AI-TECHNOLOGY IN ELECTRONICS SYSTEM

Abstract

The present invention relates to an AI-driven control and optimization system for electronic devices, designed to enhance device performance, efficiency, and reliability through real-time adjustments. The system includes a data acquisition module that collects operational and environmental data, an AI processing unit that analyzes this data using machine learning models, and a control interface that implements optimization commands to adjust device parameters. The system optimizes energy consumption, improves device adaptability, and enables predictive maintenance by continuously learning from data patterns. This invention is applicable to a wide range of electronic devices, including home appliances, industrial machinery, and medical equipment, providing significant improvements in operational efficiency, cost savings, and long-term reliability

Specification

Description:In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.

Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The invention is an artificial intelligence (AI)-powered control and optimization system designed to enhance electronic devices' adaptability, efficiency, and performance across multiple applications. This system comprises three main components: a data acquisition module, an AI processing unit, and a control interface. The data acquisition module gathers real-time data on environmental and operational conditions, the AI processing unit analyzes this data to identify patterns and make decisions, and the control interface implements these decisions by adjusting device parameters. Here, we present three specific embodiments that demonstrate the system's versatility in various scenarios.

In first embodiment, the invention is implemented in smart home appliances, such as air conditioners, refrigerators, and lighting systems, which can benefit from real-time energy management and performance optimization. Traditional appliances operate on fixed configurations and schedules, which often result in energy waste and inefficient use.

The data acquisition module includes sensors to collect real-time information on parameters like room temperature, humidity, power consumption, and even occupancy in the case of lighting systems. For example, an air conditioning system uses temperature sensors to monitor both indoor and outdoor conditions, while a refrigerator monitors internal temperature, door status, and power draw.

The AI processing unit is a machine learning model that analyzes this data and optimizes appliance settings based on real-time needs, historical usage, and environmental patterns. For instance, in the air conditioner, the AI model learns typical daily patterns and adjusts the cooling intensity to preemptively regulate the room temperature before occupancy rises, ensuring both comfort and efficiency.

The control interface then executes the AI's adjustments, such as modifying the cooling intensity or switching to energy-saving mode based on occupancy and external temperature. Over time, the AI continuously improves its decisions by learning from usage patterns and adapting to specific user preferences, significantly reducing energy usage while maintaining optimal comfort.

The second embodiment applies the invention to load management in industrial power systems, where large machinery and equipment operate under variable loads that can lead to inefficiencies or overload risks. Effective load management in this context is crucial for ensuring system stability, preventing downtime, and reducing energy waste.

The data acquisition module in this embodiment includes a series of sensors and meters distributed across the industrial site, capturing data on voltage, current, equipment load, and usage patterns. For example, load meters monitor the power draw of each machine, and sensors track fluctuations in the power grid to detect potential overloading situations.

The AI processing unit in this embodiment uses a reinforcement learning model to analyze patterns of power consumption and to predict load fluctuations. By learning optimal load distribution strategies from this data, the AI can balance power usage across machines and prevent peak loads that might cause shutdowns or inefficiencies. The AI also anticipates high-load conditions based on historical data, enabling it to make real-time adjustments and prevent strain on the system.

The control interface allows the AI system to implement adjustments directly, such as temporarily shifting non-essential loads or reducing power to certain machinery during peak usage. This intelligent load management minimizes energy costs and maintains consistent system performance, reducing both energy waste and downtime risks. Additionally, predictive maintenance insights from the AI can alert operators about potential issues before they escalate, enhancing reliability.

The third embodiment focuses on predictive diagnostics and optimization within medical equipment, where consistent performance and reliability are vital. Medical devices like MRI machines, ventilators, and surgical robots must operate with high precision and often face high-stress or high-frequency use, making predictive diagnostics essential.

In this application, the data acquisition module consists of sensors within the medical equipment that continuously monitor operational parameters such as temperature, vibration, and power consumption, along with usage frequency and environmental conditions. For instance, an MRI machine has sensors to track vibration levels and power draw, which are indicators of motor health and operational stability.

The AI processing unit uses predictive maintenance algorithms to identify early signs of wear or failure. The model is trained on historical performance and failure data, allowing it to detect abnormal patterns that may indicate potential breakdowns. For instance, an increase in vibration levels could signal motor issues, prompting early intervention. Additionally, the AI can estimate the remaining lifespan of critical components, helping operators schedule maintenance before issues become critical.

The control interface in this embodiment can initiate actions to reduce strain on vulnerable components, such as lowering operational intensity or temporarily switching to standby mode. The system also alerts maintenance personnel with diagnostic insights, facilitating timely intervention. This predictive diagnostic approach reduces the likelihood of unexpected failures, minimizes downtime, and ensures that medical devices remain safe and reliable for patient care.

These embodiments demonstrate the adaptability of the AI-powered control and optimization system across different fields, from smart home appliances and industrial power systems to medical equipment. By leveraging real-time data acquisition, advanced AI analysis, and intelligent control interfaces, the invention provides a versatile solution for enhancing the efficiency, reliability, and performance of electronic devices. This AI-driven system optimizes operations while reducing energy consumption and minimizing downtime, making it applicable to various domains and highly beneficial for end-users.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation.

, Claims:1.An AI-driven control and optimization system for an electronic device, comprising:
a data acquisition module configured to collect real-time data from one or more operational and environmental parameters of the electronic device;
an AI processing unit configured to analyze the collected data using an artificial intelligence model to generate optimization commands based on identified patterns and system requirements;
a control interface configured to receive the optimization commands from the AI processing unit and adjust one or more operational parameters of the electronic device in real time, thereby optimizing device performance and efficiency.

2.The system of claim 1, wherein the AI processing unit utilizes a machine learning model selected from the group consisting of neural networks, reinforcement learning models, and decision tree algorithms.

3.The system of claim 1, wherein the data acquisition module includes sensors for monitoring at least one of the following parameters: temperature, humidity, power consumption, load fluctuations, vibration, or device usage frequency.

4.The system of claim 1, wherein the control interface adjusts the operational parameters of the electronic device selected from the group consisting of power consumption, processing speed, cooling intensity, fan speed, or energy-saving modes.

5.The system of claim 1, wherein the AI processing unit generates predictive maintenance commands based on historical data and real-time operational data, enabling the system to anticipate and prevent potential device failures.

6.The system of claim 1, further comprising a communication module that allows the system to transmit data to an external server for continuous learning and updating of the AI model.

7.The system of claim 1, wherein the AI processing unit is trained to optimize device performance for multiple user-defined scenarios, including varying environmental conditions or usage patterns.

8.The system of claim 1, wherein the data acquisition module is configured to communicate with additional connected devices in a network, allowing the system to perform cross-device optimization in a multi-device environment.

9.The system of claim 1, wherein the control interface is capable of reducing operational load on the electronic device when an overload condition is detected, based on the analysis from the AI processing unit.

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