What data does this category include
Data related to general equipment yield rates comes from internal enterprise IoT collection systems, equipment operation and maintenance management platforms, and public general equipment trading market APIs. Data is generated as a full daily report at fixed times each day, and real-time operating auxiliary parameters can also be pulled on demand. Each document corresponds to a single unit or batch of general equipment, with daily yield-related dimension records. Fields include device unique identifier, daily output, unit energy consumption cost, market transaction average price, operation and maintenance duration, and failure frequency. The corresponding units are units/sets, yuan/unit production capacity, yuan/ton, hours, and times.
What constraints do these characteristics impose on the database and operations link
Multi-source data access requires the database to support cross-data source format verification and unified mapping, to avoid dirty data caused by field mismatches. The mixed rhythm of daily full updates and real-time synchronization requires the database to be configured with scheduled tasks and high-concurrency write queues, to balance resource usage between batch processing and real-time requests. Using the device unique identifier as the primary key requires strict control of primary key uniqueness during operation and maintenance, to prevent data overwrites. Data volume grows linearly with device deployment scale, so partitioned storage and expansion plans need to be prepared in advance to ensure fast query efficiency for historical data. At the same time, concurrent requests for daily reports from multiple terminals require the database connection pool and query cache to adapt to peak traffic, to avoid request blocking.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGO_WRITE_CONCURRENCY | 200–300 concurrent write threads | Adapts to the batch write requirements of multi-source data synchronization for general equipment, avoids blocking business processes with single-threaded writes |
SYNC_DATA_FREQUENCY | 1 real-time sync per hour + full update at 02:00 daily | Matches the update rhythm of general equipment daily reports, balances accuracy of real-time operating data and daily summary data |
DAILY_BATCH_INSERT_SIZE | 500 records per batch | Balances database write performance and memory usage, avoids write timeouts caused by overly large single batches |
DATA_CONSISTENCY_CHECK_THRESHOLD | Calibrated based on business scenarios | Verifies differences between internal operation and maintenance data and public market data, blocks dirty data writes that exceed reasonable ranges |
MAX_CONNECTION_POOL_SIZE | 800–1000 connections | Adapts to concurrent query requirements for yield rate daily reports requested by multiple terminals, avoids service interruptions caused by exhausted connections |
HISTORY_DATA_PARTITION_DAYS | Partitioned by calendar month | Improves query efficiency for historical data, facilitates data archiving and regular cleanup during operation and maintenance |
The parameter values provided on this page are all common starting points for configuration. Actual values are affected by material forms, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing the settings.
Three common mistakes
- Symptom: Unable to create the yield rate database after deployment, with the interface prompting
collection not found. Cause: The dedicated database collection naming rules for general equipment were not configured in advance, or the database permissions did not open the corresponding write path. - Symptom: Returns a
429 Too Many Requestserror when concurrent requests exceed the threshold. Cause: Concurrency limiting parameters at the FastGPT level were not configured, and external data source APIs were called directly, exceeding service provider limits. - Symptom: Some fields are empty when querying yield rate daily reports. Cause: No field mapping verification was performed during multi-source data synchronization, and field names of internal operation and maintenance data and public data sources are inconsistent, causing some data to fail to be written to the database normally.
How to confirm the configuration is correct
- Run the database initialization script, check if the dedicated collection for general equipment yield rates can be created successfully, with no permission or path-related errors.
- Simulate peak concurrent business requests, observe the interface return status codes, confirm there are no abnormal errors and that the preset traffic bearing requirements are met.
- Import a set of standard test data, verify field integrity after querying, confirm that the mapping relationship of multi-source data matches expectations.
- View the scheduled task log, confirm that the daily full data synchronization task executed normally at the fixed time each day, with no timeout or interruption 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.