What data for this category looks like
Environmental monitoring data sources include fixed monitoring stations, portable sensor terminals, and satellite remote sensing acquisition equipment. Core monitoring indicators are synchronized every 5 minutes. Full daily report data is generated at a fixed time each day. Each data record contains a unique monitoring point code, collection timestamp, pollutant category identifier, measured concentration value, daily yield, and operation and maintenance status identifier. Field and unit specifications: Monitoring point code is a string type. Collection time uses ISO 8601 format timestamp. Concentration value unit is micrograms per cubic meter. Daily yield is a floating-point numeric value. Operation and maintenance status identifier is an integer type.
Constraints from These Characteristics on Database and Operations Workflows
High-frequency real-time write requirements demand that the database support hundreds of concurrent writes per second. This avoids write blocking that disrupts data timeliness. Daily batch-generated daily report data requires the database to support efficient aggregate queries and time range filtering. Without this, query delays will reduce daily report generation efficiency. Multi-source data access requires the operations link to support compatible data validation rules for different formats. This prevents dirty data from entering and contaminating the dataset. The unique monitoring point code field requires a unique index to ensure data deduplication. It also supports bulk queries by region. The presence of daily yield and operation and maintenance status identifier fields requires the operations link to configure real-time alert rules. This enables timely handling of abnormal write data streams.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
mongodb.writeConcern | w:1 | Ensures environmental monitoring data writes are not lost, balances write performance and reliability |
mongodb.maxPoolSize | 200–300 | Adapts to 5-minute batch write requests, prevents connection exhaustion that causes interruptions |
dataSync.interval | 300 seconds | Matches the 5-minute collection and update frequency of core monitoring indicators, ensures data synchronization timeliness |
dataClean.cron | 0 2 * * * | Executes data cleanup daily at 2 AM, automatically deletes expired original collection data after daily report generation |
index.monitorPointId | Single-field unique index | Based on the uniqueness of monitoring point codes, prevents duplicate data writes, supports bulk queries by region |
batchWrite.size | 100 records | Balances network overhead per write and database load, adapts to batch submission requirements during collection cycles |
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 Mistakes
- Symptom: A MongoDB deployment via Docker shows
connection timeouterrors after running normally for several hours, and cannot continue writing monitoring data. Cause: No reasonable value was configured formongodb.maxPoolSize. Free connections are not automatically reclaimed after the connection pool is exhausted. - Symptom: When creating a new data synchronization node, using the default initialization model triggers a save error, and the interface displays
model not found. Cause: The default initialization model is not bound to the permission scope of the current workflow. Only built-in models support direct calls without configuration. - Symptom: When batch writing monitoring data, some fields are empty, causing write failure. The log shows
field required: monitorPointId. Cause: Data validation rules are not enabled, and collection data missing the unique identifier is not filtered out.
How to Confirm Configuration Is Complete
- A database connection command is executed to query the 10 most recent records in the
monitor_datacollection. Verification is performed that the collection timestamp and monitoring point code fields exist and conform to ISO 8601 and string specifications. - Write throughput metrics on the system monitoring panel are viewed. The number of write requests per second is confirmed to match the expected relationship with the configured
batchWrite.sizeanddataSync.interval. - A scheduled data cleanup task is manually triggered. Verification is performed that expired original collection data is automatically deleted, with no residual data occupying storage resources.
- A monitoring data record with an abnormal operation and maintenance status identifier is simulated for writing. Verification is performed that the system triggers the preset alert rules and pushes the corresponding alert information.
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.