Control method of wireless power transmission system

Abstract

The invention discloses a control method of a wireless power transmission system, which relates to the technical field of wireless energy transmission, and comprises the following steps: based on a comprehensive data set, using a reinforcement learning algorithm to calculate and optimize transmitting power distribution to obtain an optimal transmitting power value; based on the comprehensive data set and the optimal transmitting power value, adjusting the beam direction of the phased-array antenna of the transmitting end, and optimizing the pointing precision of the beam to the receiving end by using a hybrid beam forming method to form an optimal signal energy transmission path; based on the optimal signal energy transmission path, the receiving end monitors the receiving power in real time and returns the receiving power to the transmitting end through a feedback signal. According to the method, transmission power distribution is optimized through reinforcement learning, and the wireless power transmission efficiency and stability are improved in combination with a hybrid beam forming technology. The transmitting power is intelligently adjusted, so that the energy loss is minimized, a receiving end is ensured to obtain stable electric energy, and reliable power transmission in a complex environment is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless energy transmission, and in particular to a control method for a wireless power transmission system. Background Art

[0002] As an important means of future energy supply, wireless energy transmission technology has been widely studied and applied in recent years in fields such as electric vehicle charging, mobile terminal power supply, and remote power supply for industrial equipment. Wireless power transmission mainly includes electromagnetic induction, magnetic resonance coupling, and microwave / radio frequency energy transmission, etc. Among them, the long-distance wireless power transmission technology based on microwave / radio frequency has become one of the current research hotspots due to its advantages such as long transmission distance and strong adaptability. In a long-distance wireless power transmission system, phased array antennas are usually used for energy focusing to improve the transmission efficiency. In order to adapt to complex environmental changes, intelligent optimization algorithms are introduced to improve the accuracy of transmit power control and beamforming, thereby enhancing the stability and efficiency of the system.

[0003] The current wireless power transmission control methods mainly rely on fixed power distribution strategies or power adjustment mechanisms based on simple control algorithms, lacking the ability of adaptive optimization for complex dynamic environments. In a complex environment, traditional methods are difficult to optimize the transmit power in real time, resulting in large fluctuations in the received power at the receiving end and affecting the stability of wireless power transmission. Most of the existing beamforming technologies use fixed parameters or quasi-static adjustment methods when optimizing the beam direction, and fail to fully combine environmental data and historical reception information for intelligent optimization, resulting in insufficient pointing accuracy of the energy transmission path and affecting the overall efficiency of the system. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a control method for a wireless power transmission system to solve the problems of insufficient optimization of wireless power transmission power distribution and low beam pointing accuracy in a complex environment.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a control method for a wireless power transmission system, which includes obtaining the position information of the receiving end through GPS and millimeter-wave radar, combining with the environmental data collected by environmental sensors, and generating a comprehensive data set after filtering, denoising, and normalization processing; based on the comprehensive data set, using a reinforcement learning algorithm for calculation, optimizing the transmission power allocation, and obtaining the optimal transmission power value; based on the comprehensive data set and the optimal transmission power value, adjusting the beam direction of the phased array antenna at the transmitting end, and using a hybrid beamforming method to optimize the pointing accuracy of the beam to the receiving end, forming an optimal signal energy transmission path; based on the optimal signal energy transmission path, the receiving end monitors the received power in real time and returns it to the transmitting end through a feedback signal; the transmitting end dynamically adjusts the transmission power according to the feedback information to improve the stability and efficiency of wireless power transmission.

[0008] As a preferred solution of the control method for the wireless power transmission system of the present invention, wherein: the steps of obtaining the position information of the receiving end through GPS and millimeter-wave radar, combining with the environmental data collected by environmental sensors, and generating a comprehensive data set after filtering, denoising, and normalization processing are as follows:

[0009] Obtain the longitude and latitude coordinate information data of the receiving end through GPS, measure the distance and azimuth angle data of the receiving end through millimeter-wave radar, calculate the three-dimensional position information of the receiving end, and form a receiving end position information data set;

[0010] Collect the factors affecting the wireless power transmission path such as air humidity, atmospheric absorption coefficient, obstacle distribution, atmospheric pressure, temperature, and wind speed in real time through environmental sensors to form an environmental data set;

[0011] Perform filtering processing on the environmental data set to remove noise interference, normalize different types of environmental data, and combine with the receiving end position information data set to generate a comprehensive data set.

[0012] As a preferred solution of the control method for the wireless power transmission system of the present invention, wherein: the steps of calculating based on the comprehensive data set, using a reinforcement learning algorithm to optimize the transmission power allocation, and obtaining the optimal transmission power value are as follows:

[0013] Based on the comprehensive data set, by constructing an intelligent optimization model, calculate different transmission power allocations and beam directions, evaluate the impact on signal energy transmission, and perform iterative training, and continuously optimize the power allocation strategy using a reward function to obtain an optimal transmission scheme;

[0014] Use the reinforcement learning algorithm to calculate the optimal transmission scheme to obtain the optimal transmission power value.

[0015] As a preferred solution of the control method for the wireless power transmission system of the present invention, wherein: the steps of constructing an intelligent optimization model are as follows:

[0016] Set the state space, the receiver position information and the environmental data;

[0017] Set the action space, the adjustable power range and the beam direction parameters of the transmitter;

[0018] Define the reward function, with the maximization of the effective power received by the receiver and the minimization of the transmission loss as the optimization objectives;

[0019] Adopt deep reinforcement learning. By setting the neural network structure and using the historical transmission data as training samples, an intelligent optimization model is generated.

[0020] As a preferred scheme of the control method of the wireless power transmission system described in the present invention, wherein: based on the comprehensive data set and the optimal transmission power value, adjust the beam direction of the phased array antenna at the transmitter end. The specific steps are as follows,

[0021] Based on the comprehensive data set, through the geometric calculation method, calculate the initial beam pointing angle of the phased array antenna at the transmitter end, obtain the azimuth angle and elevation angle of the phased array antenna at the transmitter end pointing to the receiver end, and combine the optimal transmission power value to adjust the beam direction to precisely optimize the signal energy propagation path to the receiver end.

[0022] As a preferred scheme of the control method of the wireless power transmission system described in the present invention, wherein: use the hybrid beamforming method to optimize the pointing accuracy of the beam to the receiver end and form an optimal signal energy transmission path. The specific steps are as follows,

[0023] According to the obtained azimuth angle and elevation angle, calculate the optimal phased array antenna excitation weights, adjust the amplitude and phase of each sub-array, form a preliminary beam pointing, and obtain digital beamforming;

[0024] By adjusting the phase shift of each antenna element, the phased array antenna forms a main beam and performs beam alignment according to the initially calculated azimuth angle and elevation angle to obtain analog beamforming;

[0025] Combine the analog beamforming and the digital beamforming, optimize the gain and direction of the beam through the least mean square error algorithm, and obtain the feedback information of the receiver end;

[0026] Utilize the feedback information of the receiver end to adjust the beam angle and power of the transmitter end, dynamically optimize the pointing accuracy of the beam to the receiver end, and form an optimal signal energy transmission path.

[0027] As a preferred scheme of the control method of the wireless power transmission system described in the present invention, wherein: based on the optimal signal energy transmission path, the receiver end monitors the received power in real time and returns it to the transmitter end through a feedback signal. The specific steps are as follows,

[0028] Based on the optimal signal energy transmission path, a high-precision power detection circuit is used to sample the signal, and a low-pass filter is utilized to eliminate noise interference;

[0029] The sampled data is subjected to mean filtering and moving average processing to obtain the current received power;

[0030] Using the error signal calculation method, calculate the error between the current received power and the optimal transmit power value;

[0031] Based on historical received power data and environmental data, set the received power range and evaluate it against the received power error;

[0032] When the received power error is less than the received power range, maintain the current transmit power;

[0033] When the received power error exceeds the received power range, generate a feedback control signal and send it to the transmitter through a low-power communication protocol.

[0034] As a preferred solution of the control method of the wireless power transmission system described in the present invention, wherein: the transmitter dynamically adjusts the transmit power according to the feedback information to improve the stability and efficiency of wireless power transmission. The specific steps are as follows.

[0035] According to the current received power value of the feedback information, use the difference calculation method to calculate the deviation from the optimal transmit power value;

[0036] Adjust the transmit power according to the deviation and optimize the beam direction of the phased array antenna;

[0037] According to the real-time feedback of the receiver, dynamically adjust the phase and amplitude of the antenna array, optimize the signal path, and improve the stability and efficiency of wireless power transmission.

[0038] In a second aspect, the present invention provides a computer device, including a memory and a processor. The memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the control method of the wireless power transmission system as described in the first aspect of the present invention.

[0039] In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, wherein: when the computer program is executed by the processor, it implements any step of the control method of the wireless power transmission system as described in the first aspect of the present invention.

[0040] The beneficial effects of the present invention are as follows: By optimizing the transmit power allocation through reinforcement learning and combining with hybrid beamforming technology, the wireless power transmission efficiency and stability are improved. The transmit power is intelligently adjusted to minimize energy loss, ensuring a stable power supply at the receiving end. At the same time, the adaptability of the system to changes in the receiving end position, obstacles, and climatic conditions is enhanced, guaranteeing reliable power transmission in complex environments. Dynamically adjusting the transmit power reduces unnecessary energy radiation, improves electromagnetic compatibility, reduces interference to surrounding devices, and enhances safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0042] Figure 1 It is a flowchart of the control method for the wireless power transmission system in Embodiment 1.

[0043] Figure 2 It is a flowchart of data acquisition and processing in Embodiment 1.

[0044] Figure 3 It is a flowchart of optimizing the transmit power through reinforcement learning in Embodiment 1.

[0045] Figure 4 It is a flowchart of optimizing hybrid beamforming in Embodiment 1. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will give a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings of the specification.

[0047] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0048] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments.

[0049] Embodiment 1, referring to Figure 1 and Figure 2, which is the first embodiment of the present invention. This embodiment provides a control method for a wireless power transmission system, including the following steps:

[0050] S1. Obtain the position information of the receiving end through GPS and millimeter-wave radar, and combine the environmental data collected by the environmental sensor. After filtering, denoising, and normalization processing, generate a comprehensive data set.

[0051] Obtain the longitude and latitude coordinate information data of the receiving end through GPS, and measure the distance and azimuth angle data of the receiving end through millimeter-wave radar. Calculate the three-dimensional position information of the receiving end to form a receiving end position information data set;

[0052] It should be noted that GPS data is greatly affected by weather and multipath effects, while the short-distance high-precision measurement provided by millimeter-wave radar can effectively compensate for the errors of GPS and improve the three-dimensional positioning accuracy of the receiving end. The millimeter-wave radar can measure the distance and azimuth angle of the receiving end relative to the transmitting end through beam scanning and echo analysis. The millimeter-wave radar measures the round-trip time of signal propagation by emitting electromagnetic waves. The phased array antenna of the millimeter-wave radar can scan the reflected signals in different angular directions.

[0053] Real-time collect the factors such as air humidity, atmospheric absorption coefficient, obstacle distribution, atmospheric pressure, temperature, and wind speed that affect the wireless power transmission path through the environmental sensor to form an environmental data set.

[0054] Perform filtering processing on the environmental data set to remove noise interference, normalize different types of environmental data, and combine it with the receiving end position information data set to generate a comprehensive data set.

[0055] It should be noted that mean filtering is used to smooth random fluctuations in a short period of time, reduce high-frequency noise, calculate the average value of data within a certain range using a sliding window, and replace the original data points. Median filtering is used to remove burst noise or outliers, sort the data within a certain range, and take the median as the new data point. Low-pass filtering is used to eliminate high-frequency interference signals and retain the stable low-frequency data change trend, suppressing high-frequency components through Fourier transform. Kalman filtering is used for noise suppression and data prediction in dynamic systems, such as the smoothing processing of GPS positioning data, and recursive optimization is carried out using state estimation and noise covariance matrix.

[0056] S2. Based on the comprehensive data set, use the reinforcement learning algorithm for calculation, optimize the transmission power allocation, and obtain the optimal transmission power value.

[0057] Based on the comprehensive data set, by constructing an intelligent optimization model, calculate different transmission power allocations and beam directions, evaluate the impact on signal energy transmission, and perform iterative training. Continuously optimize the power allocation strategy using the reward function to obtain the optimal transmission scheme;

[0058] It should be noted that in the initial stage, the agent randomly selects different transmission powers and beam directions and evaluates the effect of signal energy transmission. If the adjusted scheme improves the received power and reduces the path loss, the reward is increased; if the adjusted scheme results in a decrease in the received power or an increase in the energy loss, the reward is decreased. The deep Q-network algorithm is used to update the policy, enabling the agent to gradually converge to the optimal power allocation scheme. After sufficient rounds of training, the model finally converges to find a stable and optimal transmission power allocation strategy, making the wireless power transmission reach an efficient state.

[0059] The optimal transmission scheme is calculated using the reinforcement learning algorithm to obtain the optimal transmission power value;

[0060] It should be noted that an optimization strategy is selected by the reinforcement learning model to adjust the transmission power. Calculate the power change at the receiving end after adjustment and record the change in the power transmission path. Evaluate the impact of the adjusted power on wireless power transmission and calculate the energy utilization rate of the system. If the received power at the receiving end increases and the energy loss decreases, a higher positive reward is given, indicating that the current transmission power adjustment is effective. If the received power at the receiving end decreases or the energy waste increases, a negative reward is given, indicating that the current adjustment strategy needs to be optimized. Through multiple rounds of iterative training, the reinforcement learning model continuously adjusts the strategy to find the optimal power allocation scheme. Learning from historical data enables the model to predict the optimal future transmission power adjustment scheme. After multiple rounds of optimization, the optimal transmission power value is finally obtained, maximizing the power transmission efficiency at the receiving end while avoiding excessive energy loss.

[0061] Construct an intelligent optimization model.

[0062] Set the state space, including the receiving end position information and environmental data;

[0063] It should be noted that the state describes the environment and power allocation of the current wireless power transmission system, including the current transmission power, the current received power at the receiving end, the wireless power transmission path loss, and the current environmental state.

[0064] Set the action space, including the adjustable power range and beam direction parameters at the transmitting end;

[0065] It should be noted that the transmission power is adjusted and the beam direction of the phased array antenna is adjusted to optimize the energy transmission path. By continuously selecting different transmission power increase and decrease strategies and calculating the corresponding received power feedback, the optimal transmission power value is found.

[0066] Define the reward function with the optimization goal of maximizing the effective power received at the receiving end and minimizing the transmission loss;

[0067] It should be noted that the optimization objectives include maximizing the effective power at the receiving end, minimizing the path loss, avoiding excessive transmission, and reducing energy consumption. If the current adjustment increases the received power and reduces the energy loss, a positive reward is given; otherwise, a negative reward is given.

[0068] Deep reinforcement learning is adopted. By setting the neural network structure and using historical transmission data as training samples, an intelligent optimization model is generated.

[0069] It should be noted that through deep reinforcement learning and neural network training, the wireless power transmission system can achieve intelligent optimization of power distribution and beam control, and can dynamically adapt even in complex and changing environments, improving the transmission efficiency and stability. Continuous training and optimization using historical data enable the transmitting end to autonomously learn the optimal strategy, maximizing the energy acquisition at the receiving end while reducing unnecessary power waste.

[0070] S3. Based on the comprehensive data set and the optimal transmission power value, adjust the beam direction of the phased array antenna at the transmitting end, and use the hybrid beamforming method to optimize the pointing accuracy of the beam to the receiving end, forming an optimal signal energy transmission path.

[0071] Based on the comprehensive data set, through the geometric calculation method, calculate the initial beam pointing angle of the phased array antenna at the transmitting end, obtain the azimuth angle and elevation angle of the phased array antenna at the transmitting end pointing to the receiving end, and combine the optimal transmission power value to accurately optimize the signal energy propagation path of the beam to the receiving end.

[0072] It should be noted that the GPS data provides the latitude and longitude coordinates (Lat, Lon) of the receiving end, the millimeter-wave radar data provides the distance and azimuth angle of the receiving end relative to the transmitting end, and the height information can be obtained through a barometer or other sensors for the height of the receiving end;

[0073] Let the coordinates of the transmitting end be (X t , Y t , Z t ), and the coordinates of the receiving end be (X r , Y r , Z r ). Through the GPS latitude and longitude information, convert the position of the receiving end into rectangular coordinates, and the expression is:

[0074] Xr = Rcos(Lat)cos(Lon); Yr = Rcos(Lat)sin(Lon); Zr = hr;

[0075] where R represents the radius of the earth, and hr represents the height of the receiving end;

[0076] The expression for calculating the azimuth angle is:

[0077]

[0078] Among them, θ represents the angle of the azimuth receiving end relative to the transmitting end in the horizontal direction, X r -X t represents the horizontal distance between the receiving end and the transmitting end in the X-axis direction, Y r -Y t represents the horizontal distance between the receiving end and the transmitting end in the Y-axis direction, and arctan represents the arctangent function to calculate the angle value so that the antenna can face the receiving end;

[0079] The expression for calculating the elevation angle is:

[0080]

[0081] Among them, φ represents the angle of the elevation receiving end relative to the transmitting end in the vertical direction, Z r -Z t represents the distance between the receiving end and the transmitting end in the Z-axis direction, represents the distance between the transmitting end and the receiving end in the horizontal direction.

[0082] According to the obtained azimuth angle and elevation angle, calculate the optimal phased array antenna excitation weights, adjust the amplitude and phase of each sub-array, form a preliminary beam direction, and obtain digital beamforming;

[0083] It should be noted that by adjusting the signals of each sub-array, making them superimpose in the target direction to enhance the main beam gain. By digitally controlling the adjustment of the excitation weights, the beam can be flexibly scanned in different directions to adapt to the changes of dynamic targets. By adjusting the shaping weights, reduce the sidelobe interference and improve the concentration of signal energy in the target direction.

[0084] By adjusting the phase shift of each antenna element, the phased array antenna forms a main beam and performs beam alignment according to the initially calculated azimuth angle and elevation angle to obtain analog beamforming.

[0085] It should be noted that by adjusting the horizontal phase of the antenna element, the beam direction is matched with the azimuth angle of the receiving end. Through gradual optimization, the maximum gain direction of the main beam is pointed to the receiving end. By adjusting the vertical phase of the antenna element, the beam can match the height direction of the receiving end. In a multipath propagation environment, appropriately adjust the elevation angle to optimize the power reception situation at the receiving end. After the phase adjustment is completed, the antenna array fixes the excitation weights to ensure that the analog beam stably radiates in the target direction. Combining with the array gain adjustment, make the signal energy as concentrated as possible, reduce the sidelobe interference, and improve the wireless power transmission efficiency. When the position of the receiving end changes, the analog beamforming can cooperate with the digital beamforming for adjustment to ensure that the beam is always aligned with the target and improve the stability of wireless power transmission.

[0086] Combining analog beamforming and digital beamforming, the gain and direction of the beam are optimized through the least mean square error algorithm to obtain the feedback information at the receiving end.

[0087] It should be noted that the main beam is formed by adjusting the phase of the phased array antenna elements to point to the azimuth and elevation angles of the receiving end calculated initially. Since analog beamforming adjusts the phase in the analog domain, it can reduce the complexity of the radio frequency link and improve the energy efficiency, but has relatively low flexibility. At the digital signal processing level, the excitation weights (amplitude and phase) of each antenna element are dynamically adjusted according to the feedback information at the receiving end. Digital beamforming supports multiple beams simultaneously, optimizes the directivity of the signal beam, reduces interference and sidelobe leakage, and improves the accuracy of wireless power transmission.

[0088] Using the feedback information at the receiving end, the beam angle and power at the transmitting end are adjusted to dynamically optimize the pointing accuracy of the beam to the receiving end, forming an optimal signal energy transmission path.

[0089] It should be noted that if the position of the receiving end changes, the latest GPS coordinates and millimeter-wave radar ranging data are used to recalculate the optimal beam pointing angles (azimuth and elevation angles). Kalman filtering or particle filtering is adopted to reduce the measurement error and improve the beam alignment accuracy. By adjusting the phase of the antenna elements, the beam pointing is reconfigured to align the main beam to the receiving end. Combining digital beamforming and analog beamforming, the amplitude and phase of the antenna array are precisely optimized to reduce sidelobe interference and improve the energy concentration. Closed-loop control is adopted, that is, after each adjustment of the beam angle, the receiving end will continue to feedback the new state, and the transmitting end continuously optimizes the beam pointing according to the latest feedback, finally achieving the optimal alignment. The main beam directly points to the receiving end, reducing energy loss and improving the receiving efficiency. The power is dynamically adjusted to ensure that the receiving end obtains stable energy without over-transmitting and causing waste. The system can adapt to external environment changes in real time, ensuring the stability and high efficiency of wireless power transmission in complex scenarios.

[0090] S4. Based on the optimal signal energy transmission path, the receiving end monitors the received power in real time and returns it to the transmitting end through a feedback signal.

[0091] Based on the optimal signal energy transmission path, a high-precision power detection circuit samples the signal, and a low-pass filter is used to eliminate noise interference.

[0092] It should be noted that the high-frequency wireless signal is converted into a DC signal, the signal is enhanced to ensure the detection accuracy, and the analog signal is converted into a digital signal. High-frequency noise is removed to avoid transient interference affecting the measurement accuracy. The signal fluctuation is smoothed to obtain stable received power data. The signal-to-noise ratio is improved, the error is reduced, and the measurement reliability is enhanced.

[0093] The sampled data is subjected to mean filtering and moving average processing to obtain the current received power.

[0094] It should be noted that mean filtering is first used for preliminary noise reduction to remove high-frequency noise interference. Then, moving average is adopted to smooth the power data to improve the adaptability to dynamic changes. The processed power data is used for feedback adjustment of the transmission power of wireless power transmission to ensure the stable and efficient operation of the system.

[0095] Using the error signal calculation method, calculate the error between the current received power and the optimal transmission power value.

[0096] It should be noted that when the received power is lower than the optimal transmission power value, it indicates that the energy obtained at the receiving end is insufficient, which may be due to path loss, interference or beam offset, and it is necessary to increase the transmission power or optimize the beam direction. When the received power is greater than the optimal transmission power value, it indicates that the transmission power may be too high, resulting in energy waste or interference with other devices, and it is necessary to appropriately reduce the transmission power. When the received power is equal to the optimal transmission power value, it indicates that the wireless power transmission system is in the best state and no adjustment is required.

[0097] Based on the historical received power data and environmental data, set the received power range and evaluate it with the received power error;

[0098] It should be noted that parameters such as air humidity, temperature, air pressure, and wind speed will affect the transmission efficiency of wireless power transmission. When the humidity is high, the transmission loss of millimeter waves increases, and the received power may decrease, and appropriate adjustment is required. When the wind speed is high, the beam may shift, and it is necessary to dynamically adjust the power range according to the change of wind speed. Through machine learning, calculate the optimal power range under different environmental conditions and dynamically adjust it.

[0099] When the received power error is less than the received power range, the current transmission power is maintained.

[0100] It should be noted that this means that the wireless power transmission system can stably and effectively transmit energy at the current transmission power, and the performance of the system is already good enough. Therefore, maintaining the current transmission power can avoid unnecessary power adjustment, save energy and improve the stability of the system.

[0101] When the received power error exceeds the received power range, a feedback control signal is generated and sent to the transmitting end through a low-power communication protocol.

[0102] It should be noted that if the error exceeds the received power range, the system determines that the transmission power needs to be dynamically adjusted. The feedback control signal is a signal generated at the receiving end and is used to transmit the difference between the current received power and the optimal power. The role of the feedback signal is to notify the transmitting end of the current transmission power deviation and prompt whether adjustment is required. The low-power communication protocol is a communication method used for feedback information transmission, and its characteristic is that it can stably transmit data without consuming too much energy. In a wireless power transmission system, the receiving end sends the calculated feedback control signal to the transmitting end through the low-power communication protocol. This method can effectively reduce the energy consumption of the system while ensuring the accurate transmission of feedback information.

[0103] S5. The transmitting end dynamically adjusts the transmission power according to the feedback information to improve the stability and efficiency of wireless power transmission.

[0104] According to the current received power value of the feedback information, the deviation from the optimal transmission power value is calculated using the difference calculation method.

[0105] It should be noted that through the deviation value, the system decides whether to adjust the transmission power and calculates the adjustment amplitude. The larger the deviation value, the greater the required adjustment amplitude. If the calculated deviation is negative, it means that the received power is lower than the optimal power, and the transmitting end needs to increase the transmission power to increase the received power and ensure that the signal can reach the optimal level. If the deviation is positive, it indicates that the received power is higher than the optimal power, and there may be excessive energy transmission. At this time, the transmitting end needs to reduce the transmission power to avoid energy waste or system instability. If the deviation is zero, it means that the current received power is already very close to the optimal value, and the system does not need to make adjustments, and the transmitting end maintains the current power output.

[0106] Adjust the transmission power according to the deviation and optimize the beam direction of the phased array antenna.

[0107] It should be noted that according to the power adjustment calculated based on the deviation, the transmitting end will adjust the beam direction of the phased array antenna to ensure that more energy can be transmitted to the receiving end. According to the comprehensive data set, the optimal beam direction of the transmitting end phased array antenna is determined by the geometric calculation method. Adjusting the beam direction of the phased array antenna is a dynamic optimization process that needs to consider the impact of environmental changes (such as obstacles, weather, etc.) on signal propagation in real time. Using the three-dimensional position data of the receiving end, the position information of the transmitting end, and the optimal transmission power value, the initial beam pointing angle of the transmitting end phased array antenna is calculated. The system will also fine-tune the beam direction based on the feedback of the receiving end to ensure that the beam direction is optimal after each adjustment.

[0108] According to the real-time feedback of the receiving end, dynamically adjust the phase and amplitude of the antenna array, optimize the signal path, and improve the stability and efficiency of wireless power transmission.

[0109] It should be noted that phase control allows each antenna element to transmit radio waves with different phases, and focuses the propagation direction of the signal through the phase difference. By finely adjusting the phases of each antenna element, the propagation path of the signal can be precisely controlled, so that the beam points more accurately to the receiving end. By adjusting the phase, the system can achieve the directional control of the beam, making the signal concentrated at the position of the receiving end and improving the received power. Amplitude control optimizes the shape and coverage area of the beam by adjusting the signal intensity of each antenna element. The amplitude adjustment of different antenna elements changes the width and focus of the beam, helping the signal to cover the receiving end more effectively. Adjusting the amplitude helps to eliminate possible interference in the signal transmission path, avoid the spread of the signal, and ensure the concentrated transmission of energy. Ensure that the signal can be transmitted to the receiving end along the shortest path, minimizing the transmission loss. Avoid unnecessary attenuation or multipath interference of the signal during propagation, and ensure the clarity and stability of the signal. By precisely controlling the direction of the beam, the signal always points to the receiving end, avoiding any unnecessary energy loss.

[0110] This embodiment also provides a computer device, which is applicable to the control method of the wireless power transmission system, 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 control method of the wireless power transmission system proposed in the above embodiment.

[0111] 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 computer programs. The internal memory provides an environment for the operation of the operating system and computer programs 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, and the wireless manner can be implemented 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 shell of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0112] 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 control method for realizing a wireless power transmission system 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 (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk or an optical disc.

[0113] In summary, the present invention: optimizes the transmission power allocation through reinforcement learning, and combines the hybrid beamforming technology to improve the wireless power transmission efficiency and stability. Intelligently adjusts the transmission power to minimize energy loss, ensures that the receiving end obtains stable electric energy, and at the same time enhances the adaptability of the system to changes in the position of the receiving end, obstacles, and climatic conditions, guaranteeing reliable power transmission in complex environments. Dynamically adjusts the transmission power to reduce unnecessary energy radiation, improves electromagnetic compatibility, reduces interference with surrounding devices, and enhances safety.

[0114] 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 control method for a wireless power transmission system, characterized in that: including Obtain the position information of the receiving end through GPS and millimeter-wave radar, and combine the environmental data collected by environmental sensors. After filtering, denoising, and normalization processing, generate a comprehensive data set; Based on the comprehensive data set, use the reinforcement learning algorithm for calculation, optimize the transmit power allocation, and obtain the optimal transmit power value; Based on the comprehensive data set and the optimal transmit power value, adjust the beam direction of the phased array antenna at the transmitting end, and use the hybrid beamforming method to optimize the pointing accuracy of the beam to the receiving end, forming an optimal signal energy transmission path; Based on the optimal signal energy transmission path, the receiving end monitors the received power in real time and returns it to the transmitting end through a feedback signal; The transmitting end dynamically adjusts the transmit power according to the feedback information to improve the stability and efficiency of wireless power transmission.

2. The control method of the wireless power transmission system according to claim 1, characterized in that: The steps of obtaining the position information of the receiving end through GPS and millimeter-wave radar, combining the environmental data collected by environmental sensors, and generating a comprehensive data set through filtering, denoising, and normalization processing are as follows. Obtain the longitude and latitude coordinate information data of the receiving end through GPS, measure the distance and azimuth angle data of the receiving end through millimeter-wave radar, calculate the three-dimensional position information of the receiving end, and form a receiving end position information data set; Real-time collect the factors affecting the wireless power transmission path, such as air humidity, atmospheric absorption coefficient, obstacle distribution, atmospheric pressure, temperature, and wind speed, through environmental sensors to form an environmental data set; Perform filtering processing on the environmental data set to remove noise interference, normalize different types of environmental data, and combine it with the receiving end position information data set to generate a comprehensive data set.

3. The control method of the wireless power transmission system according to claim 2, characterized in that: The steps of calculating based on the comprehensive data set, using the reinforcement learning algorithm to optimize the transmit power allocation, and obtaining the optimal transmit power value are as follows. Based on the comprehensive data set, by constructing an intelligent optimization model, calculate different transmit power allocations and beam directions, evaluate the impact on signal energy transmission, and perform iterative training. Continuously optimize the power allocation strategy using the reward function to obtain the optimal transmission scheme; Use the reinforcement learning algorithm to calculate the optimal transmission scheme to obtain the optimal transmit power value.

4. The control method of the wireless power transmission system according to claim 3, characterized in that: The steps of constructing an intelligent optimization model are as follows. Set the state space, including the receiving end position information and environmental data; Set the action space, including the adjustable power range and beam direction parameters of the transmitting end; Define the reward function, with the maximization of the effective power received by the receiving end and the minimization of transmission loss as the optimization objectives; Adopt deep reinforcement learning. By setting the neural network structure and using historical transmission data as training samples, generate an intelligent optimization model.

5. The control method of the wireless power transmission system according to claim 3, wherein: The steps of adjusting the beam direction of the phased array antenna at the transmitting end based on the comprehensive data set and the optimal transmit power value are as follows. Based on the comprehensive data set, through the geometric calculation method, calculate the initial beam pointing angle of the phased array antenna at the transmitting end, obtain the azimuth angle and elevation angle of the phased array antenna at the transmitting end pointing to the receiving end, and combine the optimal transmit power value to accurately optimize the signal energy propagation path of the beam to the receiving end.

6. The control method of the wireless power transmission system according to claim 5, characterized in that: The steps of using the hybrid beamforming method to optimize the pointing accuracy of the beam to the receiving end and form an optimal signal energy transmission path are as follows. According to the obtained azimuth and elevation angles, calculate the optimal phased array antenna excitation weights, adjust the amplitudes and phases of each sub-array, form a preliminary beam direction, and obtain digital beamforming; By adjusting the phase shifts of each antenna element, the phased array antenna forms a main beam and performs beam alignment according to the initially calculated azimuth and elevation angles to obtain analog beamforming; Combine the analog beamforming and digital beamforming, optimize the gain and direction of the beam through the least mean square error algorithm, and obtain the feedback information at the receiving end; Utilize the feedback information at the receiving end to adjust the beam angle and power at the transmitting end, dynamically optimize the pointing accuracy of the beam to the receiving end, and form an optimal signal energy transmission path.

7. The control method of the wireless power transmission system according to claim 6, characterized in that: Based on the optimal signal energy transmission path, the receiving end monitors the received power in real time and returns it to the transmitting end through a feedback signal. The specific steps are as follows: Based on the optimal signal energy transmission path, use a high-precision power detection circuit to sample the signal and use a low-pass filter to eliminate noise interference; Perform mean filtering and moving average processing on the sampled data to obtain the current received power; Use the error signal calculation method to calculate the error between the current received power and the optimal transmit power value; Based on the historical received power data and environmental data, set the received power range and evaluate it with the received power error; When the received power error is less than the received power range, keep the current transmit power; When the received power error exceeds the received power range, generate a feedback control signal and send it to the transmitting end through a low-power communication protocol.

8. The control method of the wireless power transmission system according to claim 7, characterized in that: The transmitting end dynamically adjusts the transmit power according to the feedback information to improve the stability and efficiency of wireless power transmission. The specific steps are as follows: According to the current received power value of the feedback information, use the difference calculation method to calculate the deviation from the optimal transmit power value; Adjust the transmit power according to the deviation and optimize the beam direction of the phased array antenna; According to the real-time feedback from the receiving end, dynamically adjust the phase and amplitude of the antenna array, optimize the signal path, and improve the stability and efficiency of wireless power transmission.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the control method of the wireless power transmission system according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the control method of the wireless power transmission system according to any one of claims 1 to 8.

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