What the data for this category looks like
Data sources for aviation equipment yield-related data include publicly disclosed equipment costs, annual maintenance quotas, flight hour statistics, and parts procurement cost announcements from the defense industry, as well as real-time operational data from some maintenance platforms. Core operational data is updated hourly. Annual cost and revenue calculation data is updated quarterly. Data is stored in structured table format, including fields such as equipment model, factory cost, annual maintenance expenditure, annual flight hours, per-hour operating cost, and per-flight-hour revenue. The corresponding units for each field are ten thousand yuan, ten thousand yuan per year, hours, yuan per hour, and yuan per hour.
What constraints these characteristics impose on deployment and upgrade
The hourly update requirement for real-time operational data requires precise scheduled pull task intervals to avoid resource redundancy or update delays. Storage and parsing of multi-field structured data require strict field validation rules to prevent parsing failures caused by format mismatches. Differences in fields across different aviation equipment models require dynamic field adaptation logic to support custom field mapping. Processing of quarterly batch accounting data requires reserving sufficient computing resources to avoid task timeouts. Upgrade processes must be compatible with old version field mapping configurations to prevent abnormal import of historical data. Multi-data source synchronization scenarios require retry mechanisms to handle temporary network fluctuations and ensure data pull success rates.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
SCHEDULE_INTERVAL | 3600 seconds | Matches the hourly update rhythm of aviation equipment real-time operational data, avoids data lag from overly long update intervals, and prevents resource waste from overly short intervals |
FIELD_VALIDATION_STRICT | Enabled | Addresses the characteristic of multiple structured fields, strictly verifies that data formats match preset fields to prevent parsing errors that affect subsequent analysis |
DYNAMIC_FIELD_MAPPING | Enabled | Adapts to field differences across different aviation equipment models, supports custom mapping rules, and covers access requirements for multi-model data |
BATCH_TASK_MEMORY_LIMIT | 4096 MB | Meets the processing resource requirements for quarterly batch accounting data, avoids task timeout interruptions caused by insufficient memory |
UPGRADE_COMPATIBILITY_MODE | v3 Compatibility Mode | Retains old version field mapping configurations during upgrades, ensures normal import of historical data, and reduces adaptation costs after upgrades |
DATA_SOURCE_SYNC_RETRY | 3 times | Handles temporary network fluctuations during multi-data source synchronization, ensures data pull success rates, and avoids data update interruptions caused by single failures |
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, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Phenomenon: The frontend page continues to spin and fails to load after Docker deployment. Cause: The
NEXT_PUBLIC_API_BASE_URLparameter is not configured correctly, preventing the frontend from connecting to the backend service address. - Phenomenon: The number of knowledge bases in the open-source version is limited to 30, and no additional ones can be created. Cause: The default configuration value of
MAX_KNOWLEDGE_BASE_COUNTis not modified, and the new configuration is not applied by rebuilding the image or restarting the service. - Phenomenon: Real-time operational data pull fails, and the log shows a
408 Request Timeouterror. Cause: TheSCHEDULE_INTERVALconfiguration is too short, causing tasks to trigger frequently and occupy system resources, leading to request timeouts.
How to Verify Correct Configuration
- Execute the scheduled task test script, check whether the pulled real-time data matches the update time of the data source, and adjust
SCHEDULE_INTERVALto a value that fits the business rhythm. - Import aviation equipment data of different models, verify that field parsing is complete, and confirm that the configurations of
FIELD_VALIDATION_STRICTandDYNAMIC_FIELD_MAPPINGcan adapt to field differences across multiple models of data. - Submit a batch accounting task, monitor system resource usage, and adjust
BATCH_TASK_MEMORY_LIMITto a reasonable value that meets task operation requirements. - Restart the service and access the frontend page, confirm that the page loads normally, and verify the correctness of the
NEXT_PUBLIC_API_BASE_URLconfiguration. - Check the system operation log, confirm that the retry count of multi-data source synchronization tasks matches the configuration of
DATA_SOURCE_SYNC_RETRY, and there are no frequent failure 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.