What the Data for This Category Looks Like
Daily data for ad delivery effect optimization typically comes from advertising platforms, attribution systems, CRM leads, and landing page data. Update frequencies are split into real-time and daily. Daily produced documents include daily reports, weekly reports, price adjustment records, material test sheets, and optimization reviews. Core fields include impressions, clicks, conversions, customer acquisition cost, and budget balance, with corresponding units of count, count, unit, yuan per conversion, and yuan respectively. Data structures are adjusted based on business scenarios. Multi-column Excel material test sheets often include multi-dimensional material parameters and performance metrics.
What Constraints Do These Characteristics Impose on Scheduled Tasks and Automation
Multiple data sources and layered update frequencies require scheduled tasks to adapt to different pull cycles. Real-time data requires short-interval triggers, while daily report documents need to be scheduled according to the business end-of-day time. Multiple fields and complex document structures require automated workflows to unify field mapping rules, while also incorporating format conversion logic to adapt to format differences across data sources and avoid data misalignment. Scenarios with high time consumption for abnormal indicator locating and budget allocation require scheduled tasks to automatically pull abnormal data and generate alerts. Data errors directly impact customer acquisition cost and budget allocation, so failure retry and alert mechanisms must be configured to ensure stability.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Task trigger cycle | Real-time pull: 30 seconds; Daily report generation: 2 AM daily | Matches the update rhythms of real-time performance monitoring and end-of-day business reports |
| Data source pull scope | Real-time pull: last 5 minutes of data; Daily report pull: full data from the previous day | Avoids pulling redundant historical data and ensures task execution efficiency |
| Abnormal alert threshold | Customer acquisition cost exceeds baseline by 20%; Budget consumption exceeds 80% of daily plan | Aligns with standard alert standards for ad delivery effect monitoring and reduces invalid alerts |
| Failure retry count | 3 times, 60-second interval | Addresses temporary API fluctuations and prevents task failure from a single exception |
| Task timeout duration | 600 seconds | Covers the typical time required for multi-data-source pulling and report generation |
| Output format | CSV format; Multiple types of reports stored in separate sheets | Adapts to the document processing habits of daily ad optimizers |
Three Common Mistakes
- The symptom is that calling the API to trigger a scheduled task returns a null value and no expected operational report is generated. The cause is that the data source key for the corresponding advertising platform was not bound in the task configuration, resulting in an inability to pull valid business data.
- The symptom is that the automatically generated material test sheets have messy chunking and cannot be aligned by single rows. The cause is that custom chunking settings were not enabled, and the default automatic chunking logic does not adapt to the row-level splitting requirements of multi-column Excel files.
- The symptom is that task execution times out but no retry mechanism is triggered. The cause is that a reasonable timeout duration and retry count were not set, resulting in no automatic recovery after task interruption due to temporary network fluctuations.
How to Confirm the Configuration Is Complete
- Manually trigger a scheduled task, and check whether there is a record of successful data source pulling in the task logs of FastGPT v4.17.0.
- Check the generated operational report files to confirm that core fields fully match those of business documents.
- Simulate an abnormal indicator trigger scenario to verify that the corresponding alert notification is sent.
- View the task historical execution records to confirm that there are no timeout or error prompts for three consecutive executions.
The configuration values listed above are common starting points and should be validated against specific operational 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.