Multi-agent system dynamic event triggering safety control and verification method and system

Abstract

The invention discloses a multi-agent system dynamic event triggering safety control and verification method and system, and belongs to the field of event triggering safety control of a multi-agent system. The method comprises the following steps: firstly, establishing a distributed control mathematical model of a multi-agent system under a multi-network attack condition, then acquiring communication network information to judge whether a network is in a malicious network attack state, then, formulating a distributed finite time security consistency control protocol, and setting a dynamic event triggering mechanism, so as to ensure that the network is in a malicious network attack state. A mathematical model of a multi-agent control system coping with multiple network attacks is constructed, finally, sufficient conditions for ensuring that the mathematical model achieves finite time safety consistency are set, the sufficient conditions comprise DoS common signal meeting conditions, and target control is achieved. According to the invention, network attacks can be resisted in limited time, and a consistency control target is realized on the premise of saving system resources.

Description

Technical Field

[0001] The present invention relates to an event-triggered security control technology for a multi-agent system under a network attack, and more specifically to a multi-agent system dynamic event-triggered security control, verification method and system. Background Art

[0002] There are some papers on event-triggered security control technology for multi-agent systems under network attacks. They mainly focus on designing a security consistency controller based on event-triggered control strategy for multi-agent systems under network attacks to resist network attacks and save system communication and control resources, and achieve system consistency goals, such as: Zhu Laixiang et al. from Qingdao University of Technology published "Event-triggered Control and Filtering of TS Fuzzy Singular Semi-Markov Jump System under Multiple Network Attacks", which uses event-triggered control technology to solve the security control problem of networked control systems under denial of service attacks and deception attacks. It can resist two types of network attacks and achieve the security control goals of the system. From the modeling point of view, although two types of network attacks, denial of service attacks and deception attacks, are considered, the saturation characteristics of system execution are not considered. Saturation phenomenon is common in many practical systems, such as the saturation characteristics of car steering angles, and the yaw angle of quadcopters is also limited; in terms of control strategy design, the event trigger mechanism designed by Zhu Laixiang et al. is static, and it is not possible to dynamically adjust the threshold in the event trigger mechanism to achieve the purpose of quickly saving communication resources; for the control target, the security consistency in the sense of asymptotic stability (that is, the performance is achieved after infinite time) cannot achieve the security consistency of the system within a finite time.

[0003] Guan Jiacheng et al. from Jilin University of Chemical Technology published a paper titled "Research on Event-Triggered Security Consistency Control of Multi-Agent Systems under Network Attacks", which designed a dynamic event-triggered mechanism to achieve the security consistency control goal of multi-agent systems. However, it only considered denial of service attacks in the network, and did not consider other types of network attacks, nor did it consider the saturation phenomenon of the system. For the control target, its control strategy is still based on asymptotically stable design, and it cannot achieve the security consistency of the system within a limited time.

[0004] In addition, Jia Xinchun et al. published "Edge-based Dynamic Event-Triggered Multi-Agent System Consistency under Hybrid Network Attacks". Although they designed a dynamic event-triggered control strategy to achieve the security consistency of the multi-agent system, they did not consider the saturation characteristics of the system for model construction; for control objectives, they achieved security consistency in the sense of asymptotic stability, not the finite-time consistency which is more engineering-significant. Summary of the invention

[0005] In order to solve the problem that the existing security control methods for multi-agent systems under multiple network attacks only achieve the asymptotic stability of the closed-loop system but have not yet achieved limited-time control, the present invention provides a multi-agent system dynamic event-triggered security control and verification method and system, which can achieve consistent control goals under the premise of resisting network attacks within a limited time and saving system resources.

[0006] The technical solution adopted by the present invention to solve the above technical problems is: a multi-agent system dynamic event triggered security control method, comprising the following steps: S1. Establish a mathematical model of distributed control of multi-agent systems under multiple network attack conditions; S2. Obtain communication network information to determine whether the network is in a malicious network attack state; S3. Develop a distributed finite-time security consistency control protocol and set a dynamic event trigger mechanism; S4. Construct a mathematical model of a multi-agent control system that can cope with multiple network attacks; S5. Set sufficient conditions to ensure that the mathematical model in S4 achieves finite-time security consistency, and the sufficient conditions include that the DoS attack signal satisfies the conditions to achieve control over the target.

[0007] As an optimization scheme for the above-mentioned multi-agent system dynamic event triggered safety control method, the multi-agent system is composed of N agents, and the agents exchange information through a communication network.

[0008] As another optimization scheme of the above-mentioned multi-agent system dynamic event triggered safety control method, in S1, the distributed control mathematical model of the multi-agent system is: ; In the formula, is the system state of the i-th agent in the multi-agent system, is the control input of the distributed control mathematical model, A and B are known constant matrices, f(t) is the uncertain nonlinear term of the system, and f(t) satisfies the following conditions: , is the Lipschitz constant; For an actuator with an upper bound The saturation function is defined as follows: .

[0009] As another optimization scheme of the above-mentioned multi-agent system dynamic event triggered security control method, the malicious network attack in S2 is DoS attack and deception attack. At this time, the state space model of the multi-agent system with actuator saturation characteristics and whose communication network is subjected to DoS attack and deception attack is described as follows: .

[0010] As another optimization scheme of the above-mentioned multi-agent system dynamic event triggered security control method, the steps of obtaining the state space model of the multi-agent system subjected to DoS attack and deception attack are as follows: 2.1) According to the characteristics of DoS attacks, set the system signal of DoS attacks for: ; In the formula, is a normal signal. is the indicator function of DoS attack, with a value of 0 or 1. =1 means DoS attack occurs. =0 means that DoS attack has not occurred; 2.2) Use the Bernoulli random variable β(t) to describe the probability of the system being attacked by deception. Its mathematical expectation is β, β∈[0,1]. At this time, the signal under deception attack for: ; In the formula, the nonlinear function is a bounded function used to describe the attack intensity, which satisfies , S is a known real constant matrix; 2.3) Based on steps 2.1) and 2.2), a state space model of a multi-agent system whose communication network is subjected to DoS attacks and deception attacks is constructed.

[0011] As another optimization scheme of the above-mentioned multi-agent system dynamic event triggered safety control method, in S3, the distributed finite time safety consistency control protocol is: ; Where K is the feedback matrix to be determined, is the weight of agents i and j in the communication topology, Z + The set of positive integers, represents the execution time of agent i, Represents the state sampling information of agent i at the execution time; Execution time It is determined by the dynamic event triggering mechanism, which is expressed as: ; In the formula, is the measurement error, which represents the difference between the current state of the ith agent and its previous sampling state. The first sampling time of all agents is set to the initial time of the system, that is, ; represents the sum of the differences between the state of the i-th agent and the states of all its neighboring agents j; is a dynamic adaptive law representing the parameter in the event generator that determines the dynamic threshold, with an initial value , The dynamic description is: ; In the formula, the parameters , , is a positive constant to be selected in the event trigger mechanism, is the positive constant to be selected in the adaptive law.

[0012] As another optimization scheme of the above-mentioned multi-agent system dynamic event triggered security control method, in S4, the mathematical model of the multi-agent control system for coping with multiple network attacks is constructed as follows: ; In the formula, is the consistency error, which is defined as ; make , , , , , , , ,use represents the N-dimensional identity matrix, represents an n-dimensional column vector whose elements are all 1, then the mathematical model of the multi-agent control system to cope with multiple network attacks is transformed into: .

[0013] As another optimization scheme of the above-mentioned multi-agent system dynamic event triggered safety control method, the specific operation of S5 is: The mathematical expression of the sufficient condition for ensuring that the system achieves finite-time safety consistency is set as: If there exists a positive definite symmetric matrix , ,matrix , so that the following conditions hold: ; Moreover, the DoS attack signal satisfies the following conditions: ; Then, the gain in the distributed finite-time safety consistency control protocol in S3 is selected as , that is, to ensure that the multi-agent control system in S4 achieves finite-time safety consistency; In the above formula: ; , ,and , , and are all selected positive numbers; .

[0014] The method for verifying the reliability of the above-mentioned multi-agent system dynamic event triggered safety control method comprises the following steps: a. For the mathematical model constructed in step S4, construct a multi-Lyapunov function as follows: ; The indicator function in the formula ; b. Calculate the time derivative of the multi-Lyapunov function in step a, bring it into the mathematical model in step S4, take the mathematical expectation, and then substitute it into the sufficient condition set in step S5 to ensure that the system achieves finite time safety consistency to obtain the equation: ; c. For the first equation in step b, both ends of the equation are in the time interval Integrate over and evaluate the two ends of the second equation in the time interval Integrating on gives the equation: ; d. Further iterate and scale the equation in step c to obtain the equation: ; e. The equation obtained in step d is combined with the conditions for DoS attack signals in S5 and the definition of finite-time stability to obtain ; That is to prove that the state of the mathematical model constructed in step S4 In the time interval [0, ) is bounded in finite time, and it is also proved that the states of all agents in the multi-agent system A finite time agreement was reached.

[0015] Multi-agent system dynamic event-triggered safety control system, including: Control system framework building module: used to build a distributed control framework for multi-agent systems; Communication network information extraction module: used to obtain parameters representing the interaction relationship between each intelligent agent, and to judge whether the network is in a malicious network attack state through the parameters; Event trigger mechanism module: used to determine the update time and frequency of the intelligent body controller based on the local information of the intelligent individual; Control strategy formulation module: used to formulate a limited time consistency safety control protocol, and to formulate the time to execute the control strategy and the parameter settings of the control protocol in collaboration with the event trigger mechanism module and the network information extraction module; Control strategy execution module: used to control the multi-agent system based on the output information of the control strategy formulation module.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1) The present invention improves the event execution efficiency of the system by introducing a dynamic adaptive law into the event triggering mechanism, which can realize the purpose of multi-agent system resisting attacks and saving communication resources under multiple network attacks, and achieve the consistent control target within a limited time; 2) In terms of the components of the system model, the existing technology takes into account multiple network attacks on the system, but does not consider the saturation characteristics of the system actuator when performing corresponding operations. The present invention takes into account the saturation characteristics of the system's physical devices in the construction of the system model, which is more realistic; 3) From the perspective of control strategy design, most existing control strategies are designed based on static event trigger mechanisms, while the present invention designs a dynamic event trigger mechanism, introducing an adaptive trigger threshold into the common static event trigger mechanism to dynamically adjust the triggering moment of the system, effectively saving communication and control resources; 4) In terms of target control, most existing technologies achieve safety consistency in the sense of asymptotic stability, while the present invention enables the system to achieve safety consistency within a limited time, which is more practical in engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of the control method of the present invention; Figure 2 This is a schematic diagram of the event trigger execution time and DoS attack sequence of the present invention; Figure 3 Tunnel diode circuit diagram used for simulation test; Figure 4 It is the communication topology diagram of the system in the simulation test; Figure 5 This is the DoS attack signal diagram in the simulation test results; Figure 6 This is a diagram of deception attack signal in simulation test results; Figure 7 This is a schematic diagram of the voltage change of the intelligent body in the simulation test; Figure 8 This is a schematic diagram of the current change of the intelligent agent in the simulation test; Fig. 9 It is a schematic diagram of the control input in the simulation test; Fig.10 A schematic diagram of the event triggering execution time in the simulation test; Fig.11 Schematic diagram of the adaptive law in the simulation test. DETAILED DESCRIPTION

[0018] The technical solution of the present invention is further described in detail below in conjunction with specific embodiments. Parts not described in the following embodiments of the present invention are regarded as prior arts known or should be known to those skilled in the art. Example 1

[0019] The multi-agent system dynamic event triggered safety control method comprises the following steps: S1. Establish a mathematical model of distributed control of multi-agent systems under multiple network attack conditions; In this step, the distributed control mathematical model of the multi-agent system is: ; In the formula, is the system state of the i-th agent in the multi-agent system, is the control input of the distributed control mathematical model, A and B are known constant matrices, f(t) is the uncertain nonlinear term of the system, and f(t) satisfies the following conditions: , is the Lipschitz constant; For an actuator with an upper bound The saturation function is defined as follows: ; S2. Obtain communication network information to determine whether the network is in a malicious network attack state; The open network environment makes networked control systems such as multi-agent systems extremely vulnerable to network attacks. In this embodiment, the communication network of the multi-agent system is considered to be subject to two types of network attacks: DoS attacks and deception attacks; In this step, the malicious network attacks are DoS attacks and deception attacks. At this time, the state space model of the multi-agent system with actuator saturation characteristics and whose communication network is subjected to DoS attacks and deception attacks is described as: ; The steps to obtain the state space model of the multi-agent system subjected to DoS attack and deception attack are as follows: 2.1) DoS attacks in the network are intended to prevent normal data transmission between intelligent individuals, but one feature of this attack is that the energy is limited, so after each attack is completed, it will enter a dormant period to accumulate energy. The duration of the hth attack is , represents the duration of the hth DoS attack, and the subsequent sleep interval is recorded as ;remember and Represents the time interval The cumulative attack time and the cumulative sleep time on the According to the characteristics of DoS attacks, set the system signal of DoS attacks for: ; In the formula, is a normal signal. is the indicator function of DoS attack, with a value of 0 or 1. =1 means DoS attack occurs. =0 means that DoS attack has not occurred; For any given time interval , the DoS attack is limited to the time interval The number of occurrences is recorded as , then the time interval DoS attack frequency Defined as: ; For any given time interval , the DoS attack is limited to the time interval The total duration of occurrence is recorded as , in the time interval DoS attack duration Defined as: ; In the formula, is the attack length rate, ; 2.2) Deception attack uses false signals to replace normal system signals. The Bernoulli random variable β(t) is used to describe the probability of the system being attacked by deception. Its mathematical expectation is β, β∈[0,1]. At this time, the signal under deception attack for: ; In the formula, the nonlinear function is a bounded function used to describe the attack intensity, which satisfies , S is a known real constant matrix; 2.3) Based on step 2.1) and step 2.2), for a multi-agent system with actuator saturation characteristics, the state space model of the multi-agent system whose communication network is subjected to DoS attack and deception attack is constructed as follows: ; S3. Develop a distributed finite-time security consistency control protocol and set a dynamic event trigger mechanism; In this step, the distributed finite-time security consistency control protocol is: ; Where K is the feedback matrix to be determined, is the weight of agents i and j in the communication topology, Z + The set of positive integers, represents the execution time of agent i, Represents the state sampling information of agent i at the execution time; Execution time It is determined by the dynamic event triggering mechanism, which is expressed as: ; In the formula, is the measurement error, which represents the difference between the current state of the ith agent and its previous sampling state. The first sampling time of all agents is set to the initial time of the system, that is, ; represents the sum of the differences between the state of the i-th agent and the states of all its neighboring agents j; is a dynamic adaptive law representing the parameter in the event generator that determines the dynamic threshold, with an initial value , The dynamic description is: ; In the formula, the parameters , , is a positive constant to be selected in the event trigger mechanism, is the positive constant to be selected in the adaptive law; S4. Construct a mathematical model of a multi-agent control system that can cope with multiple network attacks; In this step, the mathematical model of the multi-agent control system to cope with multiple network attacks is constructed as follows: ; In the formula, is the consistency error, which is defined as ; make , , , , , , , ,use represents the N-dimensional identity matrix, represents an n-dimensional column vector whose elements are all 1, then the mathematical model of the multi-agent control system to cope with multiple network attacks is transformed into: ; S5. Set sufficient conditions to ensure that the mathematical model in S4 achieves finite-time security consistency, and the sufficient conditions include that the DoS attack signal satisfies the conditions to achieve control over the target; The specific operations of this step are: The mathematical expression of the sufficient condition for ensuring that the system achieves finite-time safety consistency is set as: If there exists a positive definite symmetric matrix , ,matrix , so that the following conditions hold: ; Moreover, the DoS attack signal satisfies the following conditions: ; Then, the gain in the distributed finite-time safety consistency control protocol in S3 is selected as , that is, to ensure that the multi-agent control system in S4 achieves finite-time safety consistency; In the above formula: ; , ,and , , and are all selected positive numbers; ; In this embodiment, the multi-agent system is composed of N agents, and the agents exchange information through a communication network.

[0020] Example 2 The method for verifying the reliability of the multi-agent system dynamic event triggered safety control method in Example 1 comprises the following steps: a. For the mathematical model constructed in step S4, construct a multi-Lyapunov function as follows: ; The indicator function in the formula ; b. Calculate the time derivative of the multi-Lyapunov function in step a, bring it into the mathematical model in step S4, take the mathematical expectation, and then substitute it into the sufficient condition set in step S5 to ensure that the system achieves finite time safety consistency to obtain the equation: ; c. For the first equation in step b, both ends of the equation are in the time interval Integrate over and evaluate the two ends of the second equation in the time interval Integrating on gives the equation: ; d. Further iterate and scale the equation in step c to obtain the equation: ; e. The equation obtained in step d is combined with the conditions for DoS attack signals in S5 and the definition of finite-time stability to obtain ; That is to prove that the state of the mathematical model constructed in step S4 In the time interval [0, ) is bounded in finite time, and it is also proved that the states of all agents in the multi-agent system A finite time agreement was reached.

[0021] Example 3 Multi-agent system dynamic event-triggered safety control system, including: Control system framework building module: used to build a distributed control framework for multi-agent systems; Communication network information extraction module: used to obtain parameters representing the interaction relationship between each intelligent agent, and to judge whether the network is in a malicious network attack state through the parameters; Event trigger mechanism module: used to determine the update time and frequency of the intelligent body controller based on the local information of the intelligent individual; Control strategy formulation module: used to formulate a limited time consistency safety control protocol, and to formulate the time to execute the control strategy and the parameter settings of the control protocol in collaboration with the event trigger mechanism module and the network information extraction module; Control strategy execution module: used to control the multi-agent system based on the output information of the control strategy formulation module.

[0022] Simulation test To verify the effectiveness of the present invention, a circuit consisting of five tunnel diodes (such as Figure 3 The state space mathematical model of each agent is expressed as follows: ; Its parameters are set as follows: voltage source E=1V, resistance R=1Ω, capacitance L=1H, inductance C=1F, . Then the system parameters are: , , , .

[0023] System communication topology diagram Figure 4 As shown; The function of the spoofing attack is , its occurrence probability β=0.3; The simulation parameters are selected as follows: (Experience points), (Experience points), (random number), (Experience points), (Experience points).

[0024] Using the LMI toolbox in MATLAB to solve the sufficient conditions in step S5, we get the following feasible solution:

[0025] And the DoS attack frequency within the simulation time period of 10 seconds is 0.4, the number of attacks is 4, and the total attack duration is 3.3s. Figure 5 and Figure 6 The DoS and deception attack signal diagrams are given; Given the initial value of each agent is , , , and .

[0026] The simulation step size is selected as 0.005, and the following simulation result graph is obtained: Figure 7 and Figure 8 The voltage and current change trajectories of the intelligent system are described respectively; from Figure 7 and Figure 8 It can be seen that the voltage and current have achieved consistency within 10 seconds.

[0027] Fig. 9 The control input is characterized in , and it can be found that it has a saturation characteristic and the controller is not executed continuously.

[0028] Fig.10 The event trigger execution time is described, and it can be found that the control is executed intermittently, and the system can effectively save control resources.

[0029] The dynamics of the adaptive law of the threshold in the event triggering mechanism is given by Fig.11 Given , it converges.

[0030] From the above simulation results, it can be concluded that the present invention can ensure that the system achieves consistency within a limited time while resisting network attacks and saving system control resources, which shows the effectiveness of the technical solution of the present invention.

[0031] Those skilled in the art should be aware that the above embodiments may be method flows, executable computer programs, and systems. Therefore, the present invention may be implemented in the form of hardware embodiments, software embodiments, or software and hardware embodiments. In addition, the present invention may be implemented in the form of a computer program product on a computer storage medium (including a hard disk, a magnetic disk, an optical disk, an optical storage device, etc.) containing executable computer program code.

[0032] The present invention is described with reference to the flowcharts of the methods, systems and computer program products of the embodiments of the present invention. It should be understood that each process or a combination of multiple processes described in the flowcharts can be implemented by computer program instructions. These computer program instructions can be executed to a processor of a general / special purpose computer or a programmable data processing device to generate a machine, and after the computer or programmable data processor executes the instructions, it generates a program for implementing the process described in the flowchart. Figure 1 A device that performs the functions described in one or more processes.

Claims

1. A multi-agent system dynamic event-triggered safety control method, characterized in that: The steps include: S1. Establish a mathematical model of distributed control of multi-agent systems under multiple network attack conditions; S2. Obtain communication network information to determine whether the network is in a malicious network attack state; S3. Develop a distributed finite-time security consistency control protocol and set a dynamic event trigger mechanism; S4. Construct a mathematical model of a multi-agent control system that can cope with multiple network attacks; S5. Set sufficient conditions to ensure that the mathematical model in S4 achieves finite-time security consistency, and the sufficient conditions include that the DoS attack signal satisfies the conditions to achieve control over the target.

2. The multi-agent system dynamic event triggered safety control method according to claim 1, characterized in that: The multi-agent system consists of N agents, and the agents exchange information through a communication network.

3. The multi-agent system dynamic event triggered safety control method according to claim 1, characterized in that: In S1, the distributed control mathematical model of the multi-agent system is: ; In the formula, is the system state of the i-th agent in the multi-agent system, is the control input of the distributed control mathematical model, A and B are known constant matrices, f(t) is the uncertain nonlinear term of the system, and f(t) satisfies the following conditions: , is the Lipschitz constant; For an actuator with an upper bound The saturation function is defined as follows: 。 4. The multi-agent system dynamic event triggered safety control method according to claim 1, characterized in that: The malicious network attacks in S2 are DoS attacks and deception attacks. At this time, the state space model of the multi-agent system with actuator saturation characteristics and whose communication network is subjected to DoS attacks and deception attacks is described as: .

5. The multi-agent system dynamic event triggered safety control method according to claim 4, characterized in that: The steps of obtaining the state space model of the multi-agent system subjected to DoS attack and deception attack are as follows: 2.1) According to the characteristics of DoS attacks, set the system signal of DoS attacks for: ； In the formula, is a normal signal. is the indicator function of DoS attack, with a value of 0 or 1. =1 indicates a DoS attack occurs. =0 means that DoS attack has not occurred; 2.2) Use the Bernoulli random variable β(t) to describe the probability of the system being attacked by deception. Its mathematical expectation is β, β∈[0,1]. At this time, the signal under deception attack for: ； In the formula, the nonlinear function is a bounded function used to describe the attack intensity, which satisfies , S is a known real constant matrix; 2.3) Based on steps 2.1) and 2.2), a state space model of a multi-agent system whose communication network is subjected to DoS attacks and deception attacks is constructed.

6. The multi-agent system dynamic event triggered safety control method according to claim 1, characterized in that: In S3, the distributed finite-time security consistency control protocol is: ； Where K is the feedback matrix to be determined, is the weight of agents i and j in the communication topology, Z + The set of positive integers, represents the execution time of agent i, Represents the state sampling information of agent i at the execution time; Execution time It is determined by the dynamic event triggering mechanism, which is expressed as: ； In the formula, is the measurement error, which represents the difference between the current state of the ith agent and its previous sampling state. The first sampling time of all agents is set to the initial time of the system, that is, ; represents the sum of the differences between the state of the i-th agent and the states of all its neighboring agents j; is a dynamic adaptive law representing the parameter in the event generator that determines the dynamic threshold, with an initial value , The dynamic description is: ； In the formula, the parameters , , is a positive constant to be selected in the event trigger mechanism, is the positive constant to be selected in the adaptive law.

7. The multi-agent system dynamic event triggered safety control method according to claim 1, characterized in that: In S4, the mathematical model of constructing a multi-agent control system to cope with multiple network attacks is: ； In the formula, is the consistency error, which is defined as ; make , , , , , , , ,use represents the N-dimensional identity matrix, represents an n-dimensional column vector whose elements are all 1, then the mathematical model of the multi-agent control system to cope with multiple network attacks is transformed into: 。 8. The multi-agent system dynamic event triggered safety control method according to claim 1, characterized in that: The specific operation of S5 is: The mathematical expression of the sufficient condition for ensuring that the system achieves finite-time safety consistency is set as: If there exists a positive definite symmetric matrix , ,matrix , so that the following conditions hold: ； Moreover, the DoS attack signal satisfies the following conditions: ； Then, the gain in the distributed finite-time safety consistency control protocol in S3 is selected as , that is, to ensure that the multi-agent control system in S4 achieves finite-time safety consistency; In the above formula: ； , ,and , , and are all selected positive numbers; 。 9. A method for verifying the reliability of the multi-agent system dynamic event triggered safety control method according to any one of claims 1 to 8, characterized in that: The steps include: a. For the mathematical model constructed in step S4, construct a multi-Lyapunov function as follows: ； The characteristic function in the formula ; b. Calculate the time derivative of the multi-Lyapunov function in step a, bring it into the mathematical model in step S4, take the mathematical expectation, and then substitute it into the sufficient condition set in step S5 to ensure that the system achieves finite time safety consistency to obtain the equation: ； c. For the first equation in step b, both ends of the equation are in the time interval Integrate over and evaluate the two ends of the second equation in the time interval Integrating on gives the equation: ； d. Further iterate and scale the equation in step c to obtain the equation: ； e. The equation obtained in step d is combined with the conditions for DoS attack signals in S5 and the definition of finite-time stability to obtain ； That is to prove that the state of the mathematical model constructed in step S4 In the time interval [0, ) is bounded in finite time, and it is also proved that the states of all agents in the multi-agent system A finite time agreement was reached.

10. A multi-agent system dynamic event-triggered safety control system, characterized in that: include: Control system framework building module: used to build a distributed control framework for multi-agent systems; Communication network information extraction module: used to obtain parameters representing the interaction relationship between each intelligent agent, and to judge whether the network is in a malicious network attack state through the parameters; Event trigger mechanism module: used to determine the update time and frequency of the intelligent body controller based on the local information of the intelligent individual; Control strategy formulation module: used to formulate a limited time consistency safety control protocol, and to formulate the time to execute the control strategy and the parameter settings of the control protocol in collaboration with the event trigger mechanism module and the network information extraction module; Control strategy execution module: used to control the multi-agent system based on the output information of the control strategy formulation module.

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