What the data for this category looks like
The data for this category primarily comes from ticketing systems, customer interview records, CRM systems, and product roadmaps. Daily maintained documents include requirement pool lists, requirement evaluation forms, customer feedback records, and version planning descriptions. The data update frequency is usually once per week or synchronized with product version iterations. Common data fields include requirement source identifiers, customer association information, priority levels, processing status, and version binding fields. There are no uniform mandatory unit requirements. Some fields need to be associated with customer IDs and research and development scheduling related items.
What constraints these characteristics impose on scheduled tasks and automation
Since data sources are scattered across multiple systems, scheduled tasks must support cross-system API calls, and require configuration of permission verification and data pull rules for multiple data sources. As data update frequency changes with version iterations or weekly cycles, task triggers must support fixed-cycle or version release node triggers to avoid ineffective execution. Daily documents have diverse formats, so automated workflows must support importing requirement lists in multiple formats, and require configuration of format verification rules to filter invalid content. Since there are no unified standards for fields, custom field mapping must be supported to avoid subsequent evaluation deviations caused by misplaced requirement information.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Data source pull interval | 3600 seconds | Aligns with the weekly data update rhythm of ticketing systems and CRM, avoids frequent calls exceeding API quota limits |
| Task trigger mode | Scheduled weekly + version release event trigger | Matches the weekly/per-version update frequency of this category, balances fixed-cycle and version-linked requirements |
| Field mapping rules | Associate requirement source, priority, customer ID as needed | Adapts to the characteristic of no unified field standards for this category, ensures accurate correspondence of requirement information |
| Invalid content filtering threshold | 800–1200 characters | Filters overly short invalid feedback records, retains valid requirement information |
| Failure retry count | 3 times | Balances temporary API fluctuations and task execution efficiency, avoids resource occupation from repeated execution |
| Alert trigger condition | 2 consecutive execution failures | Notifies operation and maintenance personnel in a timely manner to troubleshoot abnormalities, aligns with FastGPT v4.17.0 alert configuration logic |
Three common configuration mistakes
- Symptom: Scheduled task execution logs show
execution timed outorno data returned, and requirement pool data in the interface is not updated as planned. Cause: Multi-data source permission verification is not configured, resulting in interception when pulling ticketing and CRM data across systems, or the pull interval is set too short, exceeding API quota limits. - Symptom: After importing an Excel-format requirement list, multiple customer feedback entries are merged into a single record, and field arrangement is disordered. Cause: Custom delimiter configuration is not enabled, using the default global delimiter which does not adapt to Excel's row-level data structure, leading to incorrect parsing logic.
- Symptom: Empty fields appear in the requirement evaluation form generated after task execution, making it unavailable for subsequent version scheduling. Cause: Field mapping rules are not configured, and the customer priority field in CRM is not mapped to the corresponding position in the requirement pool list, resulting in missing key information.
How to confirm configuration is complete
- Manually trigger a scheduled task, check whether the pulled data source data matches the latest records in the ticketing system and CRM, and verify that field mapping is correct.
- Wait for the preset trigger cycle or simulate a version release event, confirm whether the task executes automatically, and check that the execution log has no errors.
- Import a batch of test requirement lists, verify that the format verification rule filters invalid content, and that custom delimiters split data by row.
- Simulate a task execution failure scenario, confirm whether the preset alert notification is triggered, and check that the alert channel is configured 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.