Neural Premises Automation for Multi-Sensor Room Activity Control
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Solution Overview
Problem
Current building automation systems do not effectively manage the entire range of activities within a room, as they typically focus on either environmental control or security, without integrating all possible functions such as entertainment and appliance control.
Innovation Solution
A neural cognitive premises automation unit that utilizes a neural network to control and monitor equipment in response to various sensors, including movement, acoustic, temperature, light, humidity, CO2, and image sensors, with a self-learning module that analyzes events and modifies scenarios accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a building automation system focuses on environmental control or security separately, then the system design is simpler and more specialized, but the system cannot effectively manage the entire range of activities within a room
Solution Approach 1:
The patent implements a universal automation controller that integrates multiple functions including environmental control, security monitoring, entertainment system control, and appliance management into a single system. This multi-functional approach allows the system to manage the entire range of activities within a room rather than being limited to specialized functions, directly resolving the contradiction between versatility and complexity by designing complexity into the universal controller from the outset.
Solution Approach 2:
The patent merges previously separate automation systems (environmental control, security, entertainment, appliances) into a single integrated building automation system. By combining these functions under one unified controller that can process inputs from various sensors and coordinate multiple actuators, the system achieves comprehensive room activity management while avoiding the complexity of multiple independent systems through centralized intelligent control.
2Measurement precision
If multiple sensors are used to improve detection accuracy, then the system can better monitor room conditions, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent combines multiple sensor types (movement sensors, acoustic sensors, temperature sensors, light sensors, humidity sensors, CO2 sensors, and image sensors) into a unified sensing network managed by a single intelligent controller. This merging approach allows the system to achieve high detection accuracy across multiple parameters while managing complexity through centralized data processing and coordination, rather than requiring separate processing systems for each sensor type.
Solution Approach 2:
The automation controller is designed as a universal processing unit capable of handling data from diverse sensor types and coordinating responses across multiple actuator types. This multi-functional capability allows the system to process complex multi-sensor inputs and generate appropriate control actions, resolving the contradiction by making the controller universally capable rather than requiring specialized processing for each sensor-actuator pair.
3Extent of automation
If a self-learning module is implemented to analyze events and modify scenarios, then the system becomes more autonomous and adaptive, but the computational requirements and processing time increase
Solution Approach 1:
The patent implements a self-learning module that analyzes events and modifies scenarios in advance, allowing the system to proactively adjust to anticipated conditions rather than merely reacting to them. This preliminary action capability enables the system to learn from patterns in sensor data and pre-compute optimal control scenarios, reducing real-time processing requirements while enhancing autonomous adaptability.
Solution Approach 2:
The automation system incorporates self-learning capabilities that allow it to autonomously analyze its own operational data, identify patterns, and modify its control scenarios without external intervention. This self-service approach to learning and adaptation enables the system to become increasingly autonomous over time while managing computational load through efficient pattern recognition and scenario optimization algorithms.
Data Source
AI summary
A neural cognitive premises automation unit and a neural proactive premises automation unit for buildings, offices or homes includes at least one resolver including a neural network to activate pieces of equipment in response to at least one sensor, and an event resolver to control the equipment in response to events. The event resolver includes a data base, a self-learning module and either a cognitive module or a proactive module. The self-learning module finds clusters of events in the data base. The cognitive module analyzes the clusters and modifies an associated scenario. The proactive module analyzes the clusters and creates new scenarios. The automation units include a communication unit to communicate with other automation units in a network. A neural cognitive method and a proactive neural method for controlling and/or monitoring equipment in a premises includes neural network methods activating the equipment in response to at least one sensor.