What the data for this category looks like
Data related to airport revenue yield comes primarily from Civil Aviation Administration of China public operational statistics datasets, structured ledgers exported from airport operation management systems, and third-party aviation weather and traffic application programming interfaces. The data update cadence has two dimensions: real-time data such as daily flight takeoffs and landings, and passenger throughput is updated on a T+1 basis. Aggregated data such as monthly total revenue and non-aeronautical revenue is updated by the 5th of the following month. Most documents are in structured CSV or JSON format, with each row corresponding to data for a single airport during one cycle. Fields include airport ICAO code, takeoff and landing sorties, passenger throughput, total revenue, non-aeronautical revenue, and per-sortie cost. The corresponding units are: code, sorties, passenger trips, yuan, yuan, and yuan per sortie.
What constraints do these characteristics impose on workflow orchestration
The two-dimensional update cadence requires workflows to support multi-cycle trigger configurations, corresponding to daily real-time data and monthly aggregated data collection tasks respectively. Heterogeneous data formats from multiple sources require configuring corresponding data parsing nodes in the workflow to adapt to parsing rules for both CSV and JSON formats. Fields contain numeric values across different business dimensions, requiring unit mapping and format validation rules to be configured in data cleaning nodes to avoid confusion between fields with different units. The characteristic that a single airport’s data is associated with multiple dimensional indicators requires configuring variable binding nodes in the workflow to link indicators such as takeoff and landing sorties and revenue to the basic identifiers of the corresponding airport, ensuring data relevance.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Trigger Mode | Multi-cycle scheduled trigger + manual trigger | Daily real-time data requires daily updates, monthly aggregated data requires monthly updates. Manual triggers are used for temporary data replenishment scenarios |
HTTP Request Timeout | 300 seconds | Response times of civil aviation administration public data interfaces vary. 300 seconds covers most request durations |
CSV Parsing Encoding | UTF-8 | Civil aviation administration public datasets use UTF-8 encoding by default, which avoids garbled code issues during parsing |
Maximum Execution Count for Loop Nodes | Calibrated based on actual testing | Must cover all target airport samples. The specific value is adjusted based on the number of connected airports |
Variable Storage Scope | Global shared | Basic airport identifiers and general parameters need to be shared across multiple nodes, avoiding repeated data requests |
Parallel Request Limit | 5 | Avoid triggering rate limiting rules of third-party interfaces by sending too many requests simultaneously |
The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Global variables are cleared after workflow execution, only retaining the output content of the current node. Cause: The
Variable Storage Scopeis not configured asGlobal Shared. Default variables are only visible within the current node and cannot be reused across nodes. - Phenomenon: A 400 error is returned when referencing application variables in a knowledge base call node. Cause: Variable format is not verified. The referenced airport ICAO code is incomplete or contains invalid characters, leading to failed knowledge base matching.
- Phenomenon: The HTTP request returns multiple sets of airport data, and it is impossible to bind single airport data to the corresponding knowledge base. Cause: No loop node is configured to split multiple variables, or the parameters of the knowledge base call node are not bound within the loop body.
How to confirm the configuration is correct
- Trigger test: Manually trigger the workflow, check the execution logs, confirm that the generated execution records match the configured trigger cycle.
- Data validation: View the structured data output by the workflow, confirm that field names and units match the data source, with no garbled code or missing fields.
- Variable binding test: Add a print node in the loop node, check the output content, confirm that multiple sets of data are correctly split into single airport variables.
- Knowledge base call test: Call the knowledge base node bound with airport variables, confirm that the returned results match the business indicators of the corresponding airport.
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.