A method and system for tracking and optimizing communication of inspection vehicles

Abstract

This invention relates to the field of smart grid technology, and more specifically to a method and system for optimizing the tracking and communication of inspection vehicles. The method includes: constructing a RIS-ISAC-NOMA system framework for smart grid application scenarios; in non-line-of-sight (NLOS) scenarios, utilizing a reconfigurable smart metasurface (RIS) to transform NLOS paths into equivalent virtual line-of-sight (LOS) paths, and adjusting the propagation path of ISAC signals in real time; designing a vehicle tracking and prediction method based on the EKF algorithm, thereby achieving efficient real-time vehicle perception and communication; and combining NOMA technology to propose a power allocation optimization scheme for multiple inspection vehicles, further improving the system's communication rate and resource utilization efficiency. Simulation results also verify the advantages of the proposed system in terms of communication performance and resource utilization. This invention can significantly improve the overall system performance while reducing pilot signal overhead.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and more specifically to a method and system for tracking and optimizing communication of inspection vehicles. Background Technology

[0002] With the development of 6G mobile communication technology and the popularization of smart grid technology, the security and economic benefits of power systems have been significantly improved. Power inspection vehicles, as an important component of the smart grid, are widely used in various service scenarios. However, in large-scale smart grid environments, the dense power equipment limits the communication and monitoring paths of power inspection vehicles, especially under non-line-of-sight (NLOS) conditions, where traditional line-of-sight (LOS) paths are not always available. This limits the communication and sensing performance between the inspection vehicle and the base station.

[0003] A similar prior art is Chinese patent application CN117879738A, which proposes a method for analyzing the information interaction environment of a multi-UAV cooperative command and control system. The multi-UAV cooperative command and control system includes a central command and control center and several UAVs. In the interactive propagation environment, there are several reflection clusters, including single-hop clusters and multi-hop clusters. The multi-hop clusters include a first cluster on the transmitting side interacting with the UAVs and a second cluster on the receiving side interacting with the central command and control center. This invention constructs the channel impulse response matrix of the multi-UAV cooperative command and control system and calculates and selects the channel impulse response matrix at different times as the analysis result of the information interaction environment of the multi-UAV cooperative command and control system. It solves the problem that the channel model for multi-UAV cooperative communication cannot simultaneously characterize the non-stationary characteristics and consistency characteristics of the channel in the spatial and temporal domains.

[0004] Similar prior art includes Chinese patent application CN118647045A, which discloses a SAR-based ISAC UAV joint trajectory, beamforming, and resource optimization method, relating to the field of UAV exploration technology. This method includes: constructing a UAV ground target imaging and communication scenario, specifically involving the UAV flying to the detection area, using SAR in a focused working mode to image the target area, and employing integrated communication and sensing to transmit sensing data in real time; constructing a UAV ground target SAR imaging information rate model and a UAV information transmission model; constructing a UAV mobile energy consumption model and a communication and sensing energy consumption model; constructing constraints for the UAV flight process and the ground target area imaging and communication model; constructing beamforming optimization problems during UAV communication and sensing and resource optimization problems during UAV target area sensing; simplifying the system objective function, and employing alternating iteration to obtain the UAV's energy-optimized flight trajectory, beamforming, and power allocation method. This invention provides highly reliable algorithms for path planning, beamforming, and resource allocation methods.

[0005] However, neither of the above two application documents considered how to use signals to track inspection vehicles in NLOS (non-line-of-sight) scenarios, and there are still shortcomings in the communication performance between the base station and the inspection vehicle. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a method and system for optimizing the tracking and communication of inspection vehicles. Based on the RIS-ISAC-NOMA system framework for smart grid scenarios, it utilizes a reconfigurable smart metasurface (RIS) to adjust the signal propagation path in real time, assisting in the transmission of Integrated Communication and Sensing (ISAC) signals. Furthermore, it employs an extended Kalman filter (EKF) algorithm combined with real-time channel state information (CSI) from the ISAC echo signal to achieve tracking and prediction of the inspection vehicle's position. Simultaneously, it proposes a multi-vehicle power allocation optimization scheme incorporating non-orthogonal multiple access (NOMA) technology. The objective function is solved using CVX, and KKT conditions are introduced as a feedback mechanism to avoid getting trapped in local optima. This improves the communication and monitoring efficiency of power grid inspection vehicles in smart grid environments.

[0007] To achieve the above-mentioned objectives, this invention provides a method for optimizing the tracking and communication of inspection vehicles, which is implemented through the following steps:

[0008] Step S1: Construct a RIS-ISAC-NOMA system framework for smart grid application scenarios. In non-line-of-sight scenarios, a reconfigurable smart metasurface is used to transform the non-line-of-sight path into an equivalent virtual line-of-sight path. The vehicle body of the inspection vehicle is used as a reflector. The communication model and sensing model of the RIS-ISAC-NOMA system framework are established. Based on the communication model and the sensing model, the phase of the reconfigurable smart surface reflective unit is dynamically adjusted to track and locate the moving inspection vehicle.

[0009] Step S2: Define the state evolution model and tracking measurement model of the inspection vehicle, design the extended Kalman filter algorithm in the non-line-of-sight scenario, and process the signals from the dual-function radar communication base station to the reconfigurable smart surface and from the reconfigurable smart surface to the inspection vehicle through a cascaded channel model. Obtain real-time channel state information based on the echo signal reflected by the inspection vehicle body, and analyze the current motion state of the inspection vehicle based on the real-time channel state information.

[0010] Step S3: Optimize the power allocation scheme of multiple inspection vehicles based on non-orthogonal multiple access technology, and solve the objective function using the CVX toolbox. In the process of solving the objective function, a feedback mechanism is introduced, and the KKT equations of the objective function are derived using the Lagrange multiplier method to verify the solution of the objective function. Simulation numerical results are also obtained through Monte Carlo simulation.

[0011] As a preferred embodiment of the present invention, the RIS-ISAC-NOMA system framework includes:

[0012] A dual-function radar communication base station DFRC-BS equipped with H transmitting antennas, a reconfigurable smart metasurface RIS with L reflective elements, and K inspection vehicle users VU. The antenna arrays of the dual-function radar communication base station, the reconfigurable smart metasurface, and the inspection vehicles all adopt a uniform linear array ULA.

[0013] Each of the inspection vehicles (VU) carries M transceiver antennas. H, L, K, and M all represent positive integers greater than or equal to 2, but H, L, K, and M can take different values. DFRC-BS is an abbreviation for Dual Function Radar and Communication-Base Station, RIS is an abbreviation for Reflective Intelligent Surface, and VU is an abbreviation for Vehicle Users, representing inspection vehicle users or inspection vehicles.

[0014] As a preferred embodiment of the present invention, the communication model for establishing the RIS-ISAC-NOMA system framework includes:

[0015] Formula 1 represents the communication signal received by the k-th inspection vehicle at time n:

[0016] (Formula 1)

[0017] in, The array gain factor representing the signal. This indicates that the mean is 0 and the variance is . Additive white Gaussian noise (AWGN). This indicates the total power of the DFRC-BS transmitted signal. This represents the power distribution coefficient of the k-th inspection vehicle at time n, satisfying... , This indicates that the communication signal sent to the kth inspection vehicle satisfies... ; Let represent the antenna array beamforming vector of the k-th inspection vehicle, where It is the angle value at time n predicted by the k-th inspection vehicle at time n-1; This represents the beamforming vector of the DFRC-BS antenna array. Represents the combined vector of received signals, satisfying ; Represents the diagonal phase shift matrix of RIS. This represents the passive beamforming vector of the RIS. It is the first The phase shift of each reflecting unit satisfies ; This represents the communication channel gain from DFRC-BS to RIS, and the channel gain from RIS to the k-th inspection vehicle. This can be expressed as Formula 2:

[0018] (Formula 2)

[0019] in, This represents the channel gain coefficient from RIS to the inspection vehicle. This represents the predicted distance of the k-th inspection vehicle to the RIS surface at time n.

[0020] As a preferred embodiment of the present invention, the communication model for establishing the RIS-ISAC-NOMA system framework further includes:

[0021] In Non-Orthogonal Multiple Access (NOMA), the k-th inspection vehicle first detects and eliminates signal interference from all weaker inspection vehicles using Serial Interference Cancellation (SIC). Simultaneously, signals from all inspection vehicles with stronger signals than the k-th vehicle are considered noise. Based on Equation 1, at time n, the decoded signal of the k-th inspection vehicle... This can be expressed using Formula 3:

[0022] (Formula 3).

[0023] As a preferred embodiment of the present invention, establishing the sensing model of the RIS-ISAC-NOMA system framework includes:

[0024] Formula 4 expresses the following: At the nth time, the dual-function radar communication base station DFRS-BS receives the echo signal from the kth inspection vehicle:

[0025] (Formula 4)

[0026] in, Represents the combined vector of received signals, satisfying , This indicates that the mean is 0 and the variance is . AWGN, This represents the reflection coefficient of the k-th vehicle, which is related to the radar cross section (RCS) and satisfies the following condition: , Let RCS represent the RCS of the k-th vehicle at the n-th time; assuming that the RCS remains constant during the movement of the inspection vehicle, i.e. ; , The Doppler frequency shift and echo signal transmission delay of the k-th vehicle at time n are respectively represented by Equations 5 and 6:

[0027] (Formula 5)

[0028] (Formula 6)

[0029] in, This represents the straight-line distance from the DFRC-BS to the RIS surface. Indicates the signal carrier frequency. At the speed of light, , This indicates that the mean is 0 and the variances are respectively , The matched filter for filtering measurement noise can be calculated using Equation 7:

[0030] (Formula 7)

[0031] Where G represents the matched filter gain of the filter. These represent constants related to system configuration, signal design, and specific signal processing algorithms.

[0032] As a preferred embodiment of the present invention, the state evolution model of the inspection vehicle is:

[0033] A pre-defined inspection vehicle is used. A three-dimensional signal transmission model is projected onto the xy plane. The distance, angle, speed, and reflection coefficient of the inspection vehicle relative to the RIS at time n are respectively expressed as: , , and Based on the state of the inspection vehicle at time n-1, the state evolution model of the inspection vehicle is derived and expressed by Formula 8:

[0034] (Formula 8)

[0035] in, Indicates the beam sampling time interval. Represent a The distance change of the inspection vehicle described herein satisfies The state vector of the inspection vehicle is defined as follows: The state evolution model of the inspection vehicle is represented by Equation 9:

[0036] (Formula 9).

[0037] As a preferred embodiment of the present invention, the tracking and measurement model of the inspection vehicle is:

[0038] When the echo signal from the inspection vehicle reaches the DFRC-BS receiver, the dual-function radar communication base station first performs matched filtering on the received signal. After passing through the filter, the DFRC-BS can estimate the position of the k-th inspection vehicle at time n. and The estimated value obtained from the filter is used to compensate for the echo signal of the inspection vehicle. Formula 4 can be rewritten as follows:

[0039] (Formula 10)

[0040] in, This indicates that the mean is 0 and the variance is . The normalized measurement noise is calculated using Formula 7.

[0041] Based on Formulas 5, 6, and 10, the tracking and measurement model for a single inspection vehicle is represented by Formula 11:

[0042] (Formula 11)

[0043] in, .

[0044] As a preferred embodiment of the present invention, the Extended Kalman Filter (EKF) algorithm is:

[0045] After deriving the state evolution model and tracking measurement model of the inspection vehicle, the extended Kalman filter (EKF) recursive formula is further given and expressed by formula 12:

[0046] (Formula 12)

[0047] in, Representing nonlinear equations Jacobian matrix, This represents the covariance matrix composed of the AWGNs of the state evolution model. Indicates the filter gain. The predicted value represents the status of the inspection vehicle. This represents the status update value. Represents the predicted covariance matrix. Represents the covariance update matrix. Representing nonlinear equations The Jacobian matrix is ​​represented by Equation 13:

[0048] (Formula 13)

[0049] Jacobian matrix Expressed using Formula 14:

[0050] (Formula 14)

[0051] in, (Formula 15), (Formula 16);

[0052] By using the EKF algorithm to perform beam tracking and prediction on multiple inspection vehicles and allocating power to the inspection vehicles at each time step, the combined communication rate of the multiple inspection vehicles is maximized. The optimization problem is expressed by Equation 17:

[0053] (Formula 17)

[0054] in, and These represent the prediction error thresholds, and Formula 17 can also be expressed as Formula 18:

[0055] (Equation 18).

[0056] As a preferred technical solution of the present invention, the Lagrangian function of the optimization problem is constructed. Expressed using Formula 19:

[0057] (Formula 19)

[0058] in, It is a power and constraint Lagrange multiplier. It is a non-negative power Lagrange multiplier. and These are the Lagrange multipliers for the angle error and distance error constraints, respectively. Since the constraints in Equation 18 are all inequality constraints, the KKT conditions must be satisfied in the Lagrange multiplier method. The KKT conditions are necessary for finding the optimal solution at the extremum point of the objective function, and also satisfy the non-relaxation conditions of the inequality constraints and the conditions of the original constraint problem, resulting in Equation 19. The KKT condition equation system for Lagrange optimization is as follows.

[0059] The feasibility of Lagrange duality is expressed by Equation 20:

[0060] (Formula 20)

[0061] The gradient condition is expressed by Equation 21:

[0062] (Formula 21)

[0063] The complementary relaxation conditions are expressed by formulas 22, 23, and 24:

[0064] (Formula 22)

[0065] (Formula 23)

[0066] (Formula 24)

[0067] The second derivative of the objective function in Formula 21 is expressed by Formula 25:

[0068] (Formula 25)

[0069] in, , Based on the aforementioned formula 25, it can be obtained that when and When, satisfy The objective function is convex within its range of values ​​and has a global optimum. The objective function can be solved using the CVX toolbox.

[0070] The present invention also provides a patrol vehicle tracking and communication optimization system as described above, comprising the following modules:

[0071] The construction unit is used to build a RIS-ISAC-NOMA system framework for smart grid application scenarios. In non-line-of-sight scenarios, a reconfigurable smart metasurface is used to transform the non-line-of-sight path into an equivalent virtual line-of-sight path. The body of the inspection vehicle is used as a reflector to establish the communication model and sensing model of the RIS-ISAC-NOMA system framework. Based on the communication model and the sensing model, the phase of the reconfigurable smart surface reflective unit is dynamically adjusted to track and locate the moving inspection vehicle.

[0072] The analysis unit is used to define the state evolution model and tracking measurement model of the inspection vehicle, design the extended Kalman filter algorithm in the non-line-of-sight scenario, and process the signals from the dual-function radar communication base station to the reconfigurable smart surface and from the reconfigurable smart surface to the inspection vehicle through a cascaded channel model. It obtains real-time channel state information based on the echo signal reflected by the inspection vehicle body, and analyzes the current motion state of the inspection vehicle based on the real-time channel state information.

[0073] The optimization unit is used to optimize the power allocation scheme of multiple inspection vehicles based on non-orthogonal multiple access technology, and solves the objective function using the CVX toolbox. In the process of solving the objective function, a feedback mechanism is introduced, and the KKT equations of the objective function are derived using the Lagrange multiplier method to verify the solution of the objective function. Simulation numerical results are also obtained through Monte Carlo simulation.

[0074] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0075] The technical solution of this invention addresses the mobility problem of inspection vehicles under NLOS conditions in densely populated building environments by proposing a RIS-ISAC-NOMA system framework for smart grid scenarios. By utilizing RIS to adjust the propagation path of ISAC signals in real time and designing a vehicle tracking and prediction method based on the EKF algorithm, efficient real-time vehicle perception and communication are achieved. Combined with NOMA technology, a power allocation optimization scheme for multiple inspection vehicles is proposed, further improving the system's communication rate and resource utilization efficiency. Simulation results also verify the advantages of the proposed system in terms of communication performance and resource utilization, demonstrating that this scheme can significantly improve the overall system efficiency while reducing pilot signal overhead. Attached Figure Description

[0076] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0077] Figure 1 This is a flowchart illustrating the steps of a patrol vehicle tracking and communication optimization method according to the present invention.

[0078] Figure 2 This is a model diagram of the RIS-ISAC-NOMA system in this invention;

[0079] Figure 3 This is a schematic diagram of the three-dimensional vehicle state evolution model in this invention;

[0080] Figure 4 This is a diagram illustrating the simulated "ISAC-V2X-NOMA" scenario in this invention.

[0081] Figure 5 This is an angle tracking performance diagram from the sensor performance analysis of the inspection vehicle 1 in this invention;

[0082] Figure 6 This is a distance tracking performance diagram from the sensor performance analysis of the inspection vehicle 1 in this invention;

[0083] Figure 7 This is an angle tracking performance diagram from the sensor performance analysis of the inspection vehicle 2 in this invention;

[0084] Figure 8 This is a distance tracking performance diagram from the sensor performance analysis of the inspection vehicle 2 in this invention;

[0085] Figure 9 This is a graph showing the communication performance analysis of different schemes with different numbers of antennas in this invention;

[0086] Figure 10 This is a graph showing the communication performance analysis of inspection vehicles under different numbers of RIS reflection units in this invention.

[0087] Figure 11 This is a communication performance analysis diagram under different schemes in this invention;

[0088] Figure 12 This is a structural diagram of a patrol vehicle tracking and communication optimization system according to the present invention. Detailed Implementation

[0089] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0090] It is understood that the terms "first," "second," etc., used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are used only to distinguish one element from another. For example, without departing from the scope of this application, a first script may be referred to as a second script, and similarly, a second script may be referred to as a first script.

[0091] With the continuous development of 6G mobile communication technology, the widespread adoption of smart grid technology is expected to improve the security and economic efficiency of future power systems. As a crucial component of the smart grid, power grid inspection vehicles have been widely applied in various service scenarios. In large-scale smart grid environments, the internal structure is complex, and inspection is one of the daily tasks to ensure the safe and stable operation of grid equipment. Daily inspections of grid equipment record relevant parameters, monitor equipment operation, and promptly identify potential safety hazards, which are essential factors in ensuring the safe and stable operation of the power grid. Integrated Sensing and Communications (ISAC) technology improves the efficiency of spectrum, energy, and hardware utilization by integrating communication and sensing functions. Using ISAC signals, the Extended Kalman Filter (EKF) algorithm, factor graph, and deep learning methods are employed to achieve ISAC signal-based tracking and prediction of the inspection vehicle's position. Compared to traditional pilot beam training methods, the ISAC-based scheme demonstrates a significant advantage in reducing communication signal pilot overhead. In traditional power line inspection, due to the poor penetration of high-frequency signals, communication between power inspection vehicles and base stations still relies on line-of-sight (LOS) paths to maintain high-quality communication and sensing performance. However, in environments with multiple obstacles (such as dense power equipment scenarios), LOS paths are not always available, which limits the communication and sensing performance between power inspection vehicles and base stations.

[0092] To address the aforementioned technical problems, the present invention proposes the following... Figure 1 The method for optimizing the tracking and communication of inspection vehicles, as shown, includes the following steps:

[0093] Step S1: Construct a RIS-ISAC-NOMA system framework for smart grid application scenarios. In non-line-of-sight scenarios, a reconfigurable smart metasurface is used to transform the non-line-of-sight path into an equivalent virtual line-of-sight path. The vehicle body of the inspection vehicle is used as a reflector. The communication model and sensing model of the RIS-ISAC-NOMA system framework are established. Based on the communication model and sensing model, the phase of the reconfigurable smart surface reflective unit is dynamically adjusted to track and locate the moving inspection vehicle.

[0094] Specifically, the RIS-ISAC-NOMA system framework is a complex system integrating Reconfigurable Intelligent Surface (RIS), Integrated Sensing and Communication (ISAC), and Non-Orthogonal Multiple Access (NOMA) technologies. In a smart grid scenario, a dual-function radar communication base station (DFRC-BS) equipped with N transmit antennas is deployed for simultaneous communication and radar detection. A reconfigurable intelligent metasurface (RIS) with L reflector elements is introduced. These reflector elements can adjust their phase to reconfigure the signal propagation path. Through real-time adjustment by the RIS, the communication and positioning accuracy of inspection vehicles in complex power grid environments can be significantly improved. K inspection vehicle users (VUs) are incorporated into the system, each equipped with M transmit and receive antennas to communicate with the DFRC-BS. Under NLOS conditions, the RIS is used to transform the signal path from NLOS to a virtual LOS path to improve communication and sensing quality. By establishing communication and sensing models, the phase of the RIS reflector unit is dynamically adjusted to track and locate moving inspection vehicles. The RIS-ISAC-NOMA system can adapt to the dynamics of vehicles, maintaining the stability of the communication link and the accuracy of sensor data.

[0095] Step S2: Define the state evolution model and tracking measurement model of the inspection vehicle, design the extended Kalman filter algorithm in the non-line-of-sight scenario, and process the signals from the dual-function radar communication base station to the reconfigurable smart surface and from the reconfigurable smart surface to the inspection vehicle through the cascaded channel model. Obtain real-time channel state information based on the echo signal reflected by the inspection vehicle body, and analyze the current motion state of the inspection vehicle based on the real-time channel state information.

[0096] Specifically, a state evolution model for the inspection vehicle is defined, including the vehicle's distance, angle, speed, and reflection coefficient relative to the RIS (Radio Reflection System). An Extended Kalman Filter (EKF) algorithm is designed to handle state estimation for nonlinear systems. The EKF algorithm can accurately estimate the state of the inspection vehicle, maintaining high positioning accuracy even in non-line-of-sight and dynamic environments. Signals from the DFRC-BS to the RIS and from the RIS to the vehicle are processed using a cascaded channel model. Real-time Channel State Information (CSI) is obtained using the echo signal reflected from the inspection vehicle body. CSI analysis determines the vehicle's current motion state and predicts the state of the inspection vehicle at the next moment.

[0097] Step S3: Optimize the power allocation scheme of multiple inspection vehicles based on non-orthogonal multiple access technology, and solve the objective function using the CVX toolbox. A feedback mechanism is also introduced in the process of solving the objective function. The KKT equations of the objective function are derived using the Lagrange multiplier method to verify the solution of the objective function. Simulation numerical results are also obtained through Monte Carlo simulation.

[0098] Specifically, a power allocation optimization scheme is designed for multiple inspection vehicles using Non-Orthogonal Multiple Access (NOMA) technology. The objective function is constructed and solved using the CVX toolbox to maximize the combined communication rate. Karush-Kuhn-Tucker (KKT) conditions are introduced as a feedback mechanism during the objective function solution process to ensure a globally optimal solution. The KKT equations for the objective function are derived using the Lagrange multiplier method, verifying the effectiveness of the solution. The KKT conditions and Lagrange multiplier method ensure fair and efficient power allocation in complex multi-user environments. Monte Carlo simulations are used to obtain numerical results and evaluate system performance. The Monte Carlo simulations verify the superiority of the proposed system scheme in terms of communication performance and resource utilization, particularly in reducing pilot signal overhead and improving overall system efficiency. The optimized power allocation scheme significantly improves the system's communication rate and resource utilization efficiency. The combined communication rate refers to the total data transmission rate when multiple inspection vehicles (VUs) communicate simultaneously in a NOMA environment. This rate is the sum of the transmission rates of all inspection vehicles, reflecting the total communication capacity that the system can support at a specific moment.

[0099] By coordinating the above steps, the mobility problem of inspection vehicles under NLOS conditions in dense power building environments was solved. Furthermore, by utilizing RIS to adjust the propagation path of ISAC signals in real time, a vehicle tracking and prediction method based on the EKF algorithm was designed, achieving efficient real-time vehicle perception and communication. In addition, combined with NOMA technology, a power allocation optimization scheme for multiple inspection vehicles was proposed, further improving the system's communication rate and resource utilization efficiency. Simulation results also verified the advantages of the proposed system in terms of communication performance and resource utilization. This scheme can significantly improve the overall system efficiency while reducing pilot signal overhead.

[0100] Furthermore, the aforementioned RIS-ISAC-NOMA system framework includes:

[0101] A dual-function radar communication base station DFRC-BS equipped with H transmitting antennas, a reconfigurable smart metasurface RIS with L reflective elements, and K inspection vehicle users VU. The antenna arrays of the dual-function radar communication base station, the reconfigurable smart metasurface, and the inspection vehicles all adopt a uniform linear array ULA.

[0102] Each inspection vehicle (VU) carries M transceiver antennas. H, L, K, and M all represent positive integers greater than or equal to 2, but H, L, K, and M can have different values. DFRC-BS is an abbreviation for Dual Function Radar and Communication-BaseStation, representing a dual-function radar and communication base station. RIS is an abbreviation for Reflective Intelligent Surface, representing a reconfigurable intelligent metasurface. VU is an abbreviation for Vehicle Users, representing inspection vehicle users or inspection vehicles.

[0103] Specifically, such as Figure 2 As shown, the RIS-ISAC-NOMA system based on the smart grid scenario includes the above components. Assuming that all VUs are located in the NLOS non-line-of-sight region of the DFRC-BS, the deployment of the RIS smart metasurface can transform the NLOS non-line-of-sight path into the LOS line-of-sight path, ensuring that the VUs for communication and sensing are all located in the LOS region of the RIS.

[0104] Furthermore, the communication model for establishing the RIS-ISAC-NOMA system framework includes:

[0105] Formula 1 represents the communication signal received by the k-th inspection vehicle at time n:

[0106] (Formula 1)

[0107] in, The array gain factor representing the signal. This indicates that the mean is 0 and the variance is . Additive white Gaussian noise (AWGN). This indicates the total power of the DFRC-BS transmitted signal. Let the power allocation coefficient of the k-th inspection vehicle at time n satisfy the following condition: , This represents the communication signal sent to the k-th inspection vehicle, satisfying... ; Let represent the antenna array beamforming vector of the k-th inspection vehicle, where It is the angle value at time n predicted by the kth inspection vehicle at time n-1; This represents the beamforming vector of the DFRC-BS antenna array. Represents the combined vector of received signals, satisfying ; Represents the diagonal phase shift matrix of RIS. This represents the passive beamforming vector of the RIS. It is the first The phase shift of each reflecting unit satisfies ; This represents the communication channel gain from DFRC-BS to RIS, and the channel gain from RIS to the k-th inspection vehicle. This can be expressed as Formula 2:

[0108] (Formula 2)

[0109] in, This represents the channel gain coefficient from the RIS to the inspection vehicle. This represents the predicted distance of the k-th inspection vehicle to the RIS surface at time n.

[0110] Specifically, the inspection vehicles move continuously over time, and the downlink communication beams share the same channel through power domain multiplexing at the DFRC-BS. Considering there are K mobile inspection vehicles in the system, at the inspection vehicle receiver, the vehicle users can decode the interfered received signal through SIC. As the vehicles move, the DFRC-BS can allocate power to the vehicles in real time. Without loss of generality, assume that the vehicle closest to the RIS has the strongest channel gain, while the vehicle farthest from the RIS has the weakest channel gain.

[0111] Unless otherwise specified, matrices in the text are represented using bold uppercase letters (e.g., : Vectors are represented using bold lowercase letters (e.g.: , Scalars are represented using standard fonts (e.g., ...). The subscripts in the formula represent the current inspection vehicle and the time to which the inspection vehicle belongs, respectively (e.g.: (This represents the angle value of the k-th inspection vehicle at time n).

[0112] Furthermore, the communication model for establishing the RIS-ISAC-NOMA system framework also includes:

[0113] In Non-Orthogonal Multiple Access (NOMA), the k-th inspection vehicle first detects and eliminates signal interference from all weaker inspection vehicles using Serial Interference Cancellation (SIC). Simultaneously, signals from all inspection vehicles with stronger signals than the k-th vehicle are considered noise. Based on Equation 1, at time n, the decoded signal of the k-th inspection vehicle... This can be expressed using Formula 3:

[0114] (Formula 3).

[0115] Furthermore, the sensing model for establishing the RIS-ISAC-NOMA system framework includes:

[0116] Formula 4 expresses the following: At time n, the dual-function radar communication base station DFRS-BS receives the echo signal from the k-th inspection vehicle:

[0117] (Formula 4)

[0118] in, Represents the combined vector of received signals, satisfying , This indicates that the mean is 0 and the variance is . AWGN, This represents the reflection coefficient of the k-th vehicle. The reflection coefficient is related to the radar cross section (RCS) and satisfies the following condition: , Let RCS represent the RCS of the k-th vehicle at time n; assuming that the RCS remains constant during the movement of the inspection vehicle, i.e. ; , Let Doppler frequency shift and echo signal transmission delay of vehicle k at time n be represented respectively, and expressed by Equations 5 and 6:

[0119] (Formula 5)

[0120] (Formula 6)

[0121] in, This represents the straight-line distance from the DFRC-BS to the RIS surface. Indicates the signal carrier frequency. At the speed of light, , This indicates that the mean is 0 and the variances are respectively , The matched filter for filtering measurement noise can be calculated using Equation 7:

[0122] (Formula 7)

[0123] Where G represents the matched filter gain of the filter. These represent constants related to system configuration, signal design, and specific signal processing algorithms.

[0124] Furthermore, the state evolution model of the inspection vehicle is:

[0125] like Figure 3The diagram shows the three-dimensional motion state evolution model of the inspection vehicle relative to RIS and DFRC-BS. This paper will study the movement of NLOS vehicles from a two-dimensional plane perspective, taking a single inspection vehicle as an example:

[0126] Given an inspection vehicle, a 3D signal transmission model is projected onto the xy plane. The distance, angle, velocity, and reflection coefficient of the inspection vehicle relative to the RIS at time n are respectively expressed as: , , and Based on the state of the inspection vehicle at time n-1, the state evolution model of the inspection vehicle is derived and expressed by Formula 8:

[0127] (Formula 8)

[0128] in, Indicates the beam sampling time interval. Represent a The change in distance of the internal inspection vehicles meets the following requirements. The state vector of the inspection vehicle is defined as follows: The state evolution model of the inspection vehicle is represented by Equation 9:

[0129] (Formula 9).

[0130] Specifically, this paper will use the EKF algorithm to track and predict the position of inspection vehicles. EFK is a recursive algorithm commonly used for state estimation of nonlinear systems. It can handle nonlinear relationships in systems and performs well in dynamic environments. The core of its algorithm lies in defining and applying the system's evolutionary and measurement models. As mentioned above, the evolutionary model describes how the inspection vehicle's motion state changes over time, while the measurement model links the real-time CSI in the ISAC echo signal reflected by the vehicle body with the vehicle's actual state, which will be described below. Through these two sub-models, EKF can recursively update the vehicle's position estimate in highly dynamic and nonlinear environments.

[0131] Furthermore, the tracking and measurement model for the aforementioned inspection vehicle is:

[0132] When the echo signal from the inspection vehicle reaches the DFRC-BS receiver, the dual-function radar communication base station first performs matched filtering on the received signal. After passing through the filter, the DFRC-BS can estimate the position of the k-th inspection vehicle at time n. and The estimated value obtained from the filter is used to compensate for the echo signal of the inspection vehicle. Formula 4 can be rewritten as:

[0133] (Formula 10)

[0134] in, This indicates that the mean is 0 and the variance is . The normalized measurement noise is calculated using Equation 7;

[0135] Based on formulas 5, 6, and 10, the tracking and measurement model for a single inspection vehicle is expressed by formula 11:

[0136] (Formula 11)

[0137] in, .

[0138] Furthermore, the Extended Kalman Filter (EKF) algorithm is:

[0139] After deriving the state evolution model and tracking measurement model of the inspection vehicle, the recursive formula of the Extended Kalman Filter (EKF) is further given and expressed by Equation 12:

[0140] (Formula 12)

[0141] in, Representing nonlinear equations Jacobian matrix, This represents the covariance matrix composed of the AWGNs of the state evolution model. Indicates the filter gain. The predicted value representing the status of the inspection vehicle. This represents the status update value. Represents the predicted covariance matrix. Represents the covariance update matrix. Representing nonlinear equations The Jacobian matrix is ​​represented by Equation 13:

[0142] (Formula 13)

[0143] Jacobian matrix Expressed using Formula 14:

[0144] (Formula 14)

[0145] in, (Formula 15), (Formula 16);

[0146] By using the EKF algorithm to perform beam tracking and prediction on multiple inspection vehicles and allocating power to the inspection vehicles at each time step, the combined communication rate of the multiple inspection vehicles is maximized. The optimization problem is expressed by Equation 17:

[0147] (Formula 17)

[0148] in, and These represent the prediction error thresholds, and Formula 17 can also be expressed as Formula 18:

[0149] (Equation 18).

[0150] Furthermore, the KKT condition equations corresponding to the objective function are obtained through the Lagrange multiplier method. The optimization problem is then solved using the CVX toolbox. The KKT condition equations are used as a feedback mechanism to verify the solution and prevent getting trapped in local optima. The Lagrange function of the optimization problem is constructed. Expressed using Formula 19:

[0151] (Formula 19)

[0152] in, It is a power and constraint Lagrange multiplier. It is a non-negative power Lagrange multiplier. and These are the Lagrange multipliers for the angle error and distance error constraints, respectively. Since the constraints in Equation 18 are all inequality constraints, the KKT conditions must be satisfied in the Lagrange multiplier method. The KKT conditions are necessary for finding the optimal solution at the extremum point of the objective function, and also satisfy the non-relaxation conditions of the inequality constraints and the conditions of the original constraint problem, resulting in Equation 19. The KKT condition equation system for Lagrange optimization is as follows.

[0153] The feasibility of Lagrange duality is expressed by Equation 20:

[0154] (Formula 20)

[0155] The gradient condition is expressed by Equation 21:

[0156] (Formula 21)

[0157] The complementary relaxation conditions are expressed by formulas 22, 23, and 24:

[0158] (Formula 22)

[0159] (Formula 23)

[0160] (Formula 24)

[0161] The second derivative of the objective function in Formula 21 is expressed by Formula 25:

[0162] (Formula 25)

[0163] in, , Based on formula 25, when and When, satisfy The objective function is convex within its range of values ​​and has a global optimum. The objective function can be solved using the CVX toolbox.

[0164] Specifically, the detailed process for power distribution among multiple inspection vehicles, as summarized above, is shown in Table 1:

[0165] Table 1. Power Allocation Algorithm Flow for Inspection Vehicles

[0166]

[0167] The simulation results below verify the advantages of the proposed system in terms of communication performance and resource utilization. This paper uses MATLAB to evaluate the performance of the proposed model. Figure 4 This represents a simulation diagram of "RIS-ISAC-NOMA" in a smart grid scenario. Unless otherwise specified, all parameters used in the simulation are default. , , , , , , , , , , The measurement noise during the state evolution of the inspection vehicle is set as follows: , , , To evaluate the performance of the proposed system, we conducted a comparative experiment with a traditional smart grid scenario system. This traditional model uses Orthogonal Multiple Access (OMA) technology, and power allocation is equally distributed based on the number of users in the inspection vehicle scenario. The system will not optimize the communication rate of the inspection vehicle by power allocation; all other parameters will be configured using default settings. The initial state parameters of the inspection vehicle are shown in Table 2. All simulation results are... The average value was obtained from the Monte Carlo simulation experiments.

[0168] Table 2 Initial Vehicle Parameters

[0169]

[0170] Performance analysis of the sensing system under multiple inspection vehicles:

[0171] Figure 5 , Figure 6 , Figure 7 and Figure 8 The graphs show the changes in the root mean square error (RMSE) of the angle and distance for vehicles 1 and 2 over time with different numbers of RIS reflector units (L=5, 10, 15, 20) and without RIS. The figures show that the RMSE for angle and distance is significantly higher without RIS than with RIS. With the introduction of RIS, the RMSE for angle and distance gradually decreases as the number of RIS reflector units increases. At L=20, the system exhibits the best angle and distance estimation accuracy. The introduction of RIS effectively transforms the NLOS path into a LOS path, significantly improving the sensing and tracking accuracy of the ISAC signal.

[0172] Analysis of system communication performance under multiple inspection vehicles:

[0173] Figure 9 Showing , The communication reachability rate of the proposed system and the traditional system varies over time under different antenna numbers. The curves show that the communication reachability rate increases as the inspection vehicle approaches the RIS surface and decreases as the vehicle moves away. In environments with the same number of antennas, dense inspection vehicle traffic, and limited resources, the proposed system exhibits a significantly higher communication reachability rate than the traditional system. With different antenna numbers, the communication reachability rate of both systems increases significantly with the increase in the number of antennas. The proposed system demonstrates superior communication performance through more efficient resource utilization and interference management.

[0174] Figure 10 Comparison in , The paper examines the change in the reachability rate of the two systems over time under different numbers of RIS reflector units. With the increase in the number of RIS reflector units, the reachability rate of both systems significantly improves, especially as the number of RIS reflector units increases from 5 to 10, where the increase is the largest. This indicates that the use of RIS enhances the signal reflection and focusing capabilities. By increasing the number of RIS reflector units, the signal propagation path can be better controlled and optimized, thereby improving communication efficiency. Under the same number of RIS reflector units, the proposed system scheme also outperforms the traditional system, demonstrating advantages in resource utilization and interference management, and exhibiting better communication performance.

[0175] Figure 11 This section compares the communication reachability rate of the two systems with the number of RIS reflector units L under three different transmit / receive antenna counts at t=1200ms. Figure 11 As can be seen, regardless of the number of antennas or the number of RIS reflector elements, the proposed system scheme significantly outperforms the comparative scheme in terms of communication performance. With the same number of antennas, the communication rate increase of the proposed system is significantly greater than that of the comparative system as the number of RIS reflector elements increases. Combined with the simulation results above, the proposed scheme significantly improves both the sensing and communication performance of smart grid systems.

[0176] The present invention also provides, for example Figure 12 The inspection vehicle tracking and communication optimization system shown includes the following modules:

[0177] The building unit is used to construct a RIS-ISAC-NOMA system framework for smart grid application scenarios. In non-line-of-sight (NLOS) scenarios, the reconfigurable smart metasurface RIS transforms the NLOS path into an equivalent virtual line-of-sight (LOS) path. The vehicle body of the inspection vehicle is used as a reflector to establish the communication and sensing models of the RIS-ISAC-NOMA system framework. Based on the communication and sensing models, the phase of the reconfigurable smart surface RIS reflector unit is dynamically adjusted to track and locate the moving inspection vehicle.

[0178] The analysis unit is used to define the state evolution model and tracking measurement model of the inspection vehicle, design the extended Kalman filter (EKF) algorithm in the non-line-of-sight (NLOS) scenario, and process the signals from the dual-function radar communication base station to the reconfigurable smart surface and from the reconfigurable smart surface to the inspection vehicle through the cascaded channel model. It obtains real-time channel state information (CSI) based on the echo signal reflected from the vehicle body, and analyzes the current motion state of the inspection vehicle based on the real-time channel state information.

[0179] The optimization unit is used to optimize the power allocation scheme of multiple inspection vehicles based on the non-orthogonal multiple access technology (NOMA). The objective function is solved using the CVX toolbox. KKT conditions are introduced as a feedback mechanism during the solution process. The KKT equations of the objective function are derived using the Lagrange multiplier method to verify the solution of the objective function. Simulation numerical results are also obtained through Monte Carlo simulation.

[0180] In summary, the RIS-ISAC-NOMA system framework for smart grid scenarios proposed in this invention aims to solve the mobility problem of inspection vehicles under NLOS conditions in densely populated building environments. By utilizing RIS to adjust the propagation path of ISAC signals in real time and designing a vehicle tracking and prediction method based on the EKF algorithm, efficient real-time vehicle perception and communication are achieved. Combined with NOMA technology, a power allocation optimization scheme for multiple inspection vehicles is proposed, further improving the system's communication rate and resource utilization efficiency. Simulation results also verify the advantages of the proposed system in terms of communication performance and resource utilization, showing that the scheme can significantly improve the overall system efficiency while reducing pilot signal overhead.

[0181] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0182] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0183] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0184] The above-described embodiments are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0185] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the tracking and communication of inspection vehicles, characterized in that, The method includes the following steps: Step S1: Construct a RIS-ISAC-NOMA system framework for smart grid application scenarios. In non-line-of-sight scenarios, a reconfigurable smart metasurface is used to transform the non-line-of-sight path into an equivalent virtual line-of-sight path. The vehicle body of the inspection vehicle is used as a reflector. The communication model and sensing model of the RIS-ISAC-NOMA system framework are established. Based on the communication model and the sensing model, the phase of the reconfigurable smart metasurface reflection unit is dynamically adjusted to track and locate the moving inspection vehicle. The communication model for establishing the RIS-ISAC-NOMA system framework includes: the communication signal received by the k-th inspection vehicle consists of the reconfigurable smart metasurface phase shift matrix, cascaded channel gain, and a prediction angle-based signal. The beamforming vector is expressed by a formula; and the channel gain from the reconfigurable smart metasurface to the inspection vehicle is... It is the predicted distance from the inspection vehicle to the reconfigurable smart metasurface. Related functions; In the sensing model, the echo signal received by the dual-function radar communication base station from the inspection vehicle is represented by a formula including the reflection coefficient, Doppler frequency shift, and transmission delay; and the Doppler frequency shift... and the transmission delay These are the predicted distances. and based on the predicted angle The function related to the calculated value; Step S2: Define the state evolution model and tracking measurement model of the inspection vehicle, design an extended Kalman filter algorithm for the non-line-of-sight scenario, and process the signals from the dual-function radar communication base station to the reconfigurable smart metasurface and from the reconfigurable smart metasurface to the inspection vehicle through a cascaded channel model. Obtain real-time channel state information based on the echo signal reflected from the vehicle body, and analyze the current motion state of the inspection vehicle based on the real-time channel state information. The state vector in the state evolution model of the inspection vehicle includes the distance, velocity, reflection coefficient, and angle of the inspection vehicle relative to the reconfigurable smart metasurface. Step S3: Optimize the power allocation scheme of multiple inspection vehicles based on non-orthogonal multiple access technology, and solve the objective function using the CVX toolbox. In the process of solving the objective function, a feedback mechanism is introduced, and the KKT equations of the objective function are derived using the Lagrange multiplier method to verify the solution of the objective function. Simulation numerical results are also obtained through Monte Carlo simulation.

2. The method according to claim 1, characterized in that, The RIS-ISAC-NOMA system framework includes: A dual-function radar communication base station DFRC-BS equipped with H transmitting antennas, a reconfigurable smart metasurface RIS with L reflective elements, and K inspection vehicle users VU. The antenna arrays of the dual-function radar communication base station, the reconfigurable smart metasurface, and the inspection vehicles all adopt a uniform linear array ULA. Each of the inspection vehicles (VU) carries M transceiver antennas. H, L, K, and M all represent positive integers greater than or equal to 2, but the values ​​of H, L, K, and M are different. DFRC-BS is an abbreviation for Dual Function Radar and Communication-BaseStation, representing a dual-function radar and communication base station. RIS is an abbreviation for Reflective Intelligent Surface, representing a reconfigurable intelligent metasurface. VU is an abbreviation for Vehicle Users, representing inspection vehicle users or inspection vehicles.

3. The method according to claim 1, characterized in that, The communication model for establishing the RIS-ISAC-NOMA system framework includes: Formula 1 represents the communication signal received by the k-th inspection vehicle at time n: (Official 1) in, The array gain factor representing the signal. This indicates that the mean is 0 and the variance is . Additive white Gaussian noise (AWGN). This indicates the total power of the DFRC-BS transmitted signal. This represents the power distribution coefficient of the k-th inspection vehicle at time n, satisfying... , This indicates that the communication signal sent to the kth inspection vehicle satisfies... ; Let represent the antenna array beamforming vector of the k-th inspection vehicle, where It is the angle value at time n predicted by the k-th inspection vehicle at time n-1; This represents the beamforming vector of the DFRC-BS antenna array. Describes the combined vector of the received signals, satisfying ; Represents the diagonal phase shift matrix of RIS. This represents the passive beamforming vector of the RIS. It is the first The phase shift of each reflecting unit satisfies ; This represents the communication channel gain from DFRC-BS to RIS, and the channel gain from RIS to the k-th inspection vehicle. This can be expressed as Formula 2: (Official 2) in, This represents the channel gain coefficient from RIS to the inspection vehicle. This represents the predicted distance of the k-th inspection vehicle to the RIS surface at time n.

4. The method according to claim 3, characterized in that, The communication model for establishing the RIS-ISAC-NOMA system framework also includes: In Non-Orthogonal Multiple Access (NOMA), the k-th inspection vehicle first detects and eliminates signal interference from all weaker inspection vehicles using Serial Interference Cancellation (SIC). Simultaneously, signals from all inspection vehicles with stronger signals than the k-th vehicle are considered noise. Based on Equation 1, at time n, the decoded signal of the k-th inspection vehicle... Expressed using Formula 3: (Official 3).

5. The method according to claim 3, characterized in that, The sensing model for establishing the RIS-ISAC-NOMA system framework includes: Formula 4 expresses the following: At the nth time, the dual-function radar communication base station DFRS-BS receives the echo signal from the kth inspection vehicle: (Official 4) in, Describes the combined vector of the received signals, satisfying , This indicates that the mean is 0 and the variance is . AWGN, This represents the reflection coefficient of the k-th vehicle, which is related to the radar cross section (RCS) and satisfies the following condition: , Let RCS represent the RCS of the k-th vehicle at the n-th time; assuming that the RCS remains constant during the movement of the inspection vehicle, i.e. ; , The Doppler frequency shift and echo signal transmission delay of the k-th vehicle at time n are respectively represented by Equations 5 and 6: (Official 5) (Official 6) in, This represents the straight-line distance from the DFRC-BS to the RIS surface. Indicates the signal carrier frequency. At the speed of light, , This indicates that the mean is 0 and the variances are respectively , The matched filter for filtering measurement noise can be calculated using Equation 7: (Official 7) Where G represents the matched filter gain of the filter. These represent constants related to system configuration, signal design, and specific signal processing algorithms.

6. The method according to claim 5, characterized in that, The state evolution model of the inspection vehicle is: A pre-defined inspection vehicle is used. A three-dimensional signal transmission model is projected onto the xy plane. The distance, angle, speed, and reflection coefficient of the inspection vehicle relative to the RIS at time n are respectively expressed as: , , and Based on the state of the inspection vehicle at time n-1, the state evolution model of the inspection vehicle is derived and expressed by Formula 8: (Official 8) in, Indicates the beam sampling time interval. Represent a The distance change of the inspection vehicle described herein satisfies The state vector of the inspection vehicle is defined as follows: The state evolution model of the inspection vehicle is represented by Equation 9: (Official 9).

7. The method according to claim 6, characterized in that, The tracking and measurement model for the inspection vehicle is: When the echo signal from the inspection vehicle reaches the DFRC-BS receiver, the dual-function radar communication base station first performs matched filtering on the received signal. After passing through the filter, the DFRC-BS can estimate the position of the k-th inspection vehicle at time n. and The estimated value obtained from the filter is used to compensate for the echo signal of the inspection vehicle. Formula 4 can be rewritten as follows: (Official 10) in, This indicates that the mean is 0 and the variance is . The normalized measurement noise is calculated using Formula 7. Based on Formulas 5, 6, and 10, the tracking and measurement model for a single inspection vehicle is represented by Formula 11: (Official 11) in, .

8. The method according to claim 7, characterized in that, The Extended Kalman Filter (EKF) algorithm is: After deriving the state evolution model and tracking measurement model of the inspection vehicle, the extended Kalman filter (EKF) recursive formula is further given and expressed by formula 12: (Official 12) in, Representing nonlinear equations Jacobian matrix, This represents the covariance matrix composed of the AWGNs of the state evolution model. Indicates the filter gain. This represents the predicted value indicating the status of the inspection vehicle. This represents the status update value. Represents the predicted covariance matrix. Represents the covariance update matrix. Representing nonlinear equations The Jacobian matrix is ​​represented by Equation 13: (Official 13) Jacobian matrix Expressed using Formula 14: (Official 14) in, (Formula 15), (Formula 16); By using the EKF algorithm to perform beam tracking and prediction on multiple inspection vehicles and allocating power to the inspection vehicles at each time step, the combined communication rate of the multiple inspection vehicles is maximized. The optimization problem is expressed by Equation 17: (Official 17) in, and These represent the prediction error thresholds, and Formula 17 can also be expressed as Formula 18: (Official 18).

9. The method according to claim 8, characterized in that, Construct the Lagrangian function for the optimization problem. Expressed using Formula 19: (Official 19) in, It is a power and constraint Lagrange multiplier. It is a non-negative power Lagrange multiplier. and These are the Lagrange multipliers for the angle error and distance error constraints, respectively. Since the constraints in Equation 18 are all inequality constraints, the KKT conditions must be satisfied in the Lagrange multiplier method. The KKT conditions are necessary conditions for solving the optimal solution at the extremum point of the objective function, and also satisfy the non-relaxation conditions of the inequality constraints and the conditions for satisfying the original constraint problem, resulting in Equation 19. The KKT condition equation system for Lagrange optimization is as follows. The feasibility of Lagrange duality is expressed by Equation 20: (Official 20) The gradient condition is expressed by Equation 21: (Official 21) The complementary relaxation conditions are expressed by formulas 22, 23, and 24: (Official 22) (Official 23) (Official 24) The second derivative of the objective function in Formula 21 is expressed by Formula 25: (Official 25) in, , Based on the aforementioned formula 25, it can be obtained that when and When, satisfy The objective function is convex within its range of values ​​and has a global optimum. The objective function can be solved using the CVX toolbox.

10. A patrol vehicle tracking and communication optimization system, used to implement the method as described in any one of claims 1-9, characterized in that, The system includes the following modules: The construction unit is used to build a RIS-ISAC-NOMA system framework for smart grid application scenarios. In non-line-of-sight scenarios, a reconfigurable smart metasurface is used to transform the non-line-of-sight path into an equivalent virtual line-of-sight path. The body of the inspection vehicle is used as a reflector to establish the communication model and sensing model of the RIS-ISAC-NOMA system framework. Based on the communication model and the sensing model, the phase of the reconfigurable smart metasurface reflection unit is dynamically adjusted to track and locate the moving inspection vehicle. The construction unit is also used to ensure that the communication signal received by the k-th inspection vehicle is composed of the reconfigurable intelligent metasurface phase shift matrix, cascaded channel gain, and a prediction angle-based signal. The beamforming vector is expressed by a formula; and the channel gain from the reconfigurable smart metasurface to the inspection vehicle is... It is the predicted distance from the inspection vehicle to the reconfigurable smart metasurface. The relevant functions; wherein, in the sensing model, the echo signal received by the dual-function radar communication base station from the inspection vehicle is represented by a formula including the reflection coefficient, Doppler frequency shift, and transmission delay; and, the Doppler frequency shift and the transmission delay These are the predicted distances. and based on the predicted angle The function related to the calculated value; The analysis unit is used to define the state evolution model and tracking measurement model of the inspection vehicle, design the extended Kalman filter algorithm in the non-line-of-sight scenario, and process the signals from the dual-function radar communication base station to the reconfigurable smart metasurface and from the reconfigurable smart metasurface to the inspection vehicle through a cascaded channel model. It obtains real-time channel state information based on the echo signal reflected from the inspection vehicle body, and analyzes the current motion state of the inspection vehicle based on the real-time channel state information. The state vector in the state evolution model of the inspection vehicle includes the distance, velocity, reflection coefficient, and angle of the inspection vehicle relative to the reconfigurable smart metasurface. The optimization unit is used to optimize the power allocation scheme of multiple inspection vehicles based on non-orthogonal multiple access technology, and solves the objective function using the CVX toolbox. In the process of solving the objective function, a feedback mechanism is introduced, and the KKT equations of the objective function are derived using the Lagrange multiplier method to verify the solution of the objective function. Simulation numerical results are also obtained through Monte Carlo simulation.

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