What the Data for This Category Looks Like
Commercial vehicle yield rate data primarily originates from in-vehicle T-BOX terminals, fleet management systems, freight order platforms, and energy refueling platforms. Data is organized using the unique vehicle identification number (VIN) and operation date as dimensions. Each document contains detailed metrics for a single vehicle on a given day, including total orders, driving mileage, energy consumption costs, maintenance expenses, platform commissions, affiliated fees, as well as aggregated total revenue and total cost. Data is synced in batches every early morning to deliver full operational data from the previous day. Real-time writing is also supported for temporary same-day orders and supplementary data.
Constraints for Database and Operations Workflows
Multi-source data synchronization creates high-frequency concurrent write requirements, which must adapt to write time differences across different terminals and platforms. Joint queries using VIN and operation date are frequent, so joint indexes must be established to reduce query latency. Daily batch updates involve large data volumes, so batch write operations must be supported to avoid database blocking from single large writes. Post-hoc supplementary entries for operational data are possible, so UPSERT operations must be supported to prevent duplicate data generation. Real-time daily report broadcasts require controlling database query latency, and a reasonable cache strategy must be configured to reduce direct database query frequency.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
mongo.connectionPoolSize | 100-150 | Adapts to concurrent write requirements from multi-source commercial vehicle data synchronization, and supports database connection usage during daily batch updates |
batchInsertBatchSize | 500-1000 records per batch | Matches daily batch update volumes for single-vehicle data, avoids database table locking caused by overly large single writes |
index.jointKey | ["vehicle_id", "operating_date"] | Covers high-frequency query scenarios by single vehicle and operation date, joint indexes significantly reduce query latency |
dataUpsertEnable | true | Supports post-hoc supplementation and correction of operational data, prevents insertion of redundant duplicate data |
cache.ttl | 3600 seconds | Matches the daily update cycle of daily report data, reduces overhead from repeated database queries |
query.maxTimeout | 30 seconds | Adapts to real-time response requirements for AI broadcasts, avoids request timeout interruptions that prevent data from being persisted to the database |
The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by data format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Scenario: After calling the yield rate daily report generation API, the client disconnects, and no corresponding daily report data is created in the database. Root cause:
query.maxTimeoutis not configured to match batch write durations, resulting in request timeout interruptions that do not trigger data persistence callback logic. - Scenario: After starting the Docker container, port 3000 is inaccessible, and the log outputs
MongoConnectionError. Root cause: The account password or host address inmongo.connectionStringis configured incorrectly, or inter-container network connectivity is not properly configured. - Scenario: After daily batch updates, yield rate data for some vehicles is missing or duplicated. Root cause: The
index.jointKeyjoint index is not correctly set, causing the database to fail to quickly locate existing data during UPSERT operations, resulting in missed or duplicate inserts.
How to Verify Proper Configuration
- Run a database index query command to confirm the joint index has been created as configured.
- Submit single or batch simulated operational data, call the supplementary entry interface, and verify that data is updated correctly with no duplicate or missing entries.
- Review system operation logs to confirm there are no authentication failures or timeout errors for database connections.
- Initiate a simulated daily report generation request, and verify that the request response duration meets expected business thresholds.
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.