What This Category of Data Looks Like
Air governance revenue yield data mainly comes from publicly available monitoring APIs of environmental protection departments, enterprise pollution control equipment operation logs, and data pushed by third-party environmental monitoring service providers. There are three update frequency categories: real-time pollutant concentration data refreshes every 1 minute, pollution control facility operation parameters are summarized hourly, and emission reduction revenue accounting data is updated daily. Each data document uses a structured format, including fields such as monitoring point code, pollutant category, real-time concentration value, equipment operation duration, governance cost, emission reduction equivalent, and accounting cycle. Concentration units are milligrams per cubic meter, duration units are hours, cost units are yuan, and emission reduction equivalent units are tons.
Constraints for Database and Operations
Real-time pollutant data refreshes every 1 minute. This requires the database to support high-concurrency write operations to avoid data backlog caused by single-threaded writes. Daily updated emission reduction revenue accounting data requires batch import. A write strategy that supports batch SQL execution must be configured. Structures with multiple fields and enumeration types require indexes for high-frequency query fields to shorten response times. Different data sources have varying formats. A standardized cleaning link must be added before data is stored in the database. At the same time, monitor multiple data source availability. Configure an automatic reconnection mechanism after disconnection to avoid data collection interruptions affecting daily revenue accounting. When associating real-time monitoring data with revenue accounting data, design reasonable table association logic to avoid excessive cross-table query delays affecting daily report generation.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
CONCURRENT_WRITE_LIMIT | 120 times/minute | Matches the 1-minute refresh rhythm of real-time data, balances write performance and database load |
BATCH_INSERT_SIZE | 500 records per batch | Matches the scale of daily batch-imported accounting data, reduces network overhead for single write operations |
INDEX_REFRESH_INTERVAL | 300 seconds | Matches the update frequency of high-frequency queries, avoids reduced query efficiency caused by outdated indexes |
DATA_CLEANING_RULE | Based on actual field calibration | Adapts to format differences across multiple data sources, completes field standardization and null value completion |
SOURCE_MONITOR_INTERVAL | 60 seconds | Monitors the availability of environmental protection APIs and device logs, detects collection interruptions in a timely manner |
DB_CONNECTION_TIMEOUT | 30 seconds | Adapts to network latency across regional data sources, avoids invalid connections occupying resources |
The parameter values provided on this page are common recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and testing on local samples is recommended before finalizing settings.
Three Common Configuration Mistakes
- Frequent 429 status codes are returned when calling environmental monitoring APIs. Cause: No concurrent write limit is configured, exceeding the call quota of the data source causes requests to be blocked.
- Empty results are returned after the database workflow executes an SQL query. Cause: No indexes are established for high-frequency query fields, and the correct point code field is not matched during cross-table association, resulting in failure to associate corresponding data.
- A MongoDB connection error is prompted when starting local debugging. Cause: The database connection string is not configured correctly, or the local MongoDB service is not started normally, causing the application to fail to establish a database session.
How to Verify Successful Configuration
- Initiate multiple batches of data write requests within 1 minute, check database monitoring metrics, and adjust
CONCURRENT_WRITE_LIMITto a value suitable for the current business load. - Execute an SQL query containing high-frequency query fields, record the response time, and adjust index configuration to a range that meets business delay requirements.
- Simulate a third-party data source disconnection, check the alarm system trigger status, and confirm that monitoring configuration can capture exceptions in a timely manner.
- Import test data that does not conform to standard formats, check field integrity after data is stored in the database, and confirm that data cleaning rules take effect.
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.