What data for this category looks like
Environmental monitoring data primarily comes from IoT sensors deployed on-site, regional satellite remote sensing terminals, or handheld portable monitoring devices. Data update cycles range from seconds to daily. Real-time sensor data is pushed every 10 seconds to 5 minutes. Regional remote sensing data updates on a daily basis. Individual data documents include fields such as unique monitoring point identifier, collection timestamp, pollutant concentration value, device operating status, and latitude and longitude coordinates. Pollutant concentration fields mostly use units of μg/m³ or ppm. Some devices also include calibration markers and signal strength parameters.
What constraints do these characteristics impose on workflow orchestration
The high-frequency update nature of environmental monitoring data requires workflows to support event triggers or minute-level scheduled triggers. This avoids content lag caused by fixed long-cycle scheduling. Multi-source heterogeneous data formats require workflows to have built-in field mapping nodes. These nodes align the formats of data reported by different devices. The multi-field attributes of individual data require workflows to accurately call specified fields during content generation. This creates customized marketing content. Workflows also need data verification nodes. These nodes filter invalid data from abnormal concentration values or offline devices. In addition, marketing content must match regional monitoring data. Workflows must support associating user portrait tags based on latitude and longitude or monitoring point IDs. This improves customer acquisition accuracy.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
trigger_mode | event_trigger or 5min_cron | Matches the 10-second to 5-minute update frequency of environmental monitoring data to ensure real-time content generation |
data_batch_size | 100 | Balances single-batch processing efficiency and node load, avoiding workflow timeouts caused by excessive data |
field_mapping_rule | Preset mappings based on device type | Adapts to differences in field naming across monitoring devices, reducing manual configuration costs |
ai_model_timeout | 600 seconds | Supports marketing content generation with multiple field concatenations, avoiding workflow termination before content generation is complete |
rag_retrieve_count | Top 3 entries | Focuses on core monitoring data, avoiding redundant generation caused by too many retrieved contents |
error_handle_strategy | skip_invalid_data + notify_admin | Filters offline or abnormal monitoring data, while notifying administrators to troubleshoot device issues |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- The symptom is that no selectable values appear in the variable reference dropdown of the knowledge base search node in the workflow. The cause is that the output variable was not configured in the preceding node, or the output variable was not correctly bound to the workflow global context.
- The symptom is that the AI model option dropdown of the workflow problem classification node is empty. The cause is that the AI model was not deployed and permission-bound in the platform's global configuration, and the current workflow has no available model permissions.
- The symptom is that the comment trigger timing configured for the AI conversation node does not match expectations. The prompt states that it triggers after the stream reply is completed, but it actually executes early. The cause is a misunderstanding of the execution timing of stream returns: the comment node only triggers after the complete response is received.
How to verify a correct configuration
- Manually trigger the workflow once, and check whether the output log contains correct monitoring data fields and associated marketing content fragments.
- Simulate a sensor data reporting event to confirm whether the workflow starts automatically according to the configured trigger rules.
- Check the AI model dropdown of the workflow node to confirm that the target model is displayed in the selectable list, with no empty values.
- Input abnormal monitoring data to confirm that the workflow skips invalid data and triggers administrator notifications.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.