What data for this category looks like
Data related to vehicle yield rate comes primarily from three sources: publicly available vehicle-level cost accounting documents from automakers, purchase and sales ledgers from terminal dealers, and retail monitoring data from industry circulation associations. The update schedule follows this pattern: daily updates for single-vehicle price difference data from same-day terminal sales, and weekly updates for cumulative vehicle profit summary data. Documents use a structured table format, including fields such as unique vehicle code, terminal transaction price, total purchase cost, profit per vehicle, and regional circulation share. The unit for profit per vehicle is yuan. Regional circulation share is marked as a proportional value, with no percentage notation.
What constraints do these characteristics impose on deployment and upgrade
These data characteristics impose clear constraints on deployment and upgrade workflows. Multi-source heterogeneous data sources require configuring cross-source data alignment preprocessing rules during deployment to avoid field mapping deviations. High-frequency daily real-time data updates require setting reasonable incremental sync task intervals during deployment to prevent data lag. Strict validation requirements for unique vehicle codes require configuring data deduplication and format validation logic during deployment to ensure data accuracy. Weekly cumulative summary data updates require compatibility with legacy field mapping relationships during version upgrades to avoid abnormal reading of historical data.
How to set the configurations
| Config Item | Suggested Value | Rationale |
|---|---|---|
DATA_SYNC_INTERVAL_SECONDS | 300 seconds | Adapts to daily real-time data sync requirements and prevents data lag |
DATA_DUPLICATE_CHECK_FIELD | Unique vehicle code | Deduplicates based on the unique identifier of vehicle data to prevent redundant data entry |
PARSE_DATA_TIMEOUT_SECONDS | 600 seconds | Multi-source data merging and preprocessing takes longer; extend the timeout to avoid sync task interruptions |
VERSION_COMPATIBLE_FIELDS | ["terminal transaction price", "total purchase cost", "profit per vehicle"] | Compatible with legacy core field mappings during version upgrades to ensure normal reading of historical summary data |
AIPROXY_UPGRADE_MODE | Rolling upgrade | Avoids interrupting real-time broadcast services during upgrades and ensures business continuity |
EMBEDDING_CUSTOM_ENDPOINT | GPUStack private deployment bge-m3 service address | Connects to private vector models to ensure accurate semantic embedding of yield rate data |
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. It is recommended to test on your own samples before finalizing settings.
Three common errors
- Phenomenon: After performing a minor aiproxy version update, real-time market reports show data lag. Cause: A full restart upgrade strategy was used, and active sync tasks were not retained, resulting in an extended incremental sync interval.
- Phenomenon: Imported vehicle yield rate data has incorrect field mappings, with profit per vehicle displaying purchase cost values. Cause: The
VERSION_COMPATIBLE_FIELDSparameter was not specified, and legacy fields were not correctly mapped to the new data structure after the upgrade. - Phenomenon: Shared daily report broadcast links do not trigger identity verification, and unauthorized users can directly access data. Cause: The shared link authentication configuration for the open-source version was not enabled, and the permission validation logic is disabled by default.
How to confirm the configuration is properly set
- View the running logs of data sync tasks to confirm that the task execution interval matches the configured requirements, and verify that the number of synced data entries matches the source data batch.
- Import test data containing duplicate vehicle codes to check if the system automatically filters duplicate entries and verify that the deduplication configuration is effective.
- Test the access permissions of shared links to confirm that unauthorized users cannot access data, and verify that the authentication configuration is enabled.
- Call the test interface of the private vector model to compare the returned results with the FastGPT embedding results, and confirm that the custom endpoint configuration is correct.
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.