What this category of data looks like
Data sources for this category include raw datasets, ETL logs, annotation platform outputs and sampling results. Daily associated documents cover cleaning rules, quality inspection sampling tables, abnormal data lists and delivery reports. Data updates run on a daily schedule or trigger per batch task. Data fields include raw data identifier, cleaning flag, exception type, quality inspection score and delivery status. No agreed-upon units apply. Processing workflows focus on duplicate data identification, outlier handling and sampling quality inspection. Results directly impact the accuracy of business indicators and delivery compliance.
What constraints these characteristics impose on scheduled tasks and automation
Data sources are scattered, and update rhythms are inconsistent. Scheduled tasks must support pulling different data sources via batch triggers or fixed cycles. Daily documents are mostly structured rules and lists. Automated configurations must adapt to rule import and verification logic. The standardization level of data fields affects the accuracy of exception identification. Scheduled tasks must reserve space for field mapping adjustments. Data volume per batch task fluctuates greatly. Automated processes must configure reasonable resource thresholds. Real-time updates of abnormal data lists require retry mechanisms to activate after task failures, to avoid data unavailability risks.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Task trigger cycle | Set to once daily or after each batch task completes, aligned with data update rhythm | Matches daily update or batch delivery update frequencies |
| Single task data processing limit | 10000 entries | Adapts to conventional batch data processing scales, avoids resource overload |
| Failed retry count | 3 times | Addresses task interruptions from temporary network or data source fluctuations |
| Abnormal data alert threshold | Abnormal proportion exceeding 5% | Triggers manual review, prevents indicator distortion |
| Task timeout duration | 3600 seconds | Supports time requirements for complex cleaning and quality inspection workflows |
Three common misconfigurations
- Phenomenon: API request returns 404 - Resource not found. Cause: The scheduled task configuration does not correctly associate the data source's storage path or index settings.
- Phenomenon: Knowledge base search does not include automatically indexed image content. Cause: The scheduled task does not configure image metadata synchronization, and only synchronizes text data.
- Phenomenon: The abnormal data list field is empty after scheduled task execution. Cause: The task configuration does not map corresponding fields for abnormal data, so cleaning results cannot be correctly written to the target document.
How to confirm proper configuration
- Manually trigger a scheduled task, access the task log module in FastGPT v4.17.0, and confirm complete records for data source pulling, cleaning rule execution and quality inspection result generation are present.
- Check the abnormal data list for field integrity, confirm that fields including exception type and raw data identifier are correctly populated.
- Simulate a data source update, verify that the scheduled task automatically triggers per the preset cycle and completes the full workflow.
- Check that alert configurations trigger notifications when the abnormal proportion exceeds the threshold, confirm the alert link operates correctly.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.