What the data for this category looks like
Business data sources for rail transit equipment include manufacturing execution systems, equipment operation and maintenance ledger databases, employee attendance and scheduling systems, and procurement and inventory management systems. Production data updates every second. Operation and maintenance ledgers are updated per shift. Attendance and procurement data are updated daily or weekly. Data document structures include the following fields. Equipment data has fields such as equipment ID, model, maintenance cycle, last inspection time, and spare parts inventory. Employee data has fields such as employee ID, position, affiliated workshop, and scheduling information. Office application data has fields such as application number, approval node, and submission time. Units include days, pieces, hours, and similar units. Equipment IDs are unique identifiers combining letters and numbers.
What constraints these characteristics impose on workflow orchestration
The second-level update of production data requires workflows to support real-time event triggering. Scheduled polling cannot be used to obtain the latest information. Differences in update rhythms across data sources require workflows to be configured with multiple types of trigger nodes. These nodes must adapt to real-time, daily, and weekly update data sources respectively. The multi-field structure of equipment and employee data requires the workflow's variable extraction module to accurately pull specified fields. This avoids redundant data interfering with the process. Dispersed storage of employee and production data across workshops requires workflows to support dividing data access scope by node. This ensures data isolation. The daily unit of maintenance cycle requires the workflow's time judgment node to match daily-level calculation logic. This ensures that process node triggers comply with maintenance plans.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Trigger Mode | Triggered by production events | Matches the second-level update rhythm of production data, and adapts to real-time office approval requirements |
variables | Bind equipment ID and maintenance cycle fields | Corresponds to core business fields of equipment ledgers, ensuring accuracy of process data |
nodeId | Divide node IDs by workshop | Meets the management requirement of cross-workshop data isolation, ensuring data access permissions |
| Retry Count | 3 times | Responds to temporary fluctuations in production systems, reducing the probability of process interruptions |
| Timeout Duration | 600 seconds | Adapts to the delay of cross-workshop data pulling, and complies with the node configuration logic of FastGPT v4.17.0 |
| Single Call Limit | Top 20 entries | Adapts to the single-batch data volume of equipment ledgers, avoiding process overload |
Three Common Mistakes
- After a workflow calls a knowledge base, no matching results are returned. The cause is that equipment ID is not passed as a search keyword to the knowledge base retrieval module, so corresponding maintenance documents cannot be associated.
- The tool call module does not respond. The cause is that the API access permission for the corresponding production system is not configured, or the trigger node is not bound to the correct
nodeId. - The code execution module reports an error that the dependent library cannot be found. The cause is that the specified library is not installed in the Python environment of the FastGPT container, or the import path is configured incorrectly.
How to Confirm the Configuration Is Complete
- A simulated production work order is submitted to verify whether the workflow triggers automatically as expected.
- The workflow variable panel is opened to confirm that business fields such as equipment ID and maintenance cycle have been correctly extracted in
variables. - The tool call module is run to check whether real-time data from the production system can be pulled normally.
- The code execution module is triggered to verify that the specified dependent library can be loaded and used normally.
The values provided here are common starting points, and should be evaluated 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.