Active monitoring method, system and equipment for suspicious air computing system and medium

Abstract

The invention relates to the technical field of air computing, and discloses an active monitoring method, system and device for a suspicious air computing system and a medium. A suspicious receiver in the suspicious air computing system collects illegal information through a suspicious sensor, and an active monitor monitors and actively interferes the suspicious receiver to collect the illegal information. The intelligent reflecting surface enhances a monitoring channel for the active monitor to monitor illegal information collected by the suspicious receiver and weakens a suspicious channel for the suspicious receiver to collect the illegal information; constructing an optimization problem of minimizing the calculation mean square error of the active listener under the constraint of the calculation mean square error of the suspicious receiver, the transmitting power of the suspicious sensor, the active interference power of the active listener and the phase matrix of the intelligent reflecting surface; and solving the optimization problem to obtain the target receiving power of the active listener, the target active interference power of the active listener and the target phase matrix of the intelligent reflecting surface so as to realize active monitoring of the suspicious air computing system.

Description

Technical Field

[0001] The present invention relates to the field of airborne computing technology, and in particular to an active monitoring method, system, device and medium for a suspicious airborne computing system. Background Art

[0002] Nowadays, a large number of devices including sensors, coordinators and drones are deployed in various application scenarios of the Internet of Things (IoT), such as smart agriculture, smart transportation and smart cities. Unlike traditional cellular networks that only focus on single user information, the IoT focuses on the information fusion of a large number of devices, such as the sum and average of all sensor data. Air Computing (AirComp) is considered to be an efficient information fusion technology that utilizes the superposition characteristics of multiple access channels to realize the simultaneous collection and calculation of all sensor data, achieving the goals of low latency and high spectrum efficiency.

[0003] Wireless technology is a double-edged sword. While it brings convenience to people, it also brings new threats. Wireless technology can be used for legal communications, but it can also be used by criminals or terrorists to carry out illegal sabotage and threaten public safety. Therefore, in order to protect people's safety and take timely intervention measures, it is extremely necessary to conduct legal monitoring. At present, legal monitoring can be roughly divided into two categories: passive monitoring and active monitoring. In passive monitoring, the listener only eavesdrops on information. When the listener's channel is worse than that of the suspicious user, the monitoring may fail. Active monitoring uses a duplexer to simultaneously eavesdrop and send active interference to increase the success rate of eavesdropping.

[0004] However, the monitoring of airborne computing systems is currently limited to traditional communication systems, and the monitoring of airborne computing systems is still in the absence of monitoring. Airborne computing technology may also be used illegally, for example, the enemy can use sensors to collect battlefield information, and commercial spies can use sensors to steal commercial secrets, etc. Therefore, monitoring of airborne computing systems is extremely necessary. Summary of the invention

[0005] The purpose of the present invention is to provide a method, system, device and medium for actively monitoring a suspicious airborne computing system, which can realize active monitoring of the airborne computing system.

[0006] To solve the above technical problems, an embodiment of the present invention provides an active monitoring method for a suspicious aerial computing system, which is applied to an active monitoring system, wherein the suspicious aerial computing system includes a suspicious receiver and a suspicious sensor, and the active monitoring system includes an active monitor and an intelligent reflective surface, and any two of the suspicious receiver, the suspicious sensor, the active monitor, and the intelligent reflective surface are connected;

[0007] Among them, the suspicious receiver is used to collect illegal information through the suspicious sensor, the active listener is used to monitor and actively interfere with the suspicious receiver to collect illegal information, and the active listener has self-interference, and the intelligent reflective surface is used to enhance the monitoring channel of the active listener to monitor the suspicious receiver to collect illegal information and weaken the suspicious channel of the suspicious receiver to collect illegal information;

[0008] The method comprises:

[0009] According to the channel between the suspicious sensor and the suspicious receiver, the channel between the intelligent reflective surface and the suspicious receiver, the channel between the suspicious sensor and the active listener, the channel between the suspicious sensor, the intelligent reflective surface and the active listener, the channel between the active listener and the suspicious receiver, and the self-interference channel of the active listener, an active monitoring model of the suspicious air computing system is constructed;

[0010] According to the active monitoring model of the suspicious airborne computing system, the calculated mean square error of the suspicious receiver when collecting illegal information through the suspicious sensor is obtained through the transmission power of the suspicious sensor, the receiving power of the suspicious receiver and the phase matrix of the intelligent reflection surface;

[0011] According to the active monitoring model of the suspicious airborne computing system, the calculated mean square error of the active monitor when monitoring the suspicious receiver to collect illegal information is obtained through the receiving power of the active monitor, the active interference power of the active monitor and the phase matrix of the intelligent reflection surface;

[0012] Under the constraints of the calculated mean square error of the suspicious receiver, the transmit power of the suspicious sensor, the active interference power of the active listener, and the phase matrix of the smart reflective surface, an optimization problem with the goal of minimizing the calculated mean square error of the active listener is constructed.

[0013] The optimization problem with the goal of minimizing the mean square error of the active listener is solved to obtain the target receiving power of the active listener, the target active interference power of the active listener, and the target phase matrix of the smart reflective surface;

[0014] The active monitor is enabled to monitor the suspicious receiver with the target receiving power to collect illegal information, and actively interfere with the suspicious receiver with the target active interference power to collect illegal information. The intelligent reflecting surface is enabled to enhance the monitoring channel of the active monitor and weaken the suspicious channel of the suspicious receiver with the target phase matrix, so as to actively monitor the suspicious receiver in the suspicious air computing system.

[0015] In some optional embodiments, solving the optimization problem with the goal of minimizing the mean square error of active listeners includes:

[0016] The optimization problem with the goal of minimizing the mean square error of the calculation of the active listener is decomposed to obtain multiple sub-optimization problems with the goal of minimizing the mean square error of the calculation of the active listener, and the receiving power of the suspicious receiver and the active listener, the transmitting power of the suspicious sensor, the active interference power of the active listener, and the phase matrix of the smart reflection surface are used as optimization variables;

[0017] Solve each sub-optimization problem.

[0018] In some optional embodiments, solving each sub-optimization problem includes:

[0019] For each sub-optimization problem, the alternating continuous rank-one constraint relaxation iterative SROCR algorithm is used to solve it.

[0020] In some optional embodiments, the calculated mean square error of the suspicious receiver when collecting illegal information through the suspicious sensor is expressed by the following formula:

[0021]

[0022] The mean square error calculated by the active monitor when monitoring the suspicious receiver to collect illegal information is expressed by the following formula:

[0023]

[0024] In the formula, Indicates the signal misalignment error of the suspected receiver, represents the error caused by the active interference of the active listener, represents the error caused by the noise of the suspicious receiver, Indicates the signal misalignment error of the active listener, represents the error caused by the full-duplex self-interference of the active listener, represents the error caused by the noise of the active listener, Φ=diag(φ1,φ2,…,φ N ) represents the phase matrix of the smart reflector, n∈{1,2,…,N},θ n ∈[0,2π), represents the adjustment phase of the nth reflection unit of the smart reflection surface, u D and u L Respectively represent the receiving power of the suspicious receiver and the active listener, v k represents the transmission power of the kth suspicious sensor, w represents the active interference power of the active listener, and denote the noise power of the suspicious receiver and the active eavesdropper respectively, and denote the channels from the kth suspicious sensor to the suspicious receiver, the smart reflective surface, and the active listener, respectively. ID and H IL They represent the channels from the intelligent reflector surface to the suspicious receiver and the active listener, respectively. LD represents the channel from the active listener to the suspicious receiver, H LL Represents the full-duplex self-interference channel of the active listener.

[0025] In some optional embodiments, the optimization problem with the goal of minimizing the mean square error of the calculation of the active listener is expressed by the following formula:

[0026]

[0027] In the formula, MSE L represents the calculated mean square error of the active listener, C1 represents the constraint condition of the calculated mean square error of the suspicious receiver, ε represents the preset concealment constraint threshold, which is a constant, C2 represents the constraint condition of the transmit power of the suspicious sensor, P k represents the maximum transmission power of the kth suspicious sensor, C3 represents the constraint condition of the active interference power of the active listener, P L represents the maximum transmission power of the active listener, C4 represents the constraint condition of the phase matrix of the smart reflection surface, and n∈{1,2,…,N} represents the nth reflection unit of the smart reflection surface.

[0028] In some optional embodiments, before solving each sub-optimization problem, the method further includes:

[0029] By giving the transmit power of the suspicious sensor, the active interference power of the active listener, and the phase matrix of the smart reflective surface, the closed expressions of the optimal solutions for the receiving power of the suspicious receiver and the active listener are obtained, as shown in the following formulas:

[0030]

[0031]

[0032] In the formula, represents the equivalent channel from the kth suspicious sensor to the suspicious receiver, represents the equivalent channel from the kth suspicious sensor to the active listener;

[0033] By giving the receiving power of the suspicious receiver and the active listener, the active interference power of the active listener, and the phase matrix of the smart reflective surface, the Lagrange multiplier method is used to obtain the closed expression of the optimal solution of the transmit power of the suspicious sensor, as shown in the following formula:

[0034]

[0035] In the formula, represents the Lagrange multiplier corresponding to constraint C2;

[0036] By giving the receiving power of the suspicious receiver and the active listener, the transmitting power of the suspicious sensor and the phase matrix of the smart reflective surface, the closed expression of the optimal solution of the active interference power of the active listener is obtained, as shown in the following formula:

[0037]

[0038] In the formula, Represents the matrix (A D / ε1) -1 A L The eigenvector corresponding to the minimum eigenvalue of

[0039] By giving the receiving power of the suspicious receiver and the active listener, the transmitting power of the suspicious sensor and the active interference power of the active listener, the closed expression of the optimal solution of the phase matrix of the smart reflection surface is obtained, as shown in the following formula:

[0040]

[0041] In the formula, F=diag(f) represents a diagonal matrix with f as the diagonal element, t represents an auxiliary variable,

[0042] c D =([C D ] 1,1 ,[C D ] 2,2 ,…,[C D ] N,N ), c L =([C L ] 1,1 ,[C L ] 2,2 ,…,[C L ] N,N ), [A] n,n represents the nth diagonal element of matrix A,

[0043]

[0044] By optimizing the solution of P8 opt By eigendecomposition, we can get gmax For X opt Maximum eigenvalue λ max The corresponding feature vector.

[0045] In some optional embodiments, for each sub-optimization problem, using an alternating continuous rank-one constraint relaxation iteration SROCR algorithm to solve the problem includes:

[0046] Set the initial value of the calculated mean square error of the active listener The initial value of the number of iterations i=1, the iteration accuracy δ and the maximum number of iterations I max , and set the initial value of each optimization variable; the optimization variable includes the transmission power of the suspicious sensor The active interference power w of the active listener (0) And the initial value of the phase matrix of the smart reflector Φ (0) ;

[0047] Based on the value of the optimization variable in the previous iteration Φ (i-1) , w (i-1) , according to the closed expressions of the optimal solutions of the receiving powers of suspicious receivers and active listeners, the optimal receiving power of suspicious receivers is obtained respectively. and the optimal active listener receiving power

[0048] Based on the value of the optimization variable in the previous iteration Φ (i-1) ,w (i-1) , The optimal transmit power of the suspicious sensor is obtained based on the closed expression of the optimal solution of the transmit power of the suspicious sensor.

[0049] Based on the value of the optimization variable in the previous iteration Φ (i-1) , According to the closed expression of the optimal solution of the active interference power of the active listener, the optimal active interference power w of the active listener is obtained (i) ;

[0050] Based on the value of the optimization variable in the previous iteration w (i) , according to the closed expression of the optimal solution of the phase matrix of the smart reflector, the optimal phase matrix Φ of the smart reflector is obtained (i) ;

[0051] Increase the number of iterations i = i + 1;

[0052] If i>I max or Then stop the iteration.

[0053] The embodiment of the present invention further provides an active monitoring system for a suspicious aerial computing system, wherein the suspicious aerial computing system includes a suspicious receiver and a suspicious sensor, and the active monitoring system includes an active monitor and an intelligent reflective surface, and any two of the suspicious receiver, the suspicious sensor, the active monitor, and the intelligent reflective surface are connected;

[0054] Among them, the suspicious receiver is used to collect illegal information through the suspicious sensor, the active listener is used to monitor and actively interfere with the suspicious receiver to collect illegal information, and the active listener has self-interference, and the intelligent reflective surface is used to enhance the monitoring channel of the active listener to monitor the suspicious receiver to collect illegal information and weaken the suspicious channel of the suspicious receiver to collect illegal information;

[0055] The active monitoring system also includes:

[0056] A monitoring model building module is used to build an active monitoring model of a suspicious airborne computing system according to a channel between a suspicious sensor and a suspicious receiver, a channel between an intelligent reflecting surface and a suspicious receiver, a channel between a suspicious sensor and an active monitor, a channel between a suspicious sensor, an intelligent reflecting surface and an active monitor, a channel between an active monitor and a suspicious receiver, and a self-interference channel of an active monitor;

[0057] The first data acquisition module is used to obtain the calculated mean square error of the suspicious receiver when collecting illegal information through the suspicious sensor through the active monitoring model of the suspicious air computing system through the transmission power of the suspicious sensor, the receiving power of the suspicious receiver and the phase matrix of the intelligent reflection surface;

[0058] The second data acquisition module is used to obtain the calculated mean square error of the active listener when monitoring the suspicious receiver to collect illegal information through the active listener's receiving power, the active interference power of the active listener and the phase matrix of the intelligent reflection surface according to the active monitoring model of the suspicious air computing system;

[0059] An optimization problem construction module is used to construct an optimization problem with the goal of minimizing the calculated mean square error of the active listener under the constraints of the calculated mean square error of the suspicious receiver, the transmit power of the suspicious sensor, the active interference power of the active listener, and the phase matrix of the smart reflection surface;

[0060] An optimization problem solving module is used to solve the optimization problem with the goal of minimizing the mean square error of the active listener, and obtain the target receiving power of the active listener, the target active interference power of the active listener, and the target phase matrix of the smart reflection surface;

[0061] The active monitoring module is used to enable the active monitor to monitor the suspicious receiver with the target receiving power to collect illegal information, and to actively interfere with the suspicious receiver with the target active interference power to collect illegal information, so that the intelligent reflection surface can enhance the monitoring channel of the active monitor and weaken the suspicious channel of the suspicious receiver with the target phase matrix, so as to actively monitor the suspicious receiver in the suspicious air computing system.

[0062] An embodiment of the present invention also provides a computer device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned active monitoring method of the suspicious air computing system.

[0063] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which implements the above-mentioned active monitoring method of a suspicious airborne computing system when executed by a processor.

[0064] The active monitoring method of a suspicious airborne computing system provided by the present invention has at least the following beneficial effects:

[0065] The success rate of active monitoring of the aerial computing system can be improved by minimizing the calculated mean square error of the active listener when monitoring and interfering with the suspicious receiver to collect illegal information. Therefore, the present invention constructs an optimization problem with the goal of minimizing the mean square error of the active listener and solves it to obtain the working parameters of each device in the system when the mean square error of the active listener is minimized, such as the receiving power of the active listener, the active interference power of the active listener, and the phase matrix of the intelligent reflection surface, so that they can cooperate with each other with their respective working parameters to realize active monitoring of the suspicious aerial computing system.

[0066] It includes the working parameters of the intelligent reflective surface. The intelligent reflective surface can be used to enhance the monitoring channel of the active listener to monitor and actively interfere with the suspicious receiver to collect illegal information, and to weaken the suspicious channel through which the suspicious receiver collects illegal information. Therefore, by setting up an intelligent reflective surface in the suspicious air computing system, the calculation mean square error of the suspicious receiver when collecting illegal information through the suspicious sensor can be increased. On this basis, the calculation mean square error of the active listener when monitoring and interfering with the suspicious receiver to collect illegal information is minimized, thereby further improving the success rate of active monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] One or more embodiments are exemplarily described by the pictures in the corresponding drawings, and these exemplary descriptions do not constitute limitations on the embodiments.

[0068] Figure 1is a schematic diagram of a suspicious airborne computing system provided according to an embodiment of the present invention;

[0069] Figure 2 is a flow chart of a method for actively monitoring a suspicious airborne computing system provided according to an embodiment of the present invention;

[0070] Figure 3 It is a schematic diagram for comparing simulation results provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in the embodiments of the present invention, many technical details are proposed in order to enable the reader to better understand the present invention. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present invention can be implemented. The division of the following embodiments is for the convenience of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined and referenced with each other without contradiction.

[0072] The monitoring of airborne computing systems is different from that of traditional communication systems in the following ways:

[0073] (1) The research objects are different. The research object of traditional communication systems is a single user, while the research object of aerial computing systems is massive sensors.

[0074] (2) The optimization objectives are different. The traditional communication system aims to maximize the safe rate, while the air computing system aims to minimize the mean square error of the calculation.

[0075] Therefore, the research on monitoring of airborne computing systems is not a simple extension of the research on monitoring of traditional communication systems. Based on this, the present invention aims at the suspicious airborne computing system, transmits active interference to the suspicious channel and uses the intelligent reflecting surface (IRS) to simultaneously enhance the monitoring channel and weaken the suspicious channel, so that the mean square error of the suspicious system calculation is always greater than the threshold value, and jointly optimizes the full-duplex monitor transceiver beam and the intelligent reflecting surface phase to minimize the mean square error of the monitoring calculation.

[0076] An embodiment of the present invention relates to an active monitoring method for a suspicious airborne computing system, which is applied to an active monitoring system. The suspicious airborne computing system and the active monitoring system described in this embodiment are as follows: Figure 1 As shown, the suspicious air computing system includes: suspicious receivers and suspicious sensors, the active monitoring system includes active monitors and intelligent reflective surfaces, and the number of antennas of the suspicious receiver is MD , there are K suspicious sensors, and the number of transmitting antennas and receiving antennas of the active monitor are M T and M R , IRS has N reflection units. The intelligent reflection surface is a metasurface composed of a large number of low-cost passive reflection units. Each unit can independently adjust the amplitude and phase of the incident signal. Through the cooperation of each unit, the intelligent reflection surface can make each reflected signal coherently superimposed at the receiving end and coherently canceled at the eavesdropping end, thereby achieving the purpose of simultaneously ensuring system security and transmission performance.

[0077] Among them, there is a connection relationship between any two of the suspicious receivers, suspicious sensors, active listeners and intelligent reflective surfaces. The suspicious receiver is used to collect illegal information through suspicious sensors, specifically by aggregating illegal information collected by K suspicious sensors through air calculation; the active listener is used to monitor and actively interfere with the suspicious receiver to collect illegal information. The active listener specifically sends active interference while monitoring sensor information to reduce the success rate of suspicious link aggregation. The active listener has self-interference. In this embodiment, the active listener works in full-duplex mode, so there is full-duplex self-interference; the intelligent reflective surface is used to enhance the monitoring channel of the active listener to monitor the suspicious receiver to collect illegal information and weaken the suspicious channel of the suspicious receiver to collect illegal information, thereby further improving the monitoring performance.

[0078] The following is a detailed description of the implementation details of the active monitoring method for the suspicious air computing system of this embodiment. The following content is only for the convenience of understanding the implementation details provided, and is not necessary for the implementation of this solution. The specific process of the active monitoring method for the suspicious air computing system of this embodiment can be as follows Figure 2 As shown, including:

[0079] Step 201, construct an active monitoring model of a suspicious airborne computing system based on the channel between the suspicious sensor and the suspicious receiver, the channel between the intelligent reflecting surface and the suspicious receiver, the channel between the suspicious sensor and the active listener, the channel between the suspicious sensor, the intelligent reflecting surface and the active listener, the channel between the active listener and the suspicious receiver, and the self-interference channel of the active listener.

[0080] Specifically, this embodiment is based on Figure 1 In the system shown, the working principles among the suspicious receiver, suspicious sensor, active listener and intelligent reflective surface construct an active monitoring model of the suspicious aerial computing system.

[0081] Step 202, according to the active monitoring model of the suspicious airborne computing system, the calculated mean square error of the suspicious receiver when collecting illegal information through the suspicious sensor is obtained through the transmission power of the suspicious sensor, the receiving power of the suspicious receiver and the phase matrix of the intelligent reflection surface.

[0082] The mean square error calculated when the suspicious receiver collects illegal information through the suspicious sensor is the mean square error calculated when the suspicious receiver aggregates the illegal information collected by K suspicious sensors through air calculation. The suspicious receiver and the suspicious sensor are users in the legitimate network who are bribed or hijacked by the hostile party. Because they are users in the legitimate network, they can obtain the transmit power of the suspicious sensor and the receive power of the suspicious receiver.

[0083] Step 203, according to the active monitoring model of the suspicious airborne computing system, the calculated mean square error of the active monitor when monitoring the suspicious receiver to collect illegal information is obtained through the active monitor's receiving power, the active monitor's active interference power and the phase matrix of the smart reflection surface.

[0084] In step 202 and step 203, the calculated mean square error of the suspicious receiver when collecting illegal information through the suspicious sensor is expressed by the following formula:

[0085]

[0086] The mean square error calculated by the active monitor when monitoring the suspicious receiver to collect illegal information is expressed by the following formula:

[0087]

[0088] In the formula, Indicates the signal misalignment error of the suspected receiver, represents the error caused by the active interference of the active listener, represents the error caused by the noise of the suspicious receiver, Indicates the signal misalignment error of the active listener, represents the error caused by the full-duplex self-interference of the active listener, represents the error caused by the noise of the active listener, Φ=diag(φ1,φ2,…,φ N ) represents the phase matrix of the smart reflector, n∈{1,2,…,N},θ n ∈[0,2π), represents the adjustment phase of the nth reflection unit of the smart reflection surface, u D and u L Respectively represent the receiving power of the suspicious receiver and the active listener, v k represents the transmission power of the kth suspicious sensor, w represents the active interference power of the active listener, and denote the noise power of the suspicious receiver and the active eavesdropper respectively, and denote the channels from the kth suspicious sensor to the suspicious receiver, the smart reflective surface, and the active listener, respectively. ID and H IL They represent the channels from the intelligent reflector surface to the suspicious receiver and the active listener, respectively. LD represents the channel from the active listener to the suspicious receiver, H LL Represents the full-duplex self-interference channel of the active listener.

[0089] Step 204 , under the constraints of the calculated mean square error of the suspicious receiver, the transmit power of the suspicious sensor, the active interference power of the active listener and the phase matrix of the smart reflective surface, an optimization problem with the goal of minimizing the mean square error of the active listener is constructed.

[0090] The optimization problem with the goal of minimizing the mean square error of the active listener is expressed by the following formula:

[0091]

[0092] In the formula, MSE L represents the calculated mean square error of the active listener, C1 represents the constraint condition of the calculated mean square error of the suspicious receiver, ε represents the preset concealment constraint threshold, which is a constant, C2 represents the constraint condition of the transmit power of the suspicious sensor, P k represents the maximum transmission power of the kth suspicious sensor, C3 represents the constraint condition of the active interference power of the active listener, P L represents the maximum transmission power of the active listener, C4 represents the constraint condition of the phase matrix of the smart reflection surface, and n∈{1,2,…,N} represents the nth reflection unit of the smart reflection surface.

[0093] The objective function in the optimization problem P1 and the optimization variables in the constraint C1 are highly coupled, and the constraint C4 is non-convex. Therefore, the optimization problem P1 is a non-convex problem, and the Sequential Rank-One Constraint Relaxation (SROCR) iterative algorithm can be used to solve P1.

[0094] Step 205, solving the optimization problem with the goal of minimizing the mean square error of the active listener, and obtaining the target receiving power of the active listener, the target active interference power of the active listener, and the target phase matrix of the smart reflection surface.

[0095] Specifically, first, the optimization problem with the goal of minimizing the mean square error of the active listener is decomposed to obtain multiple sub-optimization problems with the goal of minimizing the mean square error of the active listener, and the receiving power of the suspicious receiver and the active listener, the transmitting power of the suspicious sensor, the active interference power of the active listener, and the phase matrix of the smart reflective surface as optimization variables. Then, each sub-optimization problem is solved.

[0096] Among them, with the goal of minimizing the mean square error of the active listener, the sub-optimization problem of minimizing the receiving power of the suspicious receiver and the active listener is expressed by the following formula:

[0097] P2:

[0098] stC1:

[0099] Taking the calculation mean square error of the active listener as the minimum as the goal, the sub-optimization problem of minimizing the transmit power of the suspicious sensor is expressed by the following formula:

[0100] P3:

[0101] stC2:

[0102] Taking the calculation mean square error of the active listener as the minimum as the goal, the sub-optimization problem of minimizing the active interference power of the active listener is expressed by the following formula:

[0103] P5:

[0104] stC1:

[0105] C3:||w||≤P

[0106] Taking the calculation mean square error of the active listener as the minimum as the goal, the sub-optimization problem of minimizing the phase matrix of the smart reflector is expressed by the following formula:

[0107] P6:

[0108] stC1:

[0109] C4:

[0110] In the specific implementation, for the sub-optimization problem P2, the receiving power {u D, u L}, by giving the transmit power of the suspicious sensor {v k}, the active interference power w of the active listener and the phase matrix Φ of the smart reflector, the sub-optimization problem P2 is {u D, u L The closed expression of the optimal solution of the receiving power of the suspicious receiver and the active listener is as follows:

[0111]

[0112] In the formula, represents the equivalent channel from the kth suspicious sensor to the suspicious receiver, represents the equivalent channel from the kth suspicious sensor to the active listener.

[0113] For sub-optimization problem P3, optimize the transmit power of the suspicious sensor {v k}, by giving the receiving power {u D, u L}, the active interference power w of the active listener and the phase matrix Φ of the smart reflector, the sub-optimization problem P3 can be further decoupled into K sub-sub-optimization problems P4 that can be executed in parallel:

[0114] P4:

[0115] stC2:|v k | 2 ≤P k

[0116] Through the sub-sub-optimization problem, the Lagrange multiplier method is used to obtain the closed expression of the optimal solution of the transmit power of the suspicious sensor, as shown in the following formula:

[0117]

[0118] In the formula, Represents the Lagrange multiplier corresponding to constraint C2.

[0119] For sub-optimization problem P5, when optimizing the active interference power w of the active listener, by giving the receiving power {u D ,u L}、The transmit power of the suspicious sensor {v k} and the phase matrix Φ of the smart reflector, the sub-optimization problem of minimizing the active interference power of the active listener is transformed into a generalized Rayleigh quotient problem. Through the generalized Rayleigh quotient problem, the closed expression of the optimal solution of the active interference power of the active listener is obtained, as shown in the following formula:

[0120]

[0121] In the formula, Represents the matrix (A D / ε1) -1 A L The eigenvector corresponding to the minimum eigenvalue of

[0122] For sub-optimization problem P6, when optimizing the phase matrix Φ of the smart reflector, by giving the receiving power {u D ,u L}、The transmit power of the suspicious sensor {v k} and the active interference power w of the active listener, using the identity tr(SFTF H ) = f H (S⊙T T )f, and introduce the preset auxiliary variable t, the optimization problem P6 can be equivalently expressed as:

[0123] P7:

[0124] stC1:

[0125] C4:

[0126] In the formula, F=diag(f) represents a diagonal matrix with f as the diagonal element, t represents an auxiliary variable,

[0127] c D =([C D ] 1,1 ,[C D ] 2,2 ,…,[C D ] N,N ), c L =([C L ] 1,1 ,[C L ] 2,2 ,…,[C L ] N,N ), [A] n,n represents the nth diagonal element of matrix A, By optimizing the solution of P8 opt By eigendecomposition, we can get g max For X opt Maximum eigenvalue λ max The corresponding feature vector.

[0128] Then, by introducing the variable The problem after equivalent transformation is converted again into the following rank 1 positive definite programming problem:

[0129] P8:

[0130] stC1:tr(R D X)≥ε2

[0131] C5:

[0132] C6:X≥0

[0133] C7:rank(X)=1

[0134] P8 is a rank 1 positive definite programming problem, which can be solved by using the alternating continuous rank 1 constraint relaxation iterative algorithm to obtain the optimal solution X of problem P8. opt After that, X opt Do feature decomposition, g max For X opt Maximum eigenvalue λ max The corresponding eigenvector is Then the closed-form expression of the optimal solution of the phase matrix of the smart reflector is

[0135] Based on the above, for each sub-optimization problem, the alternating continuous rank-one constraint relaxation iterative SROCR algorithm is used to solve it, which specifically includes the following steps:

[0136] (1) Set the initial value of the mean square error calculated by the active listener The initial value of the number of iterations i=1, the iteration accuracy δ and the maximum number of iterations I max , and set the initial value of each optimization variable; the optimization variables include the transmit power of the suspicious sensor The active interference power w of the active listener (0) And the initial value of the phase matrix of the smart reflector Φ (0) ;

[0137] (2) Based on the value of the optimization variable in the previous iteration Φ (i-1) , w (i-1) , according to the closed expressions of the optimal solutions of the receiving powers of suspicious receivers and active listeners, the optimal receiving power of suspicious receivers is obtained respectively. and the optimal active listener receiving power

[0138] (3) Based on the value of the optimization variable in the previous iteration Φ (i-1) ,w (i-1) , The optimal transmit power of the suspicious sensor is obtained based on the closed expression of the optimal solution of the transmit power of the suspicious sensor.

[0139] (4) Based on the value of the optimization variable in the previous iteration Φ (i-1) , According to the closed expression of the optimal solution of the active interference power of the active listener, the optimal active interference power w of the active listener is obtained (i) ;

[0140] (5) Based on the value of the optimized variable in the previous iteration w (i) , according to the closed expression of the optimal solution of the phase matrix of the smart reflector, the optimal phase matrix Φ of the smart reflector is obtained (i) ;

[0141] (6) Increase the number of iterations i = i + 1;

[0142] (7)If i>I max or Then stop the iteration.

[0143] Step 206, enables the active listener to monitor the suspicious receiver to collect illegal information with the target receiving power, and actively interferes with the suspicious receiver to collect illegal information with the target active interference power, so that the intelligent reflecting surface enhances the monitoring channel of the active listener and weakens the suspicious channel of the suspicious receiver with the target phase matrix, so as to actively monitor the suspicious receiver in the suspicious air computing system.

[0144] In one example, the active monitoring method of the suspicious airborne computing system of this embodiment is simulated and verified as follows:

[0145] The simulation parameters are set as follows: N = 30, M D =6,M T =M R =4,δ=0.001,I max =200, ε=K, all suspicious sensors have the same maximum transmit power constraint, that is, P1=P2=…=P K =10dBm, P L =10dBm. All suspicious sensors are evenly distributed in a circle with a center of (0, 0, 0) and a radius of 2. The coordinates of the suspicious receiver are (10, 0, 0), the coordinates of the IRS are (1, 1, 2), and the coordinates of the active listener are (2, 1, 2). Figure 1 , all channels have a mean of 0 and a variance of The cyclic complex Gaussian random distribution, d ij ,i∈{sk ,I,,L},j∈{D,I,L}, is the distance between node i and node j, α ij is the corresponding path loss factor. α IL =α ID =α LD =2.8. MATLAB software was used for simulation, and the number of Monte Carlo simulations was 200.

[0146] Figure 3 The figure is a comparison diagram of the mean square error calculated by the method of this embodiment and other methods. It can be found from the figure that the performance of calculating the mean square error of all algorithms deteriorates with the increase of the number of sensors K. This is because as the number of sensors K increases, it is more difficult for the active listener to design a receiving beam that can aggregate all sensor information. It can also be found from the figure that the method of this embodiment is far superior to the IRS-free and IRS phase random algorithms. This is because by optimizing the IRS phase, the receiving signal of the active listener and the suspicious receiver can be enhanced at the same time, thereby achieving the purpose of improving the performance of calculating the mean square error. In addition, the method of this embodiment has limited performance improvement on the passive monitoring algorithm. This is because while the active interference weakens the receiving signal of the suspicious receiver, it also generates self-interference to the active listener, limiting the performance improvement of the monitoring system.

[0147] In this embodiment, by minimizing the mean square error of the calculation when the active listener monitors and interferes with the suspicious receiver to collect illegal information, the success rate of active monitoring of the air computing system can be improved. Therefore, the present invention constructs an optimization problem with the minimum mean square error of the active listener as the goal, and solves it to obtain the working parameters of each device in the system when the mean square error of the active listener is minimized, such as the receiving power of the active listener, the active interference power of the active listener, and the phase matrix of the intelligent reflection surface, so that they can cooperate with each other with their respective working parameters to realize active monitoring of the suspicious air computing system. The working parameters of the intelligent reflection surface are included. The intelligent reflection surface can be used to enhance the monitoring channel for the active listener to monitor and actively interfere with the suspicious receiver to collect illegal information and weaken the suspicious channel for the suspicious receiver to collect illegal information. Therefore, by setting the intelligent reflection surface in the suspicious air computing system, the calculation mean square error of the suspicious receiver when collecting illegal information through the suspicious sensor can be increased. On this basis, the calculation mean square error of the active listener when monitoring and interfering with the suspicious receiver to collect illegal information is minimized, further improving the success rate of active monitoring.

[0148] The step division of the various methods above is only for clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the protection scope of the present invention. Adding insignificant modifications or introducing insignificant designs to the algorithm or process without changing the core design of the algorithm and process are all within the protection scope of the invention.

[0149] Another embodiment of the present invention relates to an active monitoring system for a suspicious aerial computing system, wherein the suspicious aerial computing system includes a suspicious receiver and a suspicious sensor, the active monitoring system includes an active monitor and an intelligent reflective surface, and a connection relationship exists between any two of the suspicious receiver, the suspicious sensor, the active monitor and the intelligent reflective surface; wherein the suspicious receiver is used to collect illegal information through the suspicious sensor, the active monitor is used to monitor and actively interfere with the suspicious receiver in collecting illegal information, and the active monitor has self-interference, and the intelligent reflective surface is used to enhance the monitoring channel through which the active monitor monitors the suspicious receiver to collect illegal information and weaken the suspicious channel through which the suspicious receiver collects illegal information.

[0150] The following is a detailed description of the implementation details of the active monitoring system of the suspicious airborne computing system of this embodiment. The following content is only provided for the convenience of understanding the implementation details and is not necessary for the implementation of this solution.

[0151] The active monitoring system of the suspicious airborne computing system of this embodiment further includes:

[0152] A monitoring model building module is used to build an active monitoring model of a suspicious airborne computing system according to a channel between a suspicious sensor and a suspicious receiver, a channel between an intelligent reflecting surface and a suspicious receiver, a channel between a suspicious sensor and an active monitor, a channel between a suspicious sensor, an intelligent reflecting surface and an active monitor, a channel between an active monitor and a suspicious receiver, and a self-interference channel of an active monitor;

[0153] The first data acquisition module is used to obtain the calculated mean square error of the suspicious receiver when collecting illegal information through the suspicious sensor through the active monitoring model of the suspicious air computing system through the transmission power of the suspicious sensor, the receiving power of the suspicious receiver and the phase matrix of the intelligent reflection surface;

[0154] The second data acquisition module is used to obtain the calculated mean square error of the active listener when monitoring the suspicious receiver to collect illegal information through the active listener's receiving power, the active interference power of the active listener and the phase matrix of the intelligent reflection surface according to the active monitoring model of the suspicious air computing system;

[0155] An optimization problem construction module is used to construct an optimization problem with the goal of minimizing the calculated mean square error of the active listener under the constraints of the calculated mean square error of the suspicious receiver, the transmit power of the suspicious sensor, the active interference power of the active listener, and the phase matrix of the smart reflection surface;

[0156] An optimization problem solving module is used to solve the optimization problem with the minimum mean square error of the active listener as the goal, and obtain the target receiving power of the active listener, the target active interference power of the active listener, and the target phase matrix of the smart reflection surface;

[0157] The active monitoring module is used to enable the active monitor to monitor the suspicious receiver with the target receiving power to collect illegal information, and to actively interfere with the suspicious receiver with the target active interference power to collect illegal information, so that the intelligent reflection surface can enhance the monitoring channel of the active monitor and weaken the suspicious channel of the suspicious receiver with the target phase matrix, so as to actively monitor the suspicious receiver in the suspicious air computing system.

[0158] It is not difficult to find that this embodiment is a system embodiment corresponding to the above method embodiment, and this embodiment can be implemented in conjunction with the above method embodiment. The relevant technical details and technical effects mentioned in the above embodiment are still valid in this embodiment, and in order to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied in the above embodiment.

[0159] It is worth mentioning that all modules involved in this embodiment are logic modules. In practical applications, a logic unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of the present invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by the present invention, but this does not mean that there are no other units in this embodiment.

[0160] Another embodiment of the present invention relates to a computer device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the active monitoring method of the suspicious airborne computing system in the above-mentioned embodiments.

[0161] Among them, the memory and the processor are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and memories together. The bus can also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor is transmitted on a wireless medium via an antenna, and further, the antenna also receives data and transmits the data to the processor.

[0162] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0163] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program, which implements the above method embodiment when executed by a processor.

[0164] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as: ROM), random access memory (Random Access Memory, referred to as: RAM), disk or optical disk and other media that can store program codes.

[0165] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present invention, and in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present invention.

Claims

1. A method for actively monitoring a suspicious airborne computing system, characterized in that: Applied to an active monitoring system, the suspicious air computing system includes a suspicious receiver and a suspicious sensor, the active monitoring system includes an active monitor and an intelligent reflective surface, and any two of the suspicious receiver, the suspicious sensor, the active monitor and the intelligent reflective surface are connected; Among them, the suspicious receiver is used to collect illegal information through the suspicious sensor, the active listener is used to monitor and actively interfere with the suspicious receiver to collect illegal information, and the active listener has self-interference, and the intelligent reflective surface is used to enhance the monitoring channel of the active listener to monitor the suspicious receiver to collect illegal information and weaken the suspicious channel of the suspicious receiver to collect illegal information; The method comprises: According to the channel between the suspicious sensor and the suspicious receiver, the channel between the intelligent reflective surface and the suspicious receiver, the channel between the suspicious sensor and the active listener, the channel between the suspicious sensor, the intelligent reflective surface and the active listener, the channel between the active listener and the suspicious receiver, and the self-interference channel of the active listener, an active monitoring model of the suspicious air computing system is constructed; According to the active monitoring model of the suspicious airborne computing system, the calculated mean square error of the suspicious receiver when collecting illegal information through the suspicious sensor is obtained through the transmission power of the suspicious sensor, the receiving power of the suspicious receiver and the phase matrix of the intelligent reflection surface; According to the active monitoring model of the suspicious airborne computing system, the calculated mean square error of the active monitor when monitoring the suspicious receiver to collect illegal information is obtained through the receiving power of the active monitor, the active interference power of the active monitor and the phase matrix of the intelligent reflection surface; Under the constraints of the calculated mean square error of the suspicious receiver, the transmit power of the suspicious sensor, the active interference power of the active listener, and the phase matrix of the smart reflective surface, an optimization problem with the goal of minimizing the calculated mean square error of the active listener is constructed. The optimization problem with the goal of minimizing the mean square error of the active listener is solved to obtain the target receiving power of the active listener, the target active interference power of the active listener, and the target phase matrix of the smart reflective surface; The active monitor is enabled to monitor the suspicious receiver with the target receiving power to collect illegal information, and actively interfere with the suspicious receiver with the target active interference power to collect illegal information. The intelligent reflecting surface is enabled to enhance the monitoring channel of the active monitor and weaken the suspicious channel of the suspicious receiver with the target phase matrix to actively monitor the suspicious air computing system.

2. The active monitoring method of a suspicious airborne computing system according to claim 1, characterized in that: The solving of the optimization problem with the goal of minimizing the mean square error of the active listener calculation includes: The optimization problem with the goal of minimizing the mean square error of the calculation of the active listener is decomposed to obtain multiple sub-optimization problems with the goal of minimizing the mean square error of the calculation of the active listener, and the receiving power of the suspicious receiver and the active listener, the transmitting power of the suspicious sensor, the active interference power of the active listener, and the phase matrix of the smart reflection surface are used as optimization variables; Solve each sub-optimization problem.

3. The active monitoring method of a suspicious airborne computing system according to claim 2, characterized in that: The solving of each sub-optimization problem includes: For each sub-optimization problem, the alternating continuous rank-one constraint relaxation iterative SROCR algorithm is used to solve it.

4. The active monitoring method of a suspicious airborne computing system according to claim 3, characterized in that: The calculated mean square error of the suspicious receiver when collecting illegal information through the suspicious sensor is expressed by the following formula: The mean square error calculated by the active monitor when monitoring the suspicious receiver to collect illegal information is expressed by the following formula: In the formula, Indicates the signal misalignment error of the suspected receiver, represents the error caused by the active interference of the active listener, represents the error caused by the noise of the suspicious receiver, Indicates the signal misalignment error of the active listener, represents the error caused by the full-duplex self-interference of the active listener, represents the error caused by the noise of the active listener, Φ=diag(φ1,φ2,…,φ N ) represents the phase matrix of the smart reflector, n∈{1,2,…,N},θ n ∈[0,2π), represents the adjustment phase of the nth reflection unit of the smart reflection surface, u D and u L Respectively represent the receiving power of the suspicious receiver and the active listener, v k represents the transmission power of the kth suspicious sensor, w represents the active interference power of the active listener, and denote the noise power of the suspicious receiver and the active eavesdropper respectively, and denote the channels from the kth suspicious sensor to the suspicious receiver, the smart reflective surface, and the active listener, respectively. ID and H IL They represent the channels from the intelligent reflector surface to the suspicious receiver and the active listener, respectively. LD represents the channel from the active listener to the suspicious receiver, H LL Represents the full-duplex self-interference channel of the active listener.

5. The active monitoring method of a suspicious airborne computing system according to claim 4, characterized in that: The optimization problem with the goal of minimizing the mean square error of the active listener is expressed by the following formula: In the formula, MSE L represents the calculated mean square error of the active listener, C1 represents the constraint condition of the calculated mean square error of the suspicious receiver, ε represents the preset concealment constraint threshold, which is a constant, C2 represents the constraint condition of the transmit power of the suspicious sensor, P k represents the maximum transmission power of the kth suspicious sensor, C3 represents the constraint condition of the active interference power of the active listener, P L represents the maximum transmission power of the active listener, C4 represents the constraint condition of the phase matrix of the smart reflection surface, and n∈{1,2,…,N} represents the nth reflection unit of the smart reflection surface.

6. The active monitoring method of a suspicious airborne computing system according to claim 5, characterized in that: Before solving each sub-optimization problem, the method further includes: By giving the transmit power of the suspicious sensor, the active interference power of the active listener, and the phase matrix of the smart reflective surface, the closed expressions of the optimal solutions for the receiving power of the suspicious receiver and the active listener are obtained, as shown in the following formulas: In the formula, represents the equivalent channel from the kth suspicious sensor to the suspicious receiver, represents the equivalent channel from the kth suspicious sensor to the active listener; By giving the receiving power of the suspicious receiver and the active listener, the active interference power of the active listener, and the phase matrix of the smart reflective surface, the Lagrange multiplier method is used to obtain the closed expression of the optimal solution for the transmit power of the suspicious sensor, as shown in the following formula: In the formula, represents the Lagrange multiplier corresponding to constraint C2; By giving the receiving power of the suspicious receiver and the active listener, the transmitting power of the suspicious sensor and the phase matrix of the smart reflective surface, the closed expression of the optimal solution of the active interference power of the active listener is obtained, as shown in the following formula: In the formula, Represents the matrix (A D / ε1) -1 A L The eigenvector corresponding to the minimum eigenvalue of By giving the receiving power of the suspicious receiver and the active listener, the transmitting power of the suspicious sensor and the active interference power of the active listener, the closed expression of the optimal solution of the phase matrix of the smart reflection surface is obtained, as shown in the following formula: In the formula, F=diag(f) represents a diagonal matrix with f as the diagonal element, t represents an auxiliary variable, c D =([C D ] 1,1 ,[C D ] 2,2 ,…,[C D ] N,N ), c L =([C L ] 1,1 ,[C L ] 2,2 ,…,[C L ] N,N ), [A] n,n represents the nth diagonal element of matrix A, By optimizing the solution of P8 opt By eigendecomposition, we can get g max For X opt Maximum eigenvalue λ max The corresponding feature vector.

7. The active monitoring method of a suspicious airborne computing system according to claim 6, characterized in that: For each sub-optimization problem, an alternating continuous rank-one constraint relaxation iteration SROCR algorithm is used to solve the problem, including: Set the initial value of the calculated mean square error of the active listener The initial value of the number of iterations i=1, the iteration accuracy δ and the maximum number of iterations I max , and set the initial value of each optimization variable; the optimization variable includes the transmission power of the suspicious sensor The active interference power w of the active listener (0) And the initial value of the phase matrix of the smart reflector Φ (0) ; Based on the value of the optimization variable in the previous iteration According to the closed expressions of the optimal solutions of the receiving powers of suspicious receivers and active listeners, the optimal receiving power of suspicious receivers is calculated respectively. and the optimal active listener receiving power Based on the value of the optimization variable in the previous iteration The optimal transmit power of the suspicious sensor is obtained based on the closed expression of the optimal solution of the transmit power of the suspicious sensor. Based on the value of the optimization variable in the previous iteration According to the closed expression of the optimal solution of the active interference power of the active listener, the optimal active interference power w of the active listener is obtained (i) ; Based on the value of the optimization variable in the previous iteration According to the closed expression of the optimal solution of the phase matrix of the smart reflector, the optimal phase matrix Φ of the smart reflector is obtained. (i) ; Increase the number of iterations i = i + 1; If i>I max or , the iteration stops.

8. An active monitoring system for suspicious airborne computing systems, characterized in that: The suspicious air computing system includes a suspicious receiver and a suspicious sensor, the active monitoring system includes an active monitor and an intelligent reflective surface, and any two of the suspicious receiver, the suspicious sensor, the active monitor and the intelligent reflective surface are connected; Among them, the suspicious receiver is used to collect illegal information through the suspicious sensor, the active listener is used to monitor and actively interfere with the suspicious receiver to collect illegal information, and the active listener has self-interference, and the intelligent reflective surface is used to enhance the monitoring channel of the active listener to monitor the suspicious receiver to collect illegal information and weaken the suspicious channel of the suspicious receiver to collect illegal information; The active monitoring system also includes: A monitoring model building module is used to build an active monitoring model of a suspicious airborne computing system according to a channel between a suspicious sensor and a suspicious receiver, a channel between an intelligent reflecting surface and a suspicious receiver, a channel between a suspicious sensor and an active monitor, a channel between a suspicious sensor, an intelligent reflecting surface and an active monitor, a channel between an active monitor and a suspicious receiver, and a self-interference channel of an active monitor; The first data acquisition module is used to obtain the calculated mean square error of the suspicious receiver when collecting illegal information through the suspicious sensor through the active monitoring model of the suspicious air computing system through the transmission power of the suspicious sensor, the receiving power of the suspicious receiver and the phase matrix of the intelligent reflection surface; The second data acquisition module is used to obtain the calculated mean square error of the active listener when monitoring the suspicious receiver to collect illegal information through the active listener's receiving power, the active interference power of the active listener and the phase matrix of the intelligent reflection surface according to the active monitoring model of the suspicious air computing system; An optimization problem construction module is used to construct an optimization problem with the goal of minimizing the calculated mean square error of the active listener under the constraints of the calculated mean square error of the suspicious receiver, the transmit power of the suspicious sensor, the active interference power of the active listener, and the phase matrix of the smart reflection surface; An optimization problem solving module is used to solve the optimization problem with the minimum mean square error of the active listener as the goal, and obtain the target receiving power of the active listener, the target active interference power of the active listener, and the target phase matrix of the smart reflection surface; The active monitoring module is used to enable the active monitor to monitor the suspicious receiver with the target receiving power to collect illegal information, and to actively interfere with the suspicious receiver with the target active interference power to collect illegal information, so that the intelligent reflection surface can enhance the monitoring channel of the active monitor and weaken the suspicious channel of the suspicious receiver with the target phase matrix, so as to actively monitor the suspicious receiver in the suspicious air computing system.

9. A computer device, characterized in that: include: at least one processor; And, a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the active monitoring method of the suspicious airborne computing system as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the active monitoring method for a suspicious airborne computing system according to any one of claims 1 to 7 is implemented.

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