What Data for This Category Looks Like
Water utility revenue yield and market data primarily comes from public operational reports released by local water utility regulatory authorities, third-party public utility industry data APIs, and internal operational systems of water utility enterprises. Data is updated daily with the previous day’s operational snapshot. Weekly aggregated revenue summaries are released, and full monthly reports are issued each month. The document structure of a single data entry includes fields such as main entity code, statistical date, water supply unit price, sewage treatment fee standard, operational cost ratio, net operational revenue, and other fields. The units for each field are string, YYYY-MM-DD format, yuan/cubic meter, yuan/ton, ratio coefficient, and ten thousand yuan respectively. No additional unlabeled statistical values are included.
What Constraints Do These Characteristics Impose on Tool Calling and Plugins
The multi-source nature of water utility data requires plugins to support multiple authentication methods. Plugins must adapt to API_KEY authentication for official interfaces and token authentication for internal systems. Fixed field structures and units require strict matching of preset field mapping rules during tool calls. This avoids data parsing failures caused by mismatched field names or units. The daily update rhythm requires scheduled tasks to trigger after data aggregation is complete. Call frequency must also be controlled to avoid triggering third-party API rate limits. Fields with professional metering dimensions require plugins to include basic unit validation logic. This ensures pulled data complies with business rules.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
plugin_request_timeout | 300 seconds | Adapts to the response delay range of most third-party public utility data APIs |
schedule_trigger_cron | 0 0 2 * * * | Matches the rhythm of water utility data aggregation completed before 1 AM daily, triggering data pulling 1 hour in advance |
field_mapping_strategy | Exclusive water utility field mapping | Adapts to the fixed field structure of daily water utility reports, avoiding field mismatches caused by general-purpose mapping |
data_cleaning_threshold | 0.8 | Matching threshold for filtering abnormal data, adapted to the normal fluctuation range of water utility data |
plugin_auth_type | Prioritize API_KEY authentication | Adapts to the standard authentication method of most third-party public utility data APIs, with support for internal system extensions |
max_request_retries | 3 times | Addresses temporary API rate limits, avoiding repeated requests that trigger stricter restriction rules |
Three Common Mistakes
- Phenomenon: Plugin calls return a
401 Unauthorizederror, prompting that an app key must be used instead of an account secret. Cause: The application-level app key was not correctly filled in the plugin authentication configuration, and an account login token was mistakenly passed as an authentication parameter. - Phenomenon: Generated SQL query statements cannot match the exclusive fields of water utility data, returning empty query results. Cause: Exclusive field mapping was not configured for water utility data, and general-purpose field mapping logic was used instead.
- Phenomenon: Scheduled pulled water utility data contains a large number of abnormal values, leading to errors in subsequent broadcast content. Cause: No data cleaning threshold was set, and abnormal data outside the normal fluctuation range was not filtered.
How to Confirm Proper Configuration
- Manually trigger a plugin call, check if the returned fields fully match the configured mapping rules, with no missing or incorrectly mapped fields.
- Check the plugin’s authentication logs to confirm that the request carries an application-level app key, and no account login token is included.
- Verify the scheduled task’s trigger time, confirming that the set time is later than the daily data aggregation completion node for water utility data.
- Run a data cleaning test to confirm that the filtering logic can identify and mark abnormal data outside the normal range.
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, and it is recommended to test on your own samples before finalizing settings.
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.