What this category's data looks like
Profit margin data for small home appliances comes primarily from national energy efficiency label databases, official brand test reports, and third-party testing agency energy consumption verification reports. Data updates follow no fixed cycle. Updates are synchronized when new products launch, updated in batches when new energy efficiency standards are released, and no mandatory daily updates are required.
Each individual data entry includes fields such as model identifier, brand name, rated power, average daily standard usage duration, local electricity unit price, initial purchase cost, annual usage duration, annual energy consumption cost, annual profit amount, and profit margin value. Field units are watts, hours, yuan per kilowatt-hour, yuan, hours, yuan, yuan, and dimensionless values.
Constraints on database and operations from these characteristics
Small home appliance data sources are scattered, and most parameters are converted from unstructured to structured formats. Adaptation rules for multi-source data pulling must be configured.
Update rhythms are flexible, so support for both scheduled batch synchronization and manual one-time synchronization is required.
Strong correlations exist between fields. For example, electricity unit price must be bound to the region dimension, so associated tables need to be designed to avoid data redundancy.
The number of small home appliance models is large, but single data entry size is small. Database indexes should prioritize covering frequently queried fields: model, brand, and update time.
Some data requires conversion to calculate profit margin. Validation rules must be configured before data import to filter invalid parameters.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGO_CONNECTION_STRING | mongodb://fastgpt:yourpassword@mongo:27017/fastgpt?authSource=admin | Adapts to FastGPT's official MongoDB authentication format, supports local or remote database connections for containerized deployments |
DATA_SYNC_CRON | 0 0 2 * * * | Executes synchronization tasks at 2:00 AM daily to avoid business peak hours, adapts to the daily update requirements of small home appliance data |
INDEX_FIELD_LIST | ["model_number", "brand", "update_time"] | Covers frequently queried dimensions for small home appliance data, optimizes database query performance |
DATA_VALIDATION_RULES | {"required": ["model_number", "rated_power", "purchase_cost"], "range": {"rated_power": [5, 2000]}} | Filters invalid data, ensures imported parameters fall within the actual parameter range of small home appliances |
DB_CONNECTION_TIMEOUT | 30 seconds | Adapts to the time required for batch pulling small home appliance data, prevents connection interruptions during synchronization |
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 individual analysis, and it is recommended to test on sample datasets before finalizing settings.
Three common configuration errors
- Issue: Docker deployment fails to start, with error
failed to connect to MongoDB. Cause: TheMONGO_CONNECTION_STRINGconfiguration item was not modified correctly, or the container network did not open the corresponding MongoDB port. - Issue: The profit margin field returned by database queries is empty. Cause:
DATA_VALIDATION_RULESwas not configured, so invalid electricity unit price or usage duration data was not filtered, causing the profit margin calculation logic to fail. - Issue: Scheduled synchronization tasks do not execute as expected, leading to delayed data updates. Cause: The
DATA_SYNC_CRONexpression format is incorrect, or the time zone of the deployment server does not match the configured time zone.
How to confirm configurations are correct
- Execute the FastGPT built-in database connection test tool, enter the connection address configured in
MONGO_CONNECTION_STRING, and confirm that a normal connection can be established and read/write tests are completed. - Manually trigger a data synchronization task, review synchronization logs, confirm there are no data pulling or import errors, and verify that the configured validation rules take effect.
- For known small home appliance models, run database queries with specified conditions, confirm that returned fields are complete and comply with the configured indexes and validation rules.
- Check the scheduled task execution records, confirm that tasks configured via
DATA_SYNC_CRONtrigger at the expected time, with no abnormal interruptions.
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.