What data for this category looks like
Data related to construction machinery yield rates comes from construction equipment IoT platforms, enterprise ERP systems, equipment leasing settlement systems, and fuel replenishment record systems. Core statistical dimensions cover daily operating hours, fuel consumption, maintenance expenses, leasing revenue, and direct operating costs per single device per day. Each record corresponds to daily operating data for one device. It includes identifying fields such as device unique ID, affiliated project ID, and statistical date, plus quantitative fields for operating, cost, and revenue metrics. There are two update cadences: Core operating data syncs full records from the previous day daily at midnight. Real-time operating data syncs every 15 minutes to support refined yield rate calculations. Field units use international standard metrics: operating hours in hours, fuel consumption in liters, revenue and costs in yuan.
Constraints on Database and Operations From These Data Characteristics
Heterogeneity of multi-source data creates data cleaning constraints: Field formats and units collected from different systems must be unified to avoid calculation deviations. The daily batch-synced full operating data volume is large, so date-partitioned storage strategies must be configured to reduce single-table query pressure. High-frequency write requirements for real-time operating data demand databases with low-latency streaming write capabilities. These systems must also perform association checks with batch-synced daily closing data. Rich field dimensions per record require joint indexes for frequently queried fields to improve query efficiency. Some quantitative fields have reasonable range limits, so data validation rules must be configured to filter invalid incorrectly entered data.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
mongo_write_concern | w:1 | Balances latency and data reliability for high-frequency small-batch real-time writes, suitable for multi-source data synchronization scenarios |
pgvector_max_connection | 200 | Matches 200 concurrent query requests, prevents service interruptions caused by exhausted connection pools |
batch_process_size | 800–1200 records | Adapts to the volume of daily batch-synced daily closing data, avoids single-run processing timeouts |
data_cleanup_policy | Retain raw data for 180 days, aggregated data for 365 days | Balances storage costs and historical data query needs |
stream_process_timeout | 600 seconds | Adapts to real-time data stream processing windows, prevents data loss from mid-process interruptions |
ollama_model_context_window | 32768 tokens | Adapts to large language model input length requirements, supports complete device operating data context |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- A
MongoConnectionTimeoutErrorerror appears, and daily closing data synchronization cannot be completed: The cause is failure to adjust database connection pool parameters to adapt to concurrent requests, leading to exhausted connections that cannot establish new sessions. - Unable to execute SQL commands after entering the pgvector container, with the prompt
invalid input syntax for type numeric: The cause is failure to unify field units from multi-source data, resulting in cleaned numerical formats that do not meet database field requirements. - Packet loss occurs during real-time operating data synchronization, with the operating hours field empty for some devices: The cause is the
stream_write_batch_sizeparameter being set too large, leading to some write requests being rejected by the database.
How to Confirm Proper Configuration
- Run a joint query by device ID and statistical date, verify that the number of returned results matches the expected covered device count. Thresholds must be adjusted based on actual deployed device scale.
- Check database operation logs, confirm that batch sync tasks complete according to the preset cycle, with no timeout or connection failure records. Verify that task execution times match the daily closing generation cadence.
- Initiate query requests at the corresponding concurrent scale, confirm no connection exhaustion or service interruption errors occur. Adjust test parameters based on actual business concurrent peak values.
- Manually enter quantitative field values outside reasonable ranges, confirm that the system automatically intercepts invalid data. Set validation rules based on device rated operating parameters.
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.