This document applies to FastGPT v4.17.0.
What the data for this category looks like
Data for this category comes from monitoring systems, ticketing systems, contract documents, and customer communication records. The update cycle follows a monthly or quarterly rhythm. Core output documents include SLA terms, monthly service reports, availability statistics reports, and customer retrospective minutes. Data fields include service cycle identifiers, SLA agreed metrics, associated abnormal event information, and customer feedback records. Fields must match service terms agreed in contracts. There is no fixed unified unit format, and adjustments should be made based on specific business scenarios.
What Constraints These Characteristics Impose on Scheduled Tasks and Automation
Since data updates follow a monthly or quarterly cycle, scheduled task trigger frequencies must match this rhythm to avoid excessive execution that wastes resources. Multi-data source integration requires tasks to pull data from different systems such as monitoring, ticketing, and contracts. Pre-set data format verification rules must be configured to ensure pulled fields match the metrics agreed in SLA terms. The abnormal event attribution link needs to associate multi-source data, so tasks must include built-in association logic. Failure retry and alert mechanisms must also be configured to avoid compensation disputes caused by missing data or format errors.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Scheduled trigger cycle | Monthly / Quarterly | Matches the monthly/quarterly update frequency of data for this category, avoids ineffective execution |
| Data source pull timeout threshold | 300–600 seconds | Adapts to network latency when pulling from multiple systems, prevents single task timeout interruptions |
| Data format verification switch | Enabled | Ensures pulled fields comply with format requirements agreed in SLA terms |
| Abnormal event association rule | Match by ticket number and monitoring event ID | Enables accurate attribution of abnormal events, meets retrospective requirements |
| Task failure retry count | 2–3 times | Addresses temporary network fluctuations or temporary unavailability of data sources |
| Alert trigger threshold | 2 consecutive task failures | Notifies relevant personnel in a timely manner to handle exceptions, avoids impacting report generation |
Three Common Mistakes
- Phenomenon: The SLA category call count statistics field is empty and is not updated when the scheduled task runs. Cause: The trigger rule for variable updates is not configured, and the statistical variable is only updated when triggered manually.
- Phenomenon: Abnormal event descriptions from multiple conflicting documents appear in the monthly service report, with no anomaly alert. Cause: Multi-source data conflict verification configuration is not enabled, and caliber alignment rules for data from different sources are not set.
- Phenomenon: No notification is sent to the WeChat Work external group after the scheduled task completes execution. Cause: The message push trigger condition for external groups is not configured, and only internal group push permissions are enabled.
How to Verify Correct Configuration
- Check the scheduled task list and confirm the trigger cycle is set to monthly or quarterly, matching the report cycle agreed by the business.
- Manually trigger a test task, and confirm that pulled data source fields match preset SLA term requirements.
- Simulate a data source pull failure, and confirm that preset alert rules are triggered.
- Check variable update configuration, and confirm that the variable for counting category call times is bound to scheduled trigger update logic.
The values provided are common starting points and should be evaluated using samples from the actual 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.