What this category of data looks like
Data sources include ship block construction ledgers, classification society inspection documents, component delivery records from main engine supporting suppliers, and internal production scheduling work orders of shipyards. Updates advance with production milestones. Document lengths vary widely, ranging from dozens-of-page specialized inspection reports to thousands-of-page assembly design drawings. Document fields include block number, construction progress milestone, inspection item status, spare part model, and supplier information. Units used include millimeters, tons, operating hours, and voyages.
What Constraints These Characteristics Impose on Workflow Orchestration
Shipbuilding data has dispersed sources, wide variation in document length, and uses engineering-specific units. These factors create multiple constraints for workflow orchestration. Dispersed multi-source heterogeneous data requires workflows to configure multiple nodes to pull data from different sources, avoiding overloading single nodes. Wide variation in document length requires workflow context processing nodes to dynamically adapt to input scale, preventing content truncation or excessive resource usage. Engineering-specific units require workflows to include built-in unit validation rules, ensuring accurate field matching across data sources. Data updates driven by production milestones require workflows to support on-demand triggering, adapting to temporary production data synchronization needs.
How to Set Configurations
| Config Item | Recommended Setting | Rationale |
|---|---|---|
| Context Capacity | 800–1200 token as calibrated by actual testing | Adapt to the variation in shipbuilding document lengths and avoid content truncation |
variables | Bind block number, inspection item ID | Match core fields of shipbuilding data to enable accurate data pulling |
| Node Concurrency | 10–20 concurrent processes | Adapt to 4c16g server configurations and avoid overwhelming single nodes |
| Trigger Rule | Trigger on production milestone update events | Match the data update rhythm of shipbuilding to enable on-demand synchronization |
| API Access Timeout | 600 seconds | Adapt to processing durations for large shipbuilding documents and avoid mid-run interruptions |
Three Common Configuration Mistakes
- Workflows fail to return image recognition results after triggering, and interface display fields are empty. The cause is that no binding rule for image paths is configured in
variables, causing nodes to fail to obtain target image resources. - Processes are terminated during single-node operation, and server load alerts are triggered. The cause is that the configured node concurrency exceeds the server's hardware carrying limit, and the value is not adjusted based on actual testing.
- API calls to published workflows return timeout status codes. The cause is that the API access timeout setting is too short, failing to adapt to processing durations for large shipbuilding documents.
How to Verify Proper Configuration
- Access the workflow editing page of FastGPT v4.17.0, and check whether the
variablesconfiguration item is bound to core shipbuilding business fields. - Simulate a production milestone update event to trigger workflow operation, and check whether node logs successfully pull documents from the corresponding data sources.
- Call the API to access the published workflow, and check whether the returned results include expected business data fields.
- Adjust the node concurrency to the set value, and use a stress testing tool to verify whether the single-node load falls within a reasonable range.
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.