What the Data for This Category Looks Like
Ledger data for urban public transport mainly comes from on-board smart terminals, dispatching command systems, ticket settlement systems, and passenger flow collection equipment. Two update frequency categories exist: real-time operation location data is updated every second, while daily operation ledgers, passenger flow statistics, and energy consumption records are updated daily. A single ledger document includes fields such as route number, vehicle unique identifier, daily operation duration, total passenger trips, number of station stops, and energy consumption per 100 kilometers. The corresponding units are minutes, trips, times, and kilowatt-hours per 100 kilometers, respectively.
What Constraints Do These Characteristics Impose on Workflow Orchestration
Constraints from multi-source data access: Both offline database query nodes and real-time stream data access nodes must be configured to adapt to data sources with different update frequencies. Inconsistent field types and units require a standardized field mapping node in the workflow to unify units and data formats for fields such as passenger volume and energy consumption. The route number variable preset via variables can accurately filter ledger data for target routes. The batch nature of daily ledger data requires the workflow to support processing 800–1200 entries at a time to avoid single request timeouts. The second-level update requirement for real-time location data requires the workflow trigger interval to be set to no more than 60 seconds to ensure data timeliness.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Data Source Node | Configured as a multi-data source aggregation node | Adapt to multi-source data access requirements such as on-board terminals and dispatching systems |
| Trigger Mode | Configured with both daily trigger and minute-level scheduled trigger | Match the different update frequencies of real-time location data and daily ledgers |
| Field Mapping Rules | Unify the units and data formats of passenger volume and energy consumption | Adapt to the characteristics of mixed ledger field types and inconsistent units |
| Single-Process Data Volume | 800–1200 entries | Adapt to the batch processing scale of daily ledgers and avoid request timeouts |
| Timeout Threshold | 600 seconds | Adapt to the time consumption requirements of multi-data source joint queries |
| nodeId | Assign a unique identifier to each workflow node | Facilitate locating specific nodes with operational anomalies |
| variables | Preset route number and date range variables | Support filtering ledger data for specific routes and time periods as needed |
Three Common Misconfigurations
- Workflow tests run normally locally, but returns an error stating "undefined" is not valid JSON when invoked via a channel link. The cause is that nested fields returned by multiple data sources are not standardized, leading to the output format not matching the JSON structure required by the channel, and the legality of output fields is not verified via
variables. - Workflow cannot obtain specified public transport ledger data. The cause is that the connection parameters of the specified database are not configured correctly, or the database query node is not added to the workflow.
- Plugins are mistakenly used as workflows, failing to complete multi-step ledger summary processes. The cause is that the role of workflows is not clearly defined: workflows are used to chain multi-step operations, while plugins are only single-function call units.
How to Verify a Correct Configuration
- Manually trigger the workflow, check if the output ledger data includes core fields such as preset route numbers and vehicle IDs.
- Configure a scheduled trigger task, verify that the trigger time matches the update frequency of the corresponding data source.
- Invoke the channel link, check if the JSON format of the returned content meets expectations, and there are no undefined field errors.
- View workflow operation logs, confirm that the single-process data volume does not exceed the preset reasonable range.
The values provided are common starting points and should be measured against the user’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.