Event-triggered industrial system consistency control method and system

Abstract

The invention belongs to the field of industrial system consistency control, and discloses an event-triggered industrial system consistency control method and system, and the method comprises the steps: firstly generating estimation data based on an obtained false information injection attack, and preliminarily coping with the interference of the attack on system state estimation; the generated current estimation data is compared with the estimation data sent last time, whether the data is sent to the neighbor subsystem node at the current moment or not is determined according to the comparison result, the mechanism effectively reduces unnecessary data transmission, and network burden is reduced; and finally, combining the estimation data from the neighbor subsystem node and the self-generated estimation data, updating a consistency control signal, and gradually driving each subsystem to reach a consistency state. According to the method, by continuously repeating the steps, the consistency state of the network control system can be maintained or recovered under the condition of network attack.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial system consistency control, and in particular relates to a control method and system for event-triggered industrial system consistency. Background Art

[0002] In industrial systems, network control systems enable remote monitoring, automated control, and data analysis by realizing network communication and data exchange between devices, thereby improving production efficiency, ensuring system reliability and security, and promoting the intelligent and digital transformation of industrial systems.

[0003] As an important component of industrial systems, networked control systems (NCSs) are a new generation of multi-dimensional complex control systems that integrate computing resources, communication media, and control processes. Compared with the traditional point-to-point control systems with complex wiring, high system deployment costs, poor flexibility and maintainability, the transmission of data information between the components of NCSs through the network can effectively solve the problems of location restrictions, site restrictions, and state restrictions. Due to its excellent performance, NCSs have been widely used in a large number of control engineering fields such as civil infrastructure, smart grids, drone clusters, and self-driving cars. When designing control strategies for network control systems, it is necessary to know the state information of the system. Generally, the system state information cannot be obtained directly. It is usually necessary to convert the measurement information obtained by sensors in NCSs into state information through state estimation methods. However, during the operation of NCSs, data transmission within and between systems usually requires networked communication media for transmission. Due to the networked working conditions of NCSs, the system is extremely vulnerable to network attacks, which in turn causes the system to be unable to obtain accurate state estimation data; therefore, when the system cannot obtain accurate state estimation data due to network attacks on sensors, the system performance is degraded.

[0004] It can be seen that with the existing consistency control method, when the network control system is attacked by the network, the measurement data information is tampered with due to the false data injection attack, resulting in the inability to obtain accurate system state estimation, and thus the inability to drive each subsystem to a consistent state. Summary of the invention

[0005] The present invention provides an event-triggered industrial system consistency control method and system to solve the technical problem that when the network control system is attacked by the network, the measurement data information is tampered with due to the false data injection attack, resulting in the inability to obtain accurate system state estimation, and further resulting in the inability to drive each subsystem to reach a consistent state.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A control method for event-triggered industrial system consistency, applied to any subsystem node in a network control system, comprising: Generate estimation data based on the obtained false information injection attack; Generate a comparison result based on the generated estimated data and the estimated data sent last time, and determine whether to send the generated estimated data to the neighbor subsystem node at the current moment based on the comparison result; In combination with the estimated data sent by the neighboring subsystem node and the generated estimated data, the consistency control signal at the current moment is updated; the consistency control signal is used to be input to each subsystem node to drive the network control system to reach a consistent state; It is determined whether the network control system has reached a consistent state, and if not, the estimation data generated based on the acquired false information injection attack is returned until the network control system reaches a consistent state.

[0007] Further, the generating of estimated data based on the acquired false information injection attack includes: Construct dynamic models of networked control systems; Based on the dynamic model of network control system and the data dimension of false information injection attack, an augmented system model is constructed; The augmented system model is combined with the safety state estimator built based on the Kalman filter idea to generate estimation data.

[0008] Furthermore, the dynamic model of the network control system is expressed as:

[0009] In the formula, Represents a subsystem node of a network control system The current k state vector of ; Represents a subsystem node of a network control system The state vector of the next moment k+1; For system nodes The current measurement vector of and They are the state transfer matrix and control input matrix of the network control system respectively; and They represent the measurement matrix and attack injection matrix of the network control system respectively; represents two uncorrelated zero-mean Gaussian noises; Represents the control input of the subsystem node; represents the false information injection attack at the current time k; the randomness of the false information injection attack is expressed as ,in, The mean is 0 and the variance is A normal random variable of Indicates the false information injection attack at the next moment k+1.

[0010] Further, the augmented system model is expressed as:

[0011] In the formula, is the augmented system state vector, is the augmented system noise vector, is the variance matrix of the augmented system noise; , , are the augmented system matrices of corresponding dimensions respectively; Representation and The identity matrix of the same dimension; Q i Subsystem Node The variance matrix of the process noise.

[0012] Furthermore, the estimation algorithm adopted by the safety state estimator is as follows:

[0013] In the formula, and are the prior estimates and their estimation error covariances, and are the posterior estimate and its estimation error covariance respectively; is the estimated gain matrix; is the subsystem observation vector; R is the variance matrix of the subsystem measurement noise.

[0014] Furthermore, the specific steps of the event triggering process include: Calculate the residual between the generated estimate and the last estimate sent; The calculated residual is compared with the preset trigger threshold to generate a comparison result; If the comparison result is that the residual is greater than the trigger threshold, the generated estimated data is sent to the neighbor subsystem node at the current moment; otherwise, the generated estimated data is not sent.

[0015] Furthermore, the specific steps of the control signal generation process are as follows: Combining the estimated data sent by the neighboring subsystem node and the generated estimated data, generating a new consistency control signal; The new consistency control signal is used to update the consistency control signal at the current moment. The specific formula is as follows:

[0016] In the formula, the variable Indicates event-triggered communication updates, is a predefined event trigger threshold; Subsystem Node The estimated data from the last send; The estimated result representing the current moment will be sent to the neighboring subsystem node to generate control input, and When the control input is generated by the previously sent estimation result; the control input of the subsystem node for:

[0017] In the formula, Subsystem nodes and the neighbor subsystem nodes of the subsystem node Corresponding estimated data; is the corresponding node weight, is the control gain matrix; Subsystem Node The set of all neighbor subsystem nodes.

[0018] Further, the determining whether the network control system has reached a consistent state, if not, returning the estimated data generated based on the acquired false information injection attack until the network control system reaches a consistent state, includes: The consistency status of each subsystem node in the current network control system is judged. If each subsystem node reaches a consistent state, the consistency status result of each subsystem node is output; otherwise, the estimated data generated based on the acquired false information injection attack is returned until each subsystem node reaches a consistent state.

[0019] Furthermore, the constructed Lyapunov function is used to judge the consistency state of each subsystem node in the current network control system. The specific formula is as follows:

[0020]

[0021] In the formula, and Represents subsystems and the consistency error of the network control system, is a given symmetric positive definite matrix, is the value of the Lyapunov function; express The transposed vector of ; Represents the identity matrix whose dimension is the number of subsystem nodes.

[0022] A control system for event-triggered industrial system consistency, comprising: A security state estimation module, used for generating estimation data based on the acquired false information injection attack; An event triggering module, used to generate a comparison result based on the generated estimated data and the estimated data sent last time, and determine whether to send the generated estimated data to the neighbor subsystem node at the current moment based on the comparison result; A control signal generation module, used to update the consistency control signal at the current moment in combination with the estimated data sent by the neighboring subsystem node and the generated estimated data; the consistency control signal is used to be input to each subsystem node to drive the network control system to reach a consistent state; The repeated execution module is used to determine whether the network control system has reached a consistent state. If not, the estimation data generated based on the acquired false information injection attack is returned until the network control system reaches a consistent state.

[0023] Compared with the prior art, the present invention has the following beneficial effects: The method of the present invention first generates estimated data based on the acquired false information injection attack, and preliminarily copes with the interference of the attack on the system state estimation; compares the generated current estimated data with the estimated data sent last time, and decides whether to send data to the neighbor subsystem node at the current moment according to the comparison result. This mechanism effectively reduces unnecessary data transmission and reduces the network burden; finally, combines the estimated data from the neighbor subsystem node and the estimated data generated by itself, updates the consistency control signal, and gradually drives each subsystem to reach a consistent state; by continuously repeating the above steps, the method can maintain or restore the consistency state of the network control system in the case of a network attack, thereby effectively avoiding system performance degradation; the use of this method significantly improves the robustness and reliability of NCSs under network attacks.

[0024] The present invention also provides a control system for event-triggered industrial system consistency, which forms a complete set of event-triggered industrial system consistency control schemes by integrating a security state estimation module, an event triggering module, a control signal generation module, and a repeated execution module. Among them, the security state estimation module can generate estimation data based on the acquired false information injection attack, effectively deal with the impact of network attacks on system state estimation, and improve the accuracy of the estimation data; the event triggering module compares the generated estimation data with the estimation data sent last time, and intelligently decides whether to send data to the neighbor subsystem node, thereby reducing unnecessary communication overhead while ensuring data accuracy; the control signal generation module combines the estimation data of the neighbor subsystem node and the estimation data of the current node to generate an updated consistency control signal, ensuring the consistency and stability of the system; the repeated execution module ensures that the entire control process is continuously iterated until the network control system reaches a consistent state, reflecting the flexibility and robustness of the system. The use of this control system can effectively deal with network attacks, improve the accuracy of system state estimation, ensure the consistency and stability of the system, thereby improving the security and reliability of the network control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of the overall system structure and network attack model of NCSs provided in an embodiment of the present invention; Figure 2 A schematic diagram of a communication network topology and network attacks provided by an embodiment of the present invention; Figure 3 A schematic diagram of the state estimation results of an attacked node under different estimators provided in an embodiment of the present invention; Figure 4 A schematic diagram of triggering situations of external communication of each subsystem node is provided for an embodiment of the present invention; Figure 5 A schematic diagram of the consistency state changes of each subsystem node and the overall consistency error of NCSs provided in an embodiment of the present invention; Figure 6 A flow chart of a control method for consistency of an event-triggered industrial system provided by an embodiment of the present invention; Figure 7 A flow chart of a control method for consistency of an event-triggered industrial system provided by the present invention; Figure 8 A schematic diagram of the structure of a control system for event-triggered industrial system consistency provided by the present invention. DETAILED DESCRIPTION

[0026] As described in the background technology, due to the excellent performance of NCSs, NCSs have been widely used in a large number of control engineering fields such as civil infrastructure, smart grids, drone swarms and self-driving cars. Consistency control provides valuable help for NCSs to perform various tasks. For example, drone swarm performances are common working scenes of NCSs. Sometimes, in order to show a specific pattern in the air, drone swarms need to reach a consensus on state information such as position or speed through consistency control. Therefore, due to its important theoretical significance and potential for application in practical engineering, the consistency problem has always been one of the hot issues in NCSs research. During the working process of NCSs, data transmission within and between systems generally requires networked communication media for transmission, and the networked working conditions of NCSs make the system extremely vulnerable to network attack threats, which in turn causes the system to be unable to obtain accurate state estimation data. As we all know, for NCSs, the measurement data obtained by the system sensor cannot be directly used for the generation of control signals, and the measurement data needs to be converted into system state information through a state estimation method. Therefore, when the system cannot obtain accurate state estimation data because the sensor is attacked by a network, the system performance is degraded.

[0027] Example 1 In order to solve the above problems, this embodiment provides an event-triggered industrial system consistency control method. This control method jointly designs the event triggering mechanism and the system consistency control to obtain a consistency control strategy based on event-triggered communication, thereby ensuring the performance of the system.

[0028] The glossary is explained as follows: False data injection attack (FDI) is a typical type of network attack against NCSs, which can forge system information or data and cause damage to NCSs.

[0029] like Figure 1 As shown, this embodiment provides an overall system structure of NCSs and a network attack model. Among them, each subsystem node of NCSs exchanges information through a communication network. Inside the node, the sensor observes the system state and sends the measurement data to the security estimator for state estimation. The estimator sends the estimation result to the event-triggered transmitter, and the event-triggered transmitter decides whether to send the current estimation data to the neighbor subsystem node. At the same time, the node receiver will receive the state estimation data sent by the neighbor subsystem node and store the received data in the memory. When no data information from any neighbor subsystem node is received, the controller will use the data previously stored in the memory to generate the control signal at the current moment. Assume that the false information injection attack exists in the sensor of the subsystem node.

[0030] In this embodiment, the network control system belongs to a linear discrete time information physical system model, and its system equation is: (1) In the formula, and Respectively represent the subsystem nodes of the network control system The state vector of the current time k and the state vector of the next time k+1, For system nodes The current measurement vector of and They are the state transfer matrix and control input matrix of the system respectively; denote the system measurement matrix and attack injection matrix respectively; represents two uncorrelated zero-mean Gaussian noises; Represents the control input of the subsystem node; represents the false information injection attack at the current time k; Among them, false information injection attack Randomness is expressed as: (2) In the formula, ; Indicates the false information injection attack at the next time k+1; Where, on the other hand, the subsystem node The consistent control input of a subsystem node depends on its neighbor subsystem node and its own state estimation. The control input of a subsystem node Expressed as:

[0031] In the formula, Subsystem nodes and subsystem nodes Neighbor subsystem node Corresponding estimated data; is the corresponding node weight, is the control gain matrix, Subsystem Node The set of all neighbor subsystem nodes.

[0032] Typically, the estimated data of each subsystem node in NCSs will be exchanged with its neighbor subsystem nodes at each sampling time to generate control signals, which places high demands on the communication capability of the system. However, due to the extremely harsh working environment and narrow loading space, NCSs are always limited by the dilemma of lack of communication resources. To solve this problem, an event-triggered communication mechanism is proposed to determine whether to schedule the estimated data, thereby reducing the communication overhead. The event-triggered communication mechanism consists of variables It means that the formula is: (4) In the formula, is a predefined event trigger threshold; Subsystem Node The last estimate sent; that is, the subsystem node The estimated data used by the neighbor subsystem nodes when generating control signals.

[0033] Under the proposed event-triggered communication mechanism, the residual between the current estimated data and the previously sent estimated data is compared with the trigger threshold. If the residual is greater than the trigger threshold, an event is triggered and the subsystem node The current estimated data will be sent to its neighbor subsystem node; otherwise, the event will not be triggered, the currently generated estimated data will not be sent, and the subsystem node The neighboring subsystem node will use the previously sent estimated data (the estimated data sent last time) in the generation of the current control signal. Neighbor subsystem nodes can use the estimated data , specifically expressed as follows:

[0034] Based on this, under the proposed event-triggered communication mechanism, the subsystem nodes Control input Generates the following:

[0035] In the formula, Neighbor subsystem node The estimated data available at time k.

[0036] For the NCSs under consideration, the goal is to achieve accurate system state estimation under network attacks (false information injection attacks) and generate control inputs based on the estimation results to achieve consistent control of the NCSs; therefore, in this embodiment, the security state estimation of the subsystem nodes is designed as follows: First, an augmented state system is constructed, the system state is combined with the sensor attack to obtain the augmented state, and the corresponding augmented system matrix is ​​obtained based on the augmented state as follows:

[0037] In the formula, is the augmented system state vector, is the augmented system noise vector, is the variance matrix of the augmented system noise; , , are the augmented system matrices of corresponding dimensions respectively; Representation and The identity matrix of the same dimension; Q i Subsystem Node The variance matrix of the process noise.

[0038] Based on the obtained augmented state vector and system matrix, the augmented state system dynamics equation is obtained as follows: (9) After the above transformation, the subsystem node dynamics model with sensor false information injection attack is transformed into a non-attack dynamics model similar to the traditional control system. Therefore, based on the Kalman filter idea, an augmented state estimator is proposed to realize the security estimation of NCSs subsystem nodes. In this embodiment, in order to make the expression concise, the subscript is omitted:

[0039] in, and is the prior estimate and its estimation error covariance, and is the estimated gain matrix; is the subsystem observation vector; R is the variance matrix of the subsystem measurement noise; the accurate system state estimation can be obtained by get.

[0040] In this embodiment, the consistency control strategy under event-triggered communication is designed as follows: Define subsystem nodes in NCSs The consistency error for:

[0041] Then the dynamic model of consistency error can be obtained as:

[0042] In the formula, Subsystem Node The consistency error at the next moment k+1, Subsystem Node The state vector at the next moment k+1, and Subsystem nodes The state vector and control vector, Subsystem Node The estimated data, Subsystem Node Available estimates, Subsystem Node For subsystem nodes The node weight of .

[0043] Replace all the estimated data triggered by events with the expression in , and construct the Lyapunov function, as follows: (12) in, and Represents subsystems and the consistency error of the network control system, For a given symmetric positive definite matrix, use the symmetric positive definite matrix of the corresponding dimension; is the value of the Lyapunov function; express The transposed vector of ; Represents the unit matrix with the dimension of the number of subsystem nodes; by transforming and simplifying the Lyapunov function, a Riccati equation is obtained. By solving the obtained Riccati equation, the control gain matrix K can be obtained. The obtained control gain matrix solution equation is as follows:

[0044] in, According to the matrix Determine the parameter matrix.

[0045] It can be seen that the control method of industrial system consistency using event-triggered control is obtained by combining the augmented state security estimator and event-triggered communication consistency control. Compared with the traditional algorithm, the proposed method can obtain accurate system state estimation under the influence of network attacks because the FDI attack is regarded as part of the system state. In addition, by introducing the event-triggered communication mechanism, the proposed method can keep the consistency performance of NCSs at a relatively high level while effectively reducing the communication requirements.

[0046] like Figure 6 As shown, this implementation provides a control method for event-triggered industrial system consistency, combined with Figure 1 , the specific steps are as follows: Step 1: Initialize the states of each subsystem node in NCSs, as well as related estimation parameters, etc., set the system time k to 1, and start the algorithm; Step 2: The sensor inside the subsystem node obtains the measurement value y_(i,k) that may be attacked by FDI at the current moment and sends it to the security state estimator inside the node; Step 3: The safety state estimator inside the subsystem node obtains accurate state estimation data and sends it to the data transmitter with event trigger mechanism; Step 4: The data transmitter inside the subsystem node compares the difference between the current estimated data and the previously sent data with the predefined event trigger threshold; wherein the data transmitter is Figure 1 The event triggers the transmitter; Step 5: When the estimated residual is greater than the trigger threshold, the data transmitter sends the current estimated data to the neighboring node, otherwise, the current data is not sent; Step 6: Combine the estimated data of the node itself with the estimated data sent by the neighbor to generate a consistency control signal (when the current estimated data sent by the neighbor node is not received, the stored previously sent data is used); Step 7: Input the consistency control signal into the subsystem node to drive the states of each subsystem node to reach consistency; Step 8: Determine whether the consistency state is reached. If not, return to step 2. Otherwise, end and output the consistency state results of each subsystem node in the NCSs; that is, all subsystem nodes of the network control system reach a consistent state.

[0047] The implementation effect of this embodiment is further described with reference to the accompanying drawings: A 2D non-self-stable networked control system with 5 subsystem nodes is introduced, where the matrix parameters of the system are set as:

[0048] The initial positions of each subsystem node are set to , , , , The variances of the process noise and measurement noise of the system are , The communication network topology of the system and the corresponding weights are as follows: Figure 2 As shown, the network attack is set to exist in the sensor of subsystem node 2.

[0049] The required symmetric positive definite matrix and the appropriate matrix Set to: (15) Based on the above matrix and system model parameters, the consistency control gain matrix can be obtained as: (16) Under the proposed sensor false information injection attack, the different estimation results of subsystem node 2 using the traditional Kalman filter-based estimator and the proposed security estimator are compared as shown in the figure. Figure 3 shown.

[0050] Depend on Figure 3 It can be seen that the traditional Kalman filter-based estimator cannot achieve accurate estimation of the system state under the influence of network attacks. The proposed security estimator can quickly track the system state and maintain a high estimation accuracy. The simulation results are helpful to prove that by adopting the augmented state-based security estimation algorithm proposed in this embodiment, the influence of network attacks on system state estimation can be effectively resisted, and accurate system state estimation can be achieved.

[0051] The event trigger threshold is set to 0.01, and the triggering conditions of each subsystem node's external communication are as follows: Figure 4 As shown in FIG. 1 , node represents a subsystem node. “○” represents that the subsystem node sends the current state estimation value to the neighboring node.

[0052] Depend on Figure 4 It can be seen that the subsystem nodes of the network control system send estimated data at each sampling time at the beginning of the simulation, and as the system gradually enters a stable state, the communication rate of each node will decrease. Compared with full-time communication, the total communication rate of the control method proposed in this embodiment is reduced by 15%. These simulations show that this control method can effectively reduce the communication requirements of NCSs.

[0053] The NCSs consistency control performance at the above transmission rate is as follows: Figure 5 As shown, Figure 5 middle, represents the consistency error of the entire system, ρ represents the predefined event trigger threshold, and k represents the time.

[0054] Figure 5 The simulation in shows that: under the proposed control strategy with event-triggered communication mechanism, each subsystem node of NCSs can achieve a consistent state, and the consistency error is converged. Compared with the performance of the algorithm under full-time communication (ρ=0), by setting an appropriate event trigger threshold (ρ=0.01), the performance of consistency control can also be maintained at a high level under event-triggered communication. In the steady state, the difference in consistency performance is about 0.02. At the same time, combined with Figure 4 and Figure 5 ,It can be proved that the application of the proposed event-triggered control method for ,consistency of industrial systems can effectively reduce the communication ,overhead and have excellent system consistency performance.

[0055] Example 2 The present invention provides an event-triggered industrial system consistency control method, which is applied to any subsystem node in a network control system, such as Figure 7 As shown, the following steps are included: Generate estimation data based on the obtained false information injection attack; Generate a comparison result based on the generated estimated data and the estimated data sent last time, and determine whether to send the generated estimated data to the neighbor subsystem node at the current moment based on the comparison result; In combination with the estimated data sent by the neighboring subsystem node and the generated estimated data, the consistency control signal at the current moment is updated; the consistency control signal is used to be input to each subsystem node to drive the network control system to reach a consistent state; It is determined whether the network control system has reached a consistent state, and if not, the estimation data generated based on the acquired false information injection attack is returned until the network control system reaches a consistent state.

[0056] In this embodiment, the estimation data is generated based on the acquired false information injection attack, and the specific steps are as follows: Construct dynamic models of networked control systems; Based on the dynamic model of network control system and the data dimension of false information injection attack, an augmented system model is constructed; The augmented system model is combined with the safety state estimator built based on the Kalman filter idea to generate estimation data.

[0057] It can be seen that in this embodiment, the specific steps of generating estimated data based on the acquired false information injection attack include: by constructing a dynamic model and an augmented system model of the network control system, combined with a security state estimator, it is possible to accurately generate estimated data, providing a reliable basis for subsequent event triggering and control signal generation.

[0058] In this embodiment, the dynamic model of the network control system is expressed as:

[0059] In the formula, Represents a subsystem node of a network control system The state vector of the current moment k; Represents a subsystem node of a network control system The state vector of the next moment k+1; For system nodes The current measurement vector of and They are the state transfer matrix and control input matrix of the network control system respectively; and They represent the measurement matrix and attack injection matrix of the network control system respectively; represents two uncorrelated zero-mean Gaussian noises; Represents the control input of the subsystem node; represents the false information injection attack at the current time k; the randomness of the false information injection attack is expressed as ,in, The mean is 0 and the variance is A normal random variable of Indicates the false information injection attack at the next moment k+1.

[0060] It can be seen that in the dynamic model of this embodiment, the relationship between factors such as system state, measurement vector, control input, and attack injection is clarified, providing a clear mathematical model for understanding and analyzing system behavior.

[0061] In this embodiment, the augmented system model is expressed as:

[0062] In the formula, is the augmented system state vector, is the augmented system noise vector, is the variance matrix of the augmented system noise; , , are the augmented system matrices of corresponding dimensions respectively; Representation and The identity matrix of the same dimension; Q i Subsystem Node The variance matrix of the process noise.

[0063] It can be seen that in the augmented system model of this embodiment, the false information injection attack is incorporated into the system model, making the system model more complete and accurate, and providing more comprehensive information for security status estimation.

[0064] In this embodiment, the estimation algorithm adopted by the safety state estimator is as follows:

[0065] In the formula, and are the prior estimates and their estimation error covariances, and are the posterior estimate and its estimation error covariance respectively; is the estimated gain matrix; is the subsystem observation vector; R is the variance matrix of the subsystem measurement noise.

[0066] It can be seen that in this embodiment, the estimation algorithm of the safety state estimator can achieve accurate estimation of the system state based on the augmented system model and combined with the Kalman filter idea, and provide reliable estimation data for the event triggering process.

[0067] In this embodiment, the specific steps of the event triggering process include: Calculate the residual between the generated estimate and the last estimate sent; The calculated residual is compared with the preset trigger threshold to generate a comparison result; If the comparison result is that the residual is greater than the trigger threshold, the generated estimated data is sent to the neighbor subsystem node at the current moment; otherwise, the generated estimated data is not sent.

[0068] Here, the event triggering process includes: determining whether the estimated data needs to be sent by calculating the residual between the estimated data and the last sent data and comparing it with a preset triggering threshold, thereby effectively reducing the communication burden and improving system efficiency.

[0069] In this embodiment, the specific steps of the control signal generation process are as follows: Combining the estimated data sent by the neighboring subsystem node and the generated estimated data, generating a new consistency control signal; The new consistency control signal is used to update the consistency control signal at the current moment. The specific update formula is as follows:

[0070] In the formula, the variable Indicates event-triggered communication updates, is a predefined event trigger threshold; Subsystem Node The estimated data from the last send; The estimated result representing the current moment will be sent to the neighboring subsystem node to generate control input, and When the control input is generated by the previously sent estimation result; the control input of the subsystem node for:

[0071] In the formula, Subsystem nodes and the neighbor subsystem nodes of the subsystem node Corresponding estimated data; is the corresponding node weight, is the control gain matrix; Subsystem Node The set of all neighbor subsystem nodes.

[0072] Here, a new consistency control signal is generated by combining the estimated data of the neighbor subsystem nodes and the estimated data of the current node, ensuring the consistency and stability of the system.

[0073] Specifically, the determining whether the network control system has reached a consistent state, and if not, returning the estimated data generated based on the acquired false information injection attack until the network control system reaches a consistent state, includes: The consistency status of each subsystem node in the current network control system is judged. If each subsystem node reaches a consistent state, the consistency status result of each subsystem node is output; otherwise, the estimated data generated based on the acquired false information injection attack is returned until each subsystem node reaches a consistent state.

[0074] In this embodiment, the constructed Lyapunov function is used to judge the consistency state of each subsystem node in the current network control system. The specific formula is as follows:

[0075]

[0076] In the formula, and Represents subsystems and the consistency error of the network control system, is a given symmetric positive definite matrix, is the value of the Lyapunov function; express The transposed vector of ; Represents the identity matrix whose dimension is the number of subsystem nodes.

[0077] It can be seen that this method constructs a Lyapunov function to judge the consistency state of each subsystem node in the current network control system, ensuring the accuracy and reliability of the system reaching a consistent state. At the same time, by repeatedly executing the above steps until the system reaches a consistent state, the flexibility and robustness of the method are reflected.

[0078] like Figure 8As shown, the present invention also provides an event-triggered industrial system consistency control system, including: a security state estimation module, used to generate estimated data based on the acquired false information injection attack; an event triggering module, used to generate a comparison result based on the generated estimated data and the estimated data sent last time, and determine whether to send the generated estimated data to the neighbor subsystem node at the current moment based on the comparison result; a control signal generation module, used to update the consistency control signal at the current moment in combination with the estimated data sent by the neighbor subsystem node and the generated estimated data; the consistency control signal is used to input into each subsystem node to drive the network control system to reach a consistent state; a repeated execution module, used to determine whether the network control system has reached a consistent state, and if not, return the estimated data generated based on the acquired false information injection attack until the network control system reaches a consistent state.

[0079] The present invention also provides a device, comprising: a memory for storing a computer program; and a processor for implementing the steps of the control method for consistency of an event-triggered industrial system when executing the computer program.

[0080] When the processor executes the computer program, the steps of controlling the consistency of the industrial system triggered by the above-mentioned event are implemented, for example: generating estimated data based on the acquired false information injection attack; generating a comparison result based on the generated estimated data and the estimated data sent last time, and determining whether to send the generated estimated data to the neighbor subsystem node at the current moment based on the comparison result; updating the consistency control signal at the current moment in combination with the estimated data sent by the neighbor subsystem node and the generated estimated data; the consistency control signal is used to input into each subsystem node to drive the network control system to reach a consistent state; judging whether the network control system has reached a consistent state, and if not, returning the estimated data generated based on the acquired false information injection attack until the network control system reaches a consistent state.

[0081] Alternatively, the processor implements the functions of each module in the above-mentioned system when executing the computer program, for example: a security state estimation module, which is used to generate estimation data based on the acquired false information injection attack; an event triggering module, which is used to generate a comparison result based on the generated estimation data and the estimation data sent last time, and determine whether to send the generated estimation data to the neighbor subsystem node at the current moment based on the comparison result; a control signal generation module, which is used to update the consistency control signal at the current moment in combination with the estimation data sent by the neighbor subsystem node and the generated estimation data; the consistency control signal is used to be input into each subsystem node to drive the network control system to reach a consistency state; a repeated execution module, which is used to determine whether the network control system has reached a consistency state, and if not, returns the estimation data generated based on the acquired false information injection attack until the network control system reaches a consistency state.

[0082] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of completing preset functions, and the instruction segments are used to describe the execution process of the computer program in the control device for triggering consistency of the industrial system by the event. For example, the computer program can be divided into a security state estimation module, an event triggering module, a control signal generating module and a repeated execution module; the specific functions of each module are as follows: a security state estimation module, used to generate estimation data based on the acquired false information injection attack; an event triggering module, used to generate a comparison result based on the generated estimation data and the estimation data sent last time, and determine whether to send the generated estimation data to the neighbor subsystem node at the current moment based on the comparison result; a control signal generating module, used to update the consistency control signal at the current moment in combination with the estimation data sent by the neighbor subsystem node and the generated estimation data; the consistency control signal is used to input into each subsystem node to drive the network control system to reach a consistent state; a repeated execution module, used to determine whether the network control system has reached a consistent state, and if not, return the estimation data generated based on the acquired false information injection attack until the network control system reaches a consistent state.

[0083] The control device for event-triggered industrial system consistency can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The control device for event-triggered industrial system consistency can include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above is an example of an event-triggered industrial system consistency control device, and does not constitute a limitation on an event-triggered industrial system consistency control device, and can include more components than the above, or a combination of certain components, or different components. For example, the control device for event-triggered industrial system consistency can also include input and output devices, network access devices, buses, etc.

[0084] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The processor is the control center of the control of the consistency of the event-triggered industrial system, and uses various interfaces and lines to connect the various parts of the control device of the consistency of the event-triggered industrial system.

[0085] The memory can be used to store the computer program and / or module, and the processor implements various functions of the control device for industrial system consistency triggered by an event by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0086] The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0087] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the control method for consistency of an event-triggered industrial system are implemented.

[0088] The module / unit integrated in the control system for event-triggered industrial system consistency can be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as an independent product.

[0089] Based on such understanding, the present invention implements all or part of the processes in the above-mentioned event-triggered industrial system consistency control method, and can also be completed by instructing related hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned event-triggered industrial system consistency control method can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or preset intermediate form, etc.

[0090] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0091] It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electrical carrier signals and telecommunication signals.

[0092] The above embodiment is only one of the implementation methods that can realize the technical solution of the present invention. The scope of protection claimed by the present invention is not limited only to this embodiment, but also includes changes, replacements and other implementation methods that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention.

Claims

1. An event-triggered industrial system consistency control method, applied to any subsystem node in a network control system, characterized in that: include: Generate estimation data based on the obtained false information injection attack; Generate a comparison result based on the generated estimated data and the estimated data sent last time, and determine whether to send the generated estimated data to the neighbor subsystem node at the current moment based on the comparison result; Combine the estimated data sent by the neighboring subsystem node and the generated estimated data to update the consistency control signal at the current moment; The consistency control signal is used to input into each subsystem node to drive the network control system to reach a consistent state; It is determined whether the network control system has reached a consistent state, and if not, the estimation data generated based on the acquired false information injection attack is returned until the network control system reaches a consistent state.

2. The method for controlling consistency of an event-triggered industrial system according to claim 1, characterized in that: The method of generating estimated data based on the acquired false information injection attack includes: Construct dynamic models of networked control systems; Based on the dynamic model of network control system and the data dimension of false information injection attack, an augmented system model is constructed; The augmented system model is combined with the safety state estimator built based on the Kalman filter idea to generate estimation data.

3. The method for controlling consistency of an event-triggered industrial system according to claim 2, characterized in that: The dynamic model of the network control system is expressed as: In the formula, Represents a subsystem node of a network control system The current k state vector of ; Represents a subsystem node of a network control system The state vector of the next moment k+1; For system nodes The current measurement vector of and They are the state transfer matrix and control input matrix of the network control system respectively; and They represent the measurement matrix and attack injection matrix of the network control system respectively; represents two uncorrelated zero-mean Gaussian noises; Represents the control input of the subsystem node; represents the false information injection attack at the current time k; the randomness of the false information injection attack is expressed as ,in, The mean is 0 and the variance is A normal random variable of Indicates the false information injection attack at the next moment k+1.

4. The event-triggered industrial system consistency control method according to claim 3, characterized in that: The augmented system model is expressed as: In the formula, is the augmented system state vector, is the augmented system noise vector, is the variance matrix of the augmented system noise; , , are the augmented system matrices of corresponding dimensions respectively; Representation and The identity matrix of the same dimension; Q i Subsystem Node The variance matrix of the process noise.

5. The event-triggered industrial system consistency control method according to claim 4, characterized in that: The estimation algorithm adopted by the safety state estimator is as follows: In the formula, and are the prior estimates and their estimation error covariances, and are the posterior estimate and its estimation error covariance respectively; is the estimated gain matrix; is the subsystem observation vector; R is the variance matrix of the subsystem measurement noise.

6. The event-triggered industrial system consistency control method according to claim 1, characterized in that: The specific steps of the event triggering process include: Calculate the residual between the generated estimate and the last estimate sent; The calculated residual is compared with the preset trigger threshold to generate a comparison result; If the comparison result is that the residual is greater than the trigger threshold, the generated estimated data is sent to the neighbor subsystem node at the current moment; otherwise, the generated estimated data is not sent.

7. The event-triggered industrial system consistency control method according to claim 1, characterized in that: The specific steps of the control signal generation process are as follows: Combining the estimated data sent by the neighboring subsystem node and the generated estimated data, generating a new consistency control signal; The new consistency control signal is used to update the consistency control signal at the current moment. The specific formula is as follows: In the formula, the variable Indicates event-triggered communication updates, is a predefined event trigger threshold; Subsystem Node The estimated data from the last send; The estimated result representing the current moment will be sent to the neighboring subsystem node to generate control input, and When the control input is generated by the previously sent estimation result; the control input of the subsystem node for: In the formula, Subsystem nodes and the neighbor subsystem nodes of the subsystem node Corresponding estimated data; is the corresponding node weight, is the control gain matrix; Subsystem Node The set of all neighbor subsystem nodes.

8. The method for controlling consistency of an event-triggered industrial system according to claim 1, characterized in that: The determining whether the network control system has reached a consistent state, and if not, returning the estimated data generated based on the acquired false information injection attack until the network control system reaches a consistent state, includes: The consistency status of each subsystem node in the current network control system is judged. If each subsystem node reaches a consistent state, the consistency status result of each subsystem node is output; otherwise, the estimated data generated based on the acquired false information injection attack is returned until each subsystem node reaches a consistent state.

9. The event-triggered industrial system consistency control method according to claim 8, characterized in that: The constructed Lyapunov function is used to judge the consistency state of each subsystem node in the current network control system. The specific formula is as follows: In the formula, and Represents subsystems and the consistency error of the network control system, is a given symmetric positive definite matrix, is the value of the Lyapunov function; express The transposed vector of ; Represents the identity matrix whose dimension is the number of subsystem nodes.

10. An event-triggered industrial system consistency control system, characterized in that: include: A security state estimation module, used for generating estimation data based on the acquired false information injection attack; An event triggering module, used to generate a comparison result based on the generated estimated data and the estimated data sent last time, and determine whether to send the generated estimated data to the neighbor subsystem node at the current moment based on the comparison result; A control signal generation module, used to update the consistency control signal at the current moment in combination with the estimation data sent by the neighbor subsystem node and the generated estimation data; The consistency control signal is used to input into each subsystem node to drive the network control system to reach a consistent state; The repeated execution module is used to determine whether the network control system has reached a consistent state. If not, the estimation data generated based on the acquired false information injection attack is returned until the network control system reaches a consistent state.

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