What this type of data looks like
Data for channel attribution and lead distribution comes from ad platforms, CRM systems, attribution SDKs, and form systems. Update cadences are mostly daily or weekly aggregations. Core daily maintenance documents include attribution rule configuration tables, lead distribution tables, channel performance reports, and exception checklists. Data fields include channel identifiers, lead source timestamps, conversion nodes, assigned sales IDs, conversion amounts, and others. There is no unified fixed format, and verification dimensions must be adjusted based on connected data sources.
What constraints these characteristics impose on scheduled tasks and automation
Multi-source heterogeneous data sources require scheduled tasks to support cross-system data pulling and format adaptation, to prevent task interruptions caused by missing fields. Daily or weekly update cadences require task trigger times to match the synchronization windows of each data source, to avoid pulling incomplete data that has not completed settlement. Verification dependencies for core documents require automated workflows to include built-in field verification logic, to reduce manual check time. At the same time, error consequences such as distorted channel ROI and sales attribution disputes require tasks to be configured with failure retry and alert mechanisms, to ensure process stability.
How to set configurations
The following configurations follow the official specifications of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Task Trigger Time | 2:00-4:00 daily (calibrated against data source daily settlement windows) | Matches the daily settlement cycles of most ad platforms and CRM systems, avoids pulling incomplete data |
| Data Pull Scope | Full channel and lead data from the previous calendar day | Covers all conversion leads from the day, ensures completeness of attribution and distribution |
| Field Verification Rules | Channel ID, lead source timestamp, and assigned sales ID are required fields | Filters invalid data, prevents task exceptions caused by missing fields |
| Failure Retry Count | 3 times, 60 seconds between each retry | Addresses temporary network fluctuations or short-lived data source unavailability |
| Task Timeout | 1800 seconds | Avoids long tasks occupying cluster resources, adapts to the execution duration of most daily tasks |
| Exception Alert Threshold | Trigger alert when task failure rate exceeds 5% | Detects batch anomalies promptly, prevents incorrect data from flowing into subsequent processes |
Three common misconfigurations
- A scheduled task returns a 404 status code after execution. Troubleshooting steps include verifying that server deployment configurations for
AIPROXY_API_ENDPOINT(AI Proxy service root address, do not append path suffixes such as /v1 or /chat/completions) andAIPROXY_API_TOKEN(AI Proxy administrator token, must match server configuration and be non-empty; not application API keys, model vendor keys, or other tokens) are correct. Incorrect configuration causes dependent services to fail normal access. - Lead deduplication results are empty or have abnormal quantities. The cause is that verification rules for corresponding duplicate fields are not configured, so duplicate leads cannot be automatically identified.
- A scheduled task outputs no data after triggering. The cause is that the task trigger time is later than the daily settlement window of the data source, so valid data from the current day is not pulled.
How to confirm configurations are correct
- Verify that server deployment configurations for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare correct. - Manually trigger the scheduled task once, and check for pull and processing records for connected data sources in the task logs.
- Cross-check that lead distribution table fields output by the task match connected source data fields, with no missing or abnormal entries.
- Simulate input of a test data entry that does not meet verification rules, and check whether the task triggers a corresponding exception prompt.
The values provided here are common starting points, and should be evaluated against the user’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.