What data for this category looks like
Data related to tourist attraction revenue yields primarily comes from ticket sales and verification systems, secondary consumption POS terminals, and park passenger flow statistics systems. Data updates follow two tiers: real-time daily transaction data is synchronized hourly, and full daily revenue data is compiled and updated after the park closes each day. Each individual data entry is categorized by date, attraction location, and revenue type, and includes fields such as attraction location ID, revenue category (ticket, catering, cultural and creative retail, etc.), number of transactions, total transaction amount, etc. Units are location ID, classification tag, transaction count, and Chinese Yuan respectively. No pre-configured aggregated statistical fields are included.
What constraints these characteristics impose on conversation logs and auditing
Since data is split by location and revenue type, conversation logs must accurately associate the location ID and date range carried in requests. Otherwise, it will not be possible to match specific operating data to query requests during audits. The hourly real-time data update requirement means log timestamps must be precise to the minute, to avoid mismatches between log and data timelines due to insufficient time precision. Multi-dimensional query parameters must be fully recorded in logs, to support operational traceability during compliance audits. Additionally, scenic spot revenue data is sensitive business information. Log retention must comply with industry compliance requirements, and reasonable retention periods and access permissions must be set.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
LOG_RETENTION_DAYS | 90 days | Covers the monthly operational review cycle of tourist attractions, and complies with standard compliance requirements for operating data audits |
LOG_QUERY_PARAMS_ENABLE | Enabled | Revenue yield queries for tourist attractions often carry location ID and date range parameters. Enabling this option allows full recording of request context |
MAX_LOG_STORAGE_SIZE | 500 GB | Adapts to daily log output of tens of MB per single attraction, and meets single-node log storage requirements for more than six months |
WORKFLOW_LOG_SYNC | Enabled | Revenue yield broadcasts for tourist attractions often call multi-node workflows. Synchronizing child node logs allows complete restoration of the audit trail |
USER_ID_MAPPING_ENABLE | Enabled | Tourist attraction operations involve multiple account scenarios. Associating user IDs clarifies audit responsibility subjects |
LOG_THRESHOLD_LEVEL | INFO | Covers key operations throughout the request and response process. The INFO level balances log completeness and storage usage |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- The symptom is that after calling a deployed sub-application or plugin, the main workflow's conversation logs do not record interaction details from the sub-application. The cause is that the
WORKFLOW_LOG_SYNCconfiguration item is not enabled, and child process node execution logs are not synchronized. - The symptom is that AI thought process output is configured, but thought steps are not displayed in conversation history. The cause is that the
LOG_THINKING_CONTENTparameter is not enabled, or the log level is set higher thanINFO, which filters logs related to thought processes. - The symptom is that conversation logs cannot be associated with the specific tourist attraction operation staff who initiated the query, or logs cannot be deleted in batches by account. The cause is that the
USER_ID_MAPPING_ENABLEconfiguration item is not enabled, and internal accounts are not mapped and bound to FastGPT user IDs.
How to confirm configurations are correctly set
- Initiate a revenue yield query request for a specific attraction location, and check whether all parameters carried in the request and return results are recorded in the conversation logs.
- Log in to the log management backend, and verify that multi-condition filtering is supported based on user identification, request time, and query dimensions.
- Execute a workflow that calls external plugins, and confirm that plugin interaction records are synchronized in the main workflow logs.
- Enable the thought process log switch, initiate a request for AI to generate broadcast content, and check whether the logs contain complete thought process records.
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.