rates mainly comes from regional power trading center public transaction ledgers, provincial grid dispatch operation settlement data, and public industry power generation cost accounting reports. The data update cadence is daily, with full data for the previous calendar day produced by the next morning. Individual data documents are stored in structured format, including fields such as accounting period, region code, unit type, benchmark settlement electricity price, actual transaction electricity price, unit utilization duration, and yield indicator. The unit of electricity price is yuan/megawatt-hour, the unit of duration is hours, and the yield indicator is a dimensionless value.
What constraints do these characteristics impose on the model access and configuration phase
The daily update cadence requires access tasks to be configured with a fixed daily trigger cycle. This avoids high-frequency pulling that occupies resources, or low-frequency pulling that misses the daily data production window. Multiple structured fields require precise field matching for power-specific fields such as region code and unit type. This prevents data misalignment caused by generic field mapping. The dimensionless yield indicator requires that normalization rules for model input be adapted to the value range of this category, and processed based on actual data intervals. For multi-source data scenarios, configure data priority verification rules. Prioritize official settlement data from grid dispatch, and filter outliers from non-authoritative sources.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
schedule_cron | 0 8 1 * * ? | Adapts to the T+1 update cadence of power data, triggers the pull task the next morning |
field_mapping | {"accounting_cycle": "date", "base_settlement_electricity_price": "base_price", "actual_trading_electricity_price": "trade_price", "yield_rate_indicator": "yield_rate"} | Matches the specific field naming rules of power data, avoids data misalignment caused by generic mapping |
data_normalization_method | Calibrate dimensionless indicator range based on actual measurements | Power yield indicators are dimensionless values, requiring normalization based on actual data intervals |
data_source_priority | ["grid_dispatch", "power_exchange", "public_report"] | Prioritize official settlement data from grid dispatch, filter abnormal records from non-authoritative sources |
max_retries | 3 times | Adapts to the fixed update window of power data, avoids missing daily data production due to excessive retries |
parse_timeout | 600 seconds | Adapts to the parsing duration of batch power data, avoids timeouts caused by large single-batch data volume |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, 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 mistakes
- Phenomenon: A model call returns a
403 Forbiddenstatus code, with an interface prompt of "Insufficient permissions". Cause: No self-owned model API key is configured, and the platform's built-in shared call quota is used. Permission restrictions are triggered after the quota is exhausted. - Phenomenon: Specified advanced Embedding and chat models cannot be found in the model selection list, with a prompt of "Model not included". Cause: No third-party model proxy configuration is added, only the platform's built-in model access channels are used, and the corresponding manufacturer's model access address and key are not synchronized.
- Phenomenon: Data pull tasks fail repeatedly, with logs showing "Field matching failed". Cause: The
field_mappingparameter is not configured according to the specific fields of power data, and generic field mapping rules are used, leading to data misalignment.
How to confirm the configuration is complete
- Manually trigger a data pull task, and check whether the task logs correctly match the power data's specific fields, with no field missing or misalignment prompts.
- Call the model test interface, input simulated power industry yield-related data, and check whether the model output adapts to the dimensionless indicator processing logic, with no format errors.
- Check the multi-source data priority configuration, simulate accessing non-official source data, and check whether the system prioritizes the preset authoritative data source.
- Check the scheduled task running logs, confirm that the daily trigger time matches the power data's update cadence, with no repeated or missed triggers.
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.