What this category of data looks like
Data mainly comes from fixed monitoring stations, mobile monitoring devices, and satellite remote sensing collection terminals. Update frequency ranges from seconds to minutes. Each document includes fields such as monitoring point code, collection time, pollutant concentration value, device operating status, and latitude and longitude coordinates. Pollutant concentration units include μg/m³, mg/L, %VOL, etc. The device status field has three enumerated values: normal, abnormal, and offline. Some documents also include calibration records and abnormal alarm tags.
What constraints these characteristics impose on database and operations workflows
High-frequency updates from seconds to minutes require database write throughput to meet standards to avoid data accumulation and loss. Multi-dimensional numeric fields and enumerated status fields need targeted indexes to improve the efficiency of frequent spatial-temporal associated queries in research scenarios. The requirement to retain full historical data creates storage capacity expansion pressure, so a reasonable data archiving strategy must be planned. Frequent cross-monitoring point spatial-temporal associated queries require selecting latitude and longitude and collection time as sharding keys to avoid cross-shard query delays. At the same time, data consistency must be ensured in research scenarios, and frequent batch deletion operations should be avoided.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGO_WRITE_CONCURRENCY | 16–32 | Matches the concurrent write requirements of second-level environmental monitoring data, avoids write blocking |
INDEX_POLICY | Create a composite index using monitoring point ID + collection time | Covers the most frequent spatial-temporal associated queries in research scenarios, reduces query latency |
DATA_RETENTION_DAYS | 180–365 days | Meets the need for retrospective research on historical data, while controlling long-term storage costs |
DB_CONNECTION_POOL_SIZE | 64–128 | Adapts to connection reuse requirements in high-frequency read/write scenarios, avoids connection exhaustion errors |
BATCH_INSERT_SIZE | 500–1000 entries | Balances throughput and network overhead for single writes, avoids timeouts caused by overly large single requests |
QUERY_TIMEOUT | 30 seconds | Matches the average latency of cross-shard spatial-temporal queries, prevents long queries from being forcibly terminated |
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 Misconfigurations
- Symptom: When deploying an environmental monitoring knowledge base with Docker and connecting to MongoDB, the connection works normally at startup, but a connection failure error appears after several hours of operation. The log shows
ETIMEDOUT. Cause: No connection pool timeout automatic reconnection parameter is configured. After idle connections in the connection pool are actively disconnected by the Docker network or MongoDB service, they are not automatically rebuilt. - Symptom: After upgrading from version 4.9.13 to 4.10.1, previously uploaded environmental monitoring data does not display synchronously in the knowledge base, and no new entries appear in the knowledge base list. Cause: No database migration script was executed during the upgrade process, and monitoring data from the old database was not mapped to the document structure of the new version database.
- Symptom: When configuring a problem classification node, using an initialized AI model to trigger a save or publish operation causes a
500 Internal Server Errorpop-up on the interface. The error disappears when switching to another AI model. Cause: The vector generation parameters of the initialized AI model do not adapt to the field length of environmental monitoring data, causing node errors after vector generation fails.
How to Verify Proper Configuration
- Run a simulated high-frequency write test, observe whether database write latency meets business expectations. Set the latency threshold according to the real-time requirements of the research scenario.
- Initiate a cross-monitoring point spatial-temporal associated query, verify that the number of query results matches expectations, and no shard query omissions occur.
- Simulate an idle connection disconnection scenario, check whether the database connection is automatically rebuilt, and services can recover without manual intervention.
- Execute the database migration script after version upgrade, verify that historical monitoring data is fully synchronized to the new version knowledge base.
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.