Real Estate Usage Analysis System for Integrated Data Monitoring
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Solution Overview
Problem
Current systems for managing property-related usage data are siloed and not integrated, making it difficult to obtain a comprehensive view of real estate usage and user behavior in real-time, and they lack the ability to react promptly to exceptions and emergencies.
Innovation Solution
A method and apparatus that collect and analyze data from various independent data sources using a machine learning algorithm, generating control signals and visualizing user behavior on maps or floor plans, with feedback used to train the algorithm and harmonize data for integrated analysis and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from multiple independent data sources are collected and integrated for comprehensive analysis, then the completeness and comprehensiveness of usage data analysis is improved, but the system complexity and difficulty of integration increase
Solution Approach 1:
The system segments data collection by maintaining independent data sources (access control systems, surveillance cameras, presence detectors) that operate autonomously, while the analysis apparatus processes them separately before integration. This reduces integration complexity while preserving data completeness.
Solution Approach 2:
The analysis apparatus acts as an intermediary that receives data from multiple independent sources, harmonizes their formats, and integrates them for comprehensive analysis. This mediator role simplifies the integration process by providing a centralized processing point.
2Speed
If real-time analysis of usage data is performed to enable immediate reaction to threats, then the response time to emergencies is improved, but the processing power and computational resources required increase
Solution Approach 1:
The system performs preliminary actions by continuously analyzing data in real-time and establishing baseline normal behavior patterns before threats occur. This allows the system to detect deviations immediately without requiring intensive computational resources during actual emergency responses.
Solution Approach 2:
The analysis apparatus performs partial analysis by focusing computational resources on detecting specific threat indicators and anomalies rather than analyzing all data equally. This selective approach enables real-time threat detection while conserving computational resources.
3Measurement precision
If machine learning algorithms are used to analyze user behavior patterns, then the accuracy of threat detection is improved, but the time required for training and processing increases
Solution Approach 1:
The system implements feedback mechanisms where analysis results and user responses are continuously fed back to retrain and refine the machine learning algorithms. This iterative process improves detection accuracy over time while distributing training across normal operational periods rather than requiring extensive upfront training.
Solution Approach 2:
The machine learning algorithms operate continuously in production mode, performing lightweight inference on incoming data streams. This continuous operation maintains high detection accuracy without requiring repeated full training cycles, as the models are already trained on historical data.
4Reliability
If comprehensive data harmonization is performed across independent data sources, then the quality and consistency of analysis output is improved, but the processing time and computational overhead increase
Solution Approach 1:
The system applies local quality harmonization by processing and standardizing data from each data source according to its specific characteristics and format requirements. Rather than imposing a single rigid structure on all data, the system adapts harmonization rules to local data properties, maintaining consistency while preserving source-specific valuable features.
Data Source
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AI summary
The embodiments relate to a method and technical equipment for monitoring a usage of a monitoring target, such as a real estate. The method comprises receiving data from one or more data sources, wherein the data relates to usage of the monitoring target; processing the data to detect deviations compared to a normal usage of the monitoring target; generating output based on the deviations; and generating a control signal based on the output, and transmitting the control signal to an external device and/or system for controlling its operation.