This document is written for FastGPT v4.17.0.
What this category of data looks like
It typically comes from ticketing systems, customer interviews, CRM, and product roadmaps. Update cadence follows weekly or per version release cycles. Four types of documents are regularly produced: demand pool lists, demand assessment forms, customer feedback records, and version planning descriptions. Fields include demand source channel, number of associated customers, priority rating, R&D scheduling nodes, and assessment cost items. There are no unified standard units, and configurations are adjusted based on internal enterprise rules.
What constraints these characteristics impose on scheduled tasks and automation
The dispersed nature of multi-source data requires scheduled tasks to adapt to cross-system pull permissions and format compatibility. Fixed update cadences require matching task cycles to weekly or version release nodes. The non-standardized attributes of document fields require automated processes to configure field verification rules, preventing invalid data from flowing into summary results. The time-consuming links of duplicate demand merging and value assessment require preset matching logic via automated rules, while manual review nodes must be reserved. The risk of distorted demand commitment requires tasks to configure failure alerts and retry mechanisms, ensuring the timeliness and accuracy of summary data.
How to set configurations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Task Trigger Cycle | Execute every 7 days / Execute 3 days before version release | Match the weekly or version-level update cadence of demand pool data |
| Data Source Pull Scope | Recent 7 days of records from ticketing systems, CRM customer feedback, latest version of product roadmaps | Cover all demand sources within the current cycle |
| Duplicate Demand Matching Threshold | 85% semantic matching | Merge similar customer feedback to avoid duplicate entries |
| Task Failure Retry Count | 2 retries | Address temporary network exceptions during single pull attempts |
| Alert Trigger Conditions | Task execution failure, summary data field missing rate exceeding 10% | Detect summary anomalies in a timely manner |
| Automatic Output Format | Markdown-formatted demand pool list and assessment form | Adapt to daily viewing habits of product operations |
Three common mistakes
- Phenomenon: No latest ticketing system demand records are synchronized after a scheduled task runs. Cause: Access permissions for the corresponding data source were not configured, preventing the task from pulling data normally.
- Phenomenon: A large number of entries with missing fields appear in the automatically generated demand assessment form. Cause: Field verification rules were not configured, and invalid demand data with no associated customers or scheduling nodes was not filtered out.
- Phenomenon: Task execution times out, resulting in incomplete summary data. Cause: A reasonable task timeout period was not set, and the preset duration was exceeded when pulling multi-source data.
How to confirm proper configuration
- Manually trigger a scheduled task, and check if the pulled data sources cover ticketing, CRM, and product roadmap data from the current cycle.
- Review the task execution log to confirm that the duplicate demand matching rule has taken effect, and similar entries have been merged.
- Simulate a data source access failure, and check if the preset alert notification is triggered.
- Export the automatically generated summary document, and verify whether field completeness and format meet preset requirements.
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.