Database and Operations for Water Treatment Yield Rates

What Data Looks Like for This Category

Data related to water treatment project yields for the financial sector comes primarily from plant SCADA systems, online water quality analyzers, production MES systems, and revenue accounting modules from financial endpoints. Core metrics are summarized and updated daily. Real-time process parameters are collected every 5 minutes. Full daily report documents are generated each early morning.

Each daily report includes these fields: unique plant identifier, monitoring period, water production volume, chemical dosage, water and electricity consumption per ton of water, chemical cost per ton of water, compliant water quality indicators, daily revenue changes for corresponding financial products, and cumulative revenue balances.

Unit specifications:

Water production volume: cubic meters
Energy consumption: kilowatt-hours
Chemical cost: yuan
COD and ammonia nitrogen water quality indicators: mg/L

Constraints for Database and Operations Workflows

High-frequency writes of real-time parameters create high database concurrent write pressure. Stable throughput for write queues must be maintained.

Large volumes of daily batch-generated report data must comply with financial industry data retention regulations. Strict scheduled archiving policies must be configured to prevent data directory bloat.

Multiple data source integrations require handling differences in data formats across systems. Strict schema validation must be implemented to prevent dirty data from entering the system and compromising the accuracy of financial revenue calculations.

Correlation analysis of yield metrics requires real-time reading of historical data. Database read latency must be controlled within business allowable ranges. Cross-system connector stability must also be ensured.

How to Set Configurations

Configuration Item	Recommended Value	Rationale
`MONGODB_WRITE_CONCURRENCY`	`20-30`	Real-time water treatment data is written every 5 minutes, write volume per plant per node is moderate. 20-30 concurrency avoids queue backlog
`DAILY_REPORT_SYNC_INTERVAL`	`86400 seconds`	Matches the business schedule of daily early morning aggregation, avoids duplicate synchronization or delays
`PARSE_DATA_TIMEOUT`	`300 seconds`	A single daily report includes indicators from multiple plants, parsing and validation take significant time. 300 seconds covers full processing
`DATABASE_ARCHIVE_RETENTION_DAYS`	`730 days`	Complies with financial industry compliance requirements for operational data retention
`MAX_CONNECTION_POOL_SIZE`	`64`	Adapts to concurrent read requirements for multiple data sources across SCADA and MES systems
`MILVUS_VECTOR_DIMENSION`	`768`	Matches the embedding dimension of general large models, supports vector retrieval for yield rate metrics

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. It is recommended to test on your own samples before finalizing settings.

Three Common Configuration Errors

Symptom: MongoDB fails to start after migration. Cleaning the mount directory does not restore functionality, and logs show Permission denied. Cause: User permissions for the MongoDB data directory were not reset after migration. The FastGPT container runtime user lacks write permissions.
Symptom: A single-node deployment triggers process termination by the operating system when handling 50 concurrent requests. Cause: The MAX_WORKER_THREADS parameter was not limited. The number of threads during concurrent processing of real-time data and daily report parsing exceeds the server memory capacity limit.
Symptom: Database connection error Failed to connect to 192.168.xx.xx.:1433 - 38FBA17 occurs, and MES system data cannot be read. Cause: The database whitelist was not configured. The FastGPT server IP is not allowed access by the target database, or port mapping configuration is incorrect.

How to Verify Correct Configuration

Run the mongo --eval "db.stats()" command to check if write throughput matches the configured concurrency. Adjust MONGODB_WRITE_CONCURRENCY to a stable range.
Review daily scheduled task logs to confirm no timeout errors occur after DAILY_REPORT_SYNC_INTERVAL triggers. Adjust PARSE_DATA_TIMEOUT to cover full processing duration.
Check cross-system connection status. Use the telnet 192.168.xx.xx. 1433 command to verify port connectivity. Adjust database whitelist and port configurations as needed.
Review container resource monitoring to confirm connection counts do not exceed server capacity limits under the MAX_CONNECTION_POOL_SIZE configuration. Adjust parameters as needed.

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.