Wireless charging safety protection system based on magnetic field modulation

Abstract

The invention discloses a wireless charging safety protection system based on magnetic field modulation, and relates to the technical field of wireless charging optimization, and the system comprises a data collection module which is used for collecting and preprocessing data of charging equipment; the safety detection module is used for detecting the state of the charging equipment according to the collected charging equipment data and adjusting the electromagnetic intensity in real time for human body safety protection; the management optimization module is used for performing energy management and recovery on the charging equipment and optimizing a charging strategy; the display storage module is used for displaying the charging equipment data and the charging equipment state and storing the charging equipment data and the charging equipment state in a database; and the central control module is used for connecting all the modules and generating a control command for module interaction. The state detection accuracy of the charging equipment is effectively improved, the electromagnetic intensity is adjusted according to the human body distance, the charging safety is improved, meanwhile, accurate charging and discharging of the charging equipment are achieved by conducting energy management and recovery on the charging equipment, and the energy utilization rate is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless charging optimization, and in particular to a wireless charging safety protection system based on magnetic field modulation. Background Art

[0002] With the popularization of mobile Internet and smart devices, wireless charging technology, as a convenient way of power transmission, has been widely used in mobile phones, smart watches, electric vehicles and other fields. Wireless charging technology mainly relies on the principle of magnetic induction to transmit electrical energy from the charging device to the battery through an electromagnetic field. However, with the deepening of wireless charging applications, its safety and efficiency issues have received increasing attention. Existing wireless charging systems usually rely on simple electromagnetic field adjustment schemes and cannot dynamically adjust the electromagnetic radiation intensity in real time during the charging process to ensure human safety. At the same time, in terms of energy management and recovery, many systems fail to effectively achieve efficient energy recovery and charging device status detection, resulting in inefficient battery charging process and even safety hazards such as overcharging and overheating. Summary of the invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a wireless charging safety protection system based on magnetic field modulation to solve the problem of failure to effectively realize efficient energy recovery and charging device status detection.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: The present invention provides a wireless charging safety protection system based on magnetic field modulation, which comprises: Data collection module, used to collect charging equipment data and perform pre-processing; Safety detection module, used to detect the status of charging equipment according to the collected charging equipment data, and adjust the electromagnetic intensity in real time to protect human body safety; Management optimization module, used to manage and recycle energy from charging equipment and optimize charging strategies; A display and storage module is used to display the charging device data and the charging device status and store them in a database; The central control module is used to connect all modules and generate control commands for module interaction.

[0006] As a preferred solution of the wireless charging safety protection system based on magnetic field modulation described in the present invention, wherein: the collection of charging device data and pre-processing refers to deploying sensors on the charging device, connecting all sensors wirelessly to form a sensor network, aligning the sensor network timestamps and setting the data collection cycle to collect charging device data, cleaning the collected charging device data, and using wavelet transform combined with Kalman filtering to denoise the charging device data, and standardizing the denoised charging device data. The charging device includes a wireless charging device and a wireless charging battery.

[0007] As a preferred solution of the wireless charging safety protection system based on magnetic field modulation of the present invention, wherein: the detection of the charging device status according to the collected charging device data refers to obtaining the standardized charging device data, and each type of data in the charging device data is represented by a polynomial : in For the i-th type of input data, are the polynomial coefficients, is the degree of the polynomial; Extract Polynomial Each item in is combined to form a column vector of the i-th type of input data : According to the column vector Use GRS code to generate the mapping matrix G of charging equipment data: in is the code location element of the GRS code, and n is the data type in the charging device data; Select a multi-layer fully connected neural network to build a charging device detection model, including an input layer, a hidden layer, and an output layer. Define the input layer input as the charging device data, and the output layer output as the charging device status value; The mapping matrix G is added to the input layer, and each type of charging device data is converted into a high-dimensional vector in the input layer by multiplying it with the mapping matrix G. Then enter the hidden layer; The hidden layer uses multiple fully connected layers, and each fully connected layer uses the ReLU activation function and mapping matrix G for nonlinear transformation: Where h is the output of the fully connected layer, is the weight of the fully connected layer, is the bias of the fully connected layer; The output layer processes the output of the fully connected layer through the sigmoid function and outputs the charging device status value; The training data is input into the charging equipment detection model, and the parameters of the charging equipment detection model are iteratively optimized using the cross entropy loss function and the Adam optimizer until the loss converges; The charging device data is input into the trained charging device detection model to obtain the charging device status value, and the charging device status value is compared with the status threshold. If the charging device status value is greater than the status threshold, it is judged that the charging device status is abnormal and the charging device is powered off.

[0008] As a preferred solution of the wireless charging safety protection system based on magnetic field modulation described in the present invention, the real-time adjustment of electromagnetic intensity for human safety protection refers to deploying ultrasonic sensors and infrared sensors on the charging device to detect the distance r between the human body and the charging device in real time, and calculating the magnetic field intensity B during normal operation according to the charging device data, and setting the safety distance ; When it is detected that r≤ When the charging device is connected to the human body, the charging device adjusts the magnetic field strength in real time according to the distance r between the human body and the charging device: in is the adjusted magnetic field strength.

[0009] As a preferred solution of the wireless charging safety protection system based on magnetic field modulation of the present invention, wherein: the energy management and recovery of the charging device includes: Get the remaining power of the wireless charging battery and rated capacity , through the rated capacity Subtract remaining power Calculate the charge level , and calculate the wireless charging battery energy state y: in For wireless charging battery density, For the remaining battery life of wireless charging, Battery cooling rate for wireless charging, is the charging factor; Calculate the energy recovery value m according to the wireless charging battery energy state y: When the energy recovery value m exceeds the set recovery threshold, the wireless charging battery enters the energy recovery mode; The ratio of the charging current and charging voltage of the wireless charging battery is used as the right shift probability amplitude , the ratio of discharge current to discharge voltage is taken as the left shift probability amplitude , and calculate the charging power and discharge power , the charging power and discharge power The sum is the total power of the wireless charging battery , the charging power With total power The ratio of , discharge power With total power The ratio of the discharge excitation amplitude , and the charging excitation amplitude and discharge excitation amplitude The sum of , through the commutation relation to determine the excitation operator ; The quantum state formula that defines the charging and discharging state of a wireless charging battery is: in It is the quantum state of the charging and discharging state of the wireless charging battery. The quantum ground state of the charging and discharging state of the wireless charging battery; By the excitation operator and deexcitation operator Calculating the Hamiltonian for Wirelessly Rechargeable Batteries and the coupled Hamiltonian operator between the wireless charging device and the battery : in The frequency of battery charge and discharge; calculate and The sum is used as the Hamiltonian operator H of the wireless charging battery, and the Schrödinger equation is used to define the change of the quantum state of the wireless charging battery's charging and discharging state over time: Where t is time and j is an imaginary unit; The numerical integration method is used to solve the Schrödinger equation to obtain the quantum state of the wireless charging battery charging and discharging state at time t , and calculate the battery energy value at time t : Will Compared with the energy recovery value m, if If is greater than m, the wireless charging battery is in charging state. If it is less than m, the wireless charging battery is in a discharging state. When it is equal to m, the wireless charging battery stops charging and discharging; After the charging and discharging state of the wireless charging battery is obtained, the charging and discharging amount of the wireless charging battery is calculated according to the difference between the time t and the current time and the power corresponding to the charging and discharging state.

[0010] As a preferred solution of the wireless charging safety protection system based on magnetic field modulation described in the present invention, wherein: the energy management and recovery of the charging device also includes converting the DC discharge of the wireless charging battery into AC power through an inverter and incorporating it into the power grid.

[0011] As a preferred solution of the wireless charging safety protection system based on magnetic field modulation described in the present invention, wherein: the optimization of the charging strategy refers to constructing a charging strategy optimization model through a convolutional neural network, setting the input of the charging strategy optimization model as charging device data, and the output as an optimized charging strategy, and using a training set to train the charging strategy optimization model; The trained charging strategy optimization model is further optimized through the reinforcement learning algorithm, and the charging equipment data is input into the optimized charging strategy optimization model to obtain the optimized charging strategy.

[0012] As a preferred solution of the wireless charging safety protection system based on magnetic field modulation described in the present invention, wherein: the display of charging device data and charging device status refers to visually displaying the collected charging device data and charging device status, and generating a charging device detection report according to the timestamp for synchronous display.

[0013] As a preferred solution of the wireless charging safety protection system based on magnetic field modulation described in the present invention, wherein: the storage in the database refers to storing the charging device data and the charging device status in the database, the database regularly performs integrity detection and security detection on the stored data, and uploads the stored data to the cloud for backup.

[0014] As a preferred solution of the wireless charging safety protection system based on magnetic field modulation described in the present invention, wherein: the connecting all modules and generating control commands for module interaction refers to the central control module being connected to the data collection module, the safety detection module, the management optimization module and the display storage module through wireless connection, and generating control commands when module collaborative operation is required and sending them to the corresponding modules for execution.

[0015] The beneficial effects of the present invention are as follows: the present invention collects charging device data, uses GRS code to optimize and construct a charging device detection model to detect the status of the charging device, thereby effectively improving the accuracy of charging device status detection, and adjusts the electromagnetic intensity according to the distance from the human body to improve charging safety. At the same time, by managing and recovering the energy of the charging device, the accurate charging and discharging of the charging device is achieved, thereby improving energy utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 This is a structural diagram of the wireless charging safety protection system based on magnetic field modulation in Example 1. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0019] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0021] Example 1, reference Figure 1 , which is the first embodiment of the present invention, provides a wireless charging safety protection system based on magnetic field modulation, including the following modules: S1, data collection module, used to collect charging equipment data and perform preprocessing; Specifically, collecting charging device data and preprocessing it refers to deploying sensors on the charging device, connecting all sensors wirelessly to form a sensor network, aligning the sensor network timestamps and setting the data collection cycle to collect charging device data, cleaning the collected charging device data, and using wavelet transform combined with Kalman filtering to denoise the charging device data, and standardizing the denoised charging device data. The sensors include temperature sensors, current sensors, voltage sensors, magnetic field sensors, etc. The charging device includes a wireless charging device and a wireless charging battery. The wireless discharge device is used to provide electrical energy, such as a wireless charger, and the wireless charging device is used to receive electrical energy.

[0022] By deploying various types of sensors (such as temperature, current, voltage and magnetic field sensors) in charging equipment, it is possible to comprehensively monitor all key parameters of the charging process in real time. This multi-sensor configuration can provide comprehensive data feedback on the charging equipment during charging, discharging and other working conditions. Each sensor is responsible for monitoring different physical quantities to ensure that the system can accurately reflect the status and working environment of the equipment. By using wavelet transform combined with Kalman filtering, multi-scale analysis of the data can be performed to further improve the smoothness and reliability of the data, especially to provide accurate estimates of dynamic signal changes during battery charging.

[0023] S2, safety detection module, used to detect the status of the charging device according to the collected charging device data, and adjust the electromagnetic intensity in real time for human safety protection; Specifically, detecting the charging device status according to the collected charging device data refers to obtaining the standardized charging device data, and each type of data in the charging device data is represented by a polynomial : in For the i-th type of input data, are the polynomial coefficients, The degree of the polynomial is usually determined based on the feature complexity of the training data to ensure that the polynomial form of the input data can effectively capture the nonlinear relationship between features. By using the polynomial form, the nonlinear characteristics of the equipment data can be better captured in the model, thereby improving the accuracy of equipment status detection; Extract Polynomial Each item in is combined to form a column vector of the i-th type of input data : According to the column vector Use GRS code to generate the mapping matrix G of charging equipment data: in is the code locator of the GRS code, usually a special constant is selected, n is the data type in the charging device data, GRS (Generalized Reed-Solomon) code is a mathematical tool for data encoding, often used for error correction and signal encoding, the selection of the code locator and codeword of the GRS code helps to improve the stability of the mapping and the redundancy of the data, thereby enhancing the robustness of the model; Select a multi-layer fully connected neural network to build a charging device detection model, including an input layer, a hidden layer, and an output layer. Define the input layer input as the charging device data, and the output layer output as the charging device status value; The mapping matrix G is added to the input layer, and each type of charging device data is converted into a high-dimensional vector in the input layer by multiplying it with the mapping matrix G. Then enter the hidden layer; The hidden layer uses multiple fully connected layers, and each fully connected layer uses the ReLU activation function and mapping matrix G for nonlinear transformation: Where h is the output of the fully connected layer, is the weight of the fully connected layer, is the bias of the fully connected layer; Through the design of multiple hidden layers, FCNN can capture complex nonlinear relationships in charging device data, thereby improving the accuracy of charging device status detection; The output layer processes the output of the fully connected layer through the sigmoid function and outputs the charging device status value in the range of ; The training data is input into the charging equipment detection model, and the parameters of the charging equipment detection model are iteratively optimized using the cross entropy loss function and the Adam optimizer until the loss converges; The charging device data is input into the trained charging device detection model to obtain the charging device status value, and the charging device status value is compared with the status threshold. If the charging device status value is greater than the status threshold, it is judged that the charging device status is abnormal and the charging device is powered off.

[0024] A multi-layer fully connected neural network (FCNN) is used as the charging device status detection model. It can learn the complex features in the input data through multiple hidden layers. In the nonlinear conversion process of each layer, the neural network can gradually abstract the deep features in the data, and further improve the modeling ability of complex data relationships through the mapping of GRS codes. Through the ReLU activation function of the hidden layer, the neural network can not only introduce nonlinear characteristics, but also effectively process sparse data, thereby improving the learning efficiency of the model. The trained charging device detection model can predict the status value of the device based on the real-time input data. The system compares the detection result with the preset status threshold. If the status value of the charging device exceeds the threshold, the power-off mechanism is triggered to prevent the device from malfunctioning due to overheating, excessive current or other abnormal conditions. This process can respond to the working status of the device in real time and ensure the safety of the charging process.

[0025] Furthermore, real-time adjustment of electromagnetic intensity for human safety protection refers to deploying ultrasonic sensors and infrared sensors on the charging device to detect the distance r between the human body and the charging device in real time, and calculating the magnetic field intensity B during normal operation based on the charging device data to set the safety distance ; Ultrasonic sensors measure the distance of objects by emitting and receiving sound waves and are often used for object detection and distance measurement. Infrared sensors detect objects using the reflection principle of infrared light and are widely used for close-range object perception. In the present invention, infrared sensors and ultrasonic sensors together provide more accurate human body distance measurement, thereby providing data support for the dynamic adjustment of magnetic field strength. When it is detected that r≤ When the charging device is connected to the human body, the charging device adjusts the magnetic field strength in real time according to the distance r between the human body and the charging device: in is the adjusted magnetic field strength.

[0026] By calculating the normal operating magnetic field strength of the charging device, the system can understand the magnetic field strength that the charging device should generate under ideal conditions. This calculation process is based on the power requirements of the device, the battery charging characteristics and the electromagnetic field distribution model. It can accurately predict the magnetic field strength that the device should maintain under different operating conditions. Through a reasonable safety distance, the system can effectively avoid the risk of human exposure to high-intensity electromagnetic radiation. Once the system detects that the human body is approaching and below the safety distance, it can automatically adjust the magnetic field strength to minimize the impact of electromagnetic radiation on the human body.

[0027] S3, management optimization module, used to manage and recycle the energy of charging equipment and optimize the charging strategy; Specifically, energy management and recovery of charging equipment includes: Get the remaining power of the wireless charging battery and rated capacity , through the rated capacity Subtract remaining power Calculate the charge level , and calculate the wireless charging battery energy state y: in For wireless charging battery density, is the remaining life of the wireless charging battery, in years, The cooling rate of the wireless charging battery is estimated based on factors such as the battery working status and ambient temperature. It reflects the efficiency of the battery in dissipating or absorbing heat during the charging and discharging process. is the charging coefficient, determined experimentally; The battery energy state y reflects the current working state and health of the battery. It estimates the overall energy state of the battery by considering the battery's density, remaining life, cooling rate, and the heat dissipation and heat absorption effects during the charging and discharging process. This state directly affects the battery's charging and discharging efficiency; Calculate the energy recovery value m according to the wireless charging battery energy state y: When the energy recovery value m exceeds the set recovery threshold, the wireless charging battery enters the energy recovery mode; The recovery threshold refers to the minimum energy recovery value that the battery needs to reach before it starts to enter the energy recovery mode. It determines whether the battery enters the energy recovery state and affects the triggering conditions of the recovery mode. This threshold is usually determined through experiments; The ratio of the charging current and charging voltage of the wireless charging battery is used as the right shift probability amplitude , the ratio of discharge current to discharge voltage is taken as the left shift probability amplitude , and calculate the charging power and discharge power , the charging power and discharge power The sum is the total power of the wireless charging battery , the charging power With total power The ratio of , discharge power With total power The ratio of the discharge excitation amplitude , and the charging excitation amplitude and discharge excitation amplitude The sum of , through the commutation relation to determine the excitation operator ,Right now ; The quantum state formula that defines the charging and discharging state of a wireless charging battery is: in It is the quantum state of the charging and discharging state of the wireless charging battery. The quantum ground state of the charging and discharging state of the wireless charging battery; By the excitation operator and deexcitation operator Calculating the Hamiltonian for Wirelessly Rechargeable Batteries and the coupled Hamiltonian operator between the wireless charging device and the battery : in The frequency of battery charge and discharge; calculate and The sum is used as the Hamiltonian operator H of the wireless charging battery, and the Schrödinger equation is used to define the change of the quantum state of the wireless charging battery's charging and discharging state over time: Where t is time and j is an imaginary unit; The quantum state reflects the energy distribution and state of the battery. The quantum ground state is the basic state of the quantum state, which usually indicates the most stable state of the battery at low energy. Through the description of quantum mechanics, the microscopic state changes during the charging and discharging process can be captured more accurately. The numerical integration method is used to solve the Schrödinger equation to obtain the quantum state of the wireless charging battery charging and discharging state at time t , and calculate the battery energy value at time t : Will Compared with the energy recovery value m, if If is greater than m, the wireless charging battery is in charging state. If it is less than m, the wireless charging battery is in a discharging state. When it is equal to m, the wireless charging battery stops charging and discharging; By solving the Schrödinger equation, the changes in the battery's charge and discharge states can be accurately predicted, thereby determining the battery's energy state at any time. Numerical integration methods are used to solve the time evolution problem in the Schrödinger equation. Especially when the equation cannot be solved analytically, numerical methods can be used to approximate the quantum state changes of the system. After the charging and discharging state of the wireless charging battery is obtained, the charging and discharging amount of the wireless charging battery is calculated according to the difference between the time t and the current time and the power corresponding to the charging and discharging state.

[0028] By defining the quantum state and excitation operators of the battery's charge and discharge states, the system can accurately simulate the battery's charge and discharge process and predict the battery's energy changes through a quantum mechanics model. The quantum state describes the energy distribution of the battery under different charge states, while the excitation operator and de-excitation operator help the system accurately control the charge and discharge process to ensure the efficiency and stability of the charge and discharge.

[0029] Furthermore, energy management and recovery of the charging device also includes converting the DC discharge of the wireless charging battery into AC power through an inverter and incorporating it into the power grid.

[0030] Furthermore, optimizing the charging strategy refers to constructing a charging strategy optimization model through a convolutional neural network, setting the charging strategy optimization model input as charging device data, output as an optimized charging strategy, and using a training set to train the charging strategy optimization model; The trained charging strategy optimization model is further optimized through the reinforcement learning algorithm, and the charging equipment data is input into the optimized charging strategy optimization model to obtain the optimized charging strategy.

[0031] By processing and analyzing charging equipment data through convolutional neural networks (CNN), it is possible to accurately capture complex patterns in the data and automatically extract features related to charging efficiency, equipment health, etc. This deep learning method can identify nonlinear relationships from massive amounts of charging equipment data, thereby providing accurate guidance for the optimization of charging strategies. The reinforcement learning algorithm simulates various operating strategies in the charging process, automatically performs trial and error and feedback adjustments, and continuously optimizes the charging strategy through multiple interactions. The reinforcement learning algorithm can adaptively adjust the charging parameters, thereby dynamically improving the charging efficiency and reducing battery loss in actual applications. This method can not only improve the charging efficiency, but also optimize the model through continuous learning to adapt to possible changes in the future.

[0032] S4, a display storage module, used to display the charging device data and the charging device status, and store them in a database; Specifically, displaying the charging device data and the charging device status refers to visually displaying the collected charging device data and the charging device status, and generating a charging device detection report according to a timestamp for synchronous display.

[0033] Furthermore, storing in a database refers to storing the charging device data and the charging device status in the database, and the database regularly performs integrity and security checks on the stored data, and uploads the stored data to the cloud for backup.

[0034] S5, central control module, used to connect all modules and generate control commands for module interaction; Specifically, connecting all modules and generating control commands for module interaction means that the central control module is connected to the data collection module, the safety detection module, the management optimization module and the display storage module through wireless connection, and when module collaborative operation is required, a control command is generated and sent to the corresponding module for execution.

[0035] The central control module exchanges data and works collaboratively with the data collection module, security detection module, management optimization module and display storage module in real time through wireless connection. Wireless connection avoids the limitations of physical wiring, allowing the system to be deployed and expanded more flexibly. The central control module aggregates the real-time data of all modules to form a comprehensive system view, providing a basis for subsequent decision-making. Through wireless communication, each module can respond and adjust quickly, improving the response speed and processing efficiency of the system. Through the control commands generated by the central control module, the system can schedule and coordinate each module according to real-time needs to ensure the orderly operation of all parts of the system. When an abnormality occurs in the system, the central control module can immediately issue instructions to adjust the operation of the corresponding module to avoid the spread of faults or more serious problems. This collaborative work between modules can effectively improve the automation level of the system and reduce human intervention.

[0036] This embodiment also provides a computer device, which is suitable for the case of a wireless charging safety protection system based on magnetic field modulation, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the wireless charging safety protection system based on magnetic field modulation as proposed in the above embodiment. The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0037] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the wireless charging safety protection system based on magnetic field modulation as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0038] In summary, the present invention collects charging device data and uses GRS code to optimize and construct a charging device detection model to detect the status of the charging device, thereby effectively improving the accuracy of charging device status detection, and adjusting the electromagnetic intensity according to the distance from the human body to improve charging safety. At the same time, by managing and recovering the energy of the charging device, accurate charging and discharging of the charging device is achieved, thereby improving energy utilization.

[0039] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A wireless charging safety protection system based on magnetic field modulation, characterized in that: include, Data collection module, used to collect charging equipment data and perform pre-processing; Safety detection module, used to detect the status of charging equipment according to the collected charging equipment data, and adjust the electromagnetic intensity in real time to protect human body safety; Management optimization module, used to manage and recycle energy from charging equipment and optimize charging strategies; A display and storage module is used to display the charging device data and the charging device status and store them in a database; The central control module is used to connect all modules and generate control commands for module interaction.

2. The wireless charging safety protection system based on magnetic field modulation according to claim 1, characterized in that: The collecting of charging device data and preprocessing thereof refers to deploying sensors on the charging device, forming a sensor network through wireless connection of all sensors, aligning the timestamp of the sensor network and setting the data collection cycle to collect charging device data, cleaning the collected charging device data, and denoising the charging device data using wavelet transform combined with Kalman filtering, and standardizing the denoised charging device data. The charging device includes a wireless charging device and a wireless charging battery.

3. The wireless charging safety protection system based on magnetic field modulation according to claim 2, characterized in that: The detecting the charging device status according to the collected charging device data refers to obtaining the standardized charging device data, and each type of data in the charging device data is represented as a polynomial : in For the i-th type of input data, are the polynomial coefficients, is the degree of the polynomial; Extract Polynomial Each item in is combined to form a column vector of the i-th type of input data : According to the column vector Use GRS code to generate the mapping matrix G of charging equipment data: in is the code location element of the GRS code, and n is the data type in the charging device data; Select a multi-layer fully connected neural network to build a charging device detection model, including an input layer, a hidden layer, and an output layer. Define the input layer input as the charging device data, and the output layer output as the charging device status value; The mapping matrix G is added to the input layer, and each type of charging device data is converted into a high-dimensional vector in the input layer by multiplying it with the mapping matrix G. Then enter the hidden layer; The hidden layer uses multiple layers of fully connected layers, and each fully connected layer uses the ReLU activation function and mapping matrix G for nonlinear transformation: Where h is the output of the fully connected layer, is the weight of the fully connected layer, Bias for the fully connected layer; The output layer processes the output of the fully connected layer through the sigmoid function and outputs the charging device status value; The training data is input into the charging equipment detection model, and the parameters of the charging equipment detection model are iteratively optimized using the cross entropy loss function and the Adam optimizer until the loss converges; The charging device data is input into the trained charging device detection model to obtain the charging device status value, and the charging device status value is compared with the status threshold. If the charging device status value is greater than the status threshold, it is judged that the charging device status is abnormal and the charging device is powered off.

4. The wireless charging safety protection system based on magnetic field modulation as claimed in claim 3, characterized in that: The real-time adjustment of electromagnetic intensity for human safety protection refers to deploying ultrasonic sensors and infrared sensors on the charging device to detect the distance r between the human body and the charging device in real time, and calculating the magnetic field intensity B during normal operation based on the charging device data to set the safety distance. ; When it is detected that r≤ When the charging device is connected to the human body, the charging device adjusts the magnetic field strength in real time according to the distance r between the human body and the charging device: in is the adjusted magnetic field strength.

5. The wireless charging safety protection system based on magnetic field modulation according to claim 4, characterized in that: The energy management and recovery of the charging equipment includes: Get the remaining power of the wireless charging battery and rated capacity , through the rated capacity Subtract remaining power Calculate the charge level , and calculate the wireless charging battery energy state y: in For wireless charging battery density, For the remaining battery life of wireless charging, Battery cooling rate for wireless charging, is the charging factor; Calculate the energy recovery value m according to the wireless charging battery energy state y: When the energy recovery value m exceeds the set recovery threshold, the wireless charging battery enters the energy recovery mode; The ratio of the charging current and charging voltage of the wireless charging battery is used as the right shift probability amplitude , the ratio of discharge current to discharge voltage is taken as the left shift probability amplitude , and calculate the charging power and discharge power , the charging power and discharge power The sum is the total power of the wireless charging battery , the charging power With total power The ratio of , discharge power With total power The ratio of the discharge excitation amplitude , and the charging excitation amplitude and discharge excitation amplitude The sum of , through the commutation relation to determine the excitation operator ; The quantum state formula that defines the charging and discharging state of a wireless charging battery is: in It is the quantum state of the charging and discharging state of the wireless charging battery. The quantum ground state of the charging and discharging state of the wireless charging battery; By the excitation operator and deexcitation operator Calculating the Hamiltonian for Wirelessly Rechargeable Batteries and the coupled Hamiltonian operator between the wireless charging device and the battery : in The frequency of battery charge and discharge; calculate and The sum is used as the Hamiltonian operator H of the wireless charging battery, and the Schrödinger equation is used to define the change of the quantum state of the wireless charging battery's charging and discharging state over time: Where t is time and j is an imaginary unit; The numerical integration method is used to solve the Schrödinger equation to obtain the quantum state of the wireless charging battery charging and discharging state at time t , and calculate the battery energy value at time t : Will Compared with the energy recovery value m, if If is greater than m, the wireless charging battery is in charging state. If it is less than m, the wireless charging battery is in a discharging state. When it is equal to m, the wireless charging battery stops charging and discharging; After the charging and discharging state of the wireless charging battery is obtained, the charging and discharging amount of the wireless charging battery is calculated according to the difference between the time t and the current time and the power corresponding to the charging and discharging state.

6. The wireless charging safety protection system based on magnetic field modulation according to claim 5, characterized in that: The energy management and recovery of the charging device also includes converting the DC discharge of the wireless charging battery into AC power through an inverter and incorporating it into the power grid.

7. The wireless charging safety protection system based on magnetic field modulation according to claim 6, characterized in that: Optimizing the charging strategy refers to building a charging strategy optimization model through a convolutional neural network, setting the charging strategy optimization model input as charging device data, outputting an optimized charging strategy, and using a training set to train the charging strategy optimization model; The trained charging strategy optimization model is further optimized through the reinforcement learning algorithm, and the charging equipment data is input into the optimized charging strategy optimization model to obtain the optimized charging strategy.

8. The wireless charging safety protection system based on magnetic field modulation according to claim 7, characterized in that: The display of the charging device data and the charging device status refers to visually displaying the collected charging device data and the charging device status, and generating a charging device detection report according to the timestamp for synchronous display.

9. The wireless charging safety protection system based on magnetic field modulation according to claim 8, characterized in that: The storing in the database refers to storing the charging device data and the charging device status in the database. The database regularly performs integrity and security checks on the stored data, and uploads the stored data to the cloud for backup.

10. The wireless charging safety protection system based on magnetic field modulation according to claim 9, characterized in that: The connecting all modules and generating control commands for module interaction means that the central control module is connected to the data collection module, the safety detection module, the management optimization module and the display storage module through wireless connection, and when module collaborative operation is required, a control command is generated and sent to the corresponding module for execution.

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