What the data for this category looks like
FastGPT v4.17.0 and above support configuration for this scenario. The data for this category mainly comes from data asset inventory, database metadata, business system descriptions, and regulatory documents. Daily maintained documents include data catalogs, classification and grading rules, important data identification tables, and review minutes. Data updates occur on a quarterly basis or are triggered when the corresponding business system changes. Data fields include asset number, data classification label, sensitivity level, and affiliated business domain. There is no unified fixed unit system, and some fields require supplementary explanations based on business scenarios.
How these characteristics impose constraints on scheduled tasks and automation
Data sources are scattered and mostly cross-system files. Scheduled tasks must adapt to multi-source data pulling logic to prevent overall process failure due to a single data source pull error. Data update rhythms are inconsistent. Task scheduling must support a combination of fixed-cycle triggering and system change triggering to avoid resource waste from high-frequency execution. Daily document structures vary, including structured identification tables and unstructured review minutes. Automated processing must support multi-format parsing logic to prevent content loss from single parsing rules. Data fields have no unified units. Automated verification rules must support dynamic adaptation to field formats, avoiding hard-coded unit matching logic.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Task Trigger Mode | Fixed cycle + system change trigger | Matches the actual rhythm of quarterly data updates and system changes |
| Single Data Pull Limit | 1000 MB | Adapts to the conventional size of database metadata and asset inventory files |
| Task Timeout Threshold | 1800 seconds | Covers the time required for multi-source data pulling and multi-format parsing |
| Automated Verification Match Rate | 85%-95% | Adapts to the review accuracy requirements of classification and grading rules |
| Failure Retry Count | 3 times | Balances task stability and system resource consumption |
| Log Retention Duration | 30 days | Meets the log review requirements for compliance retrospectives |
Three common misconfigurations
- Phenomenon: After importing an Excel-formatted important data identification table, multiple rows of content are merged into a single record. Cause: No custom row-level splitting rule is configured. The default parsing logic merges content by cell line breaks, and does not adapt to the import requirement that one Excel row corresponds to one record.
- Phenomenon: An empty value is returned after calling the automated workflow via API. Cause: The corresponding data asset inventory data source is not bound in the task configuration, so the task cannot pull valid input data.
- Phenomenon: Timeout termination is triggered during scheduled task execution. Cause: The task trigger cycle is set too short, leaving insufficient time for multi-source data pulling, which exceeds the preset timeout threshold.
How to confirm the configuration is complete
- Manually trigger a scheduled task, and check whether the configured data source file list is pulled in the task log.
- Import an Excel-formatted important data identification table, and verify that each parsed record corresponds to one original Excel row.
- Verify the task configuration's timeout threshold to ensure it covers the actual time required for multi-source data pulling and parsing.
- Simulate a system change to trigger the task, and confirm that the task can start automatically and complete the full process execution.
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.