Adaptive Presence Detection Control for Intelligent Controllers
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Solution Overview
Problem
Existing control systems lack the flexibility and intelligence to adaptively select operational modes and adjust control outputs based on the presence or absence of entities within a controlled environment, leading to inefficiencies and incorrect inferences.
Innovation Solution
Intelligent controllers that utilize sensor output and electronically stored information, such as rules and parameters, to determine the presence or absence of entities, adjusting control schedules and parameters to maximize efficiency and accuracy in maintaining selected operational modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional control systems are used with predetermined behavior, then system stability is maintained, but adaptability to different operational conditions deteriorates
Solution Approach 1:
The control system transitions from static predetermined behavior to dynamic adaptive behavior by continuously learning from sensor feedback and electronically stored information. The controller modifies its operational modes and control outputs in real-time based on detected entity presence, transforming the system from rigid to flexible while managing complexity through structured learning algorithms.
Solution Approach 2:
The intelligent controller performs self-adjustment by automatically modifying its own control parameters and operational modes based on sensor feedback and stored rules. The system serves itself by continuously optimizing its behavior without external intervention, learning from environmental data to improve entity presence detection and control efficiency autonomously.
2Productivity
If control systems operate with fixed modes, then operational reliability is maintained, but efficiency in responding to presence changes deteriorates
Solution Approach 1:
The control system implements continuous feedback loops where sensor outputs about entity presence are processed against electronically stored rules and parameters. The controller uses this feedback to dynamically adjust control modes and outputs, creating a closed-loop system that continuously optimizes efficiency while maintaining reliability through validated decision-making processes.
Solution Approach 2:
The system prepares multiple operational modes in advance and pre-stores control rules and parameters for different entity presence scenarios. When entity presence is detected, the controller can immediately switch to the appropriate pre-prepared mode, enabling rapid efficient response without compromising system reliability through untested operations.
3Measurement precision
If simple presence detection is used, then device complexity is reduced, but measurement precision of entity presence deteriorates
Solution Approach 1:
The control system uses multi-functional sensor inputs that can detect various entity characteristics and processes them through a unified intelligent control framework. By storing multiple rules and parameters for different entity types and scenarios, the system achieves high measurement precision for entity presence detection without requiring separate dedicated systems for each detection function.
Solution Approach 2:
Electronically stored rules and parameters act as intermediaries between raw sensor data and control decisions. These stored knowledge structures mediate the detection process, enabling precise entity presence determination by translating complex sensor patterns into reliable presence/absence conclusions without directly increasing hardware complexity.
Data Source
AI summary
The current application is directed to intelligent controllers that use sensor output and electronically stored information, including one or more of electronically stored rules, parameters, and instructions, to determine whether or not one or more types of entities are present within an area, volume, or environment monitored by the intelligent controllers. The intelligent controllers select operational modes and modify control schedules with respect to the presence and absence of the one or more entities. The intelligent controllers employ feedback information to continuously adjust the electronically stored parameters and rules in order to minimize the number of incorrect inferences with respect to the presence or absence of the one or more entities and in order to maximize the efficiency by which various types of systems controlled by the intelligent controllers carry out selected operational modes.