What the Data for This Category Looks Like
Aquaculture investment research data comes from five sources: on-site farming monitoring, industry regulatory department reports, wholesale market price data, policy documents, and academic research materials. Data updates follow three schedules:
- Physical and chemical water quality indicators (water temperature, dissolved oxygen, etc.): sampled hourly or in real time
- Feed feeding and market price data: updated daily
- Policy documents and academic materials: updated irregularly
Each data entry includes these fields: sampling time, farming area, indicator value, corresponding unit, data source. All numeric fields use fixed, clear units. Examples include ℃ for water temperature, mg/L for dissolved oxygen, and kg/mu for feeding amount.
Constraints for Database and Operations
High-frequency real-time water monitoring data generates high-concurrency write requests. These requests set clear requirements for database connection pools and write throughput. Multi-source heterogeneous data (on-site monitoring, industry reports, policy documents) has inconsistent formats and fields. Teams must configure unified cleaning and mapping rules at the data access layer, which increases operational complexity. Numeric fields with clear units need validation logic configured at the database level. This prevents dirty data from affecting investment research analysis results. Long-term full-process farming data requires at least one year of historical archiving. This sets requirements for storage capacity expansion cycles and cold storage migration strategies. Daily updated market and feeding data must synchronize during off-peak hours. This avoids consuming business bandwidth.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
mongodb_max_pool_size | 20–30 | High-frequency sampled water monitoring data has concurrent write requests; this range avoids connection exhaustion |
data_sync_cron | 0 0 2 * * ? | Daily updated market price and feed feeding data synchronized during off-peak hours reduces business bandwidth usage |
field_unit_validation | Enabled | Aquaculture data fields have clear units (e.g., ℃, mg/L); enabling validation filters dirty data |
history_data_retention_days | 365 | Investment research analysis requires at least one year of historical farming data; expired data can be migrated to cold storage |
batch_write_threshold | 500 records | Single batch write volume matches the generation rhythm of high-frequency sampled data, balancing write performance and database load |
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, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: An error
Failed to connect to jyfkk:1433 - 38BBDDC3AF7F0000appears when connecting to the MSSQL database during orchestration, but the MongoDB visualization tool connects normally. Cause: The access whitelist for the target database is not configured, or the hostname specified in the connection string cannot be resolved by the current deployment environment. - Symptom: The MongoDB service status shows a failure, and historical farming monitoring data cannot be loaded. Cause: A reasonable
mongodb_max_pool_sizevalue is not set, and high-concurrency write requests exhaust the connection pool, leading to service interruption. - Symptom: The database connection tool only supports three specified database types, and cannot connect to Oracle Database. Cause: The current version only includes driver adapters for three mainstream databases, and the Oracle official driver package is not integrated.
How to Confirm Configuration Is Complete
- Execute the database connection test script to verify that all configured data sources can establish normal connections, and check if the returned connection status codes meet expectations.
- Import a test data entry with clear units (e.g., sampling time
2024-05-20T08:00:00Z, water temperature25℃) to confirm that data can be written normally and unit validation takes effect. - Trigger a scheduled data synchronization task to check if the synchronized market price and feed feeding data is fully written to the target database without loss or formatting errors.
- View the database monitoring panel to confirm that the connection pool usage is within a reasonable range, with no frequent connection exhaustion or timeout errors.
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.