What the Data for This Category Looks Like
Water utility revenue rate daily reports draw data from the operation ledgers, financial accounting systems, and water supply and drainage metering platforms of water utilities. The system generates full aggregated previous-day data at midnight each day, for use in that day's broadcasts. Documentation uses a structured daily report format, including core fields such as statistical date, total water supply, total water sold, tap water sales revenue, sewage treatment service revenue, total operating cost, net profit, and operating revenue rate. Corresponding units for each field are ten thousand cubic meters, yuan, yuan per ten thousand yuan of revenue. No percentage-based units are used. All data fields tie directly to core business processes of water utility operations, with no redundant non-business fields.
Constraints Imposed on Conversation Logs and Auditing
Multi-data source access requires audit logs to record the data source identifier for each conversation call. This prevents mixing fields from different water utility systems. The daily update schedule requires audit processes to verify that conversation request times match the daily data update time. This stops calls for ungenerated daily report data. The strong link between field structure and business requirements means audit logs must fully record specific fields queried by users and returned content fragments. This facilitates subsequent review of business operations. Additionally, the operational nature of water utility data requires audit logs to retain the operator's permission identifier. This prevents unauthorized access to sensitive operating revenue data.
How to Configure Settings
The following table lists configuration keys, recommended values, and their rationales:
| Configuration Key | Recommended Value | Rationale |
|---|---|---|
AUDIT_LOG_ENABLE | Enabled | Water utility operation data falls under sensitive business scope. All conversation operations must be fully recorded for compliance auditing. |
LOG_RETENTION_DAYS | 90 days | Complies with audit retention compliance requirements for water utility industry operation data, covering standard audit cycles. |
LOG_FIELD_INCLUDE_LIST | statistical date, total water supply, sales revenue, operating yield | Matches core business fields of water utility revenue rate daily reports, ensuring audit logs cover key data calls. |
DATA_SYNC_INTERVAL | 86400 seconds | Adapts to the daily update schedule of water utility daily reports, ensuring conversation calls use data aligned with official release times. |
MAX_CONTEXT_HISTORY_LENGTH | Last 20 conversation entries | Covers the context needs of users querying multiple days of revenue rate data in water utility scenarios. |
LOG_STORAGE_TYPE | MongoDB | Adapts to storage requirements for water utility structured data, enabling easy field-based retrieval of audit logs. |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by data format, data volume, and business rules. Each scenario requires individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: After configuring
LOG_STORAGE_TYPEas MongoDB, conversation audit logs cannot be retrieved from the MongoDB collection. Cause: TheLOG_MONGODB_CONN_STRINGparameter is not configured, or the database write permission of the connection string is insufficient, preventing log writing. - Phenomenon: Water utility revenue rate data fields returned in conversations are missing, such as the
运营收益率field being empty. Cause: TheLOG_FIELD_INCLUDE_LISTconfiguration does not include this field, or the data synchronization script failed to pull the source data for this field correctly. - Phenomenon: When querying yesterday's water utility revenue rate data in a conversation, outdated data that has not been updated that day is returned. Cause: The
DATA_SYNC_INTERVALconfiguration is too long, or the conversation request time is not verified against the data update time, resulting in calls to cached outdated data.
How to Verify Proper Configuration
- The FastGPT backend audit log module is accessed, water utility-related conversation keywords are searched, and complete conversation records and associated business fields are confirmed to load normally.
- The configured log storage medium is checked for the existence of the corresponding named log collection, and log write permissions are confirmed to be normal.
- A simulated conversation query for water utility revenue rate data is initiated, and the returned fields are verified to match the preset audit log included fields.
- A permission verification test is triggered, and relevant results of the permission verification are confirmed to be recorded in the audit log.
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.