What the Data for This Category Looks Like
Data related to livestock and poultry farming profitability falls into two categories: on-site farm breeding records and external market trends. On-site farms provide daily feeding, disease prevention, and slaughter records. Regional agricultural and animal husbandry departments and third-party market quotation interfaces provide external data.
Data organizes by breeding batches. Each document includes these fields: batch identification, breeding cycle, total feed consumption per head, total breeding cost per head, regional average purchase price, total sales revenue per head, and net profit per head. Units include kilograms, yuan per head, days, and others.
Data updates once per day. The system compiles and stores full previous-day data by 6:00 AM on the current day. This ensures timeliness for daily reports.
Constraints Imposed on Database and Operations
These data characteristics create constraints for database and operations work:
Dual-source data access requires the database to support cross-data source connections. Configure data validation rules to prevent abnormal data from being stored due to external market fluctuations.
Daily full updates require a scheduled task scheduling system. This system ensures atomicity of data pull, cleaning, and storage processes. It prevents missing batch data.
Multi-field net profit calculation requires database-level views or calculated fields. This reduces repeated calculations at the application layer.
Associated queries for breeding batches require an index on the batch ID. This speeds up report generation.
Increasing breeding batches expand single-table data volume. Archive historical data regularly to control storage and query performance.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_CONNECT_TIMEOUT | 30 seconds | Livestock and poultry farming data requires connecting to two sources: on-site farm records and external market quotations. An overly short timeout will cause external interface request failures, while an overly long timeout will block the scheduled task queue |
DB_MAX_CONNECTIONS | 20–30 | The number of concurrent requests for daily batch storage is controlled within 20. This value avoids exhausting database connections while meeting parallel query requirements |
MULTI_QUERY_ENABLE | Enabled | Calculating the profit margin for a single batch requires executing data storage and statistical queries simultaneously. Enabling multi-row query simplifies business logic and avoids data inconsistency caused by segmented execution |
BACKUP_RESTORE_PATH | /data/fastgpt/db_backup | Database backups and project file backups must be stored in a unified path to quickly locate backup files during subsequent recovery operations |
WHITELIST_ALLOWED_IPS | On-site server IP range, cooperative quotation interface IP | Restrict the database to only allow connection requests from trusted sources to reduce unauthorized access risks |
AUTO_ARCHIVE_DAYS | 180 days | Livestock and poultry farming historical data does not require frequent queries. This setting regularly archives historical data older than 180 days to control the storage capacity of single tables |
The parameter values provided on this page are common starting points for configuration. The actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing the configuration.
Three Common Mistakes
- Phenomenon: A database connection prompt shows
Access denied for user 'xxx'@'xxx', or the configured IP fails to establish a database connection. Cause: The server IP deploying FastGPT and the market data source IP are not added to the database whitelist. Access requests are blocked as a result. - Phenomenon: A
You have an error in your SQL syntaxerror returns when running a multi-row SQL statement with insert and select. Cause: TheMULTI_QUERY_ENABLEconfiguration is not enabled. The database driver limits execution to one SQL statement per run by default. Multi-row query requests are blocked. - Phenomenon: Background knowledge base and agent configurations appear empty after restoring a project backup file. Cause: Only the project file backup was restored. The corresponding database backup was not synchronized. Core business data remains unrecovered.
How to Confirm Correct Configuration
Complete these steps to verify the configuration:
- Run a database connection test script. Verify the configured IP, port, account, and password establish a normal connection. Check that whitelist rules cover trusted access sources.
- Submit a test request with multiple SQL statements. Confirm the request runs normally and returns expected results. Verify the multi-row query configuration works.
- Upload a project backup file and run the recovery operation. Log in to the background to confirm knowledge base and agent configurations match the backup node. Verify the backup and recovery path configuration is correct.
- Trigger a scheduled data pull task. Check if daily livestock and poultry farming profitability data generates in the database. Verify connection timeout and connection number configurations meet task requirements.
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.