A wireless charging safety protection system based on magnetic field modulation

Abstract

The present invention discloses a wireless charging safety protection system based on magnetic field modulation, which relates to the technical field of wireless charging optimization. The system includes a data collection module for collecting charging device data and performing preprocessing; a safety detection module for detecting the status of the charging device according to the collected charging device data and adjusting the electromagnetic intensity in real time for human safety protection; a management and optimization module for performing energy management and recovery on the charging device and optimizing the charging strategy; a display and storage module for displaying the charging device data and the status of the charging device and storing them in a database; and a central control module for connecting all modules and generating control commands for module interaction. The present invention effectively improves the accuracy of charging device status detection, adjusts the electromagnetic intensity according to the human body distance to improve charging safety, and at the same time realizes precise charging and discharging of the charging device through energy management and recovery of the charging device, thereby improving energy utilization efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless charging optimization, 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 power transmission method, has been widely applied in fields such as mobile phones, smart watches, and electric vehicles. Wireless charging technology mainly relies on the principle of electromagnetic induction to transmit electrical energy from a charging device to a battery through an electromagnetic field. However, with the in-depth application of wireless charging, its safety and efficiency issues have increasingly attracted 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 low efficiency during the battery charging process and even potential 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, which solves the problem of failure to effectively achieve efficient energy recovery and charging device status detection.

[0005] To solve the above technical problems, the present invention provides the following technical solutions:

[0006] The present invention provides a wireless charging safety protection system based on magnetic field modulation, which includes

[0007] a data collection module for collecting charging device data and performing preprocessing;

[0008] a safety detection module for detecting the status of the charging device according to the collected charging device data and adjusting the electromagnetic intensity in real time for human safety protection;

[0009] a management optimization module for performing energy management and recovery on the charging device and optimizing the charging strategy;

[0010] a display storage module for displaying the charging device data and the status of the charging device and storing them in a database;

[0011] a central control module for connecting all modules and generating control commands for module interaction.

[0012] As a preferred solution of the wireless charging safety protection system based on magnetic field modulation according to the present invention, wherein: collecting charging device data and performing preprocessing means deploying sensors on the charging device, forming a sensor network by wirelessly connecting all sensors, aligning the sensor network timestamps and setting a data acquisition period 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 performing normalization processing on the denoised charging device data. The charging device includes a wireless charging device and a wireless charging battery.

[0013] As a preferred solution of the wireless charging safety protection system based on magnetic field modulation according to the present invention, wherein: detecting the charging device state according to the collected charging device data means obtaining the normalized charging device data, and representing each type of data in the charging device data as a polynomial :

[0014]

[0015] where is the i-th type of input data, are polynomial coefficients, is the polynomial degree;

[0016] Extract each term in the polynomial and combine them to form a column vector of the i-th type of input data:

[0017]

[0018] According to the column vector use the GRS code to generate the mapping matrix G of the charging device data:

[0019]

[0020] where is the code locator of the GRS code, and n is the number of data types in the charging device data;

[0021] Select a multi-layer fully connected neural network to construct a charging device detection model, including an input layer, a hidden layer, and an output layer. Define the input of the input layer as the charging device data, and the output of the output layer as the charging device status value;

[0022] Add the mapping matrix G to the input layer. Each type of charging device data is converted into a high-dimensional vector by multiplying with the mapping matrix G in the input layer and then enters the hidden layer;

[0023] The hidden layer uses multi-layer fully connected layers, and each fully connected layer uses the ReLU activation function and the mapping matrix G for non-linear transformation:

[0024]

[0025] where h is the output of the fully connected layer, is the weight of the fully connected layer, and is the bias of the fully connected layer;

[0026] The output layer processes the output of the fully connected layer through the sigmoid function to output the state value of the charging device;

[0027] Input the training data into the charging device detection model, and use the cross-entropy loss function and the Adam optimizer to iteratively optimize the parameters of the charging device detection model until the loss converges;

[0028] Input the charging device data into the trained charging device detection model to obtain the state value of the charging device. Compare the state value of the charging device with the state threshold. If the state value of the charging device is greater than the state threshold, it is determined that the state of the charging device is abnormal and the charging device is powered off.

[0029] As a preferred solution of the wireless charging safety protection system based on magnetic field modulation according to the present invention, wherein: the real-time adjustment of the electromagnetic intensity for human safety protection means 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 a safety distance ;

[0030] When it is detected that r ≤ , the charging device adjusts the magnetic field intensity in real time through the distance r between the human body and the charging device:

[0031]

[0032] where is the adjusted magnetic field intensity.

[0033] As a preferred solution of the wireless charging safety protection system based on magnetic field modulation according to the present invention, wherein: the energy management and recovery of the charging device includes,

[0034] Obtain the remaining power and the rated capacity of the wireless charging battery. Calculate the charging amount by subtracting the remaining power from the rated capacity , and calculate the energy state y of the wireless charging battery:

[0035]

[0036] where is the wireless charging battery density, is the remaining life of the wireless charging battery, is the cooling rate of the wireless charging battery, is the charging coefficient;

[0037] Calculate the energy recovery value m according to the energy state y of the wireless charging battery:

[0038]

[0039] When the energy recovery value m exceeds the set recovery threshold, the wireless charging battery enters the energy recovery mode;

[0040] Take the ratio of the charging current and charging voltage of the wireless charging battery as the right shift probability amplitude , and take the ratio of the discharge current and discharge voltage as the left shift probability amplitude , and calculate the charging power and the discharge power , take the charging power and the discharge power The sum is used as the total power of the wireless charging battery , take the ratio of the charging power to the total power as the charging excitation amplitude , the discharge power to the total power as the discharge excitation amplitude , and take the charging excitation amplitude and the discharge excitation amplitude The sum is used as the excitation operator , determine the de-excitation operator through the commutation relation ;

[0041] Define the quantum state formula for the charge-discharge state of the wireless charging battery as:

[0042]

[0043] where is the quantum state of the charge-discharge state of the wireless charging battery, is the quantum ground state of the charge-discharge state of the wireless charging battery;

[0044] Calculate the Hamiltonian operator of the wireless charging battery and the coupled Hamiltonian operator between the wireless charging device and the battery through the excitation operator and the de-excitation operator :

[0045]

[0046]

[0047] wherein is the charge and discharge frequency of the battery;

[0048] Calculate and and sum them as the Hamiltonian operator H of the wireless charging battery, and define the time variation of the quantum state of the charge and discharge state of the wireless charging battery through the Schrödinger equation:

[0049]

[0050] where t is time and j is the imaginary unit;

[0051] Use the numerical integration method to solve the Schrödinger equation to obtain the quantum state of the charge and discharge state of the wireless charging battery at time t and calculate the battery energy value at time t :

[0052]

[0053] Compare with the energy recovery value m. If is greater than m, the wireless charging battery is in the charging state. If is less than m, the wireless charging battery is in the discharging state. If is equal to m, the wireless charging battery stops charging and discharging;

[0054] After obtaining the charge and discharge state of the wireless charging battery, calculate the charge and discharge amount of the wireless charging battery according to the difference between time t and the current time and the power corresponding to the charge and discharge state.

[0055] As a preferred solution of the wireless charging safety protection system based on magnetic field modulation according to the present invention, wherein: the energy management and recovery of the charging device further includes converting the DC discharge of the wireless charging battery into alternating current through an inverter and incorporating it into the power grid.

[0056] As a preferred solution of the wireless charging safety protection system based on magnetic field modulation according to 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 the charging device data, the output as the optimized charging strategy, and using the training set to train the charging strategy optimization model;

[0057] Further optimize the trained charging strategy optimization model through a reinforcement learning algorithm, and input the charging device data into the optimized charging strategy optimization model to obtain the optimized charging strategy.

[0058] As a preferred solution of the wireless charging safety protection system based on magnetic field modulation of the present invention, wherein: 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 time stamp for synchronous display.

[0059] As a preferred solution of the wireless charging safety protection system based on magnetic field modulation of 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.

[0060] As a preferred solution of the wireless charging safety protection system based on magnetic field modulation of the present invention, wherein: the connection of all modules and the generation of control commands for module interaction means that the central control module is wirelessly connected to the data collection module, the security detection module, the management optimization module, and the display storage module, and generates control commands to be sent to the corresponding modules for execution when module collaborative operations are required.

[0061] The beneficial effects of the present invention are as follows: By collecting charging device data, using the GRS code to optimize and construct a charging device detection model for charging device status detection, the accuracy of charging device status detection is effectively improved. The electromagnetic intensity is adjusted according to the human body distance to improve charging safety. At the same time, precise charging and discharging of the charging device are realized through energy management and recovery of the charging device, improving energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0063] Figure 1 It is the structural diagram of the wireless charging safety protection system based on magnetic field modulation in Embodiment 1. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] To make the above objects, features, and advantages of the present invention more obvious and understandable, the detailed implementation manners of the present invention will be described in detail below with reference to the drawings in the specification.

[0065] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways than those specifically described herein, and those skilled in the art can make similar extensions without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0066] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that are mutually exclusive with other embodiments.

[0067] Embodiment 1, referring to Figure 1 , is the first embodiment of the present invention. This embodiment provides a wireless charging safety protection system based on magnetic field modulation, including the following modules:

[0068] S1. A data collection module, which is used to collect charging device data and perform preprocessing;

[0069] Specifically, collecting charging device data and performing preprocessing means deploying sensors on the charging device, forming a sensor network by wirelessly connecting all sensors, aligning the sensor network timestamps and setting a data acquisition period for charging device data acquisition, cleaning the collected charging device data, and using wavelet transform combined with Kalman filtering to denoise the charging device data, and performing standardization processing on the denoised charging device data. The sensors include temperature sensors, current sensors, voltage sensors, magnetic field sensors, etc. The charging devices include wireless charging devices and wireless charging batteries. The wireless discharging device is used to provide electrical energy, such as a wireless charger, and the wireless charging device is used to receive electrical energy.

[0070] By deploying various types of sensors (such as temperature, current, voltage, and magnetic field sensors) in the charging device, it is possible to comprehensively and real-time monitor various key parameters during the charging process. This multi-sensor configuration can provide comprehensive data feedback of the charging device in the charging, discharging, and other working states. Each sensor is responsible for monitoring different physical quantities to ensure that the system can accurately reflect the device's state and working environment. By using wavelet transform combined with Kalman filtering, multi-scale analysis of the data can be performed, further improving the smoothness and reliability of the data, and providing accurate estimation especially in the dynamic signal changes during the battery charging process.

[0071] S2. A safety detection module, which is used to detect the charging device state according to the collected charging device data and adjust the electromagnetic intensity in real time for human safety protection;

[0072] Specifically, detecting the charging device status based on the collected charging device data means obtaining the standardized charging device data. For each type of data in the charging device data, it is represented as a polynomial :

[0073]

[0074] where is the i-th type of input data, is the polynomial coefficient, is the polynomial degree, which 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 non-linear relationships between features. By using the polynomial form, the non-linear features of the device data can be better captured into the model, thereby improving the accuracy of device status detection;

[0075] Extract each term in the polynomial and combine them to form the column vector of the i-th type of input data:

[0076]

[0077] According to the column vector use the GRS code to generate the mapping matrix G of the charging device data:

[0078]

[0079] where is the code locator of the GRS code, usually a special constant, n is the number of data types in the charging device data. The GRS (Generalized Reed-Solomon) code is a mathematical tool for data encoding, commonly used for error correction and signal encoding. The selection of the code locator and the 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;

[0080] Select a multi-layer fully connected neural network to construct a charging device detection model, including an input layer, a hidden layer, and an output layer. Define the input of the input layer as the charging device data, and the output of the output layer as the charging device status value;

[0081] Add the mapping matrix G to the input layer. Each type of charging device data is converted into a high-dimensional vector by multiplying with the mapping matrix G in the input layer and then enters the hidden layer;

[0082] The hidden layer uses multi-layer fully connected layers, and each fully connected layer uses the ReLU activation function and the mapping matrix G for non-linear transformation:

[0083]

[0084] 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;

[0085] Through the design of multiple hidden layers, the FCNN can capture complex non-linear relationships in the charging device data, thereby improving the accuracy of charging device status detection;

[0086] The output layer processes the output of the fully connected layer through the sigmoid function to output the charging device status value, and the range is ;

[0087] Input the training data into the charging device detection model, and use the cross-entropy loss function and the Adam optimizer to iteratively optimize the parameters of the charging device detection model until the loss converges;

[0088] Input the charging device data into the trained charging device detection model to obtain the charging device status value. Compare the charging device status value 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.

[0089] Using a multi-layer fully connected neural network (FCNN) as the charging device status detection model, it can learn complex features in the input data through multiple hidden layers. In the non-linear transformation 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 non-linear characteristics, but also effectively process sparse data, improving the learning efficiency of the model. Through the trained charging device detection model, it can predict the status value of the device according to the real-time input data. The system compares the detection result with the preset status threshold. If the charging device status value 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 to ensure the safety during the charging process.

[0090] Furthermore, real-time adjustment of the electromagnetic intensity for human safety protection means 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 ;

[0091] The ultrasonic sensor measures the distance of an object by emitting and receiving sound waves and is commonly used for object detection and distance measurement. The infrared sensor, on the other hand, detects objects using the reflection principle of infrared light and is widely used for short-range object perception. In the present invention, the infrared sensor, together with the ultrasonic sensor, provides more accurate measurement of the distance of a human body, thereby providing data support for the dynamic adjustment of the magnetic field strength;

[0092] When it is detected that r ≤ , the charging device adjusts the magnetic field strength in real time according to the distance r between the human body and the charging device:

[0093]

[0094] where is the adjusted magnetic field strength.

[0095] 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, and 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 it is detected that a human body approaches and is below this safety distance, the system can automatically adjust the magnetic field strength, thereby minimizing the impact of electromagnetic radiation on the human body.

[0096] S3. The management and optimization module is used for energy management and recovery of the charging device and optimization of the charging strategy;

[0097] Specifically, the energy management and recovery of the charging device includes

[0098] obtaining the remaining power and rated capacity of the wireless charging battery , calculating the charging amount by subtracting the remaining power from the rated capacity , and calculating the energy state y of the wireless charging battery:

[0099]

[0100] where is the wireless charging battery density, is the remaining life of the wireless charging battery in years, is the cooling rate of the wireless charging battery, estimated according to factors such as the battery operating state and ambient temperature, and reflects the efficiency of heat dissipation or heat absorption of the battery during charge and discharge, is the charging coefficient, determined through experiments;

[0101] ​​The battery energy state y reflects the current working state and health condition of the battery. It estimates the overall energy state of the battery by considering the battery density, remaining life, cooling rate, and heat dissipation and heat absorption effects during the charge and discharge processes. This state directly affects the charging and discharging efficiency of the battery;

[0102] Calculate the energy recovery value m based on the wireless charging battery energy state y:

[0103]

[0104] When the energy recovery value m exceeds the set recovery threshold, the wireless charging battery enters the energy recovery mode;

[0105] The recovery threshold refers to the minimum energy recovery value required for the battery to start entering 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;

[0106] Take the ratio of the charging current and charging voltage of the wireless charging battery as the right shift probability amplitude , and the ratio of the discharge current and discharge voltage as the left shift probability amplitude , and calculate the charging power and the discharge power , take the sum of the charging power and the discharge power as the total power of the wireless charging battery , take the ratio of the charging power to the total power as the charging excitation amplitude , take the ratio of the discharge power to the total power as the discharge excitation amplitude , and take the sum of the charging excitation amplitude and the discharge excitation amplitude as the excitation operator , determine the de-excitation operator through the commutation relation, that is ;

[0107] Define the quantum state formula for the charge and discharge state of the wireless charging battery as:

[0108]

[0109] where is the quantum state of the charge and discharge state of the wireless charging battery, is the quantum ground state of the charge and discharge state of the wireless charging battery;

[0110] Through the excitation operator and the de-excitation operator Calculate the Hamiltonian operator of the wireless charging battery and the coupling Hamiltonian operator of the wireless charging device and the battery :

[0111]

[0112]

[0113] where is the charging and discharging frequency of the battery;

[0114] Calculate and The sum of them is used as the Hamiltonian operator H of the wireless charging battery. The time evolution of the quantum state of the charging and discharging state of the wireless charging battery is defined by the Schrödinger equation:

[0115]

[0116] where t is the time and j is the imaginary unit;

[0117] The quantum state reflects the energy distribution and state of the battery. The quantum ground state is the basic state of the quantum state, usually representing the most stable state of the battery in the low-energy state. Through the description of quantum mechanics, the microscopic state changes during the charging and discharging process can be captured more precisely;

[0118] Use the numerical integration method to solve the Schrödinger equation to obtain the quantum state of the charging and discharging state of the wireless charging battery at time t , and calculate the battery energy value at time t :

[0119]

[0120] Compare with the energy recovery value m. If is greater than m, the wireless charging battery is in the charging state. If is less than m, the wireless charging battery is in the discharging state. If is equal to m, the wireless charging battery stops charging and discharging;

[0121] By solving the Schrödinger equation, the change of the charging and discharging state of the battery can be accurately predicted, so as to judge the energy state of the battery at any time. The numerical integration method is used to solve the time evolution problem in the Schrödinger equation. Especially when the equation has no analytical solution, the quantum state change of the system can be approximately obtained by numerical methods;

[0122] After obtaining the charging and discharging state of the wireless charging battery, calculate the charging and discharging amount of the wireless charging battery according to the difference between the time t and the current time and the power corresponding to the charging and discharging state.

[0123] By defining the quantum states and excitation operators that characterize the charge and discharge states of the battery, the system can accurately simulate the charge and discharge processes of the battery and predict the energy changes of the battery through a quantum mechanics model. The quantum states describe the energy distribution of the battery under different charging states, and the excitation and de-excitation operators help the system precisely control the charging and discharging processes to ensure the efficiency and stability of charging and discharging.

[0124] Furthermore, the energy management and recovery of the charging device also include converting the DC discharge of the wireless charging battery into alternating current and integrating it into the power grid through an inverter.

[0125] Even further, optimizing the charging strategy means constructing a charging strategy optimization model through a convolutional neural network, setting the input of the charging strategy optimization model as the charging device data, the output as the optimized charging strategy, and using the training set to train the charging strategy optimization model;

[0126] The trained charging strategy optimization model is further optimized through a reinforcement learning algorithm, and the optimized charging strategy is obtained by inputting the charging device data into the optimized charging strategy optimization model.

[0127] Processing and analyzing the charging device data through a convolutional neural network (CNN) can accurately capture the complex patterns in the data and automatically extract features related to charging efficiency, device health, etc. This deep learning method can identify non-linear relationships from a large amount of charging device data, thus providing accurate guidance for optimizing the charging strategy. The reinforcement learning algorithm automatically conducts trial and error and feedback adjustment by simulating various operation strategies during the charging process, and continuously optimizes the charging strategy in multiple interactions. The reinforcement learning algorithm can adaptively adjust the charging parameters, thereby dynamically improving the charging efficiency and reducing battery loss in practical applications. This method can not only improve the charging efficiency but also continuously learn and optimize the model to adapt to possible future changes.

[0128] S4. Display and storage module, which is used to display the charging device data and the charging device status and store them in the database;

[0129] Specifically, displaying the charging device data and the charging device status means visually displaying the collected charging device data and the charging device status and synchronously displaying the charging device detection report generated according to the timestamp.

[0130] Furthermore, storing in the database means storing the charging device data and the charging device status in the database. The database regularly conducts integrity detection and security detection on the stored data and uploads the stored data to the cloud for backup.

[0131] S5. Central control module, which is used to connect all modules and generate control commands for module interaction;

[0132] Specifically, connecting all modules and generating control commands for module interaction means that the central control module is wirelessly connected to the data collection module, the security detection module, the management optimization module, and the display and storage module, and generates control commands to be sent to the corresponding modules for execution when module collaboration operations are required.

[0133] The central control module conducts real-time data exchange and collaborative work with the data collection module, the security detection module, the management optimization module, and the display and storage module through wireless connection. The wireless connection avoids the limitations of physical wiring, enabling the system to be deployed and expanded more flexibly. The central control module forms a comprehensive system view by aggregating the real-time data of all modules, providing a basis for subsequent decision-making. Through wireless communication, each module can respond and adjust quickly, improving the system's response speed and processing efficiency. Through the control commands generated by the central control module, the system can schedule and coordinate each module according to real-time requirements to ensure the orderly operation of all parts of the system. When an anomaly occurs in the system, the central control module can immediately issue instructions to adjust the operations of the corresponding modules to avoid the spread of faults or the occurrence of more serious problems. This collaborative work between modules can effectively improve the automation level of the system and reduce manual intervention.

[0134] This embodiment also provides a computer device applicable to 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 the computer-executable instructions to implement the wireless charging safety protection system based on magnetic field modulation as proposed in the above embodiment.

[0135] This computer device can be a terminal. 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 this computer device is used to provide computing and control capabilities. The memory of this 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 this computer device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of this computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of this computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0136] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the wireless charging safety protection system based on magnetic field modulation as proposed in the above embodiment; 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, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0137] In summary, the present invention collects charging device data, uses GRS codes to optimize and construct a charging device detection model for charging device status detection, effectively improves the accuracy of charging device status detection, adjusts the electromagnetic intensity according to the human body distance to improve charging safety, and at the same time realizes precise charging and discharging of the charging device through energy management and recovery of the charging device, improving energy utilization efficiency.

[0138] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within 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: Including, a data collection module for collecting charging device data and performing preprocessing; a safety detection module for detecting the status of the charging device according to the collected charging device data and adjusting the electromagnetic intensity in real time for human safety protection; a management optimization module for performing energy management and recovery on the charging device and optimizing the charging strategy; a display storage module for displaying the charging device data and the status of the charging device and storing them in a database; a central control module for connecting all modules and generating control commands for module interaction; The collection of charging device data and preprocessing means deploying sensors on the charging device, forming a sensor network by wirelessly connecting all sensors, aligning the sensor network timestamps and setting a data acquisition period for charging device data acquisition, cleaning the collected charging device data, and using wavelet transform combined with Kalman filtering to denoise the charging device data, and normalizing the denoised charging device data. The charging device includes a wireless charging device and a wireless charging battery; The detection of the charging device status based on the collected charging device data refers to obtaining the standardized charging device data, and each type of data in the charging device data is expressed as a polynomial : wherein is the i-th type of input data, are polynomial coefficients, is the polynomial degree; Extracted polynomial Each term in it is combined to form a column vector of the $i$-th type of input data : According to the column vector Use the GRS code to generate the mapping matrix G of the charging device data: Among them is the code locator of the GRS code, and n is the number of data types in the charging device data; Select a multi-layer fully connected neural network to construct a charging device detection model, including an input layer, a hidden layer, and an output layer. Define the input of the input layer as the charging device data, and the output of the output layer as the charging device status value; Add the mapping matrix G to the input layer. Each type of charging device data is converted into a high-dimensional vector by multiplying with the mapping matrix G in the input layer. Then it enters the hidden layer. The hidden layer uses multi-layer fully connected layers, and each fully connected layer uses the ReLU activation function and the mapping matrix G for non-linear 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 to output the charging device status value; Input the training data into the charging device detection model, and use the cross-entropy loss function and the Adam optimizer to iteratively optimize the parameters of the charging device detection model until the loss converges; Input the charging device data into the trained charging device detection model to obtain the charging device status value, compare the charging device status value with the status threshold. If the charging device status value is greater than the status threshold, it is determined that the charging device status is abnormal and the charging device is powered off.

2. The wireless charging safety protection system based on magnetic field modulation according to claim 1, characterized in that: The real-time adjustment of electromagnetic intensity for human body safety protection means 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, calculating the magnetic field intensity B during normal operation based on the data of the charging device, and setting a safe distance ; When it is detected that r ≤ , the charging device adjusts the magnetic field strength in real time according to the distance r between the human body and the charging device: wherein is the adjusted magnetic field strength.

3. The wireless charging safety protection system based on magnetic field modulation according to claim 2, characterized in that: The energy management and recovery of the charging device includes, Obtain the remaining power of the wireless charging battery and the rated capacity , through the rated capacity subtract the remaining power to calculate the charging amount , and calculate the energy state y of the wireless charging battery: Among them is the wireless charging battery density, is the remaining life of the wireless charging battery, is the wireless charging battery cooling rate, is the charging coefficient; calculating the energy recovery value m according to the energy state y of the wireless charging battery: When the energy recovery value m exceeds the set recovery threshold, the wireless charging battery enters the energy recovery mode; Take the ratio of the charging current to the charging voltage of the wireless charging battery as the right shift probability amplitude , and take the ratio of the discharge current to the discharge voltage as the left shift probability amplitude , and calculate the charging power and the discharge power , take the charging power and the discharge power sum as the total power of the wireless charging battery , take the ratio of the charging power to the total power as the charging excitation amplitude , the discharge power to the total power as the discharge excitation amplitude , and take the sum of the charging excitation amplitude and the discharge excitation amplitude as the excitation operator , and determine the de-excitation operator through the commutation relation;​ Define the quantum state formula for the charge and discharge state of the wireless charging battery as: Among them is the quantum state of the charging and discharging state of the wireless charging battery, is the quantum ground state of the charging and discharging state of the wireless charging battery; By excitation operator and de-excitation operator calculate the Hamiltonian operator of the wireless charging battery and the coupling Hamiltonian operator between the wireless charging device and the battery : wherein is the charge and discharge frequency of the battery; Calculation and The sum is used as the Hamiltonian operator H of the wireless charging battery, and the quantum state of the charge and discharge state of the wireless charging battery changing with time is defined through the Schrödinger equation: where t is time and j is the imaginary unit; Using a numerical integration method to solve the Schrödinger equation to obtain the quantum state of the charging and discharging state of a wireless charging battery at time t and calculating the battery energy value at time t : Compare with the energy recovery value m. If is greater than m, the wireless charging battery is in the charging state. If is less than m, the wireless charging battery is in the discharging state. If is equal to m, the wireless charging battery stops charging and discharging; After obtaining the charge and discharge state of the wireless charging battery, calculate the charge and discharge amount of the wireless charging battery according to the difference between the time t and the current time and the power corresponding to the charge and discharge state.

4. The wireless charging safety protection system based on magnetic field modulation according to claim 3, wherein: The energy management and recovery of the charging device also includes converting the DC discharge of the wireless charging battery into alternating current and feeding it into the power grid through an inverter.

5. The wireless charging safety protection system based on magnetic field modulation according to claim 4, characterized in that: The optimization of the charging strategy means constructing a charging strategy optimization model through a convolutional neural network, setting the input of the charging strategy optimization model as the charging device data, and the output as the optimized charging strategy, and training the charging strategy optimization model using the training set; Further optimize the trained charging strategy optimization model through a reinforcement learning algorithm, and input the charging device data into the optimized charging strategy optimization model to obtain the optimized charging strategy.

6. The wireless charging safety protection system based on magnetic field modulation according to claim 5, 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.

7. The wireless charging safety protection system based on magnetic field modulation according to claim 6, characterized in that: The storage in the database refers to storing the charging device data and the charging device status in the database. The database periodically performs integrity detection and security detection on the stored data, and uploads the stored data to the cloud for backup.

8. The wireless charging safety protection system based on magnetic field modulation according to claim 7, characterized in that: The connection of all modules and the generation of control commands for module interaction means that the central control module is wirelessly connected to the data collection module, the security detection module, the management optimization module, and the display storage module, and generates control commands to be sent to the corresponding modules for execution when module collaborative operations are required.

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