Smart Sensing Techniques for Autonomous Resource Management
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Solution Overview
Problem
Conventional security and automation systems require unnecessary intervention from personnel to manage resources and services in smart environments, as they lack efficient predictive and autonomous capabilities.
Innovation Solution
A control panel within the security and automation system monitors parameters of resources and services, using real-time and historical data, along with machine learning techniques, to predict future changes and autonomously perform necessary actions such as replenishment, scheduling, or maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional sensing techniques are used to monitor resources, then personnel can be informed of sensed conditions, but unnecessary intervention by personnel is required and system efficiency is reduced
Solution Approach 1:
The system performs predictive analysis on resource conditions before critical events occur. By analyzing historical and real-time usage data to forecast future states (such as predicting when a resource will be depleted or when maintenance is needed), the system takes preliminary actions autonomously, eliminating the need for reactive personnel intervention and achieving automated resource management.
2Measurement precision
If continuous monitoring and prediction are performed, then future changes can be predicted accurately, but CPU and memory usage increase
Solution Approach 1:
Instead of continuous monitoring, the system implements periodic analysis of resource usage data. It monitors resources continuously in the background but performs predictive computations at scheduled intervals or when triggered by specific events (such as threshold crossings or usage pattern changes). This periodic approach maintains prediction accuracy while significantly reducing CPU and memory usage, thereby lowering power consumption compared to continuous predictive computation.
3Speed
If real-time data processing is performed, then future changes can be predicted timely, but latency is increased
Solution Approach 1:
The system pre-processes and stores historical usage data in optimized formats during idle periods. When prediction is needed, it leverages this pre-prepared data structure and applies predictive models that can quickly analyze current real-time data against the pre-processed historical patterns. This preliminary preparation of data and models enables rapid predictions with minimal latency, as the computationally intensive data preparation work has already been completed in advance.
Data Source
AI summary
Methods, systems, and devices for smart sensing using a security and automation system are described. One method may include monitoring a parameter of a resource associated with a structure, predicting a future change in condition associated with the parameter based on the monitoring, and performing a function using the security and automation system based on the predicting. In some examples, the resource may include a service or a product.