What the data for this category looks like
Thermal utility yield rate data falls under the market data category for the financial industry's public utilities sector. Data sources include thermal enterprise operation report information collected by financial terminals, public market data from industry monitoring platforms, and financial report data of listed thermal companies disclosed by securities markets. Data updates daily, with a statistical cycle covering the previous calendar day. The data is structured as a table, where each row corresponds to a thermal operation entity or listed company. Fields include unique entity identifier, statistical date, total heat supply, operating cost, total revenue, yield quantile, month-over-month change, and more. Total heat supply is measured in gigajoules. Operating cost and total revenue are measured in ten thousand yuan.
Constraints for Tool Calling and Plugins
Since data sources are scattered across financial terminals, industry platforms, and securities disclosure channels, tool calling must support configuring multi-source API access and adapt to authentication rules for different channels. Data is organized by entity and date dimensions. Tool calling must pass entity code and statistical date as required parameters. Failure to pass these parameters will return no matching empty data or full redundant information. Fields include thermal-specific non-general indicators such as total heat supply and operating cost. Plugins must configure dedicated field mapping rules to convert raw data into a unified format. The daily update schedule requires tool calling to set a reasonable cache expiration time to avoid reading outdated data older than 24 hours.
Configuration Parameters
| Configuration Item | Recommended Practice | Rationale |
|---|---|---|
Request Address | Fill in the official API interface address of the corresponding data source, such as https://api.finance.heat.com/daily-report | Match the compliant release channel for thermal utility yield rate data to ensure accurate data sources |
Request Header | Configure Authorization: Bearer {custom secret} and add Content-Type: application/json | Adapt to the authentication and data transmission format requirements of most financial data interfaces |
Required Request Parameter | Fill in entity_id and stat_date, with formats of valid entity code and YYYY-MM-DD respectively | Match the field rules for filtering thermal data by entity and date to avoid returning invalid data |
Field mapping rule | Map the original field heat_supply to 供热总量, operating_cost to 运营成本, and profit_rate to 收益率 | Unify field naming across different data sources to facilitate subsequent data processing and display |
Cache Expiration Time | Set to 86400 seconds | Match the daily update schedule of thermal data to avoid reading outdated historical data |
Response Parsing Mode | Select Structured Table Parsing | Adapt to the structured data format of daily thermal reports to improve the accuracy of field extraction |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by data format, data volume, and business rules. Each use case requires individual analysis. It is recommended to test against your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: The thermal data source plugin returns a
502 Bad Gatewayerror, but the same API can be called normally using a third-party testing tool. Cause: The FastGPT plugin is not configured with correct request header authentication parameters, or does not adapt to the cross-domain access restrictions of the data source interface. - Symptom: The yield data field returned by the plugin is empty or incomplete. Cause: Required request parameters
entity_idandstat_dateare not passed, causing the interface to return no matching empty results or full redundant data. - Symptom: The number of optional large models in the plugin configuration panel is less than expected, and the target inference model cannot be selected. Cause: The API access permission for the corresponding model is not enabled in the system backend, or the model group bound to the plugin does not include this model.
How to Confirm Proper Configuration
- Copy the request address and parameters from the plugin configuration, initiate a test call in a third-party testing tool, and confirm that structured thermal utility yield rate data is returned.
- View the plugin's cache logs, confirm that the cache expiration time setting matches the daily update rhythm of the data, and no outdated data is read.
- Enter the specified query instruction in the FastGPT test conversation, verify that the plugin can correctly extract and return the yield data of the corresponding entity for the current day.
- Check the field mapping rules, confirm that the correspondence between raw data fields and custom fields is correct, with no missing fields or mapping errors.
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.