What This Category of Data Looks Like
Data related to air pollution control comes primarily from real-time reports submitted by local environmental monitoring stations, internal enterprise emission control ledgers, on-site inspection records, and associated meteorological data. This data supports marketing content for financial institutions’ green credit, green insurance, and similar business activities.
Monitoring data updates every 5 minutes to hourly. Ledger data updates monthly. Inspection records are generated alongside on-site operations.
Each data document includes fields such as monitoring point code, pollutant concentration value, emission rate, governance equipment operating duration, and rectification completion status. Concentration units are μg/m³, emission rate units are kg/h, and operating duration units are hours.
What Constraints Do These Characteristics Impose on Workflow Orchestration?
High-frequency updates of real-time monitoring data require workflows to support scheduled or event triggers. This avoids data lag caused by polling delays, and ensures marketing content uses the latest governance data.
Differences in multi-source data formats require built-in format conversion nodes in workflows. These nodes unify units and data types for fields such as pollutant concentration and emission rate, preventing unit confusion in marketing content.
The unstructured nature of inspection records requires integrated text parsing nodes in workflows. These nodes extract key information such as rectification completion status to generate compliant marketing content.
Fixed field mapping rules require strict matching of preset field names during workflow configuration. These include point codes and concentration thresholds. Failure to match these will cause missing parameters during subsequent marketing content generation.
For the data analysis link bound to work orders, ensure nodes only call historical data for the corresponding point. This prevents cross-point data confusion from affecting the accuracy of marketing content.
How to Set the Configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Scheduled Trigger Interval | 300 seconds | Matches the update frequency of most environmental monitoring data, ensures pulled data is up-to-date, supports time-sensitive marketing content |
Variable Reference Format | {{dataset.field_name}} | Follows FastGPT official variable reference specifications, resolves issues with unrecognized non-standard formats, ensures marketing content can correctly call dataset fields |
Fixed Node Binding Rule | Bind to the point ID of the specified work order | Ensures data analysis operations only target the governance points of the corresponding work order, meets the requirement of fixed operations tied to work orders, improves the accuracy of marketing content |
Loop Switch | Enable loop, process 5 points per single run | Processes multi-point data in batches, reduces workflow timeout risks, adapts to scenarios with a large number of air pollution control points |
Data Filter Condition | Filter data with update time within the last 1 hour | Ensures marketing content is based on the latest governance data, improves information timeliness, complies with compliance requirements for financial business marketing |
Debug Mode Timeout | 600 seconds | Adapts to the relatively long time consumption of air pollution control data pulling and analysis links, avoids early interruption during debugging, ensures complete configuration verification |
The parameter values provided on this page are common recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: Workflows throw errors in run mode, but execute normally in debug mode. Cause: Debug mode uses locally simulated test data. Run mode calls real environmental data sources without configured interface access permissions, leading to request interception.
- Phenomenon: Target fields appear empty in generated marketing content after variable reference. Cause: Non-standard [{datasetId: xxx}] format is used. FastGPT’s {{dataset.field_name}} variable reference specification is not followed, so the system cannot parse the variable path correctly.
- Phenomenon: Workflow execution times out and stops. Cause: Loop configuration is not enabled. All historical data for all points is pulled at once, exceeding the workflow’s default runtime limit.
How to Confirm Configuration Is Complete
- Manually trigger the workflow. Check whether the output log includes pollutant concentration data matching the specified point. This confirms the data filtering rule is active.
- Insert a variable debugging node in the workflow. Enter a test point ID, then check whether the variable reference correctly pulls field values from the corresponding dataset. This confirms the variable format configuration is correct.
- Bind a test work order, then trigger the data analysis node. Check whether the output result only includes data for the point tied to the work order. This confirms the fixed node binding rule is active.
- Run the workflow in debug mode. Check whether the runtime aligns with the business’s required timeout limit. Adjust the configuration to match the required runtime.
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.