Database and Operations for District Heating Yield and

What the Data for This Category Looks Like

Data for district heating yield and market reports comes from district heating enterprise production and operation systems and municipal public utility supervision platforms. Full datasets for the previous calendar day are generated at a fixed time each day. Each entry corresponds to single-day operation data for one district heating project. The document structure includes these fields: project_id (unique identifier for the district heating project), report_date (data report date, format YYYY-MM-DD), total_operational_income (total operating income, unit: RMB yuan), unit_heat_supply_cost (unit heating cost, unit: yuan/gigajoule), daily_heat_supply_volume (daily heating supply volume, unit: gigajoule), operational_profit (operating profit, unit: RMB yuan), customer_count (number of heated households served). No percentage-based metrics are included.

Constraints Imposed by These Data Characteristics for Database and Operations

Full daily updated datasets create concentrated write traffic peaks. Connection pools must be configured to adapt to traffic fluctuations. Multiple unit fields require unit consistency checks before data is written to the database to prevent abnormal units from being included. report_date is a high-frequency query field, so a secondary index must be created to reduce query latency. The structure of one entry per project per day is suitable for time sharding by report_date to reduce the data volume of a single collection. The retention period for historical data must be set based on broadcast requirements to avoid wasted storage usage. Additionally, data accessed across systems must first undergo format validation to ensure report_date is correctly formatted and numeric fields are non-negative, preventing dirty data from entering the database.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`mongodb_max_pool_size`	`20–30`	Addresses concentrated write traffic peaks during fixed daily periods to avoid connection exhaustion
`mongodb_write_concern`	`w: majority`	Ensures data write consistency in distributed deployments, aligning with heating data accuracy requirements
`mongodb_index_expire_after_seconds`	`86400 * 180 seconds` (180 days)	Retains six months of historical data for market backtracking; automatically cleans up expired data to free storage
`mongodb_schema_validation_level`	`strict`	Enforces validation of field formats and units for incoming data to block abnormal entries
`mongodb_batch_insert_size`	`500`	Optimizes batch writes for daily full datasets by reducing network interactions to improve write efficiency
`mongodb_connection_timeout_ms`	`10000 milliseconds`	Prevents task blocking from prolonged connection waits, suitable for low-latency daily report broadcasts

The parameter values provided here are common starting points for configuration. Actual values vary based on data volume, business rules, and other factors. Conduct targeted analysis for specific scenarios, and test on your own samples before finalizing settings.

Three Common Misconfigurations

Symptom: A connection refused error is returned when connecting to the FastGPT local MongoDB database via MongoDB Compass. Cause: The mongodb_bind_ip parameter is not configured correctly; the default binding only uses the local loopback address, preventing external tools from accessing the database.
Symptom: The unit_heat_supply_cost field in heating data returned by database queries has a negative value. Cause: Strict validation for mongodb_schema_validation_level is not enabled, and non-negative checks for the cost field are not performed, allowing abnormal data to be written to the database.
Symptom: Daily yield broadcast tasks time out and fail, with logs showing operation exceeded time limit. Cause: A reasonable mongodb_batch_insert_size parameter is not configured; the single write data volume is too large, exceeding the timeout threshold and causing write failure.

How to Verify Proper Configuration

Run the mongo --eval "db.stats()" command to check if the current database connection count falls within the range specified by mongodb_max_pool_size.
Manually import a formatted district heating test data entry and verify that the units and formats of the fields after writing match expectations.
Trigger a simulated daily report generation task and check if write operation latency is within an acceptable range, with no timeout errors.
View the database index list to confirm that secondary indexes for report_date and project_id have been successfully created.

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.