Inspection vehicle tracking and communication optimization method and system

Abstract

The invention relates to the technical field of intelligent power grids, in particular to an inspection vehicle tracking and communication optimization method and system, and the method comprises the steps: constructing an RIS-ISAC-NOMA system framework oriented to an intelligent power grid application scene, converting a non-line-of-sight NLOS path into an equivalent virtual line-of-sight LOS path through a reconfigurable intelligent metasurface RIS in a non-line-of-sight NLOS scene, and carrying out the tracking and communication optimization of an inspection vehicle through the virtual line-of-sight LOS path. A propagation path of an ISAC signal is adjusted in real time, and a vehicle tracking and predicting method based on an EKF algorithm is designed, so that efficient vehicle real-time sensing and communication are realized; a multi-inspection vehicle power distribution optimization scheme is provided in combination with the NOMA technology, the communication rate and the resource utilization efficiency of the system are further improved, meanwhile, the simulation result also verifies the advantages of the proposed system in the aspects of communication performance and resource utilization, and the overall efficiency of the system can be remarkably improved while the pilot signal overhead is reduced.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and more specifically to a patrol vehicle tracking and communication optimization method and system. Background Art

[0002] With the development of 6G mobile communication technology and the popularization of smart grid technology, the safety and economic benefits of power systems have been significantly improved. As an important part of smart grids, power inspection vehicles are widely used in various service scenarios. However, in large-scale smart grid environments, dense power equipment has limited the communication and monitoring paths of power inspection vehicles, especially under non-line-of-sight (NLOS) conditions. The traditional line-of-sight (LOS) path does not always exist, which limits the communication performance and sensing performance between the inspection vehicle and the base station.

[0003] Similar prior art includes a Chinese patent application with publication number CN117879738A, which proposes an information interaction environment analysis method for a multi-UAV collaborative command and control system. The multi-UAV collaborative command and control system includes a general command and control center and several UAVs; there are several reflection clusters in the interactive propagation environment, and the several reflection clusters include single-hop clusters and multi-hop clusters. The multi-hop clusters include a first cluster on the transmitting side that interacts with the UAV and a second cluster on the receiving side that interacts with the general command and control center. The invention constructs a channel impulse response matrix for the multi-UAV collaborative command and control system, and calculates and selects the channel impulse response matrix at different times as the information interaction environment analysis result of the multi-UAV collaborative command and control system. It solves the problem that the channel model of multi-UAV collaborative communication cannot simultaneously characterize the non-stationary characteristics and consistency characteristics of the channel in the spatial domain and time domain.

[0004] Similar prior art also includes a Chinese patent application with publication number CN118647045A, which discloses a SAR-based ISAC UAV joint trajectory, beam and resource optimization method, involving the field of UAV detection technology, including: constructing a UAV ground target imaging communication scenario, specifically including the UAV flying to the detection area SAR adopting a focusing working mode to image the target area, and adopting communication perception integration to transmit perception 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 perception energy consumption model; constructing constraints for the UAV flight process and ground target area imaging communication model; constructing the beamforming optimization problem in the UAV communication perception process and the resource optimization problem in the UAV target area perception process; simplifying the system objective function, using alternating iterations, and obtaining the UAV energy-optimized flight trajectory, beamforming and power allocation method. The invention provides a highly reliable algorithm to plan the path, beam and resource allocation method.

[0005] However, neither of the above two application documents considers how to use signals to track patrol vehicles in NLOS (non-line-of-sight) scenarios, and there are still deficiencies in the communication performance between base stations and patrol vehicles. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a patrol vehicle tracking and communication optimization method and system, based on the RIS-ISAC-NOMA system framework of the smart grid scenario, using the reconfigurable intelligent metasurface (RIS) to adjust the signal propagation path in real time, assisting the communication perception integration (ISAC) signal transmission, and using the extended Kalman filter (EKF) algorithm combined with the real-time channel state information (CSI) in the ISAC echo signal to achieve the tracking and prediction of the patrol vehicle position. At the same time, a multi-vehicle power allocation optimization scheme combined with non-orthogonal multiple access technology (NOMA) is proposed, using CVX to solve the objective function, and introducing the KKT condition as a feedback mechanism to avoid falling into the local optimum. Thereby improving the communication and monitoring efficiency of power patrol vehicles under smart grids.

[0007] In order to achieve the above-mentioned object of the invention, the present invention provides a patrol vehicle tracking and communication optimization method, which is implemented through the following steps:

[0008] Step S1: construct a RIS-ISAC-NOMA system framework for smart grid application scenarios. In a non-line-of-sight scenario, a reconfigurable smart metasurface is used to convert a non-line-of-sight path into an equivalent virtual line-of-sight path. The 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. The phase of the reconfigurable smart surface reflection unit is dynamically adjusted based on the communication model and the sensing model to track and locate the inspection vehicle in motion.

[0009] Step S2: defining a state evolution model and a tracking measurement model of the inspection vehicle, designing an extended Kalman filter algorithm in the non-line-of-sight scenario, and processing signals from a dual-function radar communication base station to the reconfigurable smart surface and from the reconfigurable smart surface to the inspection vehicle respectively through a cascade channel model, acquiring real-time channel state information based on an echo signal reflected from a body of the inspection vehicle, and analyzing a 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 use the CVX toolbox to solve the objective function. At the same time, a feedback mechanism is introduced in the process of solving the objective function, and the KKT equations of the objective function are derived using the Lagrange multiplier method to verify the solution of the objective function. The simulation numerical results are also obtained through Monte Carlo simulation.

[0011] As a preferred technical solution 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 intelligent metasurface RIS with L reflective elements, and K patrol vehicle users VU, wherein the antenna arrays of the dual-function radar communication base station, the reconfigurable intelligent metasurface and the patrol vehicle all adopt uniform linear arrays 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 have different values, DFRC-BS is the abbreviation of Dual Function Radar and Communication-Base Station, which means a dual-function radar communication base station, RIS is the abbreviation of Reflective Intelligent Surface, which means a reconfigurable intelligent metasurface, and VU is the abbreviation of Vehicle Users, which means an inspection vehicle user or an inspection vehicle.

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

[0015] Formula 1 represents the communication signal received by the kth inspection vehicle at the nth time:

[0016]

[0017] in, represents the array gain factor of the signal, z c The mean is 0 and the variance is Additive white Gaussian Noise (AWGN), P T represents the total power of the DFRC-BS transmitted signal, r k,n represents the power allocation coefficient of the kth inspection vehicle at the nth moment, satisfying s k (n) represents the communication signal sent to the kth inspection vehicle, satisfying represents the antenna array beamforming vector of the kth inspection vehicle, where is the angle value at time n predicted by the k-th inspection vehicle at time n-1; represents the beamforming vector of the DFRC-BS antenna array, w crepresents the combination vector of the received signal, satisfying ||w c || 2 =1; Θ = diag(v) represents the diagonal phase shift matrix of RIS, represents the passive beamforming vector of RIS, is the phase shift of the lth reflection unit, satisfying represents the communication channel gain from DFRC-BS to RIS, and the channel gain from RIS to the kth inspection vehicle α k,n , expressed as formula 2:

[0018]

[0019] in, represents the channel gain coefficient from RIS to the inspection vehicle, It represents the predicted value of the distance from the kth inspection vehicle to the RIS surface at time n.

[0020] As a preferred technical solution of the present invention, establishing the communication model of the RIS-ISAC-NOMA system framework also includes:

[0021] In the non-orthogonal multiple access technology NOMA, the k-th inspection vehicle first detects and eliminates the signal interference from all weak inspection vehicles through the interference cancellation technology (Serial Interference Cancellation, SIC) SIC, and at the same time regards the signals of all inspection vehicles stronger than the signal of the k-th inspection vehicle as noise. Based on the formula 1, at the nth time, the decoded signal SINR of the k-th inspection vehicle is k,n Expressed in formula 3:

[0022]

[0023] As a preferred technical solution of the present invention, the sensing model of the RIS-ISAC-NOMA system framework is established including:

[0024] Formula 4 indicates that at the nth moment, the dual-function radar communication base station DFRS-BS receives the echo signal from the kth inspection vehicle:

[0025]

[0026] Among them, f r represents the combination vector of the received signal, satisfying ||f r || 2 =1, z r The mean is 0 and the variance is AWGN, b k,nrepresents the reflection coefficient of the k-th vehicle, which is related to the radar cross section (RCS) and satisfies ε k,n represents the RCS of the k-th vehicle at the n-th moment; assuming that the corresponding RCS remains unchanged during the movement of the inspection vehicle, that is, ε k,1 =ε k,2 =... =ε k,n ;μ k,n ,t k,n denote the Doppler frequency shift and echo signal transmission delay of the k-th vehicle at the n-th time, respectively, and are expressed by Formula 5 and Formula 6:

[0027]

[0028] Among them, d BR represents the straight-line distance from DFRC-BS to RIS surface, f c represents the signal carrier frequency, c is the speed of light, z μ 、z t indicates that the mean is 0 and the variances are The matched filter measurement noise can be calculated by Equation 7:

[0029]

[0030] Where G represents the matched filter gain of the filter, a i Represents constants related to system configuration, signal design, and specific signal processing algorithms.

[0031] As a preferred technical solution of the present invention, the state evolution model of the inspection vehicle is:

[0032] A patrol vehicle is preset, and the three-dimensional signal transmission model is projected on the xy plane. The distance, angle, speed and reflection coefficient of the patrol vehicle relative to the RIS at the nth moment are represented as d n ,θ n 、b n and v n , combined with 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:

[0033]

[0034] Where ΔT represents the beam sampling time interval, Δd represents the distance change of the inspection vehicle within ΔT, and satisfies Δd = v n-1 ΔT, the state vector of the inspection vehicle is defined as x n =[d n,v n ,b n ,θ n ] T , the state evolution model of the inspection vehicle is expressed by Formula 9:

[0035]

[0036] As a preferred technical solution of the present invention, the tracking measurement model of the inspection vehicle is:

[0037] When the echo signal of the inspection vehicle reaches the DFRC-BS receiving end, the dual-function radar communication base station first performs matched filtering on the received signal. After passing through the filter, DFRC-BS can estimate the μ of the kth inspection vehicle at the nth moment. k,n and t k,n , and the estimated value obtained by the filter is used to compensate the echo signal of the inspection vehicle. The formula 4 can be rewritten as:

[0038]

[0039] in, The mean is 0 and the variance is The normalized measurement noise is calculated by the formula 7;

[0040] Based on the formula 5, the formula 6 and the formula 10, the tracking measurement model of a single inspection vehicle is expressed as

[0041] Formula 11 represents:

[0042]

[0043] in,

[0044] As a preferred technical solution of the present invention, the extended Kalman filter EKF algorithm is:

[0045] After the state evolution model and tracking measurement model of the inspection vehicle are derived, the extended Kalman filter EKF recursive formula is further given and expressed as formula 12:

[0046]

[0047] Where T(x) represents the Jacobian matrix of the nonlinear equation t(x), Q z =diag(z d ,z θ ,z b ,z v ) represents the covariance matrix composed of AWGN of the state evolution model, Kn represents the filter gain, represents the predicted value of the inspection vehicle state, Indicates the state update value, P n|n-1 represents the prediction covariance matrix, P n represents the covariance update matrix, Representing nonlinear equations The Jacobian matrix of is expressed as formula 13:

[0048]

[0049] The Jacobian matrix Expressed as formula 14:

[0050]

[0051] in,

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

[0053]

[0054] Among them, ε θ and ε d They respectively represent the prediction error thresholds, and the formula 17 can also be expressed as formula 18:

[0055]

[0056] As a preferred technical solution of the present invention, the Lagrangian function L(r 1,n ,r 2,n ,r 3,n ,λ1,λ2,...,λ 10 ) is expressed by formula 19:

[0057]

[0058] where λ is the Lagrange multiplier for the power and constraint, μ k is a Lagrange multiplier with non-negative power, n θ,k,n and n d,k,nare the Lagrange multipliers of the angle error and distance error constraints respectively. Since the constraints in formula 18 are all inequality constraints, the KKT condition needs to be satisfied in the Lagrange multiplier method. The KKT condition is a necessary condition for solving the optimal solution of the extreme point of the objective function. It is also the condition for satisfying the mutual non-relaxation condition of the inequality constraint and the original constraint problem. Formula 19 is obtained. The KKT condition equation group of Lagrange optimization is as follows:

[0059] Lagrangian dual feasibility is expressed by Equation 20:

[0060]

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

[0062]

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

[0064]

[0065] Among them, the second-order derivative of the objective function of Formula 21 is expressed by Formula 25:

[0066]

[0067] in, D k =∑ i∈K,i>k N i r i,n P T Based on formula 25, when r k,n ∈[0,1] and When, meet The objective function is convex within the value range and has a global optimal solution. The CVX toolbox can be used to solve the objective function.

[0068] The present invention also provides a patrol vehicle tracking and communication optimization system as described above, comprising the following modules:

[0069] A construction unit is used to construct a RIS-ISAC-NOMA system framework for smart grid application scenarios. In a non-line-of-sight scenario, a reconfigurable smart metasurface is used to convert a non-line-of-sight path into an equivalent virtual line-of-sight path, and the body of the inspection vehicle is used as a reflector. A communication model and a sensing model of the RIS-ISAC-NOMA system framework are established, and the phase of the reconfigurable smart surface reflection unit is dynamically adjusted based on the communication model and the sensing model to track and locate the inspection vehicle in motion.

[0070] An analysis unit is used to define a state evolution model and a tracking measurement model of the inspection vehicle, design an extended Kalman filter algorithm in the non-line-of-sight scenario, and process signals from a dual-function radar communication base station to the reconfigurable smart surface and from the reconfigurable smart surface to the inspection vehicle respectively through a cascade channel model, obtain real-time channel state information based on an echo signal reflected from a body of the inspection vehicle, and analyze a current motion state of the inspection vehicle based on the real-time channel state information;

[0071] The optimization unit is used to optimize the power allocation scheme of multiple inspection vehicles based on non-orthogonal multiple access technology, and use the CVX toolbox to solve the objective function. At the same time, a feedback mechanism is introduced in the process of solving the objective function, and the KKT equation group of the objective function is derived using the Lagrange multiplier method to verify the solution of the objective function, and the simulation numerical results are obtained through Monte Carlo simulation.

[0072] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0073] The technical solution of the present invention solves the problem of patrol vehicle mobility in NLOS conditions in dense power building environments by proposing a RIS-ISAC-NOMA system framework in a smart grid scenario. By using RIS to adjust the propagation path of the ISAC signal 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 patrol vehicles is proposed, which further improves the communication rate and resource utilization efficiency of the system. At the same time, the simulation results also verify the advantages of the proposed system in communication performance and resource utilization, indicating that the scheme can significantly improve the overall performance of the system while reducing the pilot signal overhead. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0075] Figure 1 A flowchart of the steps of a patrol vehicle tracking and communication optimization method in the present invention;

[0076] Figure 2 This is a diagram of the RIS-ISAC-NOMA system model in the present invention;

[0077] Figure 3 A schematic diagram of a three-dimensional vehicle state evolution model in the present invention;

[0078] Figure 4 This is a simulated “ISAC-V2X-NOMA” scenario diagram in the present invention;

[0079] Figure 5 It is an angle tracking performance diagram in the sensor performance analysis of the inspection vehicle 1 in the present invention;

[0080] Figure 6 It is a distance tracking performance diagram in the sensor performance analysis of the inspection vehicle 1 in the present invention;

[0081] Figure 7 It is an angle tracking performance diagram in the sensor performance analysis of the inspection vehicle 2 in the present invention;

[0082] Figure 8 It is a distance tracking performance diagram in the sensor performance analysis of the inspection vehicle 2 in the present invention;

[0083] Fig. 9 The communication performance analysis diagram of different schemes under different numbers of antennas in the present invention;

[0084] Fig.10 This is a diagram of the communication performance analysis of the inspection vehicle under different numbers of RIS reflector units in the present invention;

[0085] Fig.11 It is a communication performance analysis diagram under different schemes of the present invention;

[0086] Fig.12 The present invention is a structural diagram of the patrol vehicle tracking and communication optimization system. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0088] 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 only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0089] With the continuous development of the sixth generation mobile communication technology (6G), the popularization of smart grid technology is expected to improve the safety and economic benefits of future power systems. As an important part of the smart grid, power inspection vehicles have been widely used in various service scenarios. In a large-scale smart grid environment, the internal structure is relatively complex. Inspection is one of the daily tasks to ensure the safe and stable operation of power grid equipment. Through daily inspection of power grid equipment, recording the relevant parameters of the equipment, mastering the operation status of the equipment, and timely discovering the relevant safety hazards of the equipment, it is an important factor to ensure the safe and stable operation of the power grid. Integrated Sensing and Communications (ISAC) technology improves the efficiency of spectrum, energy and hardware by integrating communication and perception functions. Using ISAC signals, the Extended Kalman Filter (EKF) algorithm, factor graph and deep learning methods are used to realize the tracking and prediction of the inspection vehicle position by ISAC signals. Compared with the traditional pilot beam training method, the ISAC-based scheme shows significant advantages in reducing the pilot overhead of communication signals. In traditional positioning inspections, due to the poor penetration of high-frequency signals, the communication between the power inspection vehicle and the base station still relies on the line of sight (LOS) path to maintain high-quality communication and sensing performance. However, in multi-obstacle environments (such as dense power equipment scenarios), the LOS path does not always exist, which limits the communication and sensing performance between the power inspection vehicle and the base station.

[0090] In view of the above technical problems, the present invention proposes Figure 1 A patrol vehicle tracking and communication optimization method shown includes the following steps:

[0091] Step S1: Construct a RIS-ISAC-NOMA system framework for smart grid application scenarios. In non-line-of-sight scenarios, use a reconfigurable intelligent metasurface to convert non-line-of-sight paths into equivalent virtual line-of-sight paths. Use the body of the inspection vehicle as a reflector, establish the communication model and sensing model of the RIS-ISAC-NOMA system framework, and dynamically adjust the phase of the reconfigurable intelligent surface reflection unit based on the communication model and the sensing model to track and locate the moving inspection vehicle.

[0092] Specifically, the RIS-ISAC-NOMA system framework is a complex system that integrates reconfigurable intelligent surface (RIS), integrated sensing and communication (ISAC) and non-orthogonal multiple access (NOMA) technologies. In the smart grid scenario, a dual-function radar communication base station (DFRC-BS) is deployed, equipped with N transmitting antennas for simultaneous communication and radar detection; a reconfigurable intelligent metasurface (RIS) with L reflective units is introduced, and the reflective units can adjust the phase to reconfigure the propagation path of the signal. Through the real-time adjustment of RIS, the communication and positioning accuracy of patrol vehicles in complex power grid environments can be significantly improved. K patrol vehicle users (VUs) are incorporated into the system, and each VU is equipped with M transceiver antennas to achieve communication with the DFRC-BS. Under NLOS conditions, RIS is used to convert the signal path from NLOS to a virtual LOS path to improve the communication and sensing quality. By building a communication model and a sensing model, the phase of the RIS reflector unit is dynamically adjusted to track and locate the moving inspection vehicle. The RIS-ISAC-NOMA system can adapt to the dynamics of the vehicle and maintain the stability of the communication link and the accuracy of the sensing data.

[0093] Step S2: define the state evolution model and tracking measurement model of the inspection vehicle, design an 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 intelligent surface and from the reconfigurable intelligent surface to the inspection vehicle respectively through the cascade channel model, obtain real-time channel state information based on the echo signal reflected by the body of the inspection vehicle, and analyze the current motion state of the inspection vehicle based on the real-time channel state information.

[0094] Specifically, the state evolution model of the inspection vehicle is defined, including the distance, angle, speed and reflection coefficient of the vehicle relative to the RIS; the extended Kalman filter (EKF) algorithm is designed to process the state estimation of nonlinear systems. The EKF algorithm can accurately estimate the state of the inspection vehicle and maintain high positioning accuracy even in non-line-of-sight and dynamic environments. The signals from DFRC-BS to RIS and from RIS to the vehicle are processed through the cascade channel model, and the echo signal reflected by the inspection vehicle body is used to obtain real-time channel state information (CSI). The CSI is analyzed to determine the current motion state of the vehicle and predict the state of the inspection vehicle at the next moment.

[0095] Step S3: Optimize the power allocation scheme of multiple inspection vehicles based on non-orthogonal multiple access technology, and use the CVX toolbox to solve the objective function. At the same time, a feedback mechanism is introduced in the process of solving the objective function, and the KKT equations of the objective function are derived using the Lagrange multiplier method to verify the solution of the objective function. The simulation numerical results are also obtained through Monte Carlo simulation.

[0096] Specifically, combined with non-orthogonal multiple access technology (NOMA), a power allocation optimization scheme is designed for multiple inspection vehicles. The objective function is constructed and solved using the CVX toolbox to maximize the communication combined rate. The Karush-Kuhn-Tucker (KKT) condition is introduced as a feedback mechanism in the objective function solution process to ensure the global optimal solution. The KKT equations of the objective function are derived using the Lagrange multiplier method to verify the effectiveness of the solution. The KKT condition and the Lagrange multiplier method ensure fair and efficient power allocation in a complex multi-user environment. The simulation numerical results are obtained through Monte Carlo simulation to evaluate the system performance. The Monte Carlo simulation verifies the superiority of the proposed system scheme in terms of communication performance and resource utilization, especially in reducing pilot signal overhead and improving the overall system efficiency. The optimized power allocation scheme significantly improves the communication rate and resource utilization efficiency of the system. Among them, the communication combined rate refers to the total data transmission rate when multiple inspection vehicles (VUs) communicate simultaneously in the non-orthogonal multiple access technology (NOMA) environment. This rate is the sum of the transmission rates of all patrol vehicles and reflects the total communication capacity that the system can support at a specific moment.

[0097] Through the coordination of the above steps, the mobility problem of inspection vehicles in NLOS conditions in dense power building environments is solved. By using RIS to adjust the propagation path of ISAC signals in real time, a vehicle tracking and prediction method based on the EKF algorithm is designed to achieve efficient real-time vehicle perception and communication. In addition, combined with NOMA technology, a power allocation optimization scheme for multiple inspection vehicles is proposed, which further improves the communication rate and resource utilization efficiency of the system. At the same time, the simulation results also verify the advantages of the proposed system in communication performance and resource utilization. While reducing the pilot signal overhead, this scheme can also significantly improve the overall efficiency of the system.

[0098] Furthermore, the above RIS-ISAC-NOMA system framework includes:

[0099] A dual-function radar communication base station DFRC-BS equipped with H transmitting antennas, a reconfigurable intelligent metasurface RIS with L reflective elements, and K patrol vehicle users VU. The antenna arrays of the dual-function radar communication base station, the reconfigurable intelligent metasurface and the patrol vehicle all use uniform linear arrays ULA (Uniform Linear Array);

[0100] Among them, each patrol 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 the abbreviation of Dual Function Radar and Communication-BaseStation, which means dual-function radar communication base station. RIS is the abbreviation of Reflective Intelligent Surface, which means reconfigurable intelligent metasurface. VU is the abbreviation of Vehicle Users, which means patrol vehicle users or patrol vehicles.

[0101] Specifically, Figure 2 As shown in the figure, 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 area of ​​DFRC-BS, the deployment of RIS intelligent metasurface can convert the NLOS non-line-of-sight path into the LOS line-of-sight path, ensuring that the communicating and sensing VUs are all located in the LOS area of ​​RIS.

[0102] Furthermore, the communication model of the RIS-ISAC-NOMA system framework is established including:

[0103] Formula 1 represents the communication signal received by the kth inspection vehicle at the nth time:

[0104]

[0105] in, represents the array gain factor of the signal, z c The mean is 0 and the variance is Additive white Gaussian Noise (AWGN), P T represents the total power of the DFRC-BS transmitted signal, r k,n It represents the power allocation coefficient of the kth inspection vehicle at the nth moment, satisfying s k (n) represents the communication signal sent to the kth inspection vehicle, satisfying represents the antenna array beamforming vector of the kth inspection vehicle, where is the angle value at time n predicted by the kth inspection vehicle at time n-1; represents the beamforming vector of the DFRC-BS antenna array, w c represents the combination vector of the received signal, satisfying ||w c || 2 =1; Θ = diag(v) represents the diagonal phase shift matrix of RIS, represents the passive beamforming vector of RIS, is the phase shift of the lth reflection unit, satisfying represents the communication channel gain from DFRC-BS to RIS, and the channel gain from RIS to the kth inspection vehicle α k,n , expressed as formula 2:

[0106]

[0107] in, Indicates the channel gain coefficient from RIS to the inspection vehicle, It represents the predicted distance from the kth inspection vehicle to the RIS surface at time n.

[0108] Specifically, the inspection vehicles are constantly moving over time, and the downlink communication beams share the same channel through power domain multiplexing at the DFRC-BS. Considering that there are K mobile inspection vehicles in the system, at the receiving end of the inspection vehicle, the inspection vehicle user can decode the interfered received signal through SIC. As the vehicle moves, the DFRC-BS can allocate power to the vehicle in real time. Without loss of generality, it is assumed that the vehicle closest to the RIS has the strongest channel gain, while the vehicle farthest from the RIS has the weakest channel gain.

[0109] Unless otherwise specified, matrices are represented by bold capital letters (e.g., G), vectors are represented by bold lowercase letters (e.g., a, u), and scalars are represented by regular fonts (e.g., α). The subscripts involved in the formulas represent the current inspection vehicle and the time to which the inspection vehicle belongs (e.g., θ k,n represents the angle value of the kth inspection vehicle at the nth moment).

[0110] Furthermore, the communication model for establishing the RIS-ISAC-NOMA system framework also includes:

[0111] In the non-orthogonal multiple access technology NOMA, the k-th patrol vehicle first detects and eliminates the signal interference from all weak patrol vehicles through the interference cancellation technology (Serial Interference Cancellation, SIC). At the same time, the signals of all patrol vehicles stronger than the k-th patrol vehicle are regarded as noise. Based on formula 1, at the nth time, the decoded signal SINR of the k-th patrol vehicle is k,n Expressed in formula 3:

[0112]

[0113] Furthermore, the sensing model for establishing the RIS-ISAC-NOMA system framework includes:

[0114] Formula 4 indicates that at the nth moment, the dual-function radar communication base station DFRS-BS receives the echo signal from the kth patrol vehicle:

[0115]

[0116] Among them, f r represents the combination vector of the received signal, satisfying ||f r || 2 =1, z r The mean is 0 and the variance is AWGN, b k,n Represents the reflection coefficient of the kth vehicle. The reflection coefficient is related to the radar cross section (RCS) and satisfies ε k,n represents the RCS of the kth vehicle at the nth moment; assuming that the corresponding RCS remains unchanged during the movement of the inspection vehicle, that is, ε k,1 =ε k,2 =... =ε k,n ;μ k,n ,t k,n They represent the Doppler frequency shift and echo signal transmission delay of the kth vehicle at the nth moment, respectively, and are expressed by Formula 5 and Formula 6:

[0117]

[0118] Among them, d BR represents the straight-line distance from DFRC-BS to RIS surface, f c represents the signal carrier frequency, c is the speed of light, z μ 、z t indicates that the mean is 0 and the variances are The matched filter measurement noise can be calculated by Equation 7:

[0119]

[0120] Where G represents the matched filter gain of the filter, a i Represents constants related to system configuration, signal design, and specific signal processing algorithms.

[0121] Furthermore, the state evolution model of the inspection vehicle is:

[0122] like Figure 3 The schematic diagram of the three-dimensional motion state evolution model of the inspection vehicle relative to RIS and DFRC-BS is shown in the figure. This paper will study the NLOS vehicle movement from a two-dimensional plane perspective, taking a single inspection vehicle as an example:

[0123] A patrol vehicle is assumed, and the three-dimensional signal transmission model is projected on the xy plane. The distance, angle, speed and reflection coefficient of the patrol vehicle relative to the RIS at the nth moment are expressed as d n ,θ n 、b n and v n , combined with 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:

[0124]

[0125] Where ΔT represents the beam sampling time interval, Δd represents the distance change of the patrol vehicle within ΔT, and satisfies Δd = v n-1 ΔT, the state vector of the inspection vehicle is defined as x n =[d n ,v n ,b n ,θ n ] T , the state evolution model of the inspection vehicle is expressed by Formula 9:

[0126]

[0127] Specifically, this paper will use the EKF algorithm to track and predict the position of the inspection vehicle. EFK is a recursive algorithm commonly used for state estimation of nonlinear systems. It can handle nonlinear relationships in the system and perform well in dynamic environments. The core of the algorithm lies in the definition and application of the evolution model and measurement model of the system. As mentioned above, the evolution model describes how the motion state of the inspection vehicle changes over time, while the measurement model links the real-time CSI in the ISAC echo signal reflected by the vehicle body to the actual state of the vehicle, which will be described below. Through two sub-models, EKF can recursively update the vehicle's position estimate in a highly dynamic and nonlinear environment.

[0128] Furthermore, the tracking measurement model of the above inspection vehicle is:

[0129] When the echo signal of the inspection vehicle reaches the DFRC-BS receiving end, the dual-function radar communication base station first performs matched filtering on the received signal. After passing through the filter, DFRC-BS can estimate the μ of the kth inspection vehicle at the nth moment. k,n and t k,n , and use the estimated value obtained by the filter to compensate the echo signal of the inspection vehicle. Formula 4 can be rewritten as:

[0130]

[0131] in, The mean is 0 and the variance is The normalized measurement noise of is calculated by Equation 7;

[0132] Based on Formula 5, Formula 6, and Formula 10, the tracking measurement model of a single inspection vehicle is expressed by Formula 11:

[0133]

[0134] in,

[0135] Further, the extended Kalman filter EKF algorithm is:

[0136] 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 as formula 12:

[0137]

[0138] Where T(x) represents the Jacobian matrix of the nonlinear equation t(x), Q z =diag(z d ,z θ ,z b ,z v ) represents the covariance matrix composed of AWGN of the state evolution model, K n represents the filter gain, represents the predicted value of the inspection vehicle status, Indicates the state update value, P n|n-1 represents the prediction covariance matrix, P n represents the covariance update matrix, Representing nonlinear equations The Jacobian matrix of is expressed as formula 13:

[0139]

[0140]

[0141] The Jacobian matrix Expressed as formula 14:

[0142]

[0143] in,

[0144] By using the EKF algorithm to track and predict the beams of multiple inspection vehicles and to allocate power to the inspection vehicles at each moment, the combined communication rate of multiple inspection vehicles can be maximized. The optimization problem is expressed as Formula 17:

[0145]

[0146] Among them, ε θ and ε d They represent the prediction error thresholds respectively, and Formula 17 can also be expressed as Formula 18:

[0147]

[0148] Furthermore, the KKT conditional equations corresponding to the objective function are obtained by the Lagrange multiplier method. The optimization problem is solved using the CVX toolbox, and the KKT conditional equations are substituted into the obtained solution as a feedback mechanism for verification to prevent falling into the local optimum. Construct the Lagrangian function L(r 1,n ,r 2,n ,r 3,n ,λ1,λ2,...,λ 10 ) is expressed by formula 19:

[0149]

[0150] where λ is the Lagrange multiplier for the power and constraint, μ k is a Lagrange multiplier with non-negative power, n θ,k,n and n d,k,n are the Lagrange multipliers of the angle error and distance error constraints respectively. Since the constraints in formula 18 are all inequality constraints, the KKT condition needs to be satisfied in the Lagrange multiplier method. The KKT condition is a necessary condition for solving the optimal solution of the extreme point of the objective function. It is also the condition for satisfying the mutual non-relaxation condition of the inequality constraint and the original constraint problem. Formula 19 is obtained. The KKT condition equation group of Lagrange optimization is as follows:

[0151] Lagrangian dual feasibility is expressed by Equation 20:

[0152]

[0153] The gradient condition is expressed by formula 21:

[0154]

[0155] The complementary relaxation conditions are expressed by Equation 22, Equation 23, and Equation 24:

[0156]

[0157] Among them, the second-order derivative of the objective function of Formula 21 is expressed by Formula 25:

[0158]

[0159] in, D k =Σ i∈K,i>k N i r i,n P T , based on formula 25, when r k,n ∈[0,1] and When, meet The objective function is convex within the value range and has a global optimal solution. The CVX toolbox can be used to solve the objective function.

[0160] Specifically, the specific process of power distribution of multiple inspection vehicles can be obtained from the above, as shown in Table 1:

[0161] Table 1 Inspection vehicle power allocation algorithm flow

[0162]

[0163] The following are simulation results that 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 The simulation diagram of "RIS-ISAC-NOMA" in the smart grid scenario is shown. Unless otherwise specified, the simulation parameters used are all default parameters. Set K = 2, f c =30GHz, ΔT=0.02s, d BR =15m,a m =6.7′10 -5 , a t =200, G=100,ε θ =1°, ε d =0.5m. The measurement noise during the evolution of the inspection vehicle state is set to s d =0.1m,s θ =0.01°, s b =0.05, s v =0.25m / s. In order to evaluate the performance of the proposed system, we conducted a comparative experiment between the system mentioned above and the traditional smart grid scenario system. The traditional model uses orthogonal multiple access (OMA) technology, and the power allocation is divided equally according to the number of patrol vehicle users, that is, r k,n =(1 / K)P TThe system will not involve power allocation optimization of the communication rate of the inspection vehicle, and the other parameters are configured according to the default parameters. The initial state parameters of the inspection vehicle are shown in Table 2. All simulation results are 10 3 The result is obtained by averaging the Monte Carlo simulation experiments.

[0164] Table 2 Vehicle initial parameters

[0165]

[0166]

[0167] Analysis of system sensing performance under multiple inspection vehicles:

[0168] Figure 5 , Figure 6 , Figure 7 and Figure 8 The changes of the root mean square error (RMSE) of the angle and distance of vehicle 1 and vehicle 2 over time with different numbers of RIS reflection units (L = 5, 10, 15, 20) and without RIS are shown respectively. It can be seen from the figure that the RMSE of the angle and distance of the system without RIS is significantly higher than that with RIS. After the introduction of RIS, the RMSE of the angle and distance gradually decreases with the increase of the number of RIS reflection units. When L = 20, the system shows the optimal angle and distance estimation accuracy. The introduction of RIS effectively converts the NLOS path into the LOS path, significantly improving the sensing and tracking accuracy of the ISAC signal.

[0169] Analysis of system communication performance under multiple inspection vehicles:

[0170] Fig. 9 It shows that L = 10, P T =5dB, the variation of the achievable combined communication rate of the proposed system and the traditional system over time under different numbers of antennas. From the curve trend, when the inspection vehicle approaches the RIS surface, the combined communication rate increases; when the inspection vehicle leaves, the combined communication rate decreases. In an environment with the same number of antennas and dense inspection vehicles and limited resources, the achievable combined communication rate of the system proposed in this paper is significantly higher than that of the traditional system. Under different numbers of antennas, the achievable combined communication rate of the two systems increases significantly with the increase in the number of antennas. The system proposed in this paper shows superior communication performance through more efficient resource utilization and interference management.

[0171] Fig.10 Compared with N t =M=8,P T=5dB, the variation of the communication achievable combined rate of the two systems with different numbers of RIS reflector units over time. With the increase of the number of RIS reflector units, the communication achievable combined rate of the two systems has been significantly improved, especially when the number of RIS reflector units increases from 5 to 10, the increase is the largest. This shows that the use of RIS can enhance the reflection and focusing capabilities of the signal. By increasing the number of RIS surfaces, the signal propagation path can be better controlled and optimized, thereby improving communication efficiency. With the same number of RIS reflector units, the system solution proposed in this paper is also better than the traditional system, indicating that the system solution in this paper has advantages in resource utilization and interference management, and exhibits better communication performance.

[0172] Fig.11 It shows the relationship between the communication achievable combined rate of the two systems and the number of RIS reflection units L under three different numbers of transmitting and receiving antennas at t = 1200ms. Fig.11 It can be seen that the proposed system is significantly better than the comparative scheme in terms of communication performance, whether under different numbers of antennas or different numbers of RIS reflection units. Under the same number of antennas, as the number of RIS reflection units increases, the communication rate increase of the proposed system is significantly better than that of the comparative system. Combined with the above simulation results, it can be seen that the proposed scheme has significantly improved the sensing performance and communication performance of the smart grid system.

[0173] The present invention also provides a Fig.12 The inspection vehicle tracking and communication optimization system shown includes the following modules:

[0174] A construction unit is used to construct a RIS-ISAC-NOMA system framework for smart grid application scenarios. In a non-line-of-sight NLOS scenario, a reconfigurable intelligent metasurface RIS is used to convert the non-line-of-sight NLOS path into an equivalent virtual line-of-sight LOS path. The body of the inspection vehicle is used as a reflector, and the communication model and sensing model of the RIS-ISAC-NOMA system framework are established. The phase of the reconfigurable intelligent surface RIS reflection unit is dynamically adjusted based on the communication model and the sensing model to track and locate the moving inspection vehicle.

[0175] 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 intelligent surface and from the reconfigurable intelligent surface to the inspection vehicle through the cascade channel model, obtain the real-time channel state information CSI 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;

[0176] The optimization unit is used to optimize the power allocation scheme of multiple inspection vehicles based on the non-orthogonal multiple access technology NOMA, and use the CVX toolbox to solve the objective function. At the same time, the KKT condition is introduced as a feedback mechanism in the process of solving the objective function, and the KKT equations of the objective function are derived using the Lagrange multiplier method to verify the solution of the objective function. The simulation numerical results are also obtained through Monte Carlo simulation.

[0177] In summary, the RIS-ISAC-NOMA system framework in the smart grid scenario proposed in this invention aims to solve the mobility problem of patrol vehicles under NLOS conditions in dense power building environments. By using RIS to adjust the propagation path of the ISAC signal 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 patrol vehicles is proposed, which further improves the communication rate and resource utilization efficiency of the system. At the same time, the simulation results also verify the advantages of the proposed system in communication performance and resource utilization, indicating that the scheme can significantly improve the overall performance of the system while reducing the pilot signal overhead.

[0178] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0179] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database 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. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0180] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned 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.

[0181] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0182] The above are only preferred embodiments of the present invention and are 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 in the protection scope of the present invention.

Claims

1. A patrol vehicle tracking and communication optimization method, characterized in that: The method comprises the following steps: Step S1: construct a RIS-ISAC-NOMA system framework for smart grid application scenarios. In a non-line-of-sight scenario, a reconfigurable smart metasurface is used to convert a non-line-of-sight path into an equivalent virtual line-of-sight path. The 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. The phase of the reconfigurable smart surface reflection unit is dynamically adjusted based on the communication model and the sensing model to track and locate the inspection vehicle in motion. Step S2: defining a state evolution model and a tracking measurement model of the inspection vehicle, designing an extended Kalman filter algorithm in the non-line-of-sight scenario, and processing signals from a dual-function radar communication base station to the reconfigurable smart surface and from the reconfigurable smart surface to the inspection vehicle respectively through a cascade channel model, acquiring real-time channel state information based on an echo signal reflected from a body of the inspection vehicle, and analyzing a current motion state of the inspection vehicle based on the real-time channel state information; Step S3: Optimize the power allocation scheme of multiple inspection vehicles based on non-orthogonal multiple access technology, and use the CVX toolbox to solve the objective function. At the same time, a feedback mechanism is introduced in the process of solving the objective function, and the KKT equations of the objective function are derived using the Lagrange multiplier method to verify the solution of the objective function. The 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 intelligent metasurface RIS with L reflective elements, and K patrol vehicle users VU, wherein the antenna arrays of the dual-function radar communication base station, the reconfigurable intelligent metasurface and the patrol vehicle all adopt uniform linear arrays ULA; Among them, each of the patrol 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 have different values, DFRC-BS is the abbreviation of Dual Function Radar and Communication-BaseStation, which means a dual-function radar communication base station, RIS is the abbreviation of Reflective Intelligent Surface, which means a reconfigurable intelligent metasurface, and VU is the abbreviation of Vehicle Users, which means a patrol vehicle user or a patrol vehicle.

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 kth inspection vehicle at the nth time: in, represents the array gain factor of the signal, z c The mean is 0 and the variance is Additive white Gaussian Noise (AWGN), P T represents the total power of the DFRC-BS transmitted signal, r k,n represents the power allocation coefficient of the kth inspection vehicle at the nth moment, satisfying Represents the communication signal sent to the kth inspection vehicle, satisfying represents the antenna array beamforming vector of the kth inspection vehicle, where is the angle value at time n predicted by the k-th inspection vehicle at time n-1; represents the beamforming vector of the DFRC-BS antenna array, w c represents the combination vector of the received signal, satisfying ||w c || 2 =1; Θ = diag(v) represents the diagonal phase shift matrix of RIS, represents the passive beamforming vector of RIS, is the phase shift of the lth reflection unit, satisfying represents the communication channel gain from DFRC-BS to RIS, and the channel gain from RIS to the kth inspection vehicle α k,n , expressed as formula 2: in, represents the channel gain coefficient from RIS to the inspection vehicle, It represents the predicted value of the distance from the kth 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 the non-orthogonal multiple access technology NOMA, the k-th inspection vehicle first detects and eliminates the signal interference from all weak inspection vehicles through the interference cancellation technology (Serial Interference Cancellation, SIC) SIC, and at the same time regards the signals of all inspection vehicles stronger than the signal of the k-th inspection vehicle as noise. Based on the formula 1, at the nth time, the decoded signal SINR of the k-th inspection vehicle is k,n Expressed in formula 3:

5. The method according to claim 3, characterized in that: The sensing model for building the RIS-ISAC-NOMA system framework includes: Formula 4 indicates that at the nth moment, the dual-function radar communication base station DFRS-BS receives the echo signal from the kth inspection vehicle: Among them, f r represents the combination vector of the received signal, satisfying ||f r || 2 =1, z r The mean is 0 and the variance is AWGN, b k,n represents the reflection coefficient of the k-th vehicle, which is related to the radar cross section (RCS) and satisfies ε k,n represents the RCS of the k-th vehicle at the n-th moment; assuming that the corresponding RCS remains unchanged during the movement of the inspection vehicle, that is, ε k,1 =ε k,2 =... =ε k,n ;μ k,n ,t k,n denote the Doppler frequency shift and echo signal transmission delay of the k-th vehicle at the n-th time, respectively, and are expressed by Formula 5 and Formula 6: Among them, d BR represents the straight-line distance from DFRC-BS to RIS surface, f c represents the signal carrier frequency, c is the speed of light, z μ 、z t indicates that the mean is 0 and the variances are The matched filter measurement noise can be calculated by Equation 7: Where G represents the matched filter gain of the filter, a i Represents 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 patrol vehicle is preset, and the three-dimensional signal transmission model is projected on the xy plane. The distance, angle, speed and reflection coefficient of the patrol vehicle relative to the RIS at the nth moment are represented as d n ,θ n , b n and v n , combined with 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: Where ΔT represents the beam sampling time interval, Δd represents the distance change of the inspection vehicle within ΔT, and satisfies Δd = v n-1 ΔT, the state vector of the inspection vehicle is defined as x n =[d n ,v n ,b n ,θ n ] T , the state evolution model of the inspection vehicle is expressed by Formula 9:

7. The method according to claim 6, characterized in that The tracking measurement model of the inspection vehicle is: When the echo signal of the inspection vehicle reaches the DFRC-BS receiving end, the dual-function radar communication base station first performs matched filtering on the received signal. After passing through the filter, DFRC-BS can estimate the μ of the kth inspection vehicle at the nth moment. k,n and t k,n , and the estimated value obtained by the filter is used to compensate the echo signal of the inspection vehicle. The formula 4 can be rewritten as: in, The mean is 0 and the variance is The normalized measurement noise is calculated by the formula 7; Based on Formula 5, Formula 6 and Formula 10, the tracking measurement model of a single inspection vehicle is expressed by Formula 11: in, 8. The method according to claim 7, characterized in that The extended Kalman filter EKF algorithm is: After the state evolution model and tracking measurement model of the inspection vehicle are derived, the extended Kalman filter EKF recursive formula is further given and expressed as formula 12: Where T(x) represents the Jacobian matrix of the nonlinear equation t(x), Q z =diag(z d ,z θ ,z b ,z v ) represents the covariance matrix composed of AWGN of the state evolution model, K n represents the filter gain, represents the predicted value of the inspection vehicle state, Indicates the state update value, P n|n-1 represents the prediction covariance matrix, P n represents the covariance update matrix, Representing nonlinear equations The Jacobian matrix of is expressed as formula 13: (Formula 13) The Jacobian matrix Expressed as formula 14: in, By using the EKF algorithm to perform beam tracking and prediction on multiple inspection vehicles, and to allocate power to the inspection vehicles at each moment, the communication combined rate under the state of multiple inspection vehicles is maximized. The optimization problem is expressed by formula 17: Among them, ε θ and ε d They respectively represent the prediction error thresholds, and the formula 17 can also be expressed as formula 18:

9. The method according to claim 8, characterized in that Construct the Lagrangian function L(r 1,n ,r 2,n ,r 3,n ,λ1,λ2,...,λ 10 ) is expressed by formula 19: where λ is the Lagrange multiplier for the power and constraint, μ k is a Lagrange multiplier with non-negative power, n θ,k,n and n d,k,n are the Lagrange multipliers of the angle error and distance error constraints respectively. Since the constraints in formula 18 are all inequality constraints, the KKT condition needs to be satisfied in the Lagrange multiplier method. The KKT condition is a necessary condition for solving the optimal solution of the extreme point of the objective function. It is also the condition for satisfying the mutual non-relaxation condition of the inequality constraint and the original constraint problem. Formula 19 is obtained. The KKT condition equation group of Lagrange optimization is as follows: Lagrangian dual feasibility is expressed by Equation 20: The gradient condition is expressed by Equation 21: The complementary relaxation conditions are expressed by Equation 22, Equation 23, and Equation 24: Among them, the second-order derivative of the objective function of Formula 21 is expressed by Formula 25: in, D k =∑ i∈K,i>k N i r i,n P T Based on formula 25, when r k,n ∈[0,1] and When, meet The objective function is convex within the value range and has a global optimal solution. The CVX toolbox can be used to solve the objective function.

10. A patrol vehicle tracking and communication optimization system, used to implement the method according to any one of claims 1 to 9, characterized in that: The system includes the following modules: A construction unit is used to construct a RIS-ISAC-NOMA system framework for smart grid application scenarios. In a non-line-of-sight scenario, a reconfigurable smart metasurface is used to convert a non-line-of-sight path into an equivalent virtual line-of-sight path, and the body of the inspection vehicle is used as a reflector. A communication model and a sensing model of the RIS-ISAC-NOMA system framework are established, and the phase of the reconfigurable smart surface reflection unit is dynamically adjusted based on the communication model and the sensing model to track and locate the inspection vehicle in motion. An analysis unit is used to define a state evolution model and a tracking measurement model of the inspection vehicle, design an extended Kalman filter algorithm in the non-line-of-sight scenario, and process signals from a dual-function radar communication base station to the reconfigurable smart surface and from the reconfigurable smart surface to the inspection vehicle respectively through a cascade channel model, obtain real-time channel state information based on an echo signal reflected from a body of the inspection vehicle, and analyze a current motion state of the inspection vehicle based on the real-time channel state information; The optimization unit is used to optimize the power allocation scheme of multiple inspection vehicles based on non-orthogonal multiple access technology, and use the CVX toolbox to solve the objective function. At the same time, a feedback mechanism is introduced in the process of solving the objective function, and the KKT equation group of the objective function is derived using the Lagrange multiplier method to verify the solution of the objective function, and the simulation numerical results are obtained through Monte Carlo simulation.

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