Database and Operations for Power Grid Equipment Yield Rates

What data for this category looks like

Data related to power grid equipment yield rates is a core data source for financial scenarios such as power asset financial management and grid enterprise cost accounting. It mainly comes from public APIs of power trading centers, grid operation management systems, and power spot market trading interfaces.

Data update rhythm matches trading cycles: spot market data updates every 15 minutes, and medium- and long-term trading data updates once daily. Each record is a structured entry containing fields including unique device asset ID, affiliated grid node code, trading period start time, benchmark yield amount (unit: revenue per MWh), actual yield amount (same unit as benchmark), operation loss coefficient (dimensionless), and trading batch number. All fields use standardized numeric or string formats.

Constraints on database and operations from these characteristics

Multi-source data access requires the database to support parallel interface pulling. Reasonable concurrent request limits must be configured to avoid triggering rate limiting from data sources.

The update rhythm matching trading cycles requires the database to use a partitioned storage strategy. Partitioning by time period enables fast periodic data filtering and simplifies historical data archiving.

Multi-field combined query needs joint indexes to improve query performance for combinations of devices and time periods.

High-frequency write scenarios require the database to support batch write operations to avoid IO overhead from single-record writes. Data must also be archived regularly per the business retention period to reduce storage pressure on the online database.

How to set configurations

Configuration Item	Recommended Value	Rationale
`external_api_request_timeout`	`30 seconds`	Power grid device data interfaces typically have response delays of 10-20 seconds. A 30-second timeout setting avoids unnecessary waiting
`db_write_batch_size`	`50 records/batch`	Excessive single-batch writes cause database table locking, while too few increases IO operations. 50 balances performance and stability
`data_sync_interval`	`15 minutes`	Spot market data updates every 15 minutes. The sync interval matches the data source's update frequency
`db_partition_strategy`	`Partition by trading_cycle monthly`	Trading data is archived by cycle. Monthly partitioning enables fast time-period queries and simplifies archiving operations
`concurrent_api_request_limit`	`20 concurrent requests`	Most power grid data interfaces have concurrency limits. 20 concurrent requests aligns with access thresholds for most platforms
`fastgpt_version_compatible`	`v4.9.14 and above`	This version fixes batch write concurrency conflicts and adapts to high-frequency data synchronization scenarios

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 specific analysis. It is recommended to test on your own samples before finalizing settings.

Three common configuration mistakes

Symptom: Calls to power grid data interfaces return 429 Too Many Requests errors. Cause: The concurrent_api_request_limit parameter is not configured, and concurrent requests exceed the threshold set by the data source interface.
Symptom: Database write fails, and the actual_yield field is empty. Cause: Parameter mapping is not configured according to the fields returned by the data source, leading to incorrect field matching and failure to write values correctly.
Symptom: Unable to connect to external power grid data interfaces during local deployment. Cause: The external interface access whitelist is not configured, or the local network does not have a proxy configured to bypass firewall restrictions, preventing cross-domain requests from being initiated.

How to confirm configurations are correct

View FastGPT external interface call logs, confirm that the response codes of the last 10 requests match the standard return values of the data source. Adjust verification standards based on the actual response rules of the data source.
Execute database query statements to verify that tables partitioned by trading_cycle can normally filter data by time period. Adjust partition granularity based on business query requirements.
View the database write monitoring panel, confirm that the number of writes per second matches the setting of the db_write_batch_size parameter. Adjust concurrent write thresholds based on system load.
Trigger the local deployment data synchronization task, confirm that content from external power grid data interfaces can be pulled normally. Adjust proxy or whitelist configurations based on the local network environment.

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.