A dual-RIS-assisted sensing-and-communication integrated system security resource allocation optimization method

Abstract

This invention belongs to the field of 6G communication technology, specifically relating to a method for optimizing the security resource allocation of a dual-RIS-assisted sensing integrated system. The method includes constructing a dual-RIS-assisted sensing integrated system; under constraints such as sensing beam pattern gain, user service quality, dual-RIS phase shift, and base station maximum transmit power, constructing a resource allocation optimization problem with the goal of maximizing system security energy efficiency; using the Tinkerbach method combined with variable substitution techniques to transform the coupled and non-convex resource allocation optimization problem into a tractable uncoupled problem; and employing alternating optimization, continuous convex approximation, and semi-definite relaxation methods to efficiently solve the tractable uncoupled problem, obtaining the optimal security resource allocation scheme. This invention can effectively improve the overall security energy efficiency of the sensing integrated system, achieving an effective balance between security energy efficiency and sensing performance.

Description

Technical Field

[0001] This invention belongs to the field of 6G communication technology, specifically relating to a method for optimizing the allocation of secure resources in a dual RIS-assisted integrated sensing system. Background Technology

[0002] Sensor integration is a key technology for 6G communication, but it still faces many challenges, such as scarce spectrum resources, susceptibility to transmission link obstruction, and security threats. Reconfigurable smart metasurfaces (RIS) can effectively alleviate some link problems by optimizing the wireless channel environment. However, existing single RIS systems struggle to balance long-range sensing and network edge coverage, and also pose certain security risks, such as eavesdropping attacks.

[0003] Current research largely focuses on single-RIS scenarios, with insufficient exploration of the potential of dual-RIS architectures in achieving sensor collaboration and enhancing edge coverage. Particularly, there is a lack of in-depth discussion on how to collaboratively optimize communication security, sensing accuracy, and system energy efficiency, making it difficult to adapt to the evolving demands of future 6G networks for green communication and high security. Therefore, researching security and energy efficiency-driven resource allocation strategies for dual-RIS-assisted sensor-integrated systems has significant theoretical and practical value. Summary of the Invention

[0004] To address the above problems, this invention provides a method for optimizing the allocation of secure resources in a dual-RIS-assisted integrated sensing system, characterized by the following steps:

[0005] S1. Construct a dual-RIS assisted sensing integrated system, which includes one base station integrating communication and sensing functions. One user, one sensing target, one eavesdropping user, and two reconfigurable smart metasurfaces RIS1 and RIS2. RIS1 supports both communication and sensing functions, while RIS2 is used to enhance edge coverage; the base station is configured with... The RIS1 and RIS2 each have a transmit antenna. and One reflection unit; at the same time, RIS1 and RIS2 are deployed close to the base station and the user, respectively, ignoring the reflection path between RIS2 and the sensing target, and only retaining the relevant channels of RIS1;

[0006] S2. Under the constraints of sensing beam pattern gain, user service quality, dual RIS phase shift, and base station maximum transmit power, construct a resource allocation optimization problem with the goal of maximizing system security and energy efficiency;

[0007] S3. By using the Tinkelbach method combined with variable substitution techniques, the coupled and non-convex resource allocation optimization problem is transformed into a tractable non-coupled problem;

[0008] S4. By employing alternating optimization, continuous convex approximation, and semidefinite relaxation methods, the optimal safe resource allocation scheme is obtained by efficiently solving the tractable uncoupled problem.

[0009] The beneficial effects of this invention are:

[0010] In a dual-RIS-assisted sensing and communication integrated system, this invention constructs a resource allocation optimization model aimed at maximizing system safety and energy efficiency by jointly considering sensing beam pattern gain, user service quality, dual-RIS phase shift constraints, and maximum transmit power constraints. To address the non-convex coupling constraints in this model, methods such as alternating optimization, continuous convex approximation, and semi-definite relaxation are employed to progressively transform the original problem into a sequence of decoupled and tractable convex optimization subproblems. Furthermore, through the alternating optimization framework, this problem is decomposed into two subproblems: active beamforming at the base station and passive beamforming with dual-RIS. Approximate optimal transmit beam and RIS phase shift configurations are obtained through iterative solutions.

[0011] Furthermore, this invention proposes a collaborative and differentiated dual-RIS deployment strategy: RIS1 simultaneously serves communication and sensing signal reflection, while RIS2 focuses on enhancing coverage for edge users. In scenarios such as emergency communication with complex obstructions or weak coverage, this scheme can effectively improve the system's security and energy efficiency, and enhance its adaptability to harsh channel environments. Attached Figure Description

[0012] Figure 1 This is a flowchart of the method of the present invention;

[0013] Figure 2 This is an overall flowchart of the present invention;

[0014] Figure 3 This is a comparison chart of the safety and energy efficiency of the present invention and different methods under different maximum transmission powers. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Please see Figures 1-2 This invention provides a method for optimizing the allocation of security resources in a dual-RIS assisted sensing integrated system, comprising the following steps:

[0017] S1. Construct a dual-RIS assisted integrated sensing system.

[0018] Preferably, the dual-RIS assisted sensing integrated system constructed in this embodiment of the invention includes a base station integrating communication and sensing functions. One user, one sensing target, one eavesdropping user, and two reconfigurable smart metasurfaces RIS1 and RIS2. RIS1 supports both communication and sensing functions, while RIS2 is used to enhance edge coverage; the base station is configured with... The RIS1 and RIS2 each have a transmit antenna. and There are two reflection units. RIS1 is deployed near the base station, within the line-of-sight link between the base station and the target, and has established effective reflection links with some users. Therefore, RIS1 simultaneously supports legitimate user communication and assists target perception. RIS2 is deployed in edge areas or areas with severe channel obstruction, mainly to enhance the effective channel gain between the base station and users. In perception modeling, to simplify the system and focus on critical links, the method of this invention ignores the reflection path between RIS2 and the target, retaining only the reflection channel related to RIS1. By co-optimizing the phase shift of the two RISs, the channel gain of legitimate users can be effectively improved, thereby enhancing the overall security efficiency of the system.

[0019] S2. Under the constraints of sensing beam pattern gain, user service quality, dual RIS phase shift, and base station maximum transmit power, construct a resource allocation optimization problem with the goal of maximizing system security and energy efficiency.

[0020] Preferably, the resource allocation optimization problem of the present invention is expressed as:

[0021] , ,

[0022] ,

[0023] ,

[0024] ,

[0025] ,

[0026] In the formula, Indicates the first A set of RIS phase shift vectors Indicates the first The first RIS One reflective unit, This indicates the total number of reflective units; specifically, Representing the first of RIS1 One reflective unit, Representing the first of RIS2 One reflective unit; Indicates the first The communication beamforming vector of a legitimate user Indicates the first =1,2,…, The root receiving antenna is used to sense the target's sensing beamforming vector. Indicates the number of receiving antennas for the sensing target; Indicates the first The confidentiality rate of a legitimate user This indicates that the eavesdropper is targeting the first The eavesdropping rate of a legitimate user; Indicates the first The safe speed for a legitimate user Indicates when hour, ; Indicates the first Minimum confidentiality rate for a legitimate user The beam pattern gain represents the beam pattern gain of the target being sensed. The threshold representing the beam pattern gain of the perceived target; This indicates the maximum transmit power of the base station. Represents the L2 norm; This indicates the total power consumption of the system. This indicates the efficiency of the transmitting power amplifier. This indicates the fixed circuit power consumption of the system.

[0027] Preferably, in a dual RIS-assisted sensing integrated system,

[0028] No. The confidentiality rate of a legitimate user , Indicates the first Signal-to-interference-to-noise ratio for each legitimate user:

[0029] ,

[0030] In the formula, Indicates the base station to the The equivalent channel for a legitimate user Indicates the base station to the A direct channel for legitimate users This indicates that the base station travels through the RIS1 reflection channel to the... A legitimate user's channel, This represents the phase shift matrix of RIS1, used to control the phase of the reflected wave. This represents the channel matrix from the base station to RIS1. This indicates that the base station travels through the RIS2 reflection channel to the... A legitimate user, This represents the phase shift matrix of RIS2, used to control the phase of the reflected wave. This represents the channel matrix from the base station to RIS2. This represents the cascaded reflection channel matrix from RIS1 to RIS2, used to describe the signal path transmitted from RIS1 to RIS2; Indicates the first Background noise power of a legitimate user;

[0031] eavesdroppers targeting the first eavesdropping rate of a legitimate user , This indicates that the eavesdropper is targeting the first Signal-to-interference-to-noise ratio for each legitimate user:

[0032] ,

[0033] In the formula, This represents the equivalent channel from the base station to the eavesdropper. This indicates the direct channel from the base station to the eavesdropper. This indicates the reflection channel from the base station to the eavesdropper via RIS1. This indicates the reflection channel from the base station to the eavesdropper via RIS2; This indicates the background noise power at the location of the eavesdropper.

[0034] Preferably, the beam pattern gain of the sensed target ,in:

[0035] ,

[0036] ,

[0037] In the formula, Indicates the signal amplitude. This represents the equivalent channel vector from the base station to the sensing target. Indicates the base station transmitting signals The autocorrelation function, , Indicates statistical expectation. Indicates a legitimate user The signal Indicates the perceived target The signal received by the root antenna, Indicates the base station antenna array in the transmission direction Phase distribution on, Indicates the transmission direction of the base station. Indicates the distance between base station antennas. Indicates the carrier wavelength; The receiving antenna array representing the target being sensed is in the direction Phase distribution on, Indicates the perceived direction of the target. This indicates the distance between the sensing target antennas.

[0038] S3. By using the Tinkelbach method combined with variable substitution techniques, the coupled and non-convex resource allocation optimization problem is transformed into a tractable non-coupled problem.

[0039] Preferably, step S3 includes:

[0040] S31. Introducing Auxiliary Variables The total system security rate can be converted into At the same time, auxiliary variables are introduced. The constraints thus transform the resource allocation optimization problem into problem P2:

[0041] ,

[0042] ,

[0043] ,

[0044] ,

[0045] ,

[0046] ,

[0047] At this point, the objective function is still in fractional form, making problem P2 difficult to solve.

[0048] S32. Using the Tinkelbach method, the objective function in problem P2 is transformed from fractional form to subtractive form, resulting in the tractable uncoupled problem P3:

[0049] ,

[0050] ,

[0051] ,

[0052] ,

[0053] ,

[0054] ,

[0055] In the formula, This indicates the system's equivalent energy efficiency.

[0056] In question P3 as well as All are non-convex. Subsequent optimizations will be used to solve the communication and sensing beamforming problems, as well as the dual RIS phase shift problem, respectively.

[0057] S4. By employing alternating optimization, continuous convex approximation, and semidefinite relaxation methods, the optimal safe resource allocation scheme is obtained by efficiently solving the tractable uncoupled problem.

[0058] Preferably, step S4 includes:

[0059] S41. Set the maximum number of inner iterations. and the maximum number of outer iterations Initialize inner index =0, outer index =0, auxiliary variable ;

[0060] S42. In the In the next inner iteration, the phase shift vector is fixed. , Solve the active beamforming problem of the base station to obtain the communication beamforming vector. Sensing beamforming vector and auxiliary variables ;

[0061] S43. Fixed phase shift vector Communication beamforming vector Sensing beamforming vector and auxiliary variables Solving for the phase shift of RIS1 yields... ;

[0062] S44. Fixed phase shift vector Communication beamforming vector Sensing beamforming vector and auxiliary variables Solving for the phase shift of RIS2 yields... ;

[0063] S45 Update If the beam and dual RIS phase shift converge or If yes, proceed to step S46; otherwise, proceed to step S42.

[0064] S46. Calculate the total security rate of the system. and total system power consumption and calculate ;like or If the output is optimal, then the optimal communication beamforming vector, optimal sensing beamforming vector, and optimal dual RIS phase shift vector will be output; otherwise, update the output vector. , Then, step S42 is executed; where, Indicates the convergence precision, used to determine whether the iteration has converged; Indicates the first The equivalent energy efficiency of the system is obtained by the outermost iteration calculation.

[0065] Preferably, obtaining the active beamforming problem of the base station includes:

[0066] definition The lack of correlation between communication and sensing beams can be clearly identified. .Will Substituting into the formula for beam pattern gain, the beam pattern gain can be simplified to... Therefore, constraints Can be equivalently converted Furthermore, it is possible to... Reconstructed into a second-order cone programming form: Based on this, the minimum signal-to-interference-plus-noise ratio is defined. This can then constrain Rewritten in the following equivalent form: Substitute the signal-to-interference-plus-noise ratio (SINR) The calculation formula will ultimately constrain This can be transformed into the following second-order cone programming form:

[0067] .

[0068] definition This indicates that the eavesdropper is decoding the first... When a legitimate user's information is accessed, the total interference caused by the communication beams and sensing beams of other legitimate users. In the [number]th [year]... In the next continuous convex approximation inner layer iteration, the disturbance term used in the current inner layer iteration is defined as follows: Its calculation depends on the beam variables obtained from the previous inner layer iteration, i.e. Therefore, the eavesdropper's signal-to-interference-plus-noise ratio can be rewritten as: .right Using the continuous convex approximation method, we can obtain its th... The innermost iteration has the following tractable lower bound: .in, Indicates the first The signal-to-interference-plus-noise ratio (SIR) of the eavesdropper in the next inner iteration. Therefore, the eavesdropper's rate satisfies ,constraint It can be converted into .

[0069] Therefore, the active beamforming problem for base stations regarding communication and sensing beams can be described as follows:

[0070]

[0071]

[0072] question It is a convex optimization problem concerning communication and sensing beams, which can be solved using the CVX toolbox.

[0073] Preferably, a fixed communication beamforming vector is used. and sensing beamforming vector The following dual RIS passive beamforming problem can be obtained:

[0074]

[0075] Indicates the first The signal-to-interference-plus-noise ratio (SIR) of a legitimate user depends on the phase shift vectors of the two RIS. and Previously written in the expression as This is for simplification, but in optimization problems, the user signal-to-interference-plus-noise ratio (SIR) is actually directly related to the beamforming of the RIS. Therefore, when performing the second-order cone programming (SOCP) transformation or convex approximation, it is necessary to explicitly define... Writing about and function form This ensures that the optimization variables cover all parameters that affect the user's SINR, thus keeping the constraints strictly consistent with the physical model.

[0076] Due to the phase shift vector and In cascaded links, strong coupling exists, and direct joint optimization leads to high problem complexity and difficulty in solving. Therefore, this invention adopts a two-layer alternating update strategy: first, fix... renew Then fix renew .

[0077] Specifically, the alternating optimization solution process includes:

[0078] Regarding the phase shift solution for RIS1:

[0079] fixed Afterwards, legitimate users The equivalent channel includes a direct link, a RIS1 reflection link, and a dual RIS cascaded link, among which... The relevant components are only the RIS1 reflection link and the dual RIS cascade link. This utilizes the core identities of diagonal matrices and vectors. These two items can be decoupled into two separate items concerning... The linear form of .

[0080] A reflection link channel containing RIS1 can be converted into Similarly, a dual-RIS cascaded link channel can be converted into... .in, , , . This indicates that the RIS1 reflection link is for legitimate users. The equivalent channel matrix; This indicates that the RIS1 to RIS2 cascade link is for legitimate users. The equivalent channel matrix, whose value is related to the phase shift vector of RIS2. Related.

[0081] Therefore, the base station to the The equivalent channel for a legitimate user can be written as: .in, , This indicates a combination of RIS1 and RIS2 for users. The equivalent channel matrix, similarly, the equivalent channel from the base station to the eavesdropper can be represented by the index... Replace with To obtain is to be satisfied , , .

[0082] Therefore, for any fixed beam ,have .in, , Therefore, its power term Transform into a standard quadratic form structure:

[0083] (1)

[0084] in, , , .

[0085] For subsequent processing of non-convex constraints, define... , For any quadratic form , Represents the constant term , Representing matrix terms , Represents vector terms Define a homogeneous matrix. :

[0086] (2)

[0087] Therefore, the above quadratic form can be transformed into:

[0088] (3)

[0089] Therefore, phase shift The constraints can be transformed into: , , .

[0090] After fixing the beam, homogenization using (1)-(3) for each power term can reduce the constraints. Transform into

[0091]

[0092] in, and Corresponding items and The homogeneous matrix, from (1) structure.

[0093] Constraining the second-order cone programming Transformed into RIS1 phase shift vector Affine forms include:

[0094] First, expand the left-hand side of the second-order cone programming constraint and separate it from the constraints. irrelevant items Similarly, the core identities of diagonal matrices and vectors can be used. It can Transform into in, , .

[0095] definition as well as Based on matrix transformations, we can obtain Therefore, constraints It can be converted into .

[0096] To handle constraints First, this constraint is transformed into the form of an equivalent signal-to-interference-plus-noise ratio, namely:

[0097] (4)

[0098] The right-hand side of the above equation with respect to the phase shift variable Exponential nonconvex coupling will directly lead to changes in phase shift. The problem is difficult to handle. Therefore, in the first... In this iteration, a conservative interior-point approximation strategy is adopted: that is, at the previous phase point... At this point, using the current value of the equivalent signal-to-interference-plus-noise ratio threshold, i.e. Therefore, equation (4) can be transformed into .

[0099] The above treatment ensures the feasibility of the constraints in each alternating optimization iteration and can reduce the constraints. This is transformed into an equivalent signal-to-interference-plus-noise ratio (SINNR) threshold constraint, which can then be further written in the form of a linear trace inequality and a semidefinite programming problem. Similarly, by homogenizing each power term using equations (1)-(3), the constraints can be transformed into... This can be transformed into an equivalent semidefinite programming form, with the following specific expression:

[0100]

[0101] In summary, fixed beam and ,about The optimization problem can be transformed into a problem about The optimization problem is as follows:

[0102]

[0103] Therefore, regarding phase shift The problem is transformed into a semidefinite programming problem, which can be solved using the CVX toolbox.

[0104] Regarding the phase shift solution for RIS2:

[0105] First, fix Afterwards, legitimate users The equivalent channels include direct links, RIS2 reflection links, and dual RIS cascaded links, among which... The only relevant ones are the RIS2 reflection link and the dual RIS cascade link. Following the same processing as when solving the RIS1 phase shift, we can obtain: ,in, , , This indicates that the RIS2 reflection link is for the user. The equivalent channel matrix, This indicates the RIS1 to RIS2 cascade link for users. The equivalent channel matrix depends on the RIS1 phase shift vector. Similarly, the equivalent channel from the base station to the eavesdropper satisfies... , Just add the subscript Replace with That's it. Similarly, for any beam... The power term can be Write it as a standard quadratic form in, , , . , .

[0106] Similarly, definition , For any quadratic form Define a homogeneous matrix Therefore, the above quadratic form can be transformed into Therefore, regarding the RIS2 phase shift... The constraints can be transformed into: , , Therefore, constraints Ultimately transformed into in, and Corresponding items and A homogeneous matrix.

[0107] definition Similarly, by adopting a conservative interior point approximation strategy, the constraints can be reduced. Transform into linear trace inequalities .

[0108] In summary, fixed communication and sensing beamforming and phase shifting... Regarding phase shift The optimization problem can be transformed into a problem concerning the phase shift lifting matrix. The optimization problem is as follows:

[0109]

[0110] Therefore, regarding phase shift The problem is transformed into a semidefinite programming problem, which can be solved using the CVX toolbox.

[0111] In some embodiments, the path loss model is assumed to be: ,in, Indicates the reference distance Path loss at time, This represents the distance between any two devices. This represents the path loss index. The path loss index is set as follows: the path loss factor from BS to RIS1 and RIS2 is 2.2, from RIS2 to the user is 2.2, and for other links it is 2.5. The base station, RIS1, RIS2, and the sensing target are located at coordinates (0,0,3)m, (50,0,3)m, (100,20,3)m, and (60,20,3)m, respectively. All legitimate users and eavesdroppers are randomly distributed within a circular area with a center of (110,20,1.5)m and a radius of 3m. Other simulation parameters are given in Table 1.

[0112] Table 1 Simulation Parameter Table

[0113]

[0114] In this embodiment, Figure 3 The method of this invention and different comparative methods are shown at the maximum transmit power of the base station. The system's security and energy efficiency performance under varying conditions. As clearly shown in the figure, the method proposed in this invention outperforms other traditional schemes across the entire transmit power range. This is mainly due to the proposed alternating optimization framework, which, through coordinated optimization of dual RIS reflection phase shift and base station transmit beamforming, can more effectively utilize additional transmit power to improve the confidentiality rate of legitimate users while simultaneously suppressing the received signal quality of eavesdroppers, thereby achieving overall optimization of system security and energy efficiency, while satisfying perception performance constraints.

[0115] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing security resource allocation of a dual-RIS-assisted integrated sensing and communication system, characterized in that, Includes the following steps: S1. Construct a dual-RIS assisted sensing integrated system, which includes one base station integrating communication and sensing functions. One user, one sensing target, one eavesdropping user, and two reconfigurable smart metasurfaces RIS1 and RIS2. RIS1 supports both communication and sensing functions, while RIS2 is used to enhance edge coverage; the base station is configured with... The RIS1 and RIS2 each have a transmit antenna. and One reflection unit; at the same time, RIS1 and RIS2 are deployed close to the base station and the user, respectively, ignoring the reflection path between RIS2 and the sensing target, and only retaining the relevant channels of RIS1; S2. Under the constraints of sensing beam pattern gain, user service quality, dual RIS phase shift, and base station maximum transmit power, a resource allocation optimization problem is constructed with the goal of maximizing system security and energy efficiency, expressed as: ， ， ， ， ， ， In the formula, Indicates the first A set of RIS phase shift vectors Indicates the first The first RIS One reflective unit, This indicates the total number of reflective units; specifically, Representing the first of RIS1 One reflective unit, Representing the first of RIS2 One reflective unit; Indicates the first The communication beamforming vector of a legitimate user Indicates the first The root receiving antenna is used to sense the target's sensing beamforming vector. Indicates the number of receiving antennas for the sensing target; Indicates the first The confidentiality rate of a legitimate user This indicates that the eavesdropper is targeting the first The eavesdropping rate of a legitimate user; Indicates the first The safe speed for a legitimate user Indicates when hour, ; Indicates the first Minimum confidentiality rate for a legitimate user The beam pattern gain represents the beam pattern gain of the target being sensed. The threshold representing the beam pattern gain of the perceived target; This indicates the maximum transmit power of the base station. Represents the L2 norm; This indicates the total power consumption of the system. This indicates the efficiency of the transmitting power amplifier. This indicates the fixed circuit power consumption of the system; S3. By using the Tinkelbach method combined with variable substitution techniques, the coupled and non-convex resource allocation optimization problem is transformed into a tractable non-coupled problem; S4. By employing alternating optimization, continuous convex approximation, and semidefinite relaxation methods, the optimal safe resource allocation scheme is obtained by efficiently solving the tractable uncoupled problem.

2. The method for optimizing the allocation of security resources in a dual-RIS assisted sensing integrated system according to claim 1, characterized in that, In a dual RIS-assisted integrated sensing system No. The confidentiality rate of a legitimate user , Indicates the first Signal-to-interference-to-noise ratio for each legitimate user: ， In the formula, Indicates the base station to the The equivalent channel for a legitimate user Indicates the base station to the A direct channel for legitimate users This indicates that the base station travels through the RIS1 reflection channel to the... A legitimate user's channel, This represents the phase shift matrix of RIS1, used to control the phase of the reflected wave. This represents the channel matrix from the base station to RIS1. This indicates that the base station travels through the RIS2 reflection channel to the... A legitimate user, This represents the phase shift matrix of RIS2, used to control the phase of the reflected wave. This represents the channel matrix from the base station to RIS2. This represents the cascaded reflection channel matrix from RIS1 to RIS2, used to describe the signal path transmitted from RIS1 to RIS2; Indicates the first Background noise power of a legitimate user; eavesdroppers targeting the first eavesdropping rate of a legitimate user , This indicates that the eavesdropper is targeting the first Signal-to-interference-to-noise ratio for each legitimate user: ， In the formula, This represents the equivalent channel from the base station to the eavesdropper. This indicates the direct channel from the base station to the eavesdropper. This indicates the reflection channel from the base station to the eavesdropper via RIS1. This indicates the reflection channel from the base station to the eavesdropper via RIS2; This indicates the background noise power at the location of the eavesdropper.

3. The method for optimizing the allocation of security resources in a dual-RIS assisted sensing integrated system according to claim 1, characterized in that, Beam pattern gain of the target ,in: ， ， In the formula, Indicates the signal amplitude. This represents the equivalent channel vector from the base station to the sensing target. Indicates the base station transmitting signals The autocorrelation function, , Indicates statistical expectation. Indicates a legitimate user The signal Indicates the perceived target The signal received by the root antenna, Indicates the base station antenna array in the transmission direction Phase distribution on, Indicates the transmission direction of the base station. Indicates the distance between base station antennas. Indicates the carrier wavelength; The receiving antenna array representing the target being sensed is in the direction Phase distribution on, Indicates the perceived direction of the target. This indicates the distance between the sensing target antennas.

4. The method for optimizing the allocation of security resources in a dual-RIS assisted sensing integrated system according to claim 1, characterized in that, Step S3 includes: S31. Introducing Auxiliary Variables The resource allocation optimization problem is transformed into problem P2: ， ， ， ， ， ， S32. Using the Tinkelbach method, the objective function in problem P2 is transformed from fractional form to subtractive form, resulting in the tractable uncoupled problem P3: ， ， ， ， ， ， In the formula, This indicates the system's equivalent energy efficiency.

5. The method for optimizing the allocation of security resources in a dual-RIS assisted sensing integrated system according to claim 1, characterized in that, Step S4 includes: S41. Set the maximum number of inner iterations. and the maximum number of outer iterations Initialize inner index =0, outer index =0, auxiliary variable ; S42. In the In the next inner iteration, the phase shift vector is fixed. , Solve the active beamforming problem of the base station to obtain the communication beamforming vector. Sensing beamforming vector and auxiliary variables ; S43. Fixed phase shift vector Communication beamforming vector Sensing beamforming vector and auxiliary variables Solving for the phase shift of RIS1 yields... ; S44. Fixed phase shift vector Communication beamforming vector Sensing beamforming vector and auxiliary variables Solving for the phase shift of RIS2 yields... ; S45 Update If the beam and dual RIS phase shift converge or If yes, proceed to step S46; otherwise, proceed to step S42. S46. Calculate the total security rate of the system. and total system power consumption and calculate ;like or If the output is optimal, then the optimal communication beamforming vector, optimal sensing beamforming vector, and optimal dual RIS phase shift vector will be output; otherwise, update the output vector. , Then, step S42 is executed; where, Indicates the convergence accuracy. Indicates the first The equivalent energy efficiency of the system is obtained by the outermost iteration calculation.

6. The method for optimizing the allocation of security resources in a dual-RIS assisted sensing integrated system according to claim 5, characterized in that, The active beamforming problem of a base station can be described as follows: ， ， ， ， ， In the formula, Indicates the system's equivalent energy efficiency. Represents auxiliary variables. Indicates the base station to the The equivalent channel for a legitimate user This represents the minimum signal-to-interference-plus-noise ratio (SINR), used to ensure that the rate of each legitimate user meets the minimum quality of service requirement, corresponding to the lower bound in the constraint conditions. This represents the equivalent channel quantity from the base station to the sensing target. , Indicates the first The signal-to-interference-plus-noise ratio of the eavesdropper in the next inner iteration.

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