What this category's data looks like
Data for communication towers and infrastructure primarily comes from the tower operation and maintenance management system, base station inspection logs, and property registration database. There are two update cycles: inspection records are synced daily, while property and site basic information is updated monthly.
Reference only; verify against the target FastGPT release and deployment documentation.
Constraints on workflow orchestration
The characteristics of this data impose specific constraints on workflow orchestration:
- The site ID as the unique identifier requires workflows to bind externally passed site ID parameters via
variablesto avoid cross-site data confusion. - The multi-source data synchronization feature requires workflows to configure multi-source data pull nodes to handle differences in field formats across systems.
- The enumerated process node field requires workflow branch judgment nodes to strictly match the system's enumerated values. Branch matching will fail if values do not align.
- The high-precision latitude and longitude fields require data preprocessing nodes to perform format verification and retain 6 decimal places of precision.
- Daily updated inspection data requires workflows to configure scheduled trigger nodes to pull incremental data daily and avoid duplicate processing of same-day records.
Recommended configuration values
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind stationId, faultLevel fields | Pass externally provided site and fault information to enable process customization |
| Scheduled trigger frequency | Trigger daily at 02:00 | Match the daily update rhythm of inspection data and avoid duplicate pulls of same-day data |
| Data preprocessing node | Verify latitude and longitude retain 6 decimal places | Match the precision requirements of data fields and avoid positioning errors |
| Branch judgment node | Match process node enumerated values | Ensure process branches align with system process statuses |
| Timeout setting | 600 seconds | Address network latency from multi-source data pulls and avoid workflow interruptions |
| Code running component | Calibrate based on actual testing | Adapt to locally deployed environment dependencies and avoid runtime errors |
Three common configuration errors
- Phenomenon: Fields bound by
variablesare empty during workflow runtime. Cause: External parameter mapping is not configured in the trigger node, resulting in unassigned variables. - Phenomenon: Two thinking process outputs appear during workflow debugging. Cause: Trigger logic overlaps between the tool call node and the large model node, leading to repeated triggering of the thinking process.
- Phenomenon: Code running component reports an error indicating missing dependencies. Cause: Corresponding Python dependency packages are not installed in the local deployment environment, which is unrelated to the code's own logic.
How to verify successful configuration
- Trigger the workflow and pass the specified site ID, then check whether the fields bound by
variablesare correctly loaded into workflow nodes. - Run the scheduled trigger task, then check whether the pulled data consists of inspection records updated on the same day, with no duplicate entries.
- Simulate a fault level change, then check whether the branch judgment node correctly matches the process node enumerated values and triggers the corresponding branch.
- Run the code running component, then check whether the output result is consistent with the expected logic and there are no dependency-related errors.
The values provided here 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 2026-09-28.