Radio frequency tracking method and system based on deep learning

Abstract

The application provides a wireless power transmission adaptive frequency tracking method and system based on deep learning, relates to the technical field of wireless power transmission, and comprises the following steps: determining a transmitting end and a plurality of receiving ends; receiving a plurality of power demand information; performing resonance frequency dynamic matching analysis under the influence of load fluctuation to generate a dynamic transmitting parameter sequence; establishing a coupling effect network, optimizing the dynamic transmitting parameter sequence to generate a target dynamic transmitting parameter sequence; performing power transmission and real-time analysis of the impedance state of the plurality of receiving ends to perform resonance frequency stable tracking. Through the application, the technical problem that it is difficult to perform fine-grained adjustment according to real-time load changes due to the uncertainty and fluctuation of the loads of the plurality of receiving ends and the frequency coupling can be solved, efficient frequency matching and energy transmission under dynamic loads and frequency fluctuation are realized, and the technical effects of improving the stability and efficiency of wireless power transmission in a multi-device sharing environment are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless power transmission, and particularly relates to a wireless power transmission adaptive frequency tracking method and system based on deep learning. BACKGROUND

[0002] Wireless power transmission technology is widely used in electric transportation, medical devices, Internet of Things and other fields. Wireless power transmission transmits power to the receiving end through wireless electromagnetic field to realize non-contact power supply. At present, most wireless power transmission systems rely on fixed frequency or simple adaptive adjustment strategy to match the resonant frequency of the transmitting end and the receiving end.

[0003] However, in actual application, due to the uncertainty and volatility of the load of the receiving end, the fixed frequency transmission scheme often cannot realize efficient and stable power transmission. Load changes will cause the resonant frequency of the receiving end to drift, thereby reducing the energy transmission efficiency of the system, and even may cause resonance mismatch, especially in multi-device shared wireless power transmission, the coupling effect between multiple receiving ends will cause resonant frequency interference, affecting the overall transmission performance of the system.

[0004] In summary, in the prior art, due to the uncertainty and volatility of the load of multiple receiving ends and the frequency coupling, it is difficult to make fine-grained adjustment according to real-time load changes. SUMMARY

[0005] The purpose of the present application is to provide a wireless power transmission adaptive frequency tracking method and system based on deep learning, to solve the technical problem in the prior art that due to the uncertainty and volatility of the load of multiple receiving ends and the frequency coupling, it is difficult to make fine-grained adjustment according to real-time load changes.

[0006] In view of the above problems, the present application provides a wireless power transmission adaptive frequency tracking method and system based on deep learning.

[0007] In a first aspect, the application provides a deep learning-based wireless power transmission adaptive frequency tracking method, which is implemented by a deep learning-based wireless power transmission adaptive frequency tracking system. The deep learning-based wireless power transmission adaptive frequency tracking method comprises: collecting and analyzing a real-time transmission scenario of a multi-device shared wireless power transmission platform, and determining a transmitting end and a plurality of receiving ends; receiving a plurality of power demand information of the plurality of receiving ends; calling a multi-frequency resonance analysis model to perform resonance frequency dynamic matching analysis on the plurality of power demand information under the influence of load fluctuation, and generating a dynamic transmission parameter sequence of the transmitting end; performing coupling effect analysis of resonance frequency on the plurality of receiving ends, establishing a coupling effect network, optimizing the dynamic transmission parameter sequence, and generating a target dynamic transmission parameter sequence; controlling the multi-device shared wireless power transmission platform to perform power transmission by using the target dynamic transmission parameter sequence, and simultaneously performing real-time analysis of impedance states of the plurality of receiving ends for stable tracking of resonance frequency.

[0008] In a second aspect, the application also provides a deep learning-based wireless power transmission adaptive frequency tracking system for performing the deep learning-based wireless power transmission adaptive frequency tracking method of the first aspect. The deep learning-based wireless power transmission adaptive frequency tracking system comprises: a scene analysis module for collecting and analyzing a real-time transmission scenario of a multi-device shared wireless power transmission platform, and determining a transmitting end and a plurality of receiving ends; a demand receiving module for receiving a plurality of power demand information of the plurality of receiving ends; a frequency matching module for calling a multi-frequency resonance analysis model to perform resonance frequency dynamic matching analysis on the plurality of power demand information under the influence of load fluctuation, and generating a dynamic transmission parameter sequence of the transmitting end; a coupling analysis module for performing coupling effect analysis of resonance frequency on the plurality of receiving ends, establishing a coupling effect network, optimizing the dynamic transmission parameter sequence, and generating a target dynamic transmission parameter sequence; and a transmission module for controlling the multi-device shared wireless power transmission platform to perform power transmission by using the target dynamic transmission parameter sequence, and simultaneously performing real-time analysis of impedance states of the plurality of receiving ends for stable tracking of resonance frequency.

[0009] The one or more technical solutions provided in the application have at least the following technical effects or advantages:

[0010] Collect the real-time transmission scene of the multi-device shared wireless power transmission platform and analyze it to determine the transmitting end and multiple receiving ends; receive multiple power demand information of the multiple receiving ends; call a multi-frequency resonance analysis model to perform resonance frequency dynamic matching analysis on the multiple power demand information under the influence of load fluctuation, generate a dynamic transmitting parameter sequence of the transmitting end; perform coupling effect analysis of the resonance frequency on the multiple receiving ends, establish a coupling effect network, optimize the dynamic transmitting parameter sequence to generate a target dynamic transmitting parameter sequence; control the multi-device shared wireless power transmission platform to perform power transmission with the target dynamic transmitting parameter sequence, and simultaneously perform real-time analysis of the impedance state of the multiple receiving ends for resonance frequency stable tracking. By collecting the real-time transmission scene of the multi-device shared wireless power transmission platform and analyzing it, receiving the power demand information of the receiving end, combining the multi-frequency resonance analysis model and the coupling effect analysis, optimizing the dynamic transmitting parameter sequence, and then performing real-time impedance state analysis and resonance frequency stable tracking, efficient frequency matching and energy transmission under dynamic load and frequency fluctuation are realized, and the stability and efficiency of wireless power transmission in a multi-device sharing environment are improved.

[0011] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the specific embodiments of the present application can be implemented in accordance with the content of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only exemplary, and other drawings can be obtained by those skilled in the art without creating any creative labor on the basis of the provided drawings.

[0013] Figure 1 Flowchart of the wireless power transmission adaptive frequency tracking method based on deep learning of the present application.

[0014] Figure 2 Structure diagram of the wireless power transmission adaptive frequency tracking system based on deep learning of the present application.

[0015] Explanation of reference numerals: scene analysis module 11, demand receiving module 12, frequency matching module 13, coupling analysis module 14, transmission module 15. DETAILED DESCRIPTION

[0016] The application provides a deep learning-based wireless power transmission adaptive frequency tracking method and system, which solves the technical problem in the prior art that it is difficult to make fine-grained adjustments according to real-time load changes due to the uncertainty and volatility of the load of multiple receiving ends and the frequency coupling. By collecting and analyzing the real-time transmission scene of a multi-device shared wireless power transmission platform, receiving end power demand information, combining a multi-frequency resonance analysis model and coupling effect analysis, and optimizing a dynamic transmission parameter sequence, real-time impedance state analysis and resonant frequency stable tracking are performed, efficient frequency matching and energy transmission under dynamic load and frequency fluctuations are achieved, and the stability and efficiency of wireless power transmission in a multi-device shared environment are improved.

[0017] Below, the technical solutions in the application will be described clearly and completely with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments of the application. It should be understood that the application is not limited by the example embodiments described herein. Based on the embodiments of the application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the application. In addition, it should be noted that, for convenience of description, only parts related to the application are shown in the drawings, not all.

[0018] Embodiment one, please refer to the attached Figure 1 The application provides a deep learning-based wireless power transmission adaptive frequency tracking method, which is applied to a deep learning-based wireless power transmission adaptive frequency tracking system. The deep learning-based wireless power transmission adaptive frequency tracking method specifically includes the following steps:

[0019] Step one: Collect and analyze the real-time transmission scene of a multi-device shared wireless power transmission platform, and determine the transmitting end and multiple receiving ends.

[0020] Specifically, a multi-device shared wireless power transmission platform is usually composed of a transmitting end and a receiving end. The transmitting end is responsible for transmitting power to the receiving end through wireless waves. In order to achieve efficient and stable power transmission, the real-time transmission scene needs to be collected and analyzed first to determine the specific state of each device. In this step, the collection of real-time transmission scene usually involves using existing sensors or wireless wave detection devices to obtain the working state, power demand and relative position of each device. These sensors can include but are not limited to power meters, signal receivers and transmission bandwidth monitoring modules. The data obtained by these devices can help to analyze a transmitting end and multiple receiving ends that need to be transmitted, and provide accurate basic data for subsequent dynamic matching and coupling effect analysis.

[0021] Step two: receiving a plurality of power demand information of the plurality of receiving ends.

[0022] Specifically, the power demand data of each receiving end is received through sensors or smart devices. Typically, each receiving end is equipped with a power sensor or an energy management module, which can monitor the required power value in real time. The power demand information usually includes the charging power, voltage, current, etc. required by each receiving end. For example, assuming that there are two receiving ends in a platform: receiving end A and B, receiving end A may be a high-power device (such as a charging device for an electric tricycle), and its power demand is relatively large, which may reach 100W, while receiving end B may be a low-power device (such as a low-power sensor), and its power demand is only 20W. These data can be transmitted to the central processing unit of the multi-device shared wireless electric energy transmission platform through existing wireless communication protocols (such as Wi-Fi, Zigbee, Bluetooth, etc.) as power demand information, thereby providing key input for subsequent resonance frequency analysis and dynamic transmission parameter optimization.

[0023] Step three: calling a multi-frequency resonance analysis model to perform resonance frequency dynamic matching analysis on the plurality of power demand information under the influence of load fluctuation, and generating a dynamic transmission parameter sequence of the transmitting end.

[0024] Specifically, in a wireless electric energy transmission system, the resonance frequency dynamic matching analysis of the power demand information of multiple receiving ends under the influence of load fluctuation is a key step to ensure efficient operation of the system. Since the power demand of different receiving ends may fluctuate during use, traditional fixed frequency transmission methods often cannot cope with such dynamic changes. Therefore, a multi-frequency resonance analysis model is needed to analyze the power demand information of each receiving end, and to adjust the transmission parameters of the transmitting end in real time according to the influence of load fluctuation, generating a dynamic transmission parameter sequence. The core of this process is to combine the power demand of each receiving end and the load fluctuation factor through a multi-frequency resonance analysis model, to calculate adaptive resonance frequency and power transmission parameters, so as to ensure that each receiving end can obtain the best power transmission under different power demands, and to achieve more efficient and stable wireless electric energy transmission.

[0025] Step four: coupling effect analysis of the resonance frequency of the plurality of receiving ends is performed, a coupling effect network is established, the dynamic transmission parameter sequence is optimized, and a target dynamic transmission parameter sequence is generated.

[0026] Specifically, in wireless power transmission, there can be a coupling effect between the resonant frequencies of multiple receiving ends, which can affect the overall frequency stability and energy transmission efficiency of the system. Therefore, in order to achieve efficient energy distribution, it is necessary to analyze the coupling effect of the resonant frequencies of these receiving ends, and optimize the dynamic transmission parameters according to the analysis results, so as to generate a target dynamic transmission parameter sequence.

[0027] First, the purpose of coupling effect analysis is to understand how the resonance frequencies of multiple receiving ends interact when they are in operation. In a multi-device shared wireless power transmission platform, the resonance frequencies of each receiving end are not completely independent, and their frequency responses may interact due to electromagnetic interference, physical structural similarity, and other factors. For example, if the resonance frequencies of receiving end A and receiving end B are close, they may interfere with each other during transmission, leading to a decrease in energy transmission efficiency, or even energy loss or stability problems. To analyze these coupling effects, a coupling effect network needs to be established first. The coupling effect network is a graph structure model, where each node represents a receiving end and each edge represents the coupling effect relationship between receiving ends. Through this network, the system can quantitatively analyze the degree of frequency coupling between receiving ends and the impact of coupling. Once the coupling effect network is established, dynamic transmission parameters are optimized. The goal of optimization is to reduce negative coupling effects between receiving ends and ensure that each receiving end can stably receive power within its resonance frequency range without being disturbed by other receiving ends. The optimization process usually involves the following aspects: frequency adjustment: by accurately adjusting the transmission frequency, interference between the resonance frequencies of receiving ends can be avoided. For example, if the frequency coupling effect between receiving end A and B is strong, the frequency of the transmitting end can be adjusted to increase the difference between the resonance frequencies of these two receiving ends, thereby reducing interference; power distribution: based on the results of coupling effect analysis, power distribution can be adjusted to ensure that each receiving end receives sufficient power while avoiding unnecessary interference caused by excessive power output. During the optimization process, existing optimization algorithms such as particle swarm optimization (PSO), genetic algorithm (GA), and other intelligent optimization algorithms can be used to simulate different adjustment schemes for transmission parameters and select the best dynamic transmission parameter sequence. The target dynamic transmission parameter sequence represents the optimal transmission power and frequency settings for the transmitting end under the resonance frequency coupling effect of multiple receiving ends. This target sequence not only ensures the stable charging needs of each receiving end, but also improves the overall efficiency of the system and reduces energy transmission losses. For example, in the experiment, if the resonance frequencies of receiving end A and receiving end B are close and their coupling effect is strong, their power requirements cannot be fully met. After coupling effect analysis, the transmission frequency is adjusted from 250 kHz to 275 kHz, and the power output is increased to ensure that these two receiving ends can stably receive power. At the same time, the power distribution of other receiving ends is also optimized. The final target dynamic transmission parameter sequence will include the adjusted transmission frequency and power value, ensuring that all receiving ends can stably transmit power. This process not only helps to reduce interference, but also improves the overall performance of the wireless power transmission system, especially in a multi-device sharing environment, ensuring that each receiving end can receive stable and efficient power transmission.

[0028] Step five: control the multi-device shared wireless power transmission platform to transmit power with the target dynamic transmission parameter sequence, and simultaneously analyze the impedance states of the multiple receiving ends in real time to track the resonance frequency stably.

[0029] Specifically, the target dynamic transmission parameter sequence includes optimized transmission frequency and power, and can realize balanced and stable energy distribution in the transmission environment of multiple receiving ends. First, the multi-device shared wireless power transmission platform is controlled using the target dynamic transmission parameter sequence. In the process of power transmission, the impedance states of the multiple receiving ends are also analyzed in real time. The change of impedance is an important factor affecting the resonance frequency in wireless power transmission, because the change of impedance directly affects the energy exchange efficiency between the receiving end and the transmitting end. Changes in the load of the receiving end, environmental changes, and the working state of the device itself can all cause changes in impedance, which can affect the resonance frequency and thus cause a decrease in energy transmission efficiency or even unstable transmission. For example, if the load of the receiving end changes and causes fluctuations in its impedance, the resonance frequency of the receiving end may shift if the parameters of the transmitting end are not adjusted in time, affecting the energy transmission of other receiving ends. To address this situation, the impedance states of the multiple receiving ends are monitored in real time, and the current impedance data of the receiving ends are obtained through impedance sensors and then input into an impedance optimization network for analysis and feedback.

[0030] Through the impedance optimization network, the current impedance change trend of each receiving end can be calculated, and the power and frequency of the transmitting end can be adjusted accordingly to stabilize the resonance frequency and ensure that each receiving end can receive power at the optimal frequency. This dynamic impedance state tracking and frequency adjustment mechanism can significantly improve the stability and reliability under dynamic load conditions, ensuring that the multi-device shared wireless power transmission platform can efficiently and stably transmit power under different working conditions, maximizing power transmission efficiency and avoiding unstable factors during transmission.

[0031] Further, step three of the present application includes:

[0032] Based on the multiple power demand information, historical matching resonance frequency retrieval is performed on the multiple receiving ends to establish multiple frequency matching spaces. Resonance frequency influence analysis under load fluctuation is performed on the multiple frequency matching spaces to establish multiple optimized frequency matching spaces. A transmitting parameter control particle is randomly selected from each of the multiple optimized frequency matching spaces for power distribution balance optimization of the multiple receiving ends, and a dynamic transmission parameter sequence of the transmitting end is generated, wherein the dynamic transmission parameters include transmission power and frequency.

[0033] Specifically, in the adaptive frequency tracking of wireless power transmission, first, based on the received multiple receiving end power demand information, historical matching resonance frequency retrieval is carried out, the goal is to understand the frequency demand of each receiving end under different loads by analyzing historical data, so as to establish multiple frequency matching spaces, the frequency matching space is established according to the relationship between the historical power demand and the resonance frequency of each receiving end, and reflects the frequency characteristics of different receiving ends under various load conditions. In actual operation, historical matching resonance frequency retrieval relies on a large-scale data set containing frequency response data of each receiving end under various load states. By retrieving these data, a frequency matching space can be established for each receiving end. For example, assume that the frequency of receiving end A is 250 kHz under low load and 300 kHz under high load. A matching space containing multiple frequency points is established for receiving end A to adapt to the power demand under different loads.

[0034] Next, the influence of load fluctuation on the resonance frequency of multiple frequency matching spaces is analyzed, and multiple optimized frequency matching spaces are generated. Load fluctuations can be caused by many factors, such as changes in internal load at the receiving end, external environmental interference, etc. In order to ensure that these fluctuations can be effectively responded to, the resonance frequency changes under different load fluctuation conditions are simulated, and their influence on the frequency matching space of each receiving end is analyzed. Based on the fluctuation information, the frequency matching space is adjusted to ensure the stability of the frequency. The optimized frequency matching space reflects the influence of load fluctuation on the frequency. In order to achieve the best power transmission efficiency, a transmitting parameter control particle is randomly selected from multiple optimized frequency matching spaces for power distribution and balance optimization. This process is similar to the particle swarm optimization algorithm (PSO), where the control particle represents different combinations of transmitting power and frequency. By simulating and analyzing the performance of each control particle, the best power distribution scheme is determined. By evaluating the matching between the power demand of multiple receiving ends and the actual transmission efficiency, the most suitable control particle is selected, and a dynamic transmitting parameter sequence of the transmitting end is generated, which includes the transmitting power and frequency that adapt to the power demand of each receiving end. The dynamic transmitting parameter sequence will be used in real time to control the power output of the transmitting end, to ensure the efficiency and stability of wireless power transmission. In this way, not only can real-time power demand changes be responded to, but also energy distribution can be optimized in a multi-device sharing environment, reducing energy waste and improving the overall performance of the system. For example, in the experiment, it is assumed that the power demand of receiving end a fluctuates between 100W and 120W, the demand of receiving end b fluctuates less and remains around 20W, and the demand of receiving end c fluctuates greatly and may be between 30W and 60W. After historical matching frequency retrieval and load fluctuation analysis, it may be decided to adjust the transmitting power to 115W and the frequency to 270kHz to meet the demands of receiving ends a and c, while avoiding providing too much power to receiving end B. This optimized dynamic transmitting parameter sequence will improve overall efficiency and ensure stable power supply to each receiving end.

[0035] Further, the present application further comprises the following steps:

[0036] extracting a first frequency matching space corresponding to a first receiving end, and randomly extracting a first frequency matching parameter in the first frequency matching space; predicting the load fluctuation trend of the first receiving end when performing wireless power transmission with the first frequency matching parameter, and generating a first load fluctuation trend; activating a fluctuation optimization module to dynamically optimize the first frequency matching parameter based on the first load fluctuation trend, generating a first optimized frequency matching space, and adding it to the multiple optimized frequency matching spaces.

[0037] Specifically, the step of establishing a plurality of optimized frequency matching spaces based on the analysis of the influence of load fluctuation on the resonant frequency of the plurality of frequency matching spaces is as follows: the first receiving end refers to any one of the plurality of receiving ends, and the first frequency matching space refers to the frequency matching space corresponding to the first receiving end in the plurality of frequency matching spaces. First, a frequency matching parameter is randomly extracted from the established first frequency matching space. These frequency matching parameters represent the optimal frequency at which the receiving end can effectively receive power under specific power requirements and load conditions. Next, based on the selected first frequency matching parameter, the load fluctuation trend of the first receiving end is predicted to obtain the first load fluctuation trend, which reflects the change law of the power demand of the receiving end under different working conditions, thereby predicting how the load fluctuation of the first receiving end affects the resonant frequency in the future period of time. For example, if the power demand of the first receiving end fluctuates due to changes in external environmental temperature, the fluctuation amplitude and frequency change trend of the first receiving end in the future seconds or minutes can be predicted based on historical data. Specifically, a relationship model between load fluctuation data and harmonic frequency can be constructed based on the existing intelligent learning model according to the historical fluctuation data of the receiving end, so as to input the first frequency matching parameter into the relationship model for load fluctuation prediction to generate the first load fluctuation trend.

[0038] Once the load fluctuation trend is predicted, the fluctuation optimization module is activated, and the first frequency matching parameter is dynamically optimized based on the first load fluctuation trend. The fluctuation optimization module adjusts the frequency matching parameter using the first load fluctuation trend to ensure that the first receiving end can continuously and stably receive power under load fluctuation. The optimized frequency matching parameter may be adjusted according to the fluctuation prediction, such as adjusting the frequency from 250 kHz to 275 kHz to better adapt to the changes brought by load fluctuation. Specifically, the fluctuation optimization module can be constructed based on the mapping relationship between historical load fluctuation data and historical frequency adjustment data, so as to identify the frequency adjustment data (such as adjustment amplitude) corresponding to the first load fluctuation trend, dynamically optimize the first frequency matching parameter, and construct the first optimized frequency matching space with the optimized frequency matching parameter and add it to the plurality of optimized frequency matching spaces. After the fluctuation optimization, more extensive load fluctuations can be coped with, and the stability of wireless power transmission is improved. It is ensured that each receiving end can dynamically adjust the frequency according to the load fluctuation, and the overall performance in the multi-receiving-end sharing environment is guaranteed. The plurality of optimized frequency matching spaces formed will provide more possible frequency schemes, thereby realizing optimal power distribution and frequency tracking and improving the overall power transmission efficiency.

[0039] Further, the present application further comprises the following steps:

[0040] In the plurality of optimization frequency matching spaces, a transmit parameter control particle is randomly selected respectively to generate a first group of control particles; the first group of control particles is subjected to power distribution balance analysis of the plurality of receiving ends to generate a first balance visual diagram; the second group of control particles is continuously generated in the plurality of optimization frequency matching spaces until the Nth group of control particles, and the second balance visual diagram is analyzed until the Nth balance visual diagram; the plurality of charging priority identifiers of the plurality of receiving ends are determined based on the real-time transmission scenario; the plurality of charging priority identifiers are subjected to matching analysis with the first balance visual diagram, the second balance visual diagram, and the Nth balance visual diagram to generate a group of control particles with the highest matching degree as the dynamic transmit parameter sequence.

[0041] Specifically, in the plurality of optimization frequency matching spaces, a transmit parameter control particle is randomly selected respectively for power distribution balance optimization of the plurality of receiving ends to generate a specific process of the dynamic transmit parameter sequence of the transmitting end, which includes: first, in the plurality of optimization frequency matching spaces, an optimization frequency is randomly selected respectively, and then transmit end control parameters satisfying the selected plurality of optimization frequencies, including transmit frequency and power, are screened based on a historical transmission database to form a first group of control particles. The selected first group of control particles is subjected to power distribution balance analysis of the plurality of receiving ends, the goal of which is to evaluate the power distribution effect of the group of control particles on different receiving ends. In order to achieve balance, the transmit power and frequency are adjusted according to the power demand of the receiving end to ensure that each receiving end can obtain the required electric energy without overload or waste. Through this analysis, a first balance visual diagram is generated, which is a chart showing the power distribution balance and can intuitively reflect the electric energy receiving situation of different receiving ends. For example, in the chart, the power demand and the actual allocated power ratio of the plurality of receiving ends are represented by different colors or marks to help identify whether there is a power distribution imbalance or deviation.

[0042] After completing the balance analysis of the first set of control particles, continue to generate the second, third, and up to the Nth set of control particles in multiple optimized frequency matching spaces, where N is an integer greater than 1. Each set of control particles undergoes the same power distribution balance analysis, and corresponding balance visualizations are generated. As the number of control particles increases, more combinations of transmission power and frequency can be evaluated, and the effects of different schemes can be compared through the balance visualizations, gradually finding the most suitable transmission parameters. Next, based on real-time transmission scenarios, determine the charging priority identifiers of multiple receiving ends. These identifiers reflect the urgency of the charging needs of each receiving end. For example, if receiving end A has a low battery level, it may be marked as high priority, while receiving end B may be marked as low priority. Real-time scenario data such as the battery status of the receiving end, changes in power demand, etc. will be used to determine the charging priority of each receiving end, or the priority can be set directly by the administrator.

[0043] Finally, match the charging priority identifiers of multiple receiving ends with each balance visualization for analysis. Through matching analysis, evaluate the performance of each set of control particles in terms of charging priority, and generate the set of control particles with the highest matching degree as the final dynamic transmission parameter sequence. This process ensures that the power and frequency distribution of the transmitting end can be optimized according to the priority requirements in the real-time transmission scenario, achieving the best power distribution and transmission efficiency. For example, in an experiment, receiving end A has high priority, receiving end B has medium priority, and receiving end C has low priority. Analyze the balance visualization of the first set of control particles to ensure that the high-priority receiving end A can obtain the required power first, while avoiding excessive occupation of system resources by the low-priority receiving end C. Finally, through matching analysis, determine the most suitable transmission power and frequency combination to form an optimized dynamic transmission parameter sequence, ensuring efficient and stable power transmission in a multi-receiving end environment. This process not only ensures balanced power distribution, but also adjusts transmission parameters according to real-time scenario requirements to maximize overall performance.

[0044] Further, step four of the present application includes:

[0045] determining an anti-interference configuration between the multiple receiving ends; modeling the wireless power transmission of the transmitting end and the multiple receiving ends in combination with the anti-interference configuration to generate a twin power transmission model; performing transmission testing of the multiple receiving ends using the twin power transmission model, and performing resonance frequency coupling effect analysis of the multiple receiving ends based on the test results to generate the coupling effect network, wherein the nodes of the coupling effect network represent each receiving end, and the edges represent the coupling effect relationship.

[0046] Specifically, in a multi-device shared wireless power transmission system, anti-interference configuration is one of the key factors to ensure the stable operation of the system. Especially when multiple receiving ends are working simultaneously, each receiving end may be interfered by other receiving ends, environmental factors, or the signal of the transmitting end. Therefore, it is crucial to design a reasonable anti-interference strategy. Based on this, the anti-interference configuration between the current receiving ends is determined, including the shielding devices that have been set and the positions of the receiving ends. The transmitting end and multiple receiving ends are modeled for wireless power transmission in combination with the anti-interference configuration. Through simulation models or actual experiments, the electromagnetic coupling relationship between the transmitting end and each receiving end is analyzed, as well as the performance of each receiving end under different interference conditions. This modeling process is the existing technology, which needs to consider multiple variables, such as transmitting power, frequency, power demand of receiving end, distance, and environmental impact, etc. Finally, this information will be used to generate a twin power transmission model. The twin power transmission model is a digital, highly similar virtual model to the actual system. By establishing this model, the wireless power transmission process can be simulated in a virtual environment, the impact of different configurations on transmission efficiency can be evaluated, and the performance of different receiving ends under various conditions can be predicted.

[0047] After generating the twin power transmission model, transmission tests of multiple receiving ends are conducted through the model. By simulating different operating conditions and interference scenarios, transmission performance data of each receiving end under different environments can be obtained, such as load, resonant frequency variation data, which serve as test results. On this basis, resonant frequency coupling effect analysis of multiple receiving ends is conducted. The goal of coupling effect analysis is to identify the mutual influence between receiving ends, especially the interaction in frequency. For example, the load change of one receiving end may affect the operating frequency of another receiving end, resulting in a decrease in overall energy transmission efficiency. Based on existing correlation analysis methods, such as gray correlation, the resonant frequency coupling relationship between each receiving end is analyzed. Finally, the generated coupling effect network represents each receiving end as a node, and the coupling relationship between receiving ends is connected through edges. The coupling effect network can help intuitively understand the interaction between receiving ends and identify which receiving ends have strong coupling effects and which receiving ends' frequencies are easily affected by interference.

[0048] By analyzing the coupling effect network, the frequency and power distribution strategy can be further optimized to reduce the negative impact of coupling effect on system performance and improve the stability and efficiency of wireless power transmission. For example, assume that during testing, the resonance frequency coupling effect of receiving end A and receiving end B is strong, and both of them have heating phenomenon, resulting in frequency drift. In the generated coupling effect network, the edges between the nodes of the two receiving ends will show strong coupling relationship. Through the analysis of the network, it can be decided to adjust the working frequency of the two receiving ends, or optimize the power distribution between them, to reduce the negative impact of coupling effect, ensure the overall performance, reduce the interference between receiving ends, optimize the wireless power transmission process, and improve the efficiency and stability of power transmission in multi-device sharing environment.

[0049] Further, step five of the present application comprises:

[0050] Collecting historical impedance variation characteristics and corresponding transmit end power and frequency optimization parameters, training an impedance optimization network; collecting multiple impedance variation data of the multiple receiving ends in real time through an impedance sensor; inputting the multiple impedance variation data into the impedance optimization network for feedback optimization, and performing resonance frequency stable tracking with the optimized power and frequency.

[0051] Specifically, in the process of multi-device sharing wireless power transmission, the impedance variation of the receiving end has a crucial influence on the performance of power transmission, and it is necessary to track the impedance state of the receiving end in real time and dynamically adjust the power and frequency of the transmitting end according to these changes. For this purpose, by collecting historical impedance variation characteristics and corresponding transmit end optimization parameters, and using these data to train an impedance optimization network based on existing machine learning models such as neural network model, the network can feedback and optimize the parameters of the transmitting end according to real-time impedance data, so as to realize stable tracking of the resonance frequency and ensure the efficiency and stability of wireless power transmission.

[0052] First, by collecting historical impedance variation characteristics and their corresponding transmit end power and frequency optimization parameters, data is provided for training the impedance optimization network. These historical data include the impedance variation of each receiving end under different working conditions, as well as the corresponding transmit power and frequency settings. These data can help understand the relationship between the impedance fluctuation trend and the transmit end parameters under different load changes and environmental conditions. For example, when the load of the receiving end changes, the impedance may change, causing the resonant frequency of the receiving end to drift. Historical data will help the impedance optimization network understand this change pattern and provide a reference for real-time adjustment of transmit parameters. Based on these historical data, an impedance optimization network is trained. The network is a deep learning model that learns the relationship between impedance variation and transmit end power and frequency, enabling the network to automatically predict how to adjust the transmit end parameters to achieve optimal power transmission under given impedance variation. During training, the network continuously optimizes its parameters to minimize the error between the predicted power and frequency and the actual optimization requirements, so that it can more accurately adjust the parameters in subsequent operations.

[0053] When entering real-time operation state, the impedance sensor will collect real-time impedance variation data of multiple receiving ends, which reflect the impedance state of each receiving end under the current load and working environment. For example, the impedance of the receiving end may fluctuate due to load changes, temperature fluctuations, or electromagnetic interference. These real-time collected impedance variation data will be input into the already trained impedance optimization network for feedback optimization. The impedance optimization network calculates how to adjust the power and frequency of the transmit end to ensure that each receiving end can work stably within its resonant frequency range.

[0054] For example, if the load of a receiving end fluctuates, causing a large change in its impedance, the impedance optimization network calculates the required frequency and power adjustment parameters for the receiving end. Through continuous feedback optimization, the power and frequency of the transmit end are adjusted to ensure stable and efficient energy transmission, significantly improving the adaptability and transmission performance of the multi-device shared wireless power transmission platform in complex environments.

[0055] Further, the present application further includes the following steps:

[0056] The multi-device shared wireless power transmission platform is connected to a parasitic monitoring module, which monitors the parasitic parameters of the multi-device shared wireless power transmission platform through the parasitic monitoring module. The parasitic parameters at least include leakage magnetic parameters and parasitic capacitance parameters. Frequency offset influence analysis is performed based on the leakage magnetic parameters and the parasitic capacitance parameters to generate a frequency offset amount. Feedback control of power transmission is performed with the frequency offset amount.

[0057] Specifically, in a multi-device shared wireless power transmission platform, parasitic effects are important factors affecting stability and energy transmission efficiency. In order to monitor these parasitic effects in real time, a parasitic monitoring module is equipped to collect and analyze parasitic parameters, and based on these parameters, frequency offset influence analysis is performed, and finally through feedback control mechanism, the power transmission is optimized and adjusted to improve the stability and transmission efficiency of the system. First of all, the parasitic monitoring module is responsible for monitoring the parasitic parameters of the multi-device shared wireless power transmission platform, including leakage magnetic parameters and parasitic capacitance parameters. Leakage magnetic parameters mainly refer to the leakage magnetic effect caused by incomplete coupling of electromagnetic field during wireless power transmission. High leakage magnetic may cause energy loss and may cause electromagnetic interference (EMI) to surrounding electronic devices. Parasitic capacitance parameters refer to non-ideal capacitance generated by coils, transmission paths and environmental coupling in the system. These parasitic capacitances may affect the resonance frequency, causing resonance matching to deviate, and thus affecting the efficiency of power transmission. In order to ensure the stability of the system, the parasitic monitoring module needs to measure these parasitic parameters in real time. For example, use a high-precision magnetic field sensor to configure the parasitic monitoring module.

[0058] Next, based on the leakage magnetic parameters and parasitic capacitance parameters collected by the parasitic monitoring module, frequency offset influence analysis is performed, and the frequency offset is calculated. In an ideal case, the transmission frequency should match the resonance frequency of the receiving end to achieve the most efficient energy transmission, but due to the influence of parasitic parameters, the actual resonance frequency may deviate, for example, the designed resonance frequency of the transmitting end is 250 kHz, but due to the influence of parasitic capacitance, the actual working frequency measured may deviate to 245 kHz or 255 kHz. This deviation will reduce the efficiency of energy transmission, and even may cause the receiving end to be unable to effectively receive power. Therefore, the frequency offset needs to be calculated, that is, the difference between the actual working frequency and the theoretical resonance frequency, for example, if the measured frequency offset is +5 kHz, it means that the transmitting end needs to be adjusted accordingly to compensate for this deviation. Specifically, collect historical leakage magnetic parameters, historical parasitic capacitance parameters and corresponding frequency offset data as training data, and based on existing machine learning models such as neural network models, train a frequency offset identification model, which is a common technical means for those skilled in the art and will not be expanded here.

[0059] After calculating the frequency offset, a feedback control mechanism is used for dynamic adjustment to ensure that wireless power transmission can be maintained in the best state. The basic principle of feedback control is to use a closed-loop control system to make real-time corrections to the operating frequency of the transmitting end according to the measured parasitic parameters and frequency offset. For example, if it is detected that the resonance frequency has shifted +5 kHz due to changes in parasitic capacitance, the transmitting frequency may be automatically reduced by 5 kHz to re-match the resonance frequency of the receiving end. By real-time monitoring of parasitic parameters, calculating frequency offset, and adjusting the transmitting end parameters based on feedback control, frequency drift caused by parasitic effects can be effectively suppressed, ensuring the stability and efficiency of wireless power transmission.

[0060] In summary, the deep learning-based wireless power transmission adaptive frequency tracking method provided by the present application has the following technical effects:

[0061] The real-time transmission scene of the multi-device shared wireless power transmission platform is collected and analyzed to determine the transmitting end and multiple receiving ends. The multiple power demand information of the multiple receiving ends is received. The multi-frequency resonance analysis model is called to perform resonance frequency dynamic matching analysis under the influence of load fluctuation on the multiple power demand information, and a dynamic transmitting parameter sequence of the transmitting end is generated. The coupling effect of the resonance frequency of the multiple receiving ends is analyzed, a coupling effect network is established, the dynamic transmitting parameter sequence is optimized, and a target dynamic transmitting parameter sequence is generated. The multi-device shared wireless power transmission platform is controlled by the target dynamic transmitting parameter sequence to perform power transmission, and the impedance state of the multiple receiving ends is analyzed in real time to track the stability of the resonance frequency. By collecting and analyzing the real-time transmission scene of the multi-device shared wireless power transmission platform, receiving the power demand information of the receiving end, combining the multi-frequency resonance analysis model and the coupling effect analysis, optimizing the dynamic transmitting parameter sequence, and then performing real-time impedance state analysis and resonance frequency stability tracking, efficient frequency matching and energy transmission under dynamic load and frequency fluctuation are realized, achieving the technical effect of improving the stability and efficiency of wireless power transmission in a multi-device sharing environment.

[0062] Embodiment two, based on the same inventive concept as the deep learning-based wireless power transmission adaptive frequency tracking method in the preceding embodiments, the present application also provides a deep learning-based wireless power transmission adaptive frequency tracking system. Please refer to the accompanying Figure 2 The deep learning-based wireless power transmission adaptive frequency tracking system comprises:

[0063] The scene analysis module 11 is used to collect and analyze the real-time transmission scene of the multi-device shared wireless power transmission platform to determine the transmitting end and multiple receiving ends.

[0064] The demand receiving module 12 is used to receive the multiple power demand information of the multiple receiving ends.

[0065] a frequency matching module 13, configured to call a multi-frequency resonance analysis model to perform dynamic matching analysis on the resonance frequency of the plurality of power demand information under load fluctuation, and generate a dynamic transmission parameter sequence of the transmission end.

[0066] a coupling analysis module 14, configured to perform coupling effect analysis on the resonance frequency of the plurality of receiving ends, establish a coupling effect network, optimize the dynamic transmission parameter sequence, and generate a target dynamic transmission parameter sequence.

[0067] a transmission module 15, configured to control the multi-device shared wireless power transmission platform to perform power transmission with the target dynamic transmission parameter sequence, and simultaneously perform real-time analysis on the impedance state of the plurality of receiving ends to perform resonance frequency stability tracking.

[0068] Further, the frequency matching module 13 in the deep learning-based wireless power transmission adaptive frequency tracking system is further configured to:

[0069] perform historical matching resonance frequency retrieval on the plurality of receiving ends based on the plurality of power demand information, establish a plurality of frequency matching spaces, perform resonance frequency influence analysis on the plurality of frequency matching spaces under load fluctuation, establish a plurality of optimized frequency matching spaces, and perform power distribution and equalization optimization of the plurality of receiving ends by randomly selecting one transmission parameter control particle in each of the plurality of optimized frequency matching spaces, to generate a dynamic transmission parameter sequence of the transmission end, wherein the dynamic transmission parameter includes transmission power and frequency.

[0070] Further, the frequency matching module 13 in the deep learning-based wireless power transmission adaptive frequency tracking system is further configured to:

[0071] extract a first frequency matching space corresponding to a first receiving end, randomly extract a first frequency matching parameter in the first frequency matching space, perform load fluctuation trend prediction on the first receiving end when performing wireless power transmission with the first frequency matching parameter, generate a first load fluctuation trend, activate a fluctuation optimization module to perform dynamic optimization on the first frequency matching parameter according to the first load fluctuation trend, generate a first optimized frequency matching space, and add the first optimized frequency matching space to the plurality of optimized frequency matching spaces.

[0072] Further, the frequency matching module 13 in the deep learning-based wireless power transmission adaptive frequency tracking system is further configured to:

[0073] randomly select one transmitting parameter control particle in each of the plurality of optimized frequency matching spaces to generate a first group of control particles; perform power distribution balance analysis of the first group of control particles at a plurality of receiving ends to generate a first balance visual diagram; continue to generate a second group of control particles until an Nth group of control particles in the plurality of optimized frequency matching spaces, and analyze to generate a second balance visual diagram until an Nth balance visual diagram; determine a plurality of charging priority identifiers of the plurality of receiving ends based on the real-time transmission scenario; perform matching analysis on the plurality of charging priority identifiers and the first balance visual diagram, the second balance visual diagram, and the Nth balance visual diagram to generate a group of control particles with the highest matching degree as the dynamic transmitting parameter sequence.

[0074] Further, the deep learning-based wireless power transmission adaptive frequency tracking system further comprises a parasitic monitoring feedback module, which is configured to:

[0075] The multi-device shared wireless power transmission platform is connected to a parasitic monitoring module; the parasitic monitoring module is used to monitor parasitic parameters of the multi-device shared wireless power transmission platform, wherein the parasitic parameters at least include leakage magnetic parameters and parasitic capacitance parameters; frequency offset influence analysis is performed based on the leakage magnetic parameters and the parasitic capacitance parameters to generate a frequency offset amount; feedback control of power transmission is performed based on the frequency offset amount.

[0076] Further, the coupling analysis module 14 in the deep learning-based wireless power transmission adaptive frequency tracking system is further configured to:

[0077] determine an anti-interference configuration between the plurality of receiving ends; model wireless power transmission of the transmitting end and the plurality of receiving ends in combination with the anti-interference configuration to generate a twin power transmission model; perform transmission testing of the plurality of receiving ends based on the twin power transmission model, and perform resonance frequency coupling effect analysis of the plurality of receiving ends based on the testing results to generate a coupling effect network, wherein nodes of the coupling effect network represent each receiving end, and edges represent coupling effect relationships.

[0078] Further, the transmission module 15 in the deep learning-based wireless power transmission adaptive frequency tracking system is further configured to:

[0079] Collect historical impedance variation characteristics and corresponding transmitting end power and frequency optimization parameters to train an impedance optimization network; real-time collect a plurality of impedance variation data of the plurality of receiving ends through an impedance sensor; input the plurality of impedance variation data into the impedance optimization network for feedback optimization, and perform resonance frequency stable tracking based on the optimized power and frequency.

[0080] The various embodiments described in this specification are intended to be exemplary only. The foregoing description of various embodiments of the application will be better understood with Figure 1 The deep learning based wireless power transmission adaptive frequency tracking method and specific examples in embodiment one are also applicable to the deep learning based wireless power transmission adaptive frequency tracking system of the present embodiment. Through the foregoing detailed description of the deep learning based wireless power transmission adaptive frequency tracking method, those skilled in the art can clearly understand the deep learning based wireless power transmission adaptive frequency tracking system of the present embodiment. Therefore, in order to make the specification brief, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant part can be referred to the method part.

[0081] The above description of disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0082] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application also intends to include these modifications and variations.

Claims

1. A deep learning based adaptive frequency tracking method for wireless power transfer, characterized in that, The method comprises the following steps: Collecting the real-time transmission scene of the multi-device shared wireless power transmission platform and analyzing it to determine the transmitting end and multiple receiving ends; Receiving multiple power demand information of the multiple receiving ends; Calling a multi-frequency resonance analysis model to perform resonance frequency dynamic matching analysis on the multiple power demand information under the influence of load fluctuation, and generating a dynamic transmission parameter sequence of the transmitting end; Performing coupling effect analysis of the resonance frequency on the multiple receiving ends, establishing a coupling effect network, optimizing the dynamic transmission parameter sequence, and generating a target dynamic transmission parameter sequence; Controlling the multi-device shared wireless power transmission platform to perform power transmission with the target dynamic transmission parameter sequence, while real-time analyzing the impedance state of the multiple receiving ends to perform resonance frequency stable tracking; Calling a multi-frequency resonance analysis model to perform resonance frequency dynamic matching analysis on the multiple power demand information under the influence of load fluctuation, and generating a dynamic transmission parameter sequence of the transmitting end, comprising: Based on the multiple power demand information, performing historical matching resonance frequency retrieval on the multiple receiving ends to establish multiple frequency matching spaces; Performing resonance frequency influence analysis on the multiple frequency matching spaces under the influence of load fluctuation to establish multiple optimized frequency matching spaces; Randomly selecting one transmission parameter control particle in each of the multiple optimized frequency matching spaces to perform power distribution balance optimization of the multiple receiving ends, and generating a dynamic transmission parameter sequence of the transmitting end, wherein the dynamic transmission parameter includes transmission power and frequency.

2. The deep learning based wireless power transfer adaptive frequency tracking method of claim 1, wherein, Performing resonance frequency influence analysis on the multiple frequency matching spaces under the influence of load fluctuation to establish multiple optimized frequency matching spaces, comprising: Extracting a first frequency matching space corresponding to a first receiving end, and randomly extracting a first frequency matching parameter in the first frequency matching space; Performing load fluctuation trend prediction of the first receiving end during wireless power transmission with the first frequency matching parameter to generate a first load fluctuation trend; Activating a fluctuation optimization module to dynamically optimize the first frequency matching parameter according to the first load fluctuation trend to generate a first optimized frequency matching space and add it to the multiple optimized frequency matching spaces.

3. The deep learning based wireless power transfer adaptive frequency tracking method of claim 1, wherein, Randomly selecting one transmission parameter control particle in each of the multiple optimized frequency matching spaces to perform power distribution balance optimization of the multiple receiving ends, and generating a dynamic transmission parameter sequence of the transmitting end, comprising: Randomly selecting one transmission parameter control particle in each of the multiple optimized frequency matching spaces to generate a first group of control particles; Performing power distribution balance analysis of the multiple receiving ends on the first group of control particles to generate a first balance visual graph; Continuing to generate a second group of control particles in the multiple optimized frequency matching spaces until the Nth group of control particles, and analyzing to generate a second balance visual graph until the Nth balance visual graph; Determining multiple charging priority identifiers of the multiple receiving ends based on the real-time transmission scene; Performing matching analysis on the multiple charging priority identifiers and the first balance visual graph, the second balance visual graph, and the Nth balance visual graph to generate a group of control particles with the highest matching degree as the dynamic transmission parameter sequence.

4. The deep learning based wireless power transfer adaptive frequency tracking method of claim 1, wherein, The multi-device shared wireless power transmission platform is connected with a parasitic monitoring module; A parasitic parameter of the multi-device shared wireless power transmission platform is monitored through the parasitic monitoring module, wherein the parasitic parameter at least includes a leakage magnetic parameter and a parasitic capacitance parameter; Frequency offset influence analysis is performed based on the leakage magnetic parameter and the parasitic capacitance parameter, and a frequency offset amount is generated; Feedback control of power transmission is performed with the frequency offset amount.

5. The deep learning based wireless power transfer adaptive frequency tracking method of claim 1, wherein, Coupling effect analysis of resonant frequencies of the multiple receiving ends is performed, and a coupling effect network is established, including: Anti-interference configuration between the multiple receiving ends is determined; Modeling of wireless power transmission of the transmitting end and the multiple receiving ends is performed in combination with the anti-interference configuration, and a twin power transmission model is generated; Transmission test of the multiple receiving ends is performed with the twin power transmission model, resonant frequency coupling effect analysis of the multiple receiving ends is performed according to test results, and the coupling effect network is generated, wherein nodes of the coupling effect network represent respective receiving ends, and edges represent coupling effect relationships.

6. The deep learning based wireless power transfer adaptive frequency tracking method of claim 1, wherein, Real-time analysis of impedance states of the multiple receiving ends is performed for resonant frequency stable tracking, including: Historical impedance change characteristics and corresponding transmitting end power and frequency optimization parameters are collected, and an impedance optimization network is trained; Multiple impedance change data of the multiple receiving ends are collected in real time through an impedance sensor; The multiple impedance change data are input into the impedance optimization network for feedback optimization, and resonant frequency stable tracking is performed with optimized power and frequency.

7. A wireless power transfer adaptive frequency tracking system based on deep learning, characterized in that, Steps for implementing the deep learning-based wireless power transmission adaptive frequency tracking method in any one of claims 1 to 6, including: A scene analysis module is configured to collect and analyze real-time transmission scenarios of a multi-device shared wireless power transmission platform, and determine a transmitting end and multiple receiving ends; A demand receiving module is configured to receive multiple power demand information of the multiple receiving ends; A frequency matching module is configured to call a multi-frequency resonance analysis model to perform resonant frequency dynamic matching analysis under load fluctuation of the multiple power demand information, and generate a dynamic transmitting parameter sequence of the transmitting end; A coupling analysis module is configured to perform coupling effect analysis of resonant frequencies of the multiple receiving ends, establish a coupling effect network, optimize the dynamic transmitting parameter sequence, and generate a target dynamic transmitting parameter sequence; A transmission module is configured to control the multi-device shared wireless power transmission platform to perform power transmission with the target dynamic transmitting parameter sequence, and simultaneously perform real-time analysis of impedance states of the multiple receiving ends for resonant frequency stable tracking.

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