Building Abnormality Diagnosis via Image and Environmental Data Fusion
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Solution Overview
Problem
Existing systems for diagnosing building abnormalities, such as gas leaks or unusual odors from pipes, are inadequate in accuracy and effectiveness using image analysis.
Innovation Solution
A computer system that acquires visible light and infrared images, analyzes them by comparing with normal images, identifies object types and abnormal parts, and diagnoses status based on environment data, including flow rate and gas data, to improve diagnosis accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only image analysis is used for diagnosing building abnormalities, then the system structure remains simple, but the diagnosis accuracy is insufficient for detecting gas leaks and unusual odors
Solution Approach 1:
The patent combines multiple detection methods (image analysis, infrared detection, environmental data analysis) into a unified diagnostic system. The computer system integrates visible light images, infrared images, and environmental data (gas concentrations, temperature, humidity) to comprehensively diagnose building abnormalities, thereby improving diagnosis accuracy while managing system complexity through integrated processing.
Solution Approach 2:
The diagnostic system is designed to perform multiple functions: it can detect various types of abnormalities including gas leaks, unusual odors, wall hollows, and other building issues using a single integrated platform. The system processes different data types (images, environmental sensors) and applies multiple analysis methods to achieve versatile diagnostic capabilities.
2Measurement precision
If multiple data types (images, environmental data) are integrated for diagnosis, then diagnosis accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent segments the diagnostic process into distinct functional modules: image acquisition (visible light and infrared), environmental data acquisition, image analysis, environmental data analysis, and integrated diagnosis. Each module processes specific data types independently before the computer system integrates the results, thereby reducing overall processing difficulty while maintaining comprehensive diagnostic accuracy.
Solution Approach 2:
The computer system acts as an intermediary that receives and processes multiple data types from different sources. It coordinates between image analysis results and environmental data analysis, integrating them to produce comprehensive diagnostic conclusions. This intermediary processing approach manages the complexity of multi-data-type integration.
3Reliability
If comprehensive environmental data (gas data, flow rate, temperature) is collected, then the ability to detect gas leaks and abnormalities improves, but the quantity of data to be processed increases
Solution Approach 1:
The system collects environmental data including gas concentrations, flow rates, temperature, and humidity, but processes and utilizes only the data relevant to specific diagnostic purposes. For example, gas concentration data is primarily used for detecting gas leaks, while flow rate data helps identify abnormal conditions. This selective processing approach manages data volume while maintaining detection reliability.
Solution Approach 2:
The system uses environmental data to diagnose abnormalities and can trigger alerts or notifications based on detected issues. The diagnostic results feed back into the monitoring process, allowing the system to focus on areas of concern and adjust data collection or analysis priorities, thereby managing data volume efficiently while maintaining high detection reliability.
Data Source
AI summary
The present invention is to provide a computer system, and a method and a program for diagnosing an object that is capable to improve the diagnosis accuracy of an object. The computer system acquires a visible light image of an object that is taken by a camera, analyzes the acquired visible light image by comparing the acquired visible light image with a normal visible light image of the object, identifies the type of the object based on the result of the image analysis, identifies an abnormal part of the object based on the result of the image analysis, acquires environment data of the object, and diagnoses the status of the object based on the identified type, the identified abnormal part, and the acquired environment data.