What this category of data looks like
Data sources include high-definition cameras installed on bridge inspection equipment, drone aerial imagery, and structural stress and displacement sensors. Update frequency adjusts based on inspection type: updated weekly for routine inspections, triggered on demand for special inspections. Each data entry includes shooting point number, corresponding timestamp, image file, real-time stress value and displacement reading. Fields include pixel resolution, stress unit MPa, displacement unit millimeter, with no extra redundant annotation fields.
Constraints these characteristics impose on workflow orchestration
Image files can reach hundreds of megabytes per single file. This requires workflow nodes to support batch loading and sharded processing to avoid excessive resource usage from single-load operations. Sensor data consists of time-series values with point identifiers, which must be bound to corresponding captured images. Workflows must bind point IDs and associated datasets via variables to meet this requirement. Inspection data is uploaded in batches by point, so workflows must trigger corresponding branches via nodeId to avoid overload caused by full-data parallel processing. Image resolution varies significantly across different inspection scenarios. Reserved parameter adjustment space must be included during orchestration to adapt to input data of different specifications.
How to set configurations
The following configuration items are set via the workflow orchestration interface of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Adapt to text descriptions and metadata parsing of bridge images, avoid exceeding the model's processing limit for a single chunk |
| Number of parallel nodes per run | First 3 | Match the batch upload scale of inspection points, avoid overloading system resources |
| Timeout threshold | 600 seconds | Adapt to the upload and parsing duration of large-volume images, avoid mid-process workflow interruptions |
variables binding | Bind by point ID and timestamp | Associate sensor data with corresponding image metadata, ensure accurate data matching |
| Branch trigger rule | Trigger by inspection type | Adapt to different processing logic for routine and special inspections |
| Rearrangement | Set based on on-site calibration | Adapt feature extraction priority for images of different resolutions, optimize recognition accuracy |
Three common mistakes
- Phenomenon: The text classification extraction node returns empty values, and logs show no matching features. Cause: Professional terms for bridge diseases such as cracks, corrosion, deflection abnormalities are not included in the classification thesaurus, resulting in failed feature matching.
- Phenomenon: Running two parallel workflows does not improve speed. Cause: The resource usage limit for parallel nodes is not configured, leading to exhaustion of system resources and failure to achieve expected parallel efficiency.
- Phenomenon: The workflow re-enters the classification node after problem classification. Cause: No termination node is configured after branch execution completes, causing the workflow to loop the classification logic.
How to confirm proper configuration
- Upload a single bridge image with known disease and corresponding sensor data, verify whether the workflow associates and triggers the corresponding branch based on point ID.
- Adjust the chunk length parameter, upload images of different resolutions, verify whether the processing duration of the parsing node meets the preset threshold.
- Trigger a parallel workflow test, check system resource usage, confirm that parallel nodes do not exceed the preset limit.
- Simulate a problem classification branch, verify whether the workflow terminates after branch execution completes and does not re-enter the classification node.
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.