A follow-up wireless microwave charging method and system

Abstract

The present application relates to the technical field of wireless power transmission, and provides a following type wireless microwave charging method and system, the method collects multiple sets of position coordinate information and speed information of a charging device in a T1 time period, constructs a trajectory prediction model to calculate motion trajectory data of the charging device in a T2 time period, divides a microwave antenna into N independently controlled subarray antenna units according to the motion trajectory data, each subarray antenna unit corresponds to cover a region segment on a predicted trajectory, a control unit dynamically adjusts phase and power parameters of each subarray antenna unit according to a real-time position of the charging device to form an adaptive beam, and the charging device is charged by region gradient through the adaptive beam. The method and system in the present application not only solve the charging alignment problem of a mobile device, but also improve energy utilization efficiency through power gradient distribution.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless power transmission, in particular to a following type wireless microwave charging method and system. BACKGROUND

[0002] With the popularity of mobile devices and the rapid development of portable electronic products, wireless charging technology as a new charging method has been widely concerned by the academic and industrial circles. Traditional wireless charging technologies mainly include electromagnetic induction, magnetic resonance and other near-field charging methods. Although these technologies show good charging efficiency in static charging scenarios, their effective charging distance is usually limited to the centimeter level. In contrast, microwave wireless power transmission technology can realize energy transmission at a distance of meters or even further due to its far-field transmission characteristics, providing a new technical path for dynamic charging of mobile devices. However, the existing microwave wireless charging system still faces many challenges in practical application: first, the formation of traditional directional microwave beams usually relies on fixed beamforming algorithms, which is difficult to adapt to the rapid movement of charging devices; second, the coverage area of a single beam is limited and the energy distribution is uneven, which can easily cause the problem of excessive concentration of energy in some areas and insufficient energy in other areas; third, the existing technology lacks accurate prediction ability for the movement trajectory of the charging device, resulting in low energy transmission efficiency.

[0003] At present, scholars at home and abroad have carried out a lot of research on microwave wireless charging technology and proposed various improvement schemes. Among them, the introduction of phased array antenna technology makes it possible to dynamically adjust the microwave beam, but most of the existing schemes use overall beam control method, which is difficult to realize accurate energy delivery in different areas. At the same time, the traditional trajectory tracking algorithm mainly focuses on the real-time position of the target, ignoring the prediction analysis of the movement trend, which leads to a certain lag in system response and cannot meet the charging demand in mobile scenarios. In addition, since the charging device is often in a moving state, the alignment accuracy of the microwave transmitting antenna and the receiving antenna is insufficient, which not only leads to low charging efficiency, but also causes obvious energy loss. This energy loss not only reduces the overall efficiency of the system, but also causes unnecessary electromagnetic interference to the surrounding environment. SUMMARY

[0004] Embodiments of the present application provide a following type wireless microwave charging method, which can at least partially solve the problem of low charging efficiency and energy loss caused by insufficient alignment accuracy of the microwave transmitting antenna and the receiving antenna due to the charging device being often in a moving state.

[0005] Other characteristics and advantages of the present application will become apparent from the following detailed description, or will be learned by practice of the present application.

[0006] According to one aspect of the present application, a follow-up wireless microwave charging method is provided, comprising:

[0007] Collecting multiple sets of position coordinate information and speed information of the charging device within a T1 time period, and constructing a trajectory prediction model to calculate motion trajectory data of the charging device within a T2 time period;

[0008] According to the motion trajectory data, the microwave antenna is divided into N independently controlled subarray antenna units, each of which corresponds to cover a region segment on the predicted trajectory;

[0009] The control unit dynamically adjusts the phase and power parameters of each subarray antenna unit according to the real-time position of the charging device, forming an adaptive beam;

[0010] The charging device is charged by the adaptive beam in a gradient manner.

[0011] In the present application, based on the foregoing scheme, the forget gate, output gate, candidate cell calculation, cell state update, and output gate structure of the trajectory prediction model;

[0012] The definition of the forget gate is as follows:

[0013]

[0014] The definition of the output gate is as follows:

[0015]

[0016] The candidate cell state calculation is as follows:

[0017]

[0018] The cell state update is as follows:

[0019]

[0020] The output gate structure is as follows:

[0021]

[0022] The final hidden state output is as follows:

[0023]

[0024] Wherein, each parameter satisfies the following constraint condition:

[0025] δ,α,β,β,η,φ∈(0,1)

[0026]

[0027] Where, θ j For trainable parameters, f t The output value of the forget gate is in the range (0,1), σ is the sigmoid activation function, and W is the output value of the forget gate. f h is the forget gate weight matrix. t-1 , represents the hidden state of the previous moment, x t For the current input, b f The term represents the forgetting gate bias, and δ is the time derivative weighting coefficient used to adjust the sensitivity to state changes. Let i be the time derivative of the hidden state. t The input gate output value, ranging from (0,1), W i Let b be the input gate weight matrix. i The input gate bias term, α is the historical information weighting coefficient, m is the time step considering historical information, and ω is the input gate bias term. k Let x be the weight of the k-th historical moment. t-k The input is k steps in advance. For candidate cell states, W c Let b be the state weight matrix. c Here, β represents the state bias term, and β is the second-order gradient weight coefficient. For the second-order gradient of the hidden state, C t The current cell state is represented by ⊙, which is the Hadamard product (element-by-element multiplication), γ is the weighting coefficient for historical cell states, and w is the cell state weighting coefficient. i Let μ be the weight of the i-th historical state. i Let $\frac{i}{i}$ be the decay rate at the $i$-th time scale, $\frac{t}{i}$ be the time step size, and $\frac{o}{i}$ be the decay rate at the $i$-th time scale. t W is the output value of the output gate. o Let b be the output gate weight matrix. o The output gate bias term is η, where η is the weighting coefficient for the rate of change of the state. h represents the rate of change in cell state. t The output represents the hidden state at the current moment, φ is the weight coefficient of the historical hidden states, p is the number of historical frames considered, and v j is the adaptive weight for the j-th frame.

[0028] In this invention, based on the aforementioned scheme, the motion trajectory data calculates multiple spatial location points according to the output of the trajectory prediction model, and connects the spatial location points through an interpolation algorithm to form a smooth and continuous predicted trajectory curve;

[0029] The spatial location point also includes h, which is the output of the trajectory prediction model at time t. t Confidence level: When the confidence level is lower than a preset threshold, data is collected again.

[0030] In the present application, based on the foregoing scheme, the state of the subarray antenna unit includes an actively powered unit, a standby functional unit and a dormant unit.

[0031] The subarray antenna units are cooperatively controlled.

[0032] In the present application, based on the foregoing scheme, the cooperative control includes determining whether to start or change the state of the target subarray antenna unit according to the distance between the charging device and the subarray antenna unit.

[0033] Wherein, it is determined whether the target subarray antenna unit interacts with the predicted trajectory curve, if it interacts, it is the target subarray antenna unit, if multiple subarray antenna units interact with the predicted trajectory curve, the overlapping area is calculated, and the subarray antenna unit with the largest overlapping area is the target subarray antenna unit.

[0034] In the present application, based on the foregoing scheme, the cooperative control further includes:

[0035] If the actual motion state of the charging device is detected to change sharply, so that the predicted trajectory model cannot respond in time, the moving speed of the charging device is detected, when the moving speed is detected to mutate, based on the current speed vector direction, multiple adjacent subarray antenna units in the direction are triggered to enter the standby state in advance; when the motion direction of the charging device is detected to mutate, the current position is taken as the center, the adjacent subarray antenna units in the surrounding sector are pre-activated at the same time, and the power output is maintained according to the coverage until the effective trajectory prediction is re-established or the motion trend of the charging device is obvious.

[0036] In the present application, based on the foregoing scheme, the adaptive beam forming includes:

[0037] The theoretical phase value of each subarray antenna unit is calculated based on the relative spatial vector between the charging device and each subarray antenna unit in the activated state;

[0038] The theoretical phase is corrected in real time by using an adaptive phase compensation algorithm to obtain a compensated actual phase value;

[0039] The actual phase value is coupled and corrected to obtain a final phase control value, and an adaptive beam is formed according to the phase control value and the power control between the subarray antenna units.

[0040] In the present application, based on the foregoing scheme, the calculation of the compensated actual phase value is as follows:

[0041]

[0042] Wherein, φ i,compβ is the actual phase value after compensation i (ω) is a frequency-dependent compensation coefficient, dynamically adjusted with the working frequency, Δφ i is a historical phase error cumulative value, γ i is an adaptive gain coefficient, controlling the compensation strength, w k is the weight coefficient of each antenna unit, ω is the working angular frequency, N is the total number of antenna array units, φ i,theory is the i-th theoretical phase value, t is the time variable, φ k,theory is the k-th antenna unit theoretical phase value.

[0043] According to one aspect of the present application, a follow-up wireless microwave charging system is provided, comprising:

[0044] The acquisition module is configured to acquire a plurality of sets of position coordinate information and speed information of the charging device within a T1 time period.

[0045] The trajectory prediction module is configured to construct a trajectory prediction model according to the data of the acquisition module to calculate the motion trajectory data of the charging device within a T2 time period.

[0046] The partition charging module is configured to partition the microwave antenna into N independently controlled subarray antenna units according to the motion trajectory data, each of the subarray antenna units corresponding to covering a region segment on the predicted trajectory.

[0047] The control unit is configured to dynamically adjust the phase and power parameters of each of the subarray antenna units according to the real-time position of the charging device to form an adaptive beam.

[0048] The charging module is configured to perform regional gradient charging on the charging device through the adaptive beam.

[0049] In the technical scheme of the present application, the neural network prediction is combined with the subarray antenna regional dynamic power supply, the trajectory prediction is used to adjust the antenna parameters in advance, and the subarray antenna is used to realize regional gradient charging, so that the alignment deviation is controlled within 3 degrees, and the charging efficiency is improved to 92%. The scheme not only solves the alignment problem of mobile device charging, but also improves the energy utilization efficiency through power gradient distribution.

[0050] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0051] The accompanying drawings, which are incorporated herein and constitute part of the specification, illustrate embodiments consistent with the present application and, together with the description, further serve to explain the principles of the application. It is to be understood that the drawings are only illustrations of some embodiments of the application and that, to one of ordinary skill in the art, other embodiments can be clearly inferred from the drawings.

[0052] Figure 1 A flow chart of a follow-up wireless microwave charging method in an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0053] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations may, however, be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the example implementations to those skilled in the art.

[0054] Moreover, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the

[0055] The implementation details of the technical solutions of the present application are described in detail as follows:

[0056] Figure 1 A flow chart of a follow-up wireless microwave charging method according to an embodiment of the present application is shown. Referring to Figure 1 shown:

[0057] S1: Collect multiple sets of position coordinate information and speed information of the charging device in a T1 time period, and calculate the motion trajectory data of the charging device in a T2 time period based on a neural network prediction algorithm;

[0058] Collect multiple sets of position coordinate information and speed information of the charging device in a T1 time period (specifically 10 seconds), the position coordinate information includes three-dimensional space coordinates (X, Y, Z) of the charging device, the position coordinate information is obtained through a GPS positioning module arranged on the charging device, and the positioning accuracy is better than 0.1 meters, the speed information includes speed components (Vx, Vy, Vz) of the charging device in three directions, the speed information is collected through a nine-axis inertial sensor arranged on the charging device, and the collection frequency is one set of data every 50 milliseconds.

[0059] The collected position coordinate information and speed information are subjected to data denoising processing by a Kalman filtering algorithm, data jitter caused by environmental interference is filtered out, and smoothed motion parameters are obtained; the position coordinate information and speed information subjected to denoising processing are arranged in time sequence to construct an input data matrix,

[0060] The input data matrix is input into a pre-trained long short-term memory neural network model, the neural network model includes a three-layer hidden layer structure and is provided with 64 neurons per layer, an Adam optimizer is used for model training, a learning rate is set to 0.001, and time sequence feature analysis and nonlinear mapping of historical data are performed.

[0061] It should be noted that the long short-term memory neural network model includes a forgetting gate, an output gate, candidate cell calculation, cell state updating, and an output gate structure.

[0062] The forgetting gate is defined as follows:

[0063]

[0064] The output gate is defined as follows:

[0065]

[0066] The candidate cell state calculation is as follows:

[0067]

[0068] The cell state updating is as follows:

[0069]

[0070] The output gate structure is as follows:

[0071]

[0072] The final hidden state output is as follows:

[0073]

[0074] Wherein, each parameter satisfies the following constraint condition:

[0075] δ, α, β, γ, η, φ ∈ (0, 1)

[0076]

[0077] Wherein, θ j is a trainable parameter, f tis the output value of the forget gate, ranging from (0, 1), is the sigmoid activation function, W f is the weight matrix of the forget gate, h t-1 is the hidden state at the previous time, x t is the current input, b f is the bias term of the forget gate, δ is the time derivative weight coefficient, used to adjust the sensitivity of state change, is the time derivative of the hidden state, i t is the output value of the input gate, ranging from (0, 1), W i is the weight matrix of the input gate, b i is the bias term of the input gate, α is the historical information weight coefficient, m is the time step of historical information considered, ω k is the weight of the k-th historical time, x t-k is the input k steps ago, is the candidate cell state, W c is the state weight matrix, b c is the state bias term, β is the second-order gradient weight coefficient, is the second-order gradient of the hidden state, C t is the current cell state, is the Hadamard product (element-wise multiplication), γ is the historical cell state weight coefficient, w i is the i-th historical state weight, μ i is the decay rate of the i-th time scale, Δt is the time step, o t is the output value of the output gate, W o is the weight matrix of the output gate, b o is the bias term of the output gate, η is the state change rate weight coefficient, is the cell state change rate, h t is the current time hidden state output, φ is the historical hidden state weight coefficient, p is the number of historical frames considered, v j is the adaptive weight of the j-th frame.

[0078] Further, the output of the final LSTM model is a six-dimensional vector, which is converted into a spatial position point, i.e. to predict the motion trajectory data of the charging device within the future T2 time period (specifically 3 seconds), the motion trajectory data is predicted for 30 spatial position points at intervals of 100 milliseconds, and the predicted position points are connected by a cubic spline interpolation algorithm to form a smooth and continuous predicted trajectory curve, i.e.

[0079] For the time point t model output is converted into a spatial position point P t = (x t , y t , z t ), wherein each spatial position point is calculated as follows:

[0080]

[0081] where the acceleration term is obtained by the difference of adjacent velocities:

[0082]

[0083] where P i is the three-dimensional spatial position coordinate at the i-th moment, P i-1 is the position coordinate representing the previous moment, is the velocity component in three directions, is the acceleration component in three directions, is the time interval.

[0084] Meanwhile, the confidence of each point on the predicted trajectory curve is calculated, and when a predicted point with a confidence lower than 85% appears, a data reacquisition mechanism is triggered to ensure the accuracy of trajectory prediction. More specifically, the state prediction confidence is evaluated as follows:

[0085]

[0086] where κ1, κ2, κ3 are dynamically adjusted weight coefficients:

[0087]

[0088] where h t -h t-1 is the state difference, |h t | is the first derivative norm, is the second derivative norm, ρ i is the weight dynamic adjustment coefficient.

[0089] It should be noted that in the mobile wireless charging scene, the charging device often exhibits variable motion characteristics, including sudden changes in motion speed, multi-scale characteristics of motion trajectories (short-term obstacle avoidance and direction changes and long-term path planning), and trajectory fluctuations caused by environmental interference. These characteristics result in the inability of traditional LSTM models to accurately predict the motion trajectory of the charging device, which in turn affects the real-time alignment of the microwave antenna. Therefore, the present embodiment improves the trajectory prediction of the charging device. Preferably, to address the problem of rapid change response, a time derivative term is introduced into the forget gate to enable the model to respond quickly to changes in speed, reducing the state prediction delay from the original 150 ms to 35 ms, meeting the real-time alignment requirements of the antenna.

[0090] More preferably, the cell state update introduces multi-time scale historical information, enabling the capture of both short-term (0.1s level) and long-term (3s level) motion characteristics, significantly improving trajectory prediction accuracy. To address the problem of trajectory fluctuations, an exponential decay weighting of historical data is added to the input gate, effectively filtering out trajectory fluctuations caused by environmental disturbances.

[0091] S2: According to the motion trajectory data, divide the microwave antenna into N independently controlled subarray antenna units, each corresponding to a region segment on the predicted trajectory;

[0092] Project the predicted motion trajectory data onto the horizontal plane directly below the antenna to obtain a two-dimensional projected trajectory; based on the spatial distribution characteristics of the two-dimensional projected trajectory, divide the entire antenna array into N subarray antenna units (N is in the range of 4 to 16), each consisting of MxM antenna elements (M is in the range of 4 to 8), and set a 2-element overlap region between adjacent subarray antenna units to ensure the continuity of beam transition.

[0093] Further, functionally partition each subarray antenna unit, set the subarray antenna unit through which the motion trajectory passes as the active energy supply unit, set the subarray antenna unit adjacent to the motion trajectory as the standby energy supply unit, and set the remaining subarray antenna units to a dormant state; the active energy supply unit uses phased array technology to achieve beamforming, and the standby energy supply unit remains in a low-power standby state and can switch to an active energy supply state within 100 milliseconds.

[0094] Further, based on the trajectory prediction data, calculate the beam coverage range of each subarray antenna unit, which is determined by two parameters: beam width and beam direction. The beam width is dynamically adjusted according to the motion speed of the charging device, with faster speed resulting in larger beam width and slower speed resulting in smaller beam width, ensuring effective coverage in different motion states. The beam direction is determined according to the average direction of the predicted trajectory segment of the charging device within the coverage area of the subarray antenna unit.

[0095] Finally, establish a cooperative control mechanism between subarray antenna units. When the charging device is about to leave the coverage range of the current subarray antenna unit, activate the next subarray antenna unit in advance and achieve smooth transition of beam energy in the overlap area of the two subarray antenna units, avoiding energy jumps. At the same time, dynamically allocate the transmit power of each subarray antenna unit, with subarray antenna units close to the charging device receiving a higher power allocation ratio and subarray antenna units far from the charging device receiving a lower power allocation ratio, achieving gradient utilization of energy.

[0096] More specifically, the cooperative control mechanism between the subarray antenna units is as follows:

[0097] Based on the real-time location information and the predicted trajectory data of the charging device, the distance between the charging device and the boundary of the coverage area of the currently activated subarray antenna unit is continuously calculated. When the distance is less than a preset first critical distance threshold (specifically 30% of the radius of the coverage area), the next target subarray antenna unit that will be entered is determined according to the predicted trajectory. When the distance is further reduced to a second critical distance threshold (specifically 20% of the radius of the coverage area), a preheating program of the target subarray antenna unit is started to switch its working state from sleep to standby, and the energy storage unit is precharged. When the distance reaches a third critical distance threshold (specifically 15% of the radius of the coverage area), the output power of the target subarray antenna unit is gradually increased, and the initial power is set to 30% of the nominal power, and is gradually increased at a rate of 5% per 100 milliseconds.

[0098] More specifically, the logic of determining the next target subarray antenna unit according to the predicted trajectory is as follows:

[0099] The adjacent subarray antenna unit along the direction of the predicted trajectory from the boundary of the coverage area of the current subarray antenna unit is taken as a candidate unit. When the predicted trajectory and the coverage area of the adjacent candidate unit produce spatial overlap, the overlap area size of the trajectory and each candidate unit coverage area is calculated. If the predicted trajectory only overlaps with one candidate unit, the candidate unit is determined as the target subarray antenna unit. If the predicted trajectory overlaps with multiple candidate units, the candidate unit with the largest overlap area is selected as the target subarray antenna unit.

[0100] The calculation of the overlap area size of the trajectory and each candidate unit coverage area includes:

[0101] For the overlap area calculation of the trajectory and the candidate unit coverage area, the sequence of spatial position points has been calculated. When a spatial position point falls within the coverage area of a candidate unit, a circle is drawn with the position point as the center and the radius of the charging device receiving surface as the radius. The intersection area of the circle and the coverage area of the candidate unit is calculated. If there are multiple continuous spatial position points located in the same candidate unit coverage area, the intersection areas corresponding to these position points are added to obtain the total overlap area of the predicted trajectory and the candidate unit. After calculating the overlap areas of all candidate units, the candidate unit with the largest overlap area is selected as the target subarray antenna unit.

[0102] In particular, if the actual motion state of the charging device is detected to change dramatically, so that the predicted trajectory model cannot respond in time, the moving speed of the charging device is detected, when the moving speed is detected to mutate (the speed change rate exceeds 30% of the current speed), based on the current speed vector direction, the adjacent 3 subarray antenna units in the direction are triggered into standby state in advance; when the motion direction of the charging device is detected to mutate (the direction change exceeds 30 degrees), the current position is taken as the center, the adjacent subarray antenna units in the surrounding 60-degree sector are pre-activated, and the power output is maintained according to the above-mentioned coverage, until the effective trajectory prediction is re-established or the motion trend of the charging device is obvious.

[0103] When the charging device completely leaves the coverage range of the current subarray antenna unit, its output power is gradually reduced at a rate of 10% per 100 milliseconds, when the power is reduced to below 20%, the working state is switched to standby, and after 60 seconds of continuous monitoring, if it is confirmed that the charging device does not return, it is switched to hibernation state to reduce energy consumption; if the charging device is detected to return during standby, the system will quickly restore the working state of the subarray antenna unit and restart the energy supply process.

[0104] S3: The control unit dynamically adjusts the phase and power parameters of each subarray antenna unit according to the real-time position of the charging device to form an adaptive beam;

[0105] The control unit calculates and dynamically adjusts the phase parameters of each activated subarray antenna unit according to the calculated spatial vector information to realize accurate beamforming; the spatial vector information is the relative spatial vector between the charging device and each activated subarray antenna unit, including relative distance and relative angle; the calculation of the spatial vector information is a technology familiar to those skilled in the art, so this embodiment will not be described in detail.

[0106] Further, based on the spatial vector information, the theoretical phase value φ i,theory of each subarray antenna unit is calculated as follows:

[0107]

[0108] Where (x i ,y i ) is the coordinate position of the i-th antenna unit in the array plane, (θ,φ) is the pitch angle and azimuth angle of the target direction, α i is the amplitude weighting coefficient, d i,target is the distance from the antenna unit to the target, R max is the maximum effective coverage radius of the antenna array, and λ is the working wavelength. The newly added nonlinear term in the formula is used to optimize the phase control of the edge area.

[0109] More preferably, in the calculation of the basic phase, the theoretical phase value is calculated according to the spatial geometric relationship, but when the traditional method is used, it is found that the phase changes sharply when the device enters the uncovered area from the covered area, and this mutation will cause the beam to be unstable and affect the charging effect, so in the calculation of the basic phase, a nonlinear term is added to smooth the mutation that easily occurs in the edge area, that is, a preliminary compensation is performed, and a gradual transition is provided in the edge area, so that the beam of the subarray antenna unit tends to be stable.

[0110] Further, an adaptive phase compensation algorithm is used for real-time correction, and the actual phase value φ i,theory is calculated as follows:

[0111]

[0112] where β i is a frequency-dependent compensation coefficient that is dynamically adjusted with the working frequency, Δφ i is a historical phase error accumulation value, γ i is an adaptive gain coefficient that controls the compensation strength, and w k is the weight coefficient of each antenna element, ω is the angular frequency, and N is the total number of antenna array elements.

[0113] More preferably, by continuously measuring the phase error between the actual phase value of each antenna element and the smoothed theoretical phase value, historical error compensation and real-time dynamic compensation are used for comprehensive compensation, that is, the historical error compensation calculates the long-term error compensation value by accumulating the phase error combined with the frequency compensation coefficient, and the real-time dynamic compensation calculates the weight coefficient by dynamically calculating the distance from the antenna element to the target device, and the closer the distance, the greater the weight. Then, combined with the adaptive gain coefficient γ i (initial value 0.3, dynamically adjusted according to system stability), the real-time compensation value is calculated. Then, the historical error compensation and real-time dynamic compensation are superimposed to form the final compensation value, which is implemented by the phase control circuit. The hierarchical compensation mechanism of this embodiment can not only handle the cumulative errors existing in the system for a long time, but also quickly respond to transient disturbances caused by environmental changes, achieving high precision (error <0.5 degrees) and fast response (<1 millisecond) of phase control. The entire adaptive process is a continuous closed-loop control, which continuously monitors, calculates and executes to ensure that the beam is always accurately pointed at the target device, providing a stable and reliable energy transmission channel for wireless charging.

[0114] Further considering the electromagnetic coupling effect between antenna elements, a dynamic correction coefficient μ ij is introduced:

[0115]

[0116] wherein η0is a standard coupling coefficient, d ij is the antenna element spacing, and κ is a frequency correction factor, f0is the center frequency. The correction factor is adaptively adjusted according to the antenna element spacing and the operating frequency. The final phase control value φ i,final is:

[0117]

[0118] It should be noted that the dynamic correction factor describes the degree of mutual influence of the electromagnetic field between any two antenna elements in the wireless charging system. When electromagnetic waves propagate in the antenna array, each antenna element not only radiates electromagnetic waves, but also is affected by the radiation field of the adjacent elements. This influence will weaken as the distance between the elements increases, and it presents a periodic phase change characteristic. At the same time, due to the frequency dispersion characteristics of the antenna material, at higher frequencies, the metal surface current distribution will change, resulting in an increase in coupling strength. The dynamic correction factor accurately predicts and compensates for the mutual interference between elements by establishing a quantitative relationship between the spatial distance between antenna elements, the operating frequency, and the coupling strength, thereby accurately controlling the radiation characteristics of each antenna element in the actual wireless charging process, and ensuring the formation of a stable and efficient focused beam.

[0119] Finally, by accumulating the phase differences of all adjacent elements (weighted by the coupling coefficient), the actual driving phase required by each element is accurately calculated, which compensates for the phase distortion caused by the coupling between elements. Similarly, by smoothing the theoretical phase values of the array antenna elements in the initial calculation process, it is ensured that the radiation phase of each element can still be accurately controlled in the case where the charging device enters the array antenna element and there is strong coupling interference, the expected beam direction and shape are maintained, the charging power is maintained, the emitted wave is continuously followed, and the overall focusing accuracy and energy transmission efficiency are improved.

[0120] In particular, when the charging device enters the overlapping coverage area of the two sub-array antenna elements, the energy smooth transition control is started, and the output power ratio of the two sub-array antenna elements is adjusted in real time by a dynamic power distribution algorithm; if the charging device is located at the starting section of the overlapping area, the current sub-array antenna element maintains a higher power output (such as 70%), and the target sub-array antenna element maintains a lower power output (such as 30%); when the charging device moves within the overlapping area, the power ratio of the two sub-array antenna elements adopts a nonlinear dynamic adjustment strategy, and the power adjustment curve follows an S-shaped change characteristic, ensuring that the total energy received by the charging device remains constant and changes smoothly; if the charging device is located at the end of the overlapping area, the power ratio of the target sub-array antenna element is increased to 70%, and the power ratio of the current sub-array antenna element is decreased to 30%.

[0121] Then, the control unit sends the calculated phase and power parameters to the phase shifters and power amplifiers of each subarray antenna unit through a high-speed digital bus, and performs adaptive charging.

[0122] S4: The charging device is charged in a gradient manner in different regions through the adaptive beam, wherein the subarray antenna units close to the device position output higher power, and the subarray antenna units far from the device position output lower power.

[0123] When the antenna array completes the initial phase control, all antenna units are charged in a gradient manner in different regions. Based on the position information of the charging device, the antenna array is divided into an inner ring region (0-20 cm), a middle region (20-40 cm), and an outer ring region (40-60 cm). For the antenna units in the inner ring region, since they are closest to the device, the output power of these units is adjusted to a higher level (85-100% of the rated power).

[0124] When extending outward, the power output of the antenna units in each region is correspondingly reduced according to the increase of the spatial distance. In particular, when the antenna units belonging to the middle region, the output power of these units is adjusted to 65-80% of the rated power. If the antenna units belong to the outermost ring region, the output power of these units is further reduced to 40-60% of the rated power.

[0125] If it is detected that the position of the charging device deviates, the system will redivide the power regions and update the power distribution of all antenna units in each region. In particular, when the final phase of each unit needs to be recalculated, the system will update the power distribution scheme synchronously. If the temperature in a certain region abnormally rises, the system will automatically reduce the power output of that region to ensure the safety and stability of the charging process.

[0126] More specifically, after the charging device enters the overlapping coverage region of the adjacent subarray, the energy smooth transition control mechanism is superimposed on the basis of maintaining the gradient charging, when the charging device is charged in a gradient manner in different regions according to the coupling coefficient. At this time, the original gradient charging scheme will be used as the reference power configuration, and the smooth transition control will be dynamically adjusted on this basis.

[0127] When the charging device just enters the initial section of the overlapping region, the antenna units of the current subarray (i.e., the region where the device originally locates) maintain a higher gradient charging power configuration, while the output power ratio is set to 70%. Although the target subarray (i.e., the region where the device will enter) should originally output lower power according to the distance gradient, the system will adjust it to a transition power of 30% to prepare to receive the charging device.

[0128] If the charging device continuously moves in the overlapping region, the system dynamically adjusts the power ratio of the two adjacent sub-arrays according to an S-shaped curve while maintaining the internal gradient charging characteristics of each region. This nonlinear adjustment strategy ensures smooth transition of the total energy received by the device, avoiding fluctuations in charging efficiency caused by sudden changes in power.

[0129] When the charging device approaches the end of the overlapping region, the antenna elements of the target sub-array gradually increase to the corresponding gradient charging power level of the region and increase the power ratio to 70%. At the same time, the power ratio of the current sub-array is reduced to 30%, but the internal power gradient distribution characteristics are still maintained.

[0130] In particular, when multiple overlapping regions are present, the system coordinates both the gradient charging and the smooth transition control mechanisms to ensure stable and continuous charging energy for the charging device at any location. By monitoring the changes in coupling coefficients in real time, the system dynamically adjusts the power configuration of each region, organically combining gradient charging and energy smooth transition to ensure the continuity and stability of the charging process.

[0131] This combination of dual control mechanisms ensures efficient charging based on coupling characteristics and smooth transition of the device during region switching.

[0132] The follow-up wireless microwave charging system according to one embodiment of the present application comprises:

[0133] The acquisition module is configured to acquire multiple sets of position coordinate information and speed information of the charging device within a T1 time period.

[0134] The trajectory prediction module is configured to construct a trajectory prediction model based on the data of the acquisition module and calculate motion trajectory data of the charging device within a T2 time period.

[0135] The partition charging module is configured to partition the microwave antenna into N independently controlled sub-array antenna units according to the motion trajectory data, with each sub-array antenna unit corresponding to a region segment on the predicted trajectory.

[0136] The control unit is configured to dynamically adjust the phase and power parameters of each sub-array antenna unit to form an adaptive beam according to the real-time position of the charging device.

[0137] The charging module is configured to perform gradient charging on the charging device in different regions through the adaptive beam.

[0138] In the technical scheme of the present application, the combination of neural network prediction and subarray antenna dynamic power supply in different regions is adopted, the antenna parameters are adjusted in advance through trajectory prediction, and the subarray antenna is used to realize gradient charging in different regions, so that the alignment deviation is controlled within 3 degrees, and the charging efficiency is improved to 92%. The scheme not only solves the charging alignment problem of mobile devices, but also improves the energy utilization efficiency through power gradient distribution.

[0139] The flowcharts and block diagrams in the drawings illustrate the possible implementation architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. Among them, each block in the flowchart or block diagram can represent a module, a program segment or a part of code, and the above module, program segment or code part contains one or more executable instructions for realizing the specified logic function. It should also be noted that in some alternative implementations, the functions marked in the blocks can also occur in different order from that marked in the drawings. For example, two blocks indicated in succession can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be realized by a special hardware-based system that performs the specified function or operation, or can be realized by a combination of special hardware and computer instructions.

[0140] The units described in the embodiments of the present application can be realized by software or by hardware, and the described units can also be arranged in a processor. In some cases, the names of these units do not constitute a limitation on the units themselves.

[0141] According to one aspect of the present application, a computer program product or computer program is provided, which includes computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to make the computer device execute the method provided in the various optional implementations described above.

[0142] From the above description of the embodiments, those skilled in the art can easily understand that the example embodiments described herein can be realized by software or by software combined with necessary hardware. Therefore, the technical scheme according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.) or a network, and includes a plurality of instructions to make a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) execute the method according to the embodiments of the present application.

[0143] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the features disclosed herein. It is intended that the application embrace any and all variations of the present application that fall within the scope of the present application. Accordingly, the application is not intended to be limited to the precise structures described in the specification, as such can vary in components and arrangement of components without departing from the scope of the application.

[0144] It is to be understood that the application is not limited to the precise construction described in the specification and as shown in the drawings, and that various modifications and changes can be effected therein by those skilled in the art without departing from the scope of the application. The scope of the application is to be limited only by the appended claims.

Claims

1. A follow-up wireless microwave charging method, characterized by, The method comprises the following steps: Collecting a plurality of sets of position coordinate information and speed information of the charging device in a T1 time period, and constructing a trajectory prediction model to calculate motion trajectory data of the charging device in a T2 time period; According to the motion trajectory data, the microwave antenna is divided into N independently controlled subarray antenna units, each of which corresponds to cover a region segment on the predicted trajectory; The control unit dynamically adjusts the phase and power parameters of each subarray antenna unit according to the real-time position of the charging device to form an adaptive beam; The charging device is charged by the adaptive beam in a gradient manner in different regions; The forget gate, input gate, candidate cell calculation, cell state update, and output gate structure of the trajectory prediction model; The definition of the forget gate is as follows: The definition of the input gate is as follows: The candidate cell state calculation is as follows: The cell state update is as follows: The output gate structure is as follows: The final hidden state output is as follows: Wherein, each parameter satisfies the following constraint condition: wherein, is a trainable parameter, is a forget gate output value, range (0, 1), is a sigmoid activation function, is a forget gate weight matrix, is a previous time step hidden state, is a current input, is a forget gate bias term, is a time derivative weight coefficient, used to adjust the sensitivity of state change, is a time derivative of hidden state, is an input gate output value, range (0, 1), is an input gate weight matrix, is an input gate bias term, is a history information weight coefficient, is a time step considered for history information, is a weight of the kth history time step, is an input k steps ago, is a candidate cell state, is a state weight matrix, is a state bias term, is a second-order gradient weight coefficient, is a second-order gradient of hidden state, is a current cell state, is a Hadamard product, is a history cell state weight coefficient, is the ith history state weight, is a decay rate of the ith time scale, is a time step, is an output gate output value, is an output gate weight matrix, is an output gate bias term, is a state change rate weight coefficient, is a cell state change rate, is a current time step hidden state output, is a history hidden state weight coefficient, is a number of history frames considered, is an adaptive weight of the jth frame; The state of the subarray antenna unit includes an active power supply unit, a standby function unit and a dormant unit; The subarray antenna units are cooperatively controlled; The cooperative control includes judging whether to start or change the state of the target subarray antenna unit according to the distance between the charging device and the subarray antenna unit; Wherein, the target subarray antenna unit is determined according to whether the predicted trajectory curve interacts with the subarray antenna unit, if it interacts, it is the target subarray antenna unit, if multiple subarray antenna units interact with the predicted trajectory curve, the overlapping area is calculated, and the subarray antenna unit with the largest overlapping area is the target subarray antenna unit; The cooperative control further includes: If the actual motion state of the charging device changes sharply, so that the prediction trajectory model cannot respond in time, the moving speed of the charging device is detected, and when the moving speed suddenly changes, based on the current speed vector direction, multiple subarray antenna units adjacent in this direction are triggered into standby state in advance; when the motion direction of the charging device suddenly changes, the surrounding subarray antenna units in the adjacent sector are pre-activated based on the current position, and the power output is maintained according to the coverage until an effective trajectory prediction is re-established.

2. The follow-me wireless microwave charging method of claim 1, wherein, The motion trajectory data is calculated according to the output of the trajectory prediction model, a plurality of spatial position points are connected through an interpolation algorithm, and a smooth and continuous predicted trajectory curve is formed; The spatial location point also includes the output of the trajectory prediction model at time t. Confidence level: When the confidence level is lower than a preset threshold, data is collected again. 3.The follow-me wireless microwave charging method of claim 1, wherein, The formation of the adaptive beam includes: Based on the relative spatial vector between the charging device and each subarray antenna unit in the active state, the theoretical phase value of each subarray antenna unit is calculated; An adaptive phase compensation algorithm is used to correct the theoretical phase in real time to obtain the compensated actual phase value; The actual phase value is coupled and corrected to obtain the final phase control value, and the adaptive beam is formed according to the phase control value and the power control between the subarray antenna units.

4. The follow-me wireless microwave charging method of claim 3, wherein, The calculation of the compensated actual phase value is as follows: wherein, is the actual phase value after compensation, is the frequency-dependent compensation coefficient, dynamically adjusted with the operating frequency, is the historical phase error cumulative value, is the adaptive gain coefficient, controlling the compensation strength, is the weight coefficient of each antenna element, is the operating angular frequency, is the total number of antenna array elements, is the i-th theoretical phase value, t is the time variable, is the k-th antenna element theoretical phase value.

5. A follow-up wireless microwave charging system for use in the follow-up wireless microwave charging method according to any one of claims 1 to 4, characterized by The method comprises the following steps: The collection module is used for collecting multiple sets of position coordinate information and speed information of the charging device in a T1 time period; The prediction trajectory module is used for constructing a trajectory prediction model according to the data of the collection module to calculate motion trajectory data of the charging device in a T2 time period; The partition charging module is used for partitioning a microwave antenna into N independently controlled subarray antenna units according to the motion trajectory data, and each subarray antenna unit corresponds to covering a region segment on the predicted trajectory; The control unit is used for dynamically adjusting phase and power parameters of each subarray antenna unit according to a real-time position of the charging device to form an adaptive beam; The charging module is used for performing regional gradient charging on the charging device through the adaptive beam.

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