What This Category of Data Looks Like
Bus operation data comes from intelligent dispatching systems, station operation management platforms, passenger flow monitoring terminals, and safety inspection ledger systems. Update rhythms fall into three categories:
- Real-time updates for dispatching commands and passenger flow data
- Daily generation of operation daily reports and revenue reports
- Monthly compilation of safety summaries and route optimization plans
Common documents include dispatching orders (includes route number, departure time, stop stations), passenger flow analysis reports (includes time-period passenger volume, stop popularity), station inspection records (includes point number, hazard level), and revenue settlement slips (includes shift revenue, card swipe count). Fields covered include route number, departure time, passenger trips, inspection point, and hazard level. Units include minutes, trips, kilometers, and level.
What Constraints These Characteristics Impose on Workflow Orchestration
Decentralized data sources require workflows to support flexible configuration of multi-system data pull nodes, to adapt to bus operation data from different sources. Large gaps in update rhythms require support for both real-time trigger and scheduled trigger modes. These modes match immediate processing of dispatching commands and batch generation of monthly reports respectively. Diverse document types and inconsistent fields require configurable custom variable extraction rules, to adapt to field differences across different bus documents. Fields with specific units require automatic matching of unit formats during content generation, to avoid data confusion. FastGPT v4.17.0’s workflow orchestration function must adjust node configurations to address these constraints.
How to Configure Settings
| Configuration Item | Recommended Setting | Basis for This Setting |
|---|---|---|
| Trigger Mode | Split into real-time dispatching trigger, daily scheduled trigger | Matches the real-time update and batch report generation rhythms of bus data |
| Variable Extraction Rules | Match corresponding fields by document type, store results in variables | Adapts to field differences across different bus documents, such as extracting route number and departure time from dispatching orders |
| Context Window Size | 800–1200 characters | Covers core content of a single bus document, avoids truncation of key information |
| Recall Result Limit | Top 6 entries | Balances sufficient information and processing efficiency, adapts to the volume of associated data for bus documents |
| Timeout Threshold | 600 seconds | Addresses delays from multi-system data pulls, avoids workflow interruption |
| Node Flow Rules | Bind pre- and post-processing nodes by nodeId | Ensures the workflow executes in the standard order for bus document processing |
Three Common Mistakes
- Phenomenon: The workflow operates normally during the knowledge base search phase, but the AI chat phase does not reference the pulled bus operation data. Cause: Extracted fields are not stored in
variables, so the chat node cannot access context-associated data. - Phenomenon: The workflow throws an error with empty fields during runtime. Cause: No targeted variable extraction rules are configured, so required fields such as route number and departure time cannot be extracted from uploaded bus documents.
- Phenomenon: The workflow does not respond for a long time after triggering. Cause: The timeout threshold is set too low, and does not match the actual delay of multi-system data pulls.
How to Confirm Proper Configuration
- Manually upload the corresponding type of bus document, check whether the workflow node’s running logs show successful data pull and variable extraction.
- Call the workflow’s test function, verify whether
variablescontains the preset bus document fields. - Adjust the trigger mode to real-time trigger, simulate the push of a bus dispatching command, check whether the workflow automatically starts and completes the full process.
- View the workflow’s node flow records, confirm that all pre- and post-processing nodes bound by
nodeIdexecute in the expected order.
The values provided are common starting points, and should be measured against the reader’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.