What this type of data looks like
The daily crop farming yield rate report serves as reference data for financial institutions’ agricultural wealth management and insurance services. Its data sources include the National Agricultural Wholesale Market Price Information System, daily production ledger uploads from farming entities, and growth monitoring data from regional agricultural technology extension stations. Data is updated daily at midnight, with full data for the previous calendar day released. Each data record includes the following fields: crop category, planting area code, per-mu input cost, per-mu output quantity, same-day regional purchase price, and per-mu revenue difference. Field units are as follows: cost is yuan/mu, output quantity is kg/mu, purchase price is yuan/kg, and revenue difference is yuan/mu.
What constraints do these characteristics impose on deployment and upgrade
The multi-source, decentralized nature of the daily crop farming yield rate report requires a multi-data source adaptation module during deployment. Upgrade phases must support new regional agricultural monitoring interfaces. The daily update rhythm requires scheduled tasks to align precisely with the calendar day cycle. Deployment must reserve redundant time for exception retries, to avoid delaying daily report timeliness due to data lags. The core calculation logic for per-mu revenue difference must be fixed. Do not adjust parameters arbitrarily during upgrades, as this will cause inconsistencies in historical data comparisons. Crop farming data has seasonal volatility peaks. Deployment must reserve sufficient computing resources. For cluster deployments, configure data synchronization consistency across multiple nodes, to avoid calculation deviations caused by inconsistent data between nodes.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
SYNC_TASK_CRON | 0 0 22 * * * | Aligns with the daily midnight update schedule for the previous day’s data. Set to trigger at 22:00 daily, reserving 2 hours for data cleaning and calculation |
MAX_CONCURRENT_REQUESTS | 10–15 | Adapts to concurrent requests from multiple data sources (wholesale markets, farming ledgers, monitoring stations), avoiding data synchronization congestion |
UPLOAD_FILE_MAX_SIZE | 600 MB | Meets batch upload requirements for daily full crop farming data, including structured data across multiple regions and crop categories |
PARSE_FILE_TIMEOUT_SECONDS | 720 seconds | Handles per-mu revenue difference calculation logic and multi-field validation, preventing timeouts due to sudden increases in daily data volume |
MONGO_IMAGE_TAG | mongo:4.4.29 | Adapts to CPU environments that do not support AVX instruction sets, preventing MongoDB startup failures |
ONEAPI_BASE_URL | Fill in the locally deployed large model service address | Adapts to offline deployment scenarios, connecting to local large model interfaces |
ENABLE_LOGIN_AUTH | Enabled | Meets identity verification requirements for enterprise-grade deployments, restricting unauthorized access to internal crop farming data |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by data format, 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 Mistakes
- Symptom: Scheduled synchronization tasks do not trigger, and container logs return the
invalid cron expressionerror. Cause: TheSYNC_TASK_CRONconfiguration uses a non-standard field order, and does not adapt to FastGPT’s cron parsing rules. - Symptom: MongoDB container fails to start, and the console outputs the
illegal instructionerror. Cause: The MongoDB image version was not updated for CPU environments that do not support AVX instruction sets, and a higher version MongoDB image was used instead. - Symptom: Large model interface calls fail after offline deployment, returning the
connection refusederror. Cause: TheONEAPI_BASE_URLparameter was not configured correctly, and it does not point to the locally deployed large model service address.
How to Confirm Successful Configuration
- Manually trigger a synchronization task. Review synchronization logs to confirm that all configured data sources return structured data, and that core fields such as crop category and planting area are included.
- Access the container management interface. Review the MongoDB container’s runtime logs to confirm that there are no instruction set incompatibility error messages.
- Access the FastGPT identity authentication configuration module. Confirm that the
ENABLE_LOGIN_AUTHsetting matches deployment requirements. - Generate a yield rate report for a single crop. Verify the matching between calculated fields and raw data, and confirm that no fields are missing or calculation anomalies exist.
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.