Following type wireless microwave charging method and system

Abstract

The invention relates to the technical field of wireless power transmission, and provides a following type wireless microwave charging method and system, and the method comprises the steps: collecting multiple groups of position coordinate information and speed information of charging equipment in a time period T1, and constructing a track prediction model to calculate the motion track data of the charging equipment in a time period T2; according to the motion trail data, the microwave antenna is partitioned into N sub-array antenna units which are independently controlled, and each sub-array antenna unit correspondingly covers a region section on a predicted trail; the control unit dynamically adjusts the phase and power parameters of each sub-array antenna unit according to the real-time position of the charging equipment to form a self-adaptive wave beam; and carrying out regional gradient charging on the charging equipment through the adaptive wave beam. According to the method and the system, the charging alignment problem of the mobile equipment is solved, and the energy utilization efficiency is improved through power gradient distribution.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless power transmission, and in particular to a follow-up wireless microwave charging method and system. Background Art

[0002] With the popularity of mobile devices and the rapid development of portable electronic products, wireless charging technology as a new charging method has attracted more and more attention from academia and industry. Traditional wireless charging technologies mainly include near-field charging methods such as electromagnetic induction and magnetic resonance. 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 energy transmission technology can achieve energy transmission at meter level or even longer distances 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 applications: 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 easily causes the problem of excessive energy concentration in some areas and insufficient energy in other areas; third, the existing technology lacks the ability to accurately predict the movement trajectory of charging devices, resulting in low energy transmission efficiency.

[0003] At present, domestic and foreign scholars have conducted a lot of research on microwave wireless charging technology and proposed a variety of improvement schemes. Among them, the introduction of phased array antenna technology makes dynamic adjustment of microwave beams possible, but most of the existing schemes use overall beam control, which makes it difficult to achieve accurate energy delivery to different areas. At the same time, traditional trajectory tracking algorithms mainly focus on the real-time position of the target and ignore the prediction and analysis of motion trends, which leads to a certain lag in system response and cannot meet the charging needs in mobile scenarios. In addition, since the charging equipment is often in a mobile state, the alignment accuracy of the microwave transmitting antenna and the receiving antenna is insufficient, which not only causes low charging efficiency, but also 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 of the invention

[0004] An embodiment of the present invention provides a follow-up wireless microwave charging method, which can at least to some extent solve the problem that the charging device is often in a mobile state, resulting in insufficient alignment accuracy between the microwave transmitting antenna and the receiving antenna, resulting in low charging efficiency and energy loss.

[0005] Other features and advantages of the present invention will become apparent from the following detailed description, or may be learned in part by practice of the present invention.

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

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

[0008] According to the motion trajectory data, the microwave antenna is partitioned into N independently controlled sub-array antenna units, each of the sub-array antenna units corresponding to a region segment on the predicted trajectory;

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

[0010] The charging device is charged with gradient charging in different regions through the adaptive beam.

[0011] In the present invention, based on the aforementioned 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 output gate is defined as follows:

[0015]

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

[0017]

[0018] The cell state is updated as follows:

[0019]

[0020] The output gate structure is shown below:

[0021]

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

[0023]

[0024] Among them, each parameter meets the following constraints:

[0025] δ,α,β,β,η,φ∈(0,1)

[0026]

[0027] Among them, θ j is a trainable parameter, f t is the output value of the forget gate, ranging from (0,1), σ is the sigmoid activation function, W f is the forget gate weight matrix, h t-1 , is the hidden state at the previous moment, x t is the current input, b f is the forget gate bias term, δ is the time derivative weight coefficient, which is used to adjust the sensitivity of state changes. is the time derivative of the hidden state, i t is the input gate output value, range (0,1), W i is the input gate weight matrix, b i is the input gate bias term, α is the historical information weight coefficient, m is the time step considered for historical information, ω k is the weight of the kth historical moment, 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 weight coefficient of the historical cell state, and w i is the weight of the i-th historical state, 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 output gate weight matrix, b o is the output gate bias term, η is the state change rate weight coefficient, is the cell state change rate, h t is the hidden state output at the current moment, φ is the historical hidden state weight coefficient, p is the number of historical frames considered, and v j is the adaptive weight of the jth frame.

[0028] In the present invention, based on the above-mentioned solution, the motion trajectory data calculates a plurality of spatial position points according to the output of the trajectory prediction model, and the spatial position points are connected by an interpolation algorithm to form a smooth and continuous prediction trajectory curve;

[0029] The spatial position point also includes h output by 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 invention, based on the aforementioned solution, the states of the subarray antenna unit include an active power supply unit, a standby function unit, and a sleep unit;

[0031] The sub-array antenna units perform cooperative control.

[0032] In the present invention, based on the aforementioned solution, the collaborative control includes determining whether to start or change the state of the target sub-array antenna unit according to the distance between the charging device and the sub-array antenna unit;

[0033] Among them, it is determined whether the target subarray antenna unit interacts with the subarray antenna unit according to the predicted trajectory curve. If so, 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 invention, based on the above solution, the collaborative control further includes:

[0035] If it is detected that the actual motion state of the charging device has changed dramatically, making the predicted trajectory model unable to respond in time, the moving speed of the charging device is detected. When a sudden change in the moving speed is detected, based on the current speed vector direction, multiple adjacent sub-array antenna units in this direction are triggered to enter the standby state in advance; when a sudden change in the movement direction of the charging device is detected, the adjacent sub-array antenna units in the surrounding sectors are pre-activated with the current position as the center, and the power output is maintained according to the coverage situation until a valid trajectory prediction is re-established or the movement trend of the charging device is obvious.

[0036] In the present invention, based on the above solution, the forming of the adaptive beam includes:

[0037] Calculate the theoretical phase value of each sub-array antenna unit based on the relative space vector between the charging device and each sub-array antenna unit in the activated state;

[0038] Adopting an adaptive phase compensation algorithm to correct the theoretical phase in real time to obtain an actual phase value after compensation;

[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 power control between the subarray antenna units.

[0040] In the present invention, based on the above scheme, the actual phase value after compensation is calculated as shown in the following formula:

[0041]

[0042] Among them, φ i,compis the actual phase value after compensation, β i (ω) is the frequency-dependent compensation coefficient, which is dynamically adjusted with the operating frequency, Δφ i is the accumulated value of historical phase error, γ i is the adaptive gain coefficient, controlling the compensation strength, w k is the weight coefficient of each antenna element, ω is the operating angular frequency, N is the total number of antenna array elements, φ i,theory is the ith theoretical phase value, t is the time variable, φ k,theory is the theoretical phase value of the kth antenna element.

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

[0044] The acquisition module is used to collect multiple sets of position coordinate information and speed information of the charging device within the T1 time period;

[0045] A trajectory prediction module, used to construct a trajectory prediction model based on the data of the acquisition module to calculate the motion trajectory data of the charging device in the T2 time period;

[0046] A partition charging module, used for partitioning the microwave antenna into N independently controlled sub-array antenna units according to the motion trajectory data, each of the sub-array antenna units corresponding to a region segment on the predicted trajectory;

[0047] A control unit, configured to dynamically adjust the phase and power parameters of each of the sub-array antenna units according to the real-time position of the charging device to form an adaptive beam;

[0048] A charging module is used to perform gradient charging of the charging device in different regions through the adaptive beam.

[0049] In the technical solution of the present invention, a combination of neural network prediction and sub-array antenna regional dynamic power supply is adopted to adjust antenna parameters in advance through trajectory prediction, and sub-array antenna is used to realize regional gradient charging, so that the alignment deviation is controlled within 3 degrees, and the charging efficiency is increased to 92%. This solution not only solves the charging alignment problem of mobile devices, but also improves energy utilization efficiency through power gradient allocation.

[0050] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings herein are incorporated into and constitute a part of the specification, showing embodiments consistent with the present invention, and together with the specification, are used to explain the principles of the present invention. Obviously, the accompanying drawings described below are only some embodiments of the present invention, and for those of ordinary skill in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0052] Figure 1 The flowchart of the following wireless microwave charging method in one embodiment of the present invention is schematically shown. DETAILED DESCRIPTION

[0053] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present invention will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0054] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present invention. However, those skilled in the art will appreciate that the technical solution of the present invention can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring various aspects of the present invention.

[0055] The implementation details of the technical solution of the present invention are described in detail below:

[0056] Figure 1 FIG. 1 is a flow chart showing a method for following wireless microwave charging according to an embodiment of the present invention. Figure 1 As shown:

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

[0058] Collect multiple sets of position coordinate information and speed information of the charging device within the T1 time period (specifically 10 seconds), the position coordinate information includes the three-dimensional spatial coordinates (X, Y, Z) of the charging device, and the position coordinate information is obtained by the GPS positioning module arranged on the charging device, and the positioning accuracy is better than 0.1 meters. The speed information includes the speed components of the charging device in three directions (Vx, Vy, Vz), and the speed information is collected by the 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 noise reduction processing by the Kalman filter algorithm to filter out data jitter caused by environmental interference and obtain smoothed motion parameters; the position coordinate information and speed information subjected to noise reduction 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, which includes a three-layer hidden layer structure with 64 neurons in each layer. The Adam optimizer is used for model training, and the learning rate is set to 0.001. The temporal characteristics of historical data are analyzed and nonlinearly mapped.

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

[0062] The definition of the forget gate is as follows:

[0063]

[0064] The output gate is defined as follows:

[0065]

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

[0067]

[0068] The cell state is updated as follows:

[0069]

[0070] The output gate structure is shown below:

[0071]

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

[0073]

[0074] Among them, each parameter meets the following constraints:

[0075] δ,α,β,γ,η,φ∈(0,1)

[0076]

[0077] Among them, θ j is a trainable parameter, f tis the output value of the forget gate, ranging from (0,1), σ is the sigmoid activation function, W f is the forget gate weight matrix, h t-1 , is the hidden state at the previous moment, x t is the current input, b f is the forget gate bias term, δ is the time derivative weight coefficient, which is used to adjust the sensitivity of state changes. is the time derivative of the hidden state, i t is the input gate output value, range (0,1), W i is the input gate weight matrix, b i is the input gate bias term, α is the historical information weight coefficient, m is the time step considered for historical information, ω k is the weight of the kth historical moment, 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 weight coefficient of the historical cell state, and w i is the weight of the i-th historical state, 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 output gate weight matrix, b o is the output gate bias term, η is the state change rate weight coefficient, is the cell state change rate, h t is the hidden state output at the current moment, φ is the historical hidden state weight coefficient, p is the number of historical frames considered, and v j is the adaptive weight of the jth frame.

[0078] Furthermore, the output of the final LSTM model is a six-dimensional vector, which is converted into a spatial position point, that is, the motion trajectory data of the charging device in the future T2 time period (specifically 3 seconds) is predicted. The motion trajectory data predicts 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 prediction trajectory curve, that is:

[0079] For time point t, the model output Convert to spatial position point P t =(x t ,y t ,z t ), where the calculation of each spatial position point is as follows:

[0080]

[0081] Among them, the acceleration term is obtained by the difference of adjacent velocities:

[0082]

[0083] Among them, P i is the three-dimensional space position coordinate at the i-th moment, P i-1 To represent the position coordinates of the previous moment, are the velocity components in three directions, are the acceleration components in three directions, For the time interval.

[0084] At the same time, the confidence of each point on the predicted trajectory curve is calculated. When a prediction point with a confidence lower than 85% appears, the data re-collection mechanism is triggered to ensure the accuracy of trajectory prediction. More specifically, the state prediction confidence evaluation is shown in the following formula:

[0085]

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

[0087]

[0088] Among them, h t -h t-1 is the state difference, |h t | is the first-order derivative norm, is the second-order derivative norm, ρ i Dynamic adjustment coefficient for weight.

[0089] It should be noted that in the mobile wireless charging scenario, the charging device often exhibits variable motion characteristics, including sudden changes in motion speed, multi-scale characteristics of the motion trajectory (short-term obstacle avoidance and long-term path planning), and trajectory fluctuations caused by environmental interference. These characteristics make it impossible for the traditional LSTM model to accurately predict the motion trajectory of the charging device, thereby affecting the real-time alignment of the microwave antenna. Therefore, this embodiment makes improvements in the prediction of the trajectory of the charging device. Preferably, for the problem of rapid change response, the time derivative term is introduced in the forget gate so that the model can respond quickly to speed changes, and the state prediction delay is reduced from the original 150ms to 35ms, meeting the requirements of real-time antenna alignment.

[0090] Preferably, the cell state update introduces multi-time scale historical information, which makes it possible to capture both short-term (0.1s level) and long-term (3s level) motion features at the same time. The trajectory prediction accuracy is significantly improved. To address the problem of trajectory fluctuations, the exponential decay weighting of historical data is added to the input gate to effectively filter out trajectory fluctuations caused by environmental interference.

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

[0092] The predicted motion trajectory data is projected onto the horizontal plane directly below the antenna to obtain a two-dimensional projection trajectory; based on the spatial distribution characteristics of the two-dimensional projection trajectory, the entire antenna array is divided into N sub-array antenna units (N ranges from 4 to 16), each of which is composed of M×M antenna elements (M ranges from 4 to 8), and an overlapping area of ​​2 elements is set between adjacent sub-array antenna units to ensure the continuity of beam transition.

[0093] Furthermore, each of the subarray antenna units is functionally partitioned, and the subarray antenna units in the area where the motion trajectory passes are set as active power supply units, the subarray antenna units adjacent to the motion trajectory are set as standby power supply units, and the remaining subarray antenna units are set to sleep mode; the active power supply unit uses phased array technology to achieve beamforming, and the standby power supply unit maintains a low-power standby state and can switch to the active power supply state within 100 milliseconds.

[0094] Furthermore, based on the trajectory prediction data, the beam coverage range of each sub-array antenna unit is calculated, and the beam coverage range is determined by two parameters: beam width and beam direction. The beam width is dynamically adjusted according to the movement speed of the charging device. The faster the speed, the larger the beam width, and the slower the speed, the smaller the beam width, to ensure effective coverage under different movement 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 sub-array antenna unit.

[0095] Finally, a collaborative control mechanism is established between subarray antenna units. When the charging device is about to leave the coverage of the current subarray antenna unit, the next subarray antenna unit is activated in advance, and a smooth transition of beam energy is achieved in the overlapping area of ​​the two subarray antenna units to avoid energy jumps. At the same time, the transmit power of each subarray antenna unit is dynamically allocated. The subarray antenna unit close to the charging device obtains a higher power allocation ratio, and the subarray antenna unit far away from the charging device obtains a lower power allocation ratio, thereby realizing gradient utilization of energy.

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

[0097] Based on the real-time location information and predicted trajectory data of the charging device, the distance between the charging device and the boundary of the coverage area of ​​the currently activated sub-array antenna unit is continuously calculated. When the distance is less than the preset first critical distance threshold (specifically 30% of the coverage area radius), the next target sub-array antenna unit to enter is determined according to the predicted trajectory; when the distance is further reduced to the second critical distance threshold (specifically 20% of the coverage area radius), the preheating program of the target sub-array antenna unit is started, and its working state is switched from sleep to standby, and the energy storage unit is pre-charged at the same time; when the distance reaches the third critical distance threshold (specifically 15% of the coverage area radius), the output power of the target sub-array 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% every 100 milliseconds.

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

[0099] The adjacent subarray antenna units along the direction of the predicted trajectory at the boundary of the coverage area of ​​the current subarray antenna unit are taken as candidate units; when the predicted trajectory spatially overlaps with the coverage area of ​​the adjacent candidate units, the overlapping area between the trajectory and the coverage area of ​​each candidate unit is calculated; if the predicted trajectory overlaps with only one candidate unit, the candidate unit is determined as the target subarray antenna unit; if the predicted trajectory overlaps with multiple candidate units at the same time, the candidate unit with the largest overlapping area is selected as the target subarray antenna unit.

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

[0101] For the calculation of the overlapping area between the trajectory and the candidate unit coverage area, the sequence of calculated spatial position points, when the spatial position point falls within the candidate unit coverage area, a circle is drawn with the position point as the center and the radius of the receiving surface of the charging device as the radius, and the intersection area of ​​the circle and the candidate unit coverage area is calculated; if there are multiple consecutive spatial position points located in the same candidate unit coverage area, the intersection areas corresponding to these position points are accumulated and added to obtain the total overlapping area between the predicted trajectory and the candidate unit; after calculating the overlapping areas of all candidate units, the candidate unit with the largest overlapping area is selected as the target subarray antenna unit.

[0102] In particular, if it is detected that the actual motion state of the charging device has changed dramatically, making the predicted trajectory model unable to respond in time, the moving speed of the charging device is detected. When a sudden change in the moving speed is detected (the speed change rate exceeds 30% of the current speed), based on the current speed vector direction, the three adjacent sub-array antenna units in this direction are triggered in advance to enter the standby state; when a sudden change in the movement direction of the charging device is detected (the direction changes by more than 30 degrees), the adjacent sub-array antenna units in the surrounding 60-degree sector are pre-activated with the current position as the center, and the power output is maintained according to the above coverage until a valid trajectory prediction is re-established or the movement trend of the charging device is obvious.

[0103] When the charging device completely leaves the coverage of the current sub-array antenna unit, its output power is gradually reduced at a rate of 10% every 100 milliseconds. When the power drops below 20%, its working state is switched to standby. After 60 seconds of continuous monitoring, if it is confirmed that the charging device has not returned, it is switched to sleep mode to reduce energy consumption. If the charging device is detected to have returned during the standby period, the system will quickly restore the working state of the sub-array antenna unit and restart the energy supply process.

[0104] S3: The control unit dynamically adjusts the phase and power parameters of each sub-array 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 for each activated subarray antenna unit based on the calculated spatial vector information to achieve accurate beamforming; the spatial vector information is the relative spatial vector between the charging device and each activated subarray antenna unit, including the relative distance and the relative angle; the calculation of the spatial vector information is a technology well known to those skilled in the art, so this embodiment will not be elaborated in detail.

[0106] Furthermore, the theoretical phase value φ of each subarray antenna element is calculated based on the space vector information. i,theory , as shown below:

[0107]

[0108] Among them, (x i ,y i ) is the coordinate position of the ith antenna unit in the array plane, (θ, φ) is the elevation 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 operating wavelength. The newly added nonlinear term in the formula is For optimized phase control in edge areas.

[0109] Preferably, in the calculation of the basic phase, the theoretical phase value is first calculated according to the spatial geometric relationship. However, when the traditional method is used for calculation, it is found that mutations are prone to occur in the edge area, that is, when the device enters the uncovered area from the covered area, the phase changes drastically. This mutation will cause the beam to be unstable and affect the charging effect. Therefore, in the calculation of the basic phase, a nonlinear term is added to smooth the mutations that are prone to occur in the edge area, that is, preliminary compensation is performed to provide a gradual transition in the edge area so that the beam of the subarray antenna unit tends to be stable.

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

[0111]

[0112] Among them, β i (ω) is the frequency-dependent compensation coefficient, which is dynamically adjusted with the operating frequency, Δφ i is the accumulated value of historical phase error, γ i is the adaptive gain coefficient, controlling the compensation strength, w k is the weight coefficient of each antenna element, ω is the operating angular frequency, and N is the total number of antenna array elements.

[0113] The better solution is to continuously measure the phase error between the actual phase value of each antenna unit and the smoothed theoretical phase value, and perform comprehensive compensation through historical error compensation and real-time dynamic compensation. That is, the historical error compensation calculates the long-term error compensation value by accumulating the phase error and combining it with the frequency compensation coefficient, while the real-time dynamic compensation calculates the weight coefficient dynamically by the distance from the antenna unit to the target device. 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), calculate the real-time compensation value. Then the historical error compensation and real-time dynamic compensation are superimposed to form the final compensation value, and the compensation is implemented through the phase control circuit. The hierarchical compensation mechanism of this embodiment can not only deal with the long-term cumulative errors of the system, 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. Through continuous monitoring, calculation and execution, it ensures that the beam always points accurately to the target device, providing a stable and reliable energy transmission channel for wireless charging.

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

[0115]

[0116] Where η0 is the standard coupling coefficient, d ij is the antenna unit spacing, κ is the frequency correction factor, and f0 is the center frequency. The correction factor is adaptively adjusted with the antenna unit spacing and the operating frequency. The final phase control value φ i,final for:

[0117]

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

[0119] Finally, by accumulating the phase differences of all adjacent units (weighted by the coupling coefficient), the actual driving phase required for each unit is accurately calculated, compensating for the phase distortion caused by the coupling between units. Similarly, since the theoretical phase value of the array antenna unit is smoothed during the initial calculation process, it is ensured that when the charging device enters this array antenna unit and there is strong coupling interference, the radiation phase of each unit can still be accurately controlled to maintain the expected beam direction and shape, maintain the charging power, and ensure that the emitted fluctuations continue to follow up, thereby improving the overall focusing accuracy and energy transmission efficiency.

[0120] In particular, when the charging device enters the overlapping coverage area of ​​two subarray antenna units, energy smooth transition control is started, and the output power ratio of the two subarray antenna units is adjusted in real time through the dynamic power allocation algorithm; if the charging device is located at the starting section of the overlapping area, the current subarray antenna unit maintains a higher power output (such as 70%), and the target subarray antenna unit maintains a lower power output (such as 30%); when the charging device moves in the overlapping area, the power ratio of the two subarray antenna units adopts a nonlinear dynamic adjustment strategy, and the power adjustment curve follows the S-shaped change characteristics to ensure that the total energy received by the charging device remains constant and changes smoothly; if the charging device is located at the ending section of the overlapping area, the power share of the target subarray antenna unit is increased to 70%, and the current subarray antenna unit is reduced to 30%.

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

[0122] S4: The charging device is gradient charged in different regions by using the adaptive beam, wherein the sub-array antenna units close to the device position output a larger power, and the sub-array antenna units far from the device position output a smaller power.

[0123] After the antenna array completes the initial phase control, all antenna units are gradient charged in different regions. Based on the location information of the charging device, the antenna array is divided into an inner circle area (0-20cm), a middle area (20-40cm), and an outer circle area (40-60cm). For the antenna units in the inner circle area, 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 area is reduced accordingly according to the increase in spatial distance. In particular, when the antenna units are divided into the middle area, the output power of these units is adjusted to 65-80% of the rated power. If the antenna units belong to the outermost area, the output power of these units is further reduced to 40-60% of the rated power.

[0125] If the position of the charging device is detected to be offset, the system will re-divide the power area and update the power allocation of all antenna units in each area. In particular, when the final phase of each unit needs to be recalculated, the system will synchronously update the power allocation plan. If the temperature in a certain area rises abnormally, the system will automatically reduce the power output in that area to ensure the safety and stability of the charging process.

[0126] More specifically, after gradient charging is performed in different regions according to the coupling coefficient, if it is detected that the charging device enters the overlapping coverage area of ​​adjacent sub-arrays, the energy smooth transition control mechanism is superimposed on the basis of maintaining gradient charging. 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 beginning of the overlapping area, the antenna units of the current subarray (i.e. the area where the device is originally located) maintain a higher gradient charging power configuration and set the output power ratio to 70%. Although the target subarray (i.e. the area where the device is about to enter) should have output a lower power according to the distance gradient, the system will adjust it to a transitional power of 30% to prepare for receiving the charging device.

[0128] If the charging device continues to move in the overlapping area, the system will dynamically adjust the power ratio of the two adjacent sub-arrays according to the S-curve while maintaining the gradient charging characteristics within each area. This nonlinear adjustment strategy ensures a smooth transition of the total energy received by the device and avoids fluctuations in charging efficiency caused by power mutations.

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

[0130] In particular, when there are multiple overlapping areas, the system will coordinate the two mechanisms of gradient charging and smooth transition control to ensure that the charging device can obtain stable and continuous charging energy at any location. The system monitors the changes in the coupling coefficient in real time and dynamically adjusts the power configuration of each area to organically combine gradient charging and smooth energy transition to jointly ensure the continuity and stability of the charging process.

[0131] The coordination of this dual control mechanism ensures both efficient charging based on coupling characteristics and smooth transition of the device when switching between regions.

[0132] According to an embodiment of the present invention, a follow-up wireless microwave charging system includes:

[0133] The acquisition module is used to collect multiple sets of position coordinate information and speed information of the charging device within the T1 time period;

[0134] A trajectory prediction module, used to construct a trajectory prediction model based on the data of the acquisition module to calculate the motion trajectory data of the charging device in the T2 time period;

[0135] A partition charging module, used for partitioning the microwave antenna into N independently controlled sub-array antenna units according to the motion trajectory data, each of the sub-array antenna units corresponding to a region segment on the predicted trajectory;

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

[0137] A charging module is used to perform gradient charging of the charging device in different regions through the adaptive beam.

[0138] In the technical solution of the present invention, a combination of neural network prediction and sub-array antenna regional dynamic power supply is adopted to adjust antenna parameters in advance through trajectory prediction, and sub-array antenna is used to realize regional gradient charging, so that the alignment deviation is controlled within 3 degrees, and the charging efficiency is increased to 92%. This solution not only solves the charging alignment problem of mobile devices, but also improves energy utilization efficiency through power gradient allocation.

[0139] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0140] The units involved in the embodiments of the present invention may be implemented by software or hardware, and the units described may also be arranged in a processor. The names of these units do not, in some cases, limit the units themselves.

[0141] According to one aspect of the present invention, a computer program product or a computer program is provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the above-mentioned various optional implementations.

[0142] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present invention 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 USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation of the present invention.

[0143] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the embodiments disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary technical means in the art that are not disclosed in the present invention.

[0144] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A follow-up wireless microwave charging method, characterized in that: include: Collect multiple sets of position coordinate information and speed information of the charging device in the T1 time period, and build a trajectory prediction model to calculate the motion trajectory data of the charging device in the T2 time period; According to the motion trajectory data, the microwave antenna is partitioned into N independently controlled sub-array antenna units, each of the sub-array antenna units corresponding to a region segment on the predicted trajectory; The control unit dynamically adjusts the phase and power parameters of each sub-array antenna unit according to the real-time position of the charging device to form an adaptive beam; The charging device is charged with gradient charging in different regions through the adaptive beam.

2. The follow-up wireless microwave charging method according to claim 1, characterized in that: The forget gate, output 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 output gate is defined as follows: The candidate cell state is calculated as follows: The cell state is updated as follows: The output gate structure is shown below: The final hidden state output is shown as follows: Among them, each parameter meets the following constraints: δ,α,β,γ,η,φ∈(0,1) Among them, θ j is a trainable parameter, f t is the output value of the forget gate, ranging from (0,1), σ is the sigmoid activation function, W f is the forget gate weight matrix, h t-1 , is the hidden state at the previous moment, x t is the current input, b f is the forget gate bias term, δ is the time derivative weight coefficient, which is used to adjust the sensitivity of state changes. is the time derivative of the hidden state, i t is the input gate output value, range (0,1), W i is the input gate weight matrix, b i is the input gate bias term, α is the historical information weight coefficient, m is the time step considered for historical information, ω k is the weight of the kth historical moment, 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 weight coefficient of the historical cell state, and w i is the weight of the i-th historical state, 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 output gate weight matrix, b o is the output gate bias term, η is the state change rate weight coefficient, is the cell state change rate, h t is the hidden state output at the current moment, φ is the historical hidden state weight coefficient, p is the number of historical frames considered, and v j is the adaptive weight of the jth frame.

3. The follow-up wireless microwave charging method according to claim 2, characterized in that: The motion trajectory data calculates a plurality of spatial position points according to the output of the trajectory prediction model, and connects the spatial position points through an interpolation algorithm to form a smooth and continuous prediction trajectory curve; The spatial position point also includes h output by the trajectory prediction model at time t t Confidence level: when the confidence level is lower than a preset threshold, data is collected again.

4. The follow-up wireless microwave charging method according to claim 1, characterized in that: The states of the sub-array antenna units include an active power supply unit, a standby function unit and a sleep unit; The sub-array antenna units perform cooperative control.

5. The follow-up wireless microwave charging method according to claim 4, characterized in that: The collaborative control includes determining whether to start or change the state of the target sub-array antenna unit according to the distance between the charging device and the sub-array antenna unit; Among them, it is determined whether the target subarray antenna unit interacts with the subarray antenna unit according to the predicted trajectory curve. If so, 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.

6. The follow-up wireless microwave charging method according to claim 5, characterized in that: The collaborative control also includes: If it is detected that the actual motion state of the charging device has changed dramatically, making the predicted trajectory model unable to respond in time, the moving speed of the charging device is detected. When a sudden change in the moving speed is detected, based on the current speed vector direction, multiple adjacent sub-array antenna units in this direction are triggered to enter the standby state in advance; when a sudden change in the moving direction of the charging device is detected, the adjacent sub-array antenna units in the surrounding sectors are pre-activated with the current position as the center, and the power output is maintained according to the coverage situation until a valid trajectory prediction is re-established.

7. The follow-up wireless microwave charging method according to claim 1, characterized in that: The adaptive beam forming includes: Calculate the theoretical phase value of each sub-array antenna unit based on the relative space vector between the charging device and each sub-array antenna unit in the activated state; Adopting an adaptive phase compensation algorithm to correct the theoretical phase in real time to obtain an actual phase value after compensation; 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 power control between the subarray antenna units.

8. The follow-up wireless microwave charging method according to claim 7, characterized in that: The actual phase value after compensation is calculated as follows: Among them, φ i,comp is the actual phase value after compensation, β i (ω) is the frequency-dependent compensation coefficient, which is dynamically adjusted with the operating frequency, Δφ i is the accumulated value of historical phase error, γ i is the adaptive gain coefficient, controlling the compensation strength, w k is the weight coefficient of each antenna element, ω is the operating angular frequency, N is the total number of antenna array elements, φ i,theory is the ith theoretical phase value, t is the time variable, φ k,theory is the theoretical phase value of the kth antenna element.

9. A follow-up wireless microwave charging system, characterized in that: include: The acquisition module is used to collect multiple sets of position coordinate information and speed information of the charging device within the T1 time period; A trajectory prediction module, used to construct a trajectory prediction model based on the data of the acquisition module to calculate the motion trajectory data of the charging device in the T2 time period; A partition charging module, used for partitioning the microwave antenna into N independently controlled sub-array antenna units according to the motion trajectory data, each of the sub-array antenna units corresponding to a region segment on the predicted trajectory; A control unit, configured to dynamically adjust the phase and power parameters of each of the sub-array antenna units according to the real-time position of the charging device to form an adaptive beam; A charging module is used to perform gradient charging of the charging device in different regions through the adaptive beam.

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