Reconfigurable intelligent surface energy transmission system for non-line-of-sight environment

Abstract

The invention proposes a reconfigurable intelligent surface energy transmission system for a non-line-of-sight environment, and belongs to the technical field of wireless power supply systems, and the system comprises a feedback module which is disposed at a mobile receiving end and is used for collecting voltage data on a local energy receiving circuit; the global scanning module is used for controlling the RIS to carry out beam scanning and determining a maximum receiving power direction as an initial focus point according to the voltage data; the three-dimensional prediction module establishes a three-dimensional voxel grid by taking the initial focus point as a center, and estimates predicted position coordinates of the mobile receiving end through a preset time sequence prediction model based on historical and real-time voltage data sequences; the control module calculates the phase configuration of the RIS unit according to the predicted position coordinates, and synchronously outputs a phase control signal through a shift register to drive the RIS array; and the energy transmission module is used for forming a focusing beam according to the phase control signal to wirelessly supply power to the mobile receiving end. According to the invention, low-delay and high-precision energy focusing of the moving target is realized.

Description

Technical Field

[0001] This invention belongs to the field of wireless power supply system technology, and particularly relates to a reconfigurable smart surface energy transfer system for non-line-of-sight environments. Background Technology

[0002] Wireless power transfer technology enables continuous operation of IoT devices and mobile robots. However, in real industrial or indoor environments, obstacles often exist between the transmitter and receiver, blocking direct line-of-sight (LOS) links and creating non-line-of-sight (NLOS) environments. This causes a sharp decline in the efficiency of traditional line-of-sight-based power transfer methods.

[0003] Reconfigurable smart surfaces (RIS), as an emerging technology, can construct virtual line-of-sight links by intelligently modulating the wavefront of electromagnetic waves, theoretically solving problems in non-line-of-sight environments. However, existing technologies have the following drawbacks: 1. Most existing reconfigurable smart surface energy transmission schemes assume the receiver is static or can only perform simple beam steering, failing to effectively track high-speed moving targets in non-line-of-sight environments. 2. In non-line-of-sight environments, due to signal blocking and reflection, the system struggles to directly obtain the precise location of moving targets. Existing methods mostly rely on instantaneous feedback, lacking the ability to predict target trajectories, resulting in beam pointing always lagging behind the actual target position. 3. The performance of reconfigurable smart surfaces is positively correlated with the number of units, but controlling large-scale reconfigurable smart surface arrays requires extremely high computational speed and parallel processing capabilities. Traditional CPU / GPU-based control systems have high processing latency, making it difficult to achieve nanosecond-level real-time response. Although FPGAs possess parallel processing potential, existing designs are insufficient in terms of drive channel expansion and synchronous control, making it difficult to efficiently control hundreds or even thousands of reconfigurable smart surface units. Summary of the Invention

[0004] To address the aforementioned technical challenges, this invention proposes a reconfigurable intelligent surface energy transfer system for non-line-of-sight environments. By coordinating Bluetooth real-time feedback, a three-dimensional motion prediction algorithm, and FPGA-based large-scale array synchronous control, low-latency, high-precision energy focusing and tracking of moving targets is achieved.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: Reconfigurable smart surface energy transfer systems for non-line-of-sight environments, including The feedback module, deployed at the mobile receiver, collects voltage data of the local load through a voltage sampling circuit and transmits it back to the global scanning module and the three-dimensional prediction module respectively. The global scanning module is used to control the RIS to perform beam scanning and determine the direction of maximum received power based on the voltage data as the initial focusing point; The three-dimensional prediction module is communicatively connected to the feedback module and the global scanning module. It establishes a three-dimensional voxel grid centered on the initial focal point and estimates the predicted position coordinates of the mobile receiver based on the historical and real-time voltage data sequences continuously transmitted back by the feedback module through a preset time-series prediction model. The control module, which is communicatively connected to the three-dimensional prediction module, is used to calculate the phase configuration of the RIS unit based on the predicted position coordinates, and synchronously output the phase control signal through a shift register to drive the RIS array. The power transmission module is used to form a focused beam based on the phase control signal to wirelessly power the mobile receiver.

[0006] Furthermore, the feedback module includes: The ARM microcontroller acquires voltage data from the local load through a voltage sampling circuit. The Bluetooth communication unit, connected to the ARM microcontroller, is used to transmit the collected voltage data back to the global scanning module and the three-dimensional prediction module, respectively.

[0007] Furthermore, the detailed process of the global scanning module's operation includes: The RIS array is controlled to load multiple sets of phase configurations from a pre-stored beamforming codebook in sequence, with each set of phase configurations corresponding to a different beam direction; Iterate through all voltage data received during all phase configuration processes and determine the beam direction corresponding to the maximum voltage value as the initial focus point.

[0008] Furthermore, the detailed process of the operation of the three-dimensional prediction module includes: A three-dimensional voxel mesh is established with the initial focal point as the center; At each time step, the historical and real-time voltage data sequences from the feedback module are mapped to a three-dimensional voxel grid to form an observation tensor, and spatial features are extracted by the encoder to obtain the feature tensor. The feature tensor is input into a preset time-series prediction model to predict the probability distribution of the position of the mobile receiver at the prediction time step. Calculate the weighted expectation of the position probability distribution at any time step to obtain the estimated predicted position coordinates.

[0009] Furthermore, the temporal prediction model in the three-dimensional prediction module is a convolutional long short-term memory network, specifically including: Input Gate: ; Forgotten Gate: ; Output gate: ; Candidate memories: ; Cell status update: ; Hidden status update: ; in, Index for the current time step; express Time input gate, express The Gate of Forgetting Time; express The output gate at any given time; It is the sigmoid activation function; express The convolution kernel weight matrix to the input gate; express The convolution kernel weight matrix leading to the forget gate; express The convolution kernel weight matrix leading to the output gate; express The convolution kernel weight matrix to the candidate state; Indicates the previous hidden state The convolution kernel weight matrix to the input gate; Indicates the previous hidden state The convolution kernel weight matrix leading to the forget gate; Indicates the previous hidden state The convolution kernel weight matrix leading to the output gate; This represents the bias vector of the input gate; The bias vector representing the forget gate; This represents the bias vector of the output gate; The bias vector representing the candidate state; express The time is determined by historical location, received voltage, and RIS status information in a three-dimensional grid. The input feature tensor formed above; express Cellular state at any given moment; Represents the hyperbolic tangent activation function; This indicates element-wise multiplication.

[0010] Furthermore, the training process of the 3D prediction module minimizes the multi-objective loss function, specifically as follows: ; in, Represents cross-entropy loss; This indicates the position mean square error loss; This indicates a loss of speed consistency. Indicates power regression loss; Indicates the L2 regularization term; The weight coefficients representing the cross-entropy loss; The weighting coefficients representing the position mean square error loss; Weighting coefficients representing the speed consistency loss; The weighting coefficients representing the power regression loss; This represents the weight coefficient of the L2 regularization term.

[0011] Furthermore, the process executed by the control module includes: Based on the predicted location coordinates Calculate the required phase configuration vector for each cell in the RIS cell array. ;in, This indicates the total number of cells contained in the RIS cell array; Represents the first RISC cell array The phase offset that each unit needs to be configured with; Phase configuration vector Each consecutive phase value in Quantized into corresponding digital control words and all Combined into data frames suitable for serial communication; The packaged serial data is shifted to a position driven by a clock signal. In a register chain composed of cascaded shift register chips, once all data is in place, a global latch signal is generated, causing all shift registers to synchronously update the data in their internal registers to the parallel output. The level signal output in parallel from the shift register is passed through a driver circuit and then applied to each corresponding cell in the RIS cell array, so that all cells operate according to their digital control words. Synchronously switch to a new electromagnetic state, thereby forming a directional... The focused beam.

[0012] Furthermore, the digital control word The calculation process is as follows: ; in, This indicates the bit width of the digital control word that controls a single RIS unit; This function represents rounding down to the nearest integer.

[0013] Furthermore, the calculation process for the phase offset is as follows: ; in, The electromagnetic carrier wavelength that indicates the system's operation; Indicates the first The total propagation path difference from the reference cell to the target point for each RIS cell; ; Indicates the first The center coordinates of each RIS unit; Indicates the location coordinates of the radio frequency source; Indicates the position coordinates of a pre-selected reference RIS cell; The operator represents the Euclidean norm.

[0014] Furthermore, the energy transfer module includes a radio frequency source and a RIS array: The radio frequency source is used to generate radio frequency signals as energy carriers; The RIS array is used to receive the radio frequency signal, reconstruct the electromagnetic wave front according to the phase control signal issued by the control module, form a focused beam pointing to the predicted position coordinates, and wirelessly power the mobile receiver.

[0015] The effects described in the invention are merely those of the embodiments, and not all the effects of the invention. One of the above technical solutions has the following advantages or beneficial effects: This invention proposes a reconfigurable intelligent surface energy transfer system for non-line-of-sight environments, belonging to the field of wireless power supply system technology. The system includes: a feedback module deployed at the mobile receiver, used to collect voltage data from the local energy receiving circuit of the mobile receiver and transmit it back to the global scanning module and the 3D prediction module; a global scanning module, used to control the RIS (Radio Resonance Array) to perform beam scanning and determine the direction of maximum received power based on the voltage data as the initial focusing point; a 3D prediction module, communicatively connected to the feedback module and the global scanning module, establishing a 3D voxel grid centered on the initial focusing point, and estimating the predicted position coordinates of the mobile receiver based on the historical and real-time voltage data sequences continuously transmitted back by the feedback module using a preset time-series prediction model; a control module, communicatively connected to the 3D prediction module, used to calculate the RIS unit phase configuration based on the predicted position coordinates and synchronously output the phase control signal through a shift register to drive the RIS array; and an energy transfer module, used to form a focused beam based on the phase control signal to wirelessly power the mobile receiver. This invention achieves low-latency, high-precision energy focusing and tracking of moving targets through the synergy of Bluetooth real-time feedback, a 3D motion prediction algorithm, and FPGA-based large-scale array synchronous control.

[0016] This invention innovatively employs a ConvLSTM spatiotemporal prediction model based on an encoder-decoder architecture, which can make high-precision predictions of the target's position within hundreds of milliseconds in the future based on historical trajectories and signal strength.

[0017] This invention employs a hardware architecture of "FPGA + cascaded shift register". The FPGA provides nanosecond-level computation and response speed, while the cascaded circuit based on SN74HC595 achieves the synchronous driving of hundreds or even thousands of RIS units using a "serial shift-synchronous latch" mechanism with very few FPGA pins. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the reconfigurable smart surface energy transfer system for non-line-of-sight environments proposed in Embodiment 1 of the present invention; Figure 2 This is a flowchart of the global scanning module proposed in Embodiment 1 of the present invention; Figure 3 This is a flowchart of the three-dimensional prediction module proposed in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram of the mesh of the three-dimensional prediction module proposed in Embodiment 1 of the present invention; Figure 5 This is a flowchart of the control module proposed in Embodiment 1 of the present invention; Figure 6 A control circuit framework diagram based on 595 is proposed for Embodiment 1 of the present invention; Figure 7 This is a schematic diagram of the FPGA control timing proposed in Embodiment 1 of the present invention. Detailed Implementation

[0019] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific embodiments and in conjunction with the accompanying drawings. The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure of the invention, components and arrangements of specific examples are described below. Furthermore, reference numerals and / or letters may be repeated in different examples. This repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. It should be noted that the components illustrated in the drawings are not necessarily drawn to scale. Descriptions of well-known components, processing techniques, and processes are omitted in this invention to avoid unnecessarily limiting the invention.

[0020] Example 1 Embodiment 1 of this invention proposes a reconfigurable intelligent surface energy transmission system for non-line-of-sight environments, which solves the technical problem that the transmitter and receiver are often in a non-line-of-sight state due to obstacles, resulting in a sharp decline in the efficiency of traditional line-of-sight energy transmission methods.

[0021] Figure 1This is a schematic diagram of a reconfigurable smart surface energy transmission system for non-line-of-sight environments proposed in Embodiment 1 of the present invention; the system includes a control center, a feedback module deployed at a mobile receiver, and a transmitter containing a radio frequency source and a reconfigurable smart surface array; the control center includes a global scanning module, a three-dimensional prediction module, and a control module.

[0022] The feedback module, deployed at the mobile receiver, is used to collect voltage data from the local energy receiving circuit of the mobile receiver and transmit it back to the global scanning module and the three-dimensional prediction module, respectively. The feedback module includes: an ARM microcontroller, which collects voltage data from the local load through a voltage sampling circuit; and a Bluetooth communication unit, which connects to the ARM microcontroller and is used to wirelessly transmit the collected voltage data back to the global scanning module and the 3D prediction module, respectively.

[0023] In this invention, the mobile receiver typically employs a mobile vehicle equipped with an ARM microcontroller (such as the STM32F407 series) and an energy receiving circuit (rectifier antenna + load). The ARM microcontroller is responsible for acquiring the voltage values ​​across the load terminals, which are positively correlated with the received power. The acquired data is wirelessly transmitted in real-time to the control center via a Bluetooth module (such as the HC-05 or BLE series). The control center periodically receives the voltage data from the vehicle, using it as a basis for system focusing optimization and prediction.

[0024] The scope of protection of this invention is not limited to the device models listed in Example 1, and those skilled in the art can make reasonable selections based on the actual situation.

[0025] The global scanning module is used to quickly determine the approximate direction of a target during system initialization or when the target is lost. The global scanning module, which communicates with the feedback module and the three-dimensional prediction module, is used to control the RIS to perform beam scanning and determine the direction of maximum received power based on voltage data as the initial focusing point. Figure 2 This is a schematic diagram of the operation of the global scanning module proposed in Embodiment 1 of the present invention; the detailed process includes: controlling the RIS array to load multiple sets of phase configurations in the pre-stored beamforming codebook in sequence, each set of phase configurations corresponding to a different beam direction; traversing all voltage data received during the process of all phase configurations, determining the RIS phase configuration corresponding to the maximum received voltage as the initial focusing coordinate point, providing a starting point for subsequent three-dimensional fine prediction.

[0026] The 3D prediction module is the core module within the control center, used to predict the future location of moving targets in non-line-of-sight environments.

[0027] The 3D prediction module communicates with the feedback module, global scanning module, and control module. It establishes a 3D voxel grid centered on the initial focal point and estimates the predicted position coordinates of the mobile receiver based on the historical and real-time voltage data sequences continuously transmitted back by the feedback module, using a preset time-series prediction model.

[0028] Figure 3 The flowchart below shows the workflow of the three-dimensional prediction module proposed in Embodiment 1 of this invention: A three-dimensional voxel grid is established with the initial focal point as the center; at each time step, the historical and real-time voltage data sequences from the feedback module are mapped to the three-dimensional voxel grid to form an observation tensor, and spatial features are extracted by the encoder to obtain a feature tensor; the feature tensor is input into a preset time-series prediction model to predict the position probability distribution of the mobile receiver at the prediction time step; the weighted expectation of the position probability distribution at any time step is calculated to obtain the estimated predicted position coordinates.

[0029] Figure 4 This is a schematic diagram of the mesh of the three-dimensional prediction module proposed in Embodiment 1 of the present invention; in this application, a temporal neural network with an encoder-decoder architecture is used, where the encoder E maps the observation tensor composed of historical voltage, RIS phase, and estimated position into a feature tensor. The temporal core employs ConvLSTM (Convolutional Long Short-Term Memory network), and its gating update mechanism is as follows: Input Gate: ; Forgotten Gate: ; Output gate: ; Candidate memories: ; Cell status update: ; Hidden status update: ; in, Index for the current time step; express Time input gate, express The Gate of Forgetting Time; express The output gate at any given time; It is the sigmoid activation function; express The convolution kernel weight matrix to the input gate; express The convolution kernel weight matrix leading to the forget gate; express The convolution kernel weight matrix leading to the output gate; express The convolution kernel weight matrix to the candidate state; Indicates the previous hidden state The convolution kernel weight matrix to the input gate; Indicates the previous hidden state The convolution kernel weight matrix leading to the forget gate; Indicates the previous hidden state The convolution kernel weight matrix leading to the output gate; This represents the bias vector of the input gate; The bias vector representing the forget gate; This represents the bias vector of the output gate; The bias vector representing the candidate state; express The time is determined by historical location, received voltage, and RIS status information in a three-dimensional grid. The input feature tensor formed above; express Cellular state at any given moment; Represents the hyperbolic tangent activation function; This represents element-wise multiplication; Decoder D will output the hidden state of ConvLSTM. The predicted position of the target is obtained by converting the voxel grid prediction score for the next H steps, normalizing it using the Softmax function, and then taking the expected value of this probability distribution.

[0030] The training process of the 3D prediction module minimizes the multi-objective loss function, specifically as follows: ; in, Represents cross-entropy loss; This indicates the position mean square error loss; This indicates a loss of speed consistency. Indicates power regression loss; Indicates the L2 regularization term; The weight coefficients representing the cross-entropy loss; The weighting coefficients representing the position mean square error loss; Weighting coefficients representing the speed consistency loss; The weighting coefficients representing the power regression loss; This represents the weight coefficient of the L2 regularization term.

[0031] The control module, which communicates with the 3D prediction module, is used to calculate the phase configuration of the RIS unit based on the predicted position coordinates and synchronously output the phase control signal through a shift register to drive the RIS array. Figure 5This is a flowchart of the control module proposed in Embodiment 1 of the present invention; the detailed process includes: Based on the predicted location coordinates Calculate the required phase configuration vector for each cell in the RIS cell array. ;in, This indicates the total number of cells contained in the RIS cell array; Represents the first RISC cell array The phase offset that each unit needs to be configured with; Phase configuration vector Each consecutive phase value in Quantized into corresponding digital control words and all Combined into data frames suitable for serial communication; The packaged serial data is shifted to a position driven by a clock signal. In a register chain composed of cascaded shift register chips, once all data is in place, a global latch signal is generated, causing all shift registers to synchronously update the data in their internal registers to the parallel output. The level signal output in parallel from the shift register is driven by the driver circuit and then applied to each corresponding cell in the RIS cell array, so that all cells respond according to their digital control words. Synchronously switch to a new electromagnetic state, thereby forming a directional... The focused beam.

[0032] Digital control word The calculation process is as follows: ; in, This indicates the bit width of the digital control word that controls a single RIS unit; This function represents rounding down to the nearest integer.

[0033] The calculation process for the phase offset is as follows: ; in, The electromagnetic carrier wavelength that indicates the system's operation; Indicates the first The total propagation path difference from the reference cell to the target point for each RIS cell; ; Indicates the first The center coordinates of each RIS unit; Indicates the location coordinates of the radio frequency source; Indicates the position coordinates of a pre-selected reference RIS cell; The operator represents the Euclidean norm.

[0034] In this invention, the control module is the key hardware for achieving low-latency, large-scale control. A Xilinx Artix-7 series FPGA is used as the control core, leveraging its parallel processing capabilities to achieve nanosecond-level phase calculations and command issuance. The scope of protection of this invention is not limited to the models listed in Example 1; those skilled in the art can make reasonable selections based on actual circumstances.

[0035] Figure 6 This invention provides a control circuit framework diagram based on the 595 microcontroller for Embodiment 1. To drive up to 800 RIS units, an SN74HC595PW shift register is used for I / O expansion. Specifically, the FPGA serially inputs control data to the first 595 microcontroller via the SPI communication protocol, and then passes it to the next microcontroller through cascading outputs, forming a long chain of shift registers. Each 595 microcontroller controls 8 RIS units, and 100 microcontrollers can control 800 RIS units. A latch signal is used to simultaneously update the outputs of all 595 microcontrollers, achieving synchronous phase switching of the 800 RIS units. A perforated board circuit is located between the 595 outputs and the RIS units, used for level conversion, signal isolation, and enhanced drive capability, ensuring that the control signal can reliably drive each RIS unit.

[0036] The power transmission module is used to form a focused beam based on a phase control signal to wirelessly power the mobile receiver. The power transmission module includes a radio frequency (RF) source and a RIS array: the RF source generates an RF signal as an energy carrier; the RIS array receives the RF signal, reconstructs the electromagnetic wave front according to the phase control signal issued by the control module, and forms a focused beam pointing towards the predicted position coordinates to wirelessly power the mobile receiver.

[0037] This application describes a rectifier antenna on a mobile vehicle that receives radio frequency energy and converts it into direct current to power loads on the vehicle, such as batteries or sensors.

[0038] The reconfigurable smart surface energy transfer system proposed in this application operates in a fixed cycle period Δt = 100 ms for non-line-of-sight environments. Figure 7This is a schematic diagram of the FPGA control timing proposed in Embodiment 1 of the present invention. During time slice allocation, approximately 90ms (90%) of each cycle is used for energy transfer, during which the RIS beam remains focused; the remaining 10ms (10%) is used for prediction updates. During this time period, the system receives Bluetooth feedback, runs the prediction algorithm, and updates the RIS phase configuration. The FPGA internally precisely coordinates feedback reception, data preprocessing, prediction calculation (or receiving results from the CPU), 595 data shifting, and latch updates to ensure that all operations are completed within the 10ms update window.

[0039] Embodiment 1 of this invention proposes a reconfigurable intelligent surface energy transfer system for non-line-of-sight environments. Through the synergy of Bluetooth real-time feedback, three-dimensional motion prediction algorithm and FPGA-based large-scale array synchronous control, it achieves low-latency, high-precision energy focusing and tracking of moving targets.

[0040] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that the elements inherent in a process, method, article, or apparatus that includes a list of elements are included. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Additionally, portions of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.

[0041] While specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art can make other modifications or variations based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A reconfigurable intelligent surface energy transfer system for non-line-of-sight environments, characterized in that, The application relates to a wireless power transmission method and device based on a reconfigurable intelligent surface (RIS) and a mobile receiving end. The application comprises: a feedback module arranged at the mobile receiving end, which collects voltage data of a local load through a voltage sampling circuit and transmits the voltage data to a global scanning module and a three-dimensional prediction module respectively; a global scanning module, which is used for controlling the RIS to perform beam scanning and determining a maximum receiving power direction according to the voltage data, so as to serve as an initial focusing point; a three-dimensional prediction module, which is in communication connection with the feedback module and the global scanning module, establishes a three-dimensional voxel grid with the initial focusing point as the center, and estimates a predicted position coordinate of the mobile receiving end based on a historical and real-time voltage data sequence continuously transmitted by the feedback module through a preset time sequence prediction model; a control module, which is in communication connection with the three-dimensional prediction module and is used for calculating an RIS unit phase configuration according to the predicted position coordinate and synchronously outputting a phase control signal through a shift register to drive the RIS array; 2. The system of claim 1, wherein, an energy transmission module, which is used for forming a focused beam according to the phase control signal and performing wireless power supply to the mobile receiving end. The feedback module comprises: an ARM microcontroller, which collects voltage data of a local load through a voltage sampling circuit; 3. The system of claim 1, wherein, a Bluetooth communication unit, which is connected with the ARM microcontroller and is used for transmitting the collected voltage data to the global scanning module and the three-dimensional prediction module respectively. The detailed process executed by the global scanning module comprises: controlling the RIS array to sequentially load a plurality of groups of phase configurations in a pre-stored beam forming codebook, and each group of phase configurations corresponds to a different beam direction; 4. The system of claim 1, wherein, traversing all voltage data received in the process of traversing all phase configurations, and determining a beam direction corresponding to a maximum voltage value as an initial focusing point. The detailed process executed by the three-dimensional prediction module comprises: establishing a three-dimensional voxel grid with the initial focusing point as the center; at each time step, mapping a historical and real-time voltage data sequence from the feedback module to the three-dimensional voxel grid to form an observation tensor, and extracting spatial features through an encoder to obtain a feature tensor; inputting the feature tensor into a preset time sequence prediction model to predict a position probability distribution of the mobile receiving end at a predicted time step; 5. The system of claim 4, wherein, calculating a weighted expectation of the position probability distribution at any time step to obtain an estimated predicted position coordinate. Input gate: ; forget gate: ; Output gate: ; Candidate memories: ; Cell state update: ; Hidden state update: ; wherein, is the current time step index; denotes the input gate at time step, denotes the forget gate at time step ; denotes the output gate at time step ; is the sigmoid activation function; express The convolution kernel weight matrix to the input gate; express The convolution kernel weight matrix leading to the forget gate; express The convolution kernel weight matrix leading to the output gate; express The convolution kernel weight matrix to the candidate state; Indicates the previous hidden state The convolution kernel weight matrix to the input gate; Indicates the previous hidden state The convolution kernel weight matrix leading to the forget gate; Indicates the previous hidden state The convolution kernel weight matrix leading to the output gate; This represents the bias vector of the input gate; The bias vector representing the forget gate; This represents the bias vector of the output gate; The bias vector representing the candidate state; express The time is determined by historical location, received voltage, and RIS status information in a three-dimensional grid. The input feature tensor formed above; express Cellular state at any given moment; Represents the hyperbolic tangent activation function; This indicates element-wise multiplication.

6. The system of claim 5, wherein, The time sequence prediction model in the three-dimensional prediction module is a convolutional long short-term memory network, and specifically comprises: ； wherein, represents a cross-entropy loss; represents a position mean square error loss; represents a velocity consistency loss; represents a power regression loss; represents an L2 regularization term; a weight coefficient representing a cross-entropy loss; a weight coefficient representing a position mean square error loss; a weight coefficient representing a velocity consistency loss; a weight coefficient representing a power regression loss; a weight coefficient representing an L2 regularization term.

7. The system of claim 6, wherein, a multi-objective loss function in a training process of the three-dimensional prediction module is: According to the predicted position coordinates The phase configuration vector required by each unit in the RIS unit array is calculated ; wherein, represents the total number of units contained in the RIS unit array; represents the phase offset amount required by the th unit in the RIS unit array to be configured; Phase configuration vector Each consecutive phase value in Quantized into corresponding digital control words and all Combined into data frames suitable for serial communication; The packaged serial data is shifted to a position driven by a clock signal. In a register chain composed of cascaded shift register chips, once all data is in place, a global latch signal is generated, causing all shift registers to synchronously update the data in their internal registers to the parallel output. The level signals output in parallel by the shift register are loaded, after passing through a driver circuit, to each corresponding cell of the array of RIS cells, so that all the cells form a focused beam directed towards by synchronously switching to a new electromagnetic state. ​ 8. The system of claim 7, wherein, The digital control word The calculation process is: ； wherein denotes the bit width of the control word for controlling the respective RIS unit; denotes the function of rounding to the nearest integer.

9. The system of claim 7, wherein, The detailed process executed by the control module comprises: ； wherein, represents a wavelength of an electromagnetic wave carrier operating the system; represents a total propagation path difference of the RIS units relative to the reference unit to the target point; ； denotes the center position coordinates of the denotes the position coordinates of the radio frequency source; denotes the position coordinates of the pre-selected reference RIS element; denotes the Euclidean norm operator.​ 10. The system of claim 1, wherein, a calculation process of the phase offset is: The energy transmission module comprises a radio frequency source and an RIS array: the radio frequency source is used for generating a radio frequency signal as an energy carrier; the RIS array is used for receiving the radio frequency signal, reconstructing an electromagnetic wave front according to a phase control signal issued by the control module, forming a focused beam pointing to the predicted position coordinate, and performing wireless power supply to the mobile receiving end.

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