What the data for this category looks like
Data sources are public transportation industry statistical APIs and road network operation monitoring systems. Full historical operation data for the previous day updates every early morning. Some real-time traffic nodes push incremental data hourly. The data structure is a standardized JSON array. Each entry includes these fields: line identifier, line owning region, statistical date, passenger and freight turnover, toll revenue, direct operating costs, net revenue. Passenger and freight turnover uses hundred-ton kilometers as its unit. Toll revenue and operating costs use ten thousand yuan as their unit. Net revenue is revenue minus direct operating costs.
What constraints these characteristics impose on workflow orchestration
The large size of the daily full update dataset requires workflow configuration for batch fetching and segmented processing nodes. This prevents single API call timeouts. Lines under different owning regions have inconsistent field naming. This requires workflow configuration of custom field mapping rules to unify standardized processing logic. Real-time incremental data uses a different fetch frequency than full data. This requires splitting workflows into parallel tasks to adapt to full and incremental data processing flows separately. Multiple unit fields are present. This requires workflow configuration of unit conversion nodes to ensure consistent cross-line data comparison and calculation.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
schedule_cron | 0 2 * * * | Matches the daily early morning full data update window of the data source, avoiding fetching incomplete same-day data |
data_fetch_batch_size | 500 | Adapts to batch fetch scenarios with many railway and highway lines, balancing API call frequency and processing efficiency |
field_mapping_mode | custom_rule | Adapts to inconsistent field naming for lines across different owning regions, unifying standardized field processing logic |
unit_conversion_switch | enabled | Handles conversion requirements for different measurement units of passenger and freight turnover and revenue, ensuring data consistency |
stream_response_wait_timeout | 600 seconds | Adapts to long-cycle full data processing workflows, preventing workflow interruption due to timeout |
default_ai_model | Railway and Highway Domain-Specific Classification Model | Adapts to semantic needs for professional classification scenarios, avoiding classification bias from general-purpose models |
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 targeted analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Symptom: An error occurs when running, saving, publishing, or switching the workflow's problem classification node, and normal operation resumes after switching to another model. Cause: The initialized built-in general-purpose model does not adapt to the professional semantics of the railway and highway domain, and cannot correctly recognize classification tags related to yield rates.
- Symptom: The workflow's text extraction node returns an empty JSON result. Cause: Custom field mapping rules are not configured, so the exclusive field naming of railway and highway data cannot be matched, and the extraction logic fails to locate target data.
- Symptom: The reply annotation node of the AI chat component does not trigger execution. Cause: The stream response trigger timing is not set correctly. The annotation node is configured for real-time triggering instead of waiting for the full AI reply to complete.
How to confirm successful configuration
- View scheduled task execution logs to confirm that daily data fetch times match the data source's update cadence.
- Trigger the field extraction process for a single line, and verify that all configured fields are correctly extracted and unit conversion is completed.
- Trigger the complete stream reply process, and confirm that the annotation node runs after the full AI reply output.
- Switch back to the initialized built-in model, and verify that the problem classification node runs as expected.
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.