Res-LSTM Crack Evaluation for Ancient Building IoT Monitoring
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Solution Overview
Problem
Existing monitoring systems for ancient buildings lack effective prediction models to accurately assess the health state in real time, leading to discrepancies between analyzed and actual conditions due to issues like data missing or inaccurate acquisition.
Innovation Solution
A monitoring and early warning system based on the Internet of Things (IoT) for ancient buildings, incorporating a Res-LSTM neural network model for crack state evaluation, which includes data preprocessing and a comprehensive sensor suite, and a user-side visualization device for management and interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sensor-based monitoring is used for ancient buildings, then data acquisition is achieved, but data accuracy and reliability deteriorate due to missing or inaccurate data
Solution Approach 1:
The patent introduces an expert module as an intermediary between sensor data acquisition and health state assessment. This expert module processes and validates sensor data, filtering out inaccurate or missing data through multiple assessment dimensions, thereby improving the reliability of health state assessment without directly addressing sensor measurement limitations
Solution Approach 2:
The system implements a feedback mechanism where the expert module continuously evaluates sensor data quality and provides feedback for data validation. This feedback loop enables the system to identify and correct inaccurate data, improving overall assessment reliability while maintaining continuous monitoring capabilities
2Loss of time
If statistical analysis of sensor data is used, then health state assessment is achieved, but real-time prediction capability is lost
Solution Approach 1:
The expert module performs preliminary actions by pre-establishing multiple assessment dimensions and evaluation criteria before actual health state assessment. This preparation enables rapid real-time evaluation without requiring complex statistical analysis during the assessment moment, thus reducing time delay while maintaining prediction accuracy
Solution Approach 2:
The assessment process is segmented into multiple independent dimensions within the expert module. Each dimension evaluates specific aspects of building health independently, allowing parallel processing that reduces overall assessment time while maintaining comprehensive and accurate real-time prediction capabilities
3Adaptability or versatility
If comprehensive sensor deployment is implemented, then monitoring coverage is improved, but system complexity increases
Solution Approach 1:
The expert module serves as a universal processing unit that handles data from multiple different sensor types through unified assessment dimensions. This multi-functional approach enables comprehensive monitoring coverage while avoiding the need for separate complex analysis systems for each sensor type, thus reducing overall system complexity
Data Source
AI summary
A monitoring and early warning system based on the Internet of Things for an ancient building includes an information acquisition system, a service platform and a user side. The information acquisition system is configured to acquire state data of the ancient building and upload the state data to the service platform by means of a 4G/5G gateway. The user side is integrated in a visualization device for a user to manage, analyze and interact with the acquired ancient building data, and the user side includes a monitoring module, a pre-alarm module, a management module and an expert module. The service platform is of a duster system integrating a plurality of applications, caches and database servers. The expert module is of a Res-long short-term memory (Res-LSTM) neural network model for evaluating a crack state of the ancient building.