What the data for this category looks like
Data for this category primarily originates from advertising platform backends, customer qualification upload systems, contract management systems, and public regulatory rule databases. Daily generated documents fall into four categories: advertising review forms, qualification attachments, delivery records, and rectification notices. Each document includes fields such as advertising identifier, delivery time period, qualification number, and rectification completion status. Data updates are triggered by individual advertising campaigns or monthly cycles, with no fixed real-time update rhythm. The volume of data per batch fluctuates based on campaign scale.
Constraints imposed on scheduled tasks and automation
Multiple scattered data sources and limited single-pull interface call frequency require limiting concurrent request volume for scheduled tasks. The non-fixed update rhythm tied to campaigns or monthly cycles demands task trigger logic support two modes: trigger by campaign node and trigger by fixed cycle. Unlike advertising material review scenarios, this category requires retention of four compliance evidence types: review, qualification, delivery, and rectification records. Categorized archiving rules by document type must be configured in automated workflows. Regulatory requirements mandate that compliance evidence retention cannot be omitted. Automated workflows must trigger alerts when task execution fails to avoid compliance risks from missing evidence.
How to configure
All following configuration items are compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Task Trigger Mode | Trigger by campaign node + fixed monthly cycle | Matches the update rhythm of this category tied to campaigns or monthly cycles |
| Concurrent Request Limit | 3-5 requests per run | Avoid exceeding interface call limits of advertising platform backends |
| Task Timeout Threshold | 600 seconds | Adapts to average time spent pulling data across multiple systems |
| Failure Retry Count | 2 retries | Balances task success rate and resource usage |
| Document Classification Rule | Classify by review form / qualification attachment / delivery record / rectification notice | Matches the daily document structure of this category |
| Alert Trigger Condition | Task failure / data missing rate exceeds threshold | Meets risk prevention and control requirements for compliance retention |
Three common misconfiguration issues
- Symptom: The directory structure is automatically removed from scheduled compliance documents, resulting in incomplete document structure. Cause: FastGPT's built-in automatic directory cleanup configuration was not disabled, leading to tampering of the compliance recordkeeping document structure.
- Symptom: Duplicate entries automatically appear in delivery records or qualification attachments in the dataset, doubling the index count. Cause: No deduplication rules were configured, causing scheduled tasks to repeatedly pull archived data from the same campaign cycle.
- Symptom: A 404 Not Found error is returned when accessing the page after automated deployment. Cause: FastGPT's static resource path was not correctly configured during deployment, preventing static files for compliance reports from loading properly.
How to confirm correct configuration
- A scheduled task may be manually triggered, and pulled document types may be verified against the configured classification rules.
- Task execution logs may be reviewed to confirm concurrent request counts do not exceed the configured limit.
- A task failure scenario may be simulated to confirm alert notifications are triggered normally.
- The volume of data pulled in a single task may be compared against the expected campaign or monthly data scale.
The values provided are common starting points and should be measured against samples relevant to the specific deployment context.
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.