What Data for This Category Looks Like
Bridge disease identification data primarily comes from high-resolution structural images captured by inspection drones, time-series values collected by bridge stress and displacement sensors, and electronic records from manual inspections. Data update frequency aligns with regular inspection cycles. Data collected for a single bridge during one inspection includes dozens to hundreds of images, hundreds of sensor readings and point notes. Core fields for individual data entries include capture time, GPS positioning coordinates, image path, disease type identifier, stress value (unit: megapascals), displacement reading (unit: millimeters). Data is stored as a mix of structured tables and image files.
Constraints on Model Access and Configuration Imposed by Data Characteristics
The characteristics of bridge disease identification data impose multiple constraints on model access and configuration. The large input volume of high-resolution inspection images requires model calls to support sufficient maxTokens capacity. This ensures full parsing of image details and associated annotations. The multimodal mixed data scenario requires enabling the vision parameter to ensure the model supports image recognition capabilities. The batch collection nature of sensor time-series data requires the concurrency limit of model calls to match the scale of inspection batches. This prevents request backlogs that would affect alarm timeliness. Structured data fields require model output to conform to a preset format. Otherwise, the output cannot be directly used in subsequent alarm judgment processes.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled | Adapts to the disease feature recognition requirements for inspection images, supports multimodal data parsing |
temperature | 0.2–0.4 | Ensures consistency of disease identification results, reduces random deviations, and meets the stability requirements for alarm judgment |
maxTokens | 8192 token | Adapts to the text description and structured data parsing capacity of a single high-resolution inspection image |
maxContext | 4000 token | Adapts to the context association parsing requirements for multiple batches of inspection data |
toolChoice | auto | Allows the model to independently choose whether to call image parsing tools to handle non-standard inspection images |
AIPROXY_API_ENDPOINT | Fill in the deployed multimodal model interface address | Adapts to the multimodal data call link for bridge disease identification |
AIPROXY_API_TOKEN | Fill in the access key for the corresponding model | Ensures secure identity authentication for model calls |
Three Common Misconfiguration Issues
- Symptom: A single inspection model call returns a 504 timeout or empty result. Cause: No multi-model redundant call mechanism is configured, and single-model stability is insufficient to meet real-time alarm requirements.
- Symptom: Format errors are returned when calling a domestic multimodal model. Cause: The model’s support for the standard input format of the
visionparameter is not confirmed, resulting in failed image parsing. - Symptom: Disease information fields in model responses are missing or formatted incorrectly. Cause: No clear configuration constraints are set for output format, and relying solely on prompts cannot guarantee consistent structured parsing.
How to Verify Correct Configuration
- Upload a single bridge image with a clear disease identifier, trigger a model call, and check if the returned result includes the preset core fields.
- View the model call logs in FastGPT v4.17.0, confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly, with no authentication error prompts. - Simulate a model call failure scenario, verify whether the preset redundant model switching logic is triggered, ensuring that alarm judgment is not interrupted due to a single model failure.
- Adjust the value of the
temperatureparameter, compare the differences between two call results, and confirm that the parameter configuration takes effect.
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.