Cloud-Premises Control System for Context-Aware Event Response
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Solution Overview
Problem
Residential and commercial monitoring and security systems traditionally rely solely on local sensor information for responses to events, neglecting external and learned information that could significantly impact action decisions, such as personnel schedules and third-party data like weather or utility status.
Innovation Solution
A control, monitoring, and alert system (CMAS) that integrates a premises device with a cloud server, utilizing local, remote, and learned information to evaluate and respond to real-time events through a combination of sensor data, third-party resources, and machine learning for improved action selection and communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional local sensor information alone is used for response decisions, then system simplicity is maintained, but response effectiveness and action precision deteriorate due to lack of contextual information
Solution Approach 1:
The system segments information sources into three distinct categories: local information from on-premises sensors, remote information from third-party sources (weather, traffic, news), and learned information from machine learning models. This segmentation allows each information type to be processed and integrated independently, improving response effectiveness while managing system complexity through modular architecture.
Solution Approach 2:
The premises device acts as an intermediary that receives and integrates information from multiple sources (local sensors, remote third-party sources, and cloud-based machine learning models). It synthesizes this diverse information to generate context-aware response decisions, resolving the contradiction by providing a centralized integration point that improves reliability without proportionally increasing overall system complexity.
2Measurement precision
If external third-party information and machine learning are integrated, then action precision is improved, but device complexity and information processing requirements increase
Solution Approach 1:
The machine learning models are trained and prepared in advance (preliminarily) to recognize patterns and predict outcomes. These pre-trained models are then deployed to the premises device or cloud server, where they can quickly evaluate sensor data and generate response recommendations without requiring complex real-time computation, thus improving action precision while managing device complexity.
Solution Approach 2:
The system replaces traditional rule-based or manual decision-making mechanisms with machine learning-based intelligent systems. The machine learning models automatically analyze sensor data, integrate it with external information, and generate optimized response actions, substituting complex mechanical or manual evaluation processes with automated computational systems that improve precision.
3Reliability
If comprehensive local, remote, and learned information is evaluated, then false alerts are reduced, but information processing time and computational requirements increase
Solution Approach 1:
The system implements periodic updates and evaluations of information from multiple sources rather than continuous real-time processing. The premises device periodically queries remote third-party sources, updates machine learning model predictions, and re-evaluates sensor data at scheduled intervals or triggered by significant events. This periodic approach reduces false alerts through comprehensive evaluation while limiting processing time by avoiding constant continuous analysis.
Data Source
AI summary
Systems and methods providing actions and communications responsive to real-time events incorporating local, remote and learned information are disclosed. The system includes a cloud application on a cloud server and a premises device at a premises. The premises device has a plurality of sensors coupled to and/or included in the premises device. The cloud application receives signals, status and other information from the premises device. The cloud application also obtains information from third party information sources. The cloud application obtains location and other pertinent information about key persons. The cloud application evaluates actions to take in response to signals, status and information received from the premises device taking into consideration information from the third party information sources and information about key persons.