Power communication coupling smart grid sensor control integration method, device and system

Abstract

The application relates to an electric power communication coupling intelligent power grid sensing control integration method, device and system, and belongs to the technical field of electric power communication. In view of the possible FDI attack in the system, a time sequence Kalman filtering method is adopted, the correlation between the optimal estimation sequence of the consistency variable and the measurement value sequence is compared, the FDI attack is accurately perceived, and the real reliability of the consistency variable data is ensured. Secondly, in the communication control aspect, when the FDI attack is perceived, the encrypted interaction mode is switched to the quantum encryption mode based on the state-action evaluation function in the Q network, and through the cooperative work of the leader node and the follower node, the quantum key distribution rate of each node is selected and dynamically adjusted. Finally, in the electrical control aspect, the consistency variable importance weight is constructed based on the encryption communication delay and the sequence correlation of attack perception, and the consistency variable is updated after normalization processing.

Description

Technical Field

[0001] The present invention relates to a method, device and system for integrating sensing, transmission and control of power communication coupled with smart grid, belonging to the technical field of power communication. Background Art

[0002] The development of advanced information and communication technologies has significantly accelerated the intelligentization and automation of smart grids. In smart grids, widely deployed IoT devices can collect real-time critical status information from energy storage devices, distributed power sources, and other equipment. This information is then transmitted back to the control center via high-speed communication networks such as 5G and optical fiber, enabling efficient, real-time control of the power generation, grid, load, and storage network.

[0003] However, the high degree of coupling between power and communication systems in smart grids means that data transmission latency and security directly impact the control performance of power systems. In untrusted environments, False Data Injection (FDI) attacks, particularly those that inject malicious data streams to tamper with real measurement data and control instructions, impacting the convergence and accuracy of control decisions and jeopardizing the balance between energy supply and demand. Therefore, effectively detecting FDI attacks and ensuring low-latency, secure data transmission and precise electrical control are critical issues that need to be addressed.

[0004] At present, there have been many studies on the integrated sensing, transmission and control methods of smart grids. The patent "Smart Grid Integrated Management System" proposes an integrated management system for smart grids, including: multiple sensor terminals, connected through a sensor network, used to obtain equipment or environmental information in each link of transmission, transformation, distribution and power consumption; a front-end application server, connected through the MIS bus, used to interact with the sensor network, and provide application services for each link itself and across links based on the equipment or environmental information of each link of transmission, transformation, distribution and power consumption; a management application platform, used to interact with the multiple front-end application servers, and provide integrated operations and data sharing of application services for each link itself and across links of transmission, transformation, distribution and power consumption. Based on the sensor network, a data sharing and integrated processing system for each link of transmission, transformation, distribution and power consumption is established to realize the management and monitoring of power resources, reduce the redundancy of work processes, and improve work efficiency. However, the above patents do not consider the impact of performance deviations from the perspective of power communication coupling, nor do they consider the impact of data transmission latency and security on grid control, causing false data and control instructions in the information domain to spread to the power domain through the communication network, triggering grid cascade oscillations and increasing control costs.

[0005] The patent "A Smart Grid Management System" proposes a smart grid management system, comprising: an information collection module connected to the smart grid for collecting power operation data; a grid adjustment module for adjusting the power transmission status of the grid; and a control module connected to the information collection module and the grid adjustment module, respectively, for adjusting the corresponding cutoff frequency of the filter when the operational stability of the smart grid is determined to be unsatisfactory based on the variance of the voltage fluctuation amplitude of the transformer output line, or for preliminarily determining that the current monitoring accuracy of the transformer output line is unsatisfactory and then secondary determining the current monitoring accuracy of the transformer output line based on the average operating current of the transformer output line; and, when the current monitoring accuracy is secondary determined to be unsatisfactory, adjusting the corresponding sampling period of the transformer output line current, thereby improving the operational stability of the smart grid. However, the above patent does not consider the importance differences of network nodes, ignores the impact of communication delay and attack perception on consistency control, and does not utilize encryption interaction mode switching and elastic step size to adjust importance weights, resulting in slower system convergence or even failure to converge.

[0006] Therefore, there is an urgent need to design an integrated method, device and system for sensing, transmission and control of power communication coupled smart grid to effectively identify and defend against FDI attacks and achieve low-latency secure data transmission and precise electrical control. Summary of the Invention

[0007] In order to solve the above technical problems, the purpose of the present invention is to provide a method, device and system for integrating sensing, transmission and control of power communication coupled smart grid.

[0008] The present invention provides a method for integrating sensing, transmission and control of power communication coupled smart grid, which comprises the following specific steps:

[0009] First, an electrical control model based on a distributed consensus algorithm is proposed to reduce the dependence of control on central nodes.

[0010] Secondly, to address the FDI attacks in the system, a communication control model that integrates quantum encryption and classical encryption is proposed. The encryption method and key distribution rate are dynamically adjusted to ensure data security while reducing the consistent variable transmission delay of quantum encryption.

[0011] Next, a communication-electrical collaborative control model under FDI attacks is proposed. Based on FDI attack awareness, the encryption interaction mode and key distribution rate in the communication domain, as well as the importance weights of consistency variables in the power domain, are dynamically adjusted to prevent the injection of false data from spreading through the communication network and achieve rapid and accurate convergence of consistency variables.

[0012] Based on the three models mentioned above, the time series Kalman filter method is used to achieve accurate perception of FDI attacks by comparing the correlation between the optimal estimation sequence of consistency variables and the measurement value sequence, ensuring the authenticity and reliability of consistency variable data;

[0013] In terms of communication control, when an FDI attack is detected, the system switches the encryption interaction mode to quantum encryption based on the state-action evaluation function in the Q network, and through the collaborative work of the leader node and follower nodes, selects and dynamically adjusts their respective quantum key distribution rates;

[0014] Finally, in terms of electrical control, the importance weights of consistency variables are constructed based on the serial correlation of encrypted communication delay and attack perception, and normalized, and the consistency variables are updated to perform integrated optimization of power communication coupled smart grid sensing, transmission and control until the entire optimization cycle is completed and the optimal output power is output.

[0015] Furthermore, the electrical control model based on the distributed consistency algorithm is specifically:

[0016] Define the number of fusion terminals as J, and its set is At the jth fusion terminal s j The cost function F of distributed generation output control in the controlled area j is j (P j )for:

[0017]

[0018] Where, α j , γ j , β j They represent the quadratic term coefficient, linear term coefficient, and constant term coefficient of the distributed generation output cost function in the grid area j, respectively. j is the output power of distributed power generation in area j;

[0019] Based on the Lagrange multiplier method and KKT conditions, the consistency variable λ is introduced j , indicating that at the jth fusion terminal s j The Lagrange multiplier of the controlled area; the definition of the consistency control process can be divided into K iterations, the consistency variable λ of the j-th fusion terminal j (k+1), the update process at the kth iteration is expressed as:

[0020]

[0021] Where, ξ j (k) represents the λ in the kth iteration j (k) update step size; τ j′,j (k) is the number of s in the kth iteration j′to s j Communication delay; ω j′,j (k) is the kth iteration s j′ to s j The importance weight of the consistency variable; j′ (k-τ j′,j (k)) represents the consideration of communication delay τ j′,j (k) next, s j From s j′ Received consistency variable; j-th fusion terminal s j Control the consistency variable λ of the controlled area j The convergence value of the optimal distributed power output power P corresponding to the area j j , expressed as

[0022]

[0023] Furthermore, the communication control model that integrates quantum encryption and classical encryption is specifically as follows:

[0024] definition is the encrypted interaction mode between fusion terminals; j,j′ (k) = 1 means s j With s j′ Quantum encryption is used to interact with each other; j,j′ (k) = 0 means using classic encryption method for interaction; definition and are the quantum node set and the classical node set respectively; define τ j,j′ (k) is the number of s in the kth iteration j With s j′ The encryption communication delay between is expressed as:

[0025]

[0026] Where, and Respectively represent the k-th iteration s j With s j′ The communication delay between classical encryption and quantum encryption.

[0027] Furthermore, the communication-electrical coordinated control model under the FDI attack is specifically as follows:

[0028] During the consistency control process, the FDI attacker intercepts s j With s j′ The classical channel data between them is injected with false data to destroy the convergence of the consistency variables and reduce the control performance; define is the attacker's number in s in the kth iteration j With sj′ The false data injected into the tampered consistency variable is expressed as:

[0029]

[0030] Where, Θ j′,j (k) represents the kth iteration of the attacker in s j With s j′ Inter-FDI attack indicator variable, Θ j′,j (k) = 1 means s j With s j′ The communication data between them is attacked by FDI, otherwise Θ j′,j (k) = 0;

[0031] Communication-electrical cooperative control model under FDI attack, consistency variable λ of the j-th fusion terminal j (k+1), the update process at the kth iteration is expressed as:

[0032]

[0033] Furthermore, the specific method of using the time series Kalman filtering method to achieve accurate perception of FDI attacks by comparing the correlation between the optimal estimation sequence of the consistency variable and the measurement value sequence is as follows:

[0034] Construct s in the kth iteration j With s j′ The optimal estimate of the consistency variable between time series X j′,j (k) = [X j′,j (kk * ),…,X j′,j (k-1),X j′,j (k)] and the measurement value time series Y j′,j (k)=[Y j′,j (kk * ),…,Y j′,j (k-1),Y j′,j (k)]; FDI attack perception is performed by comparing the correlation of the two sequences and comparing them with the threshold; the cosine similarity is used to measure the correlation between the optimal estimation value sequence and the measurement value sequence; in the kth iteration, s j With s j The correlation F between j′,j (k) and FDI attack perception are expressed as:

[0035]

[0036]

[0037] Where, ||.|| represents the Euclidean norm; X j′,j (k) = [X j′,j (kk * ),…,X j′,j (k-1),X j′,j (k)] represents the optimal estimate sequence, whose elements include the (kk * ) iterations to the optimal estimate of the kth iteration; Y j′,j (k)=[Y j′,j (kk * ),…,Y j′,j (k-1),Y j′,j (k)] represents the measurement value sequence; ΔF represents the FDI attack perception threshold; Indicates the kth iteration s j With s j′ FDI attack perception indicator variable; Indicates the perception of s j With s j′ There is an FDI attack; conversely,

[0038] Furthermore, the system switches the encryption interaction mode to quantum encryption mode based on the state-action evaluation function in the Q network, and through the collaborative work of the leader node and the follower node, selects and dynamically adjusts the specific method of their respective quantum key distribution rates as follows:

[0039] The fusion terminal that directly interacts with the distribution network control center is defined as the leader node, and the set is represented as Other terminals that cannot directly interact with information are follower nodes, and the set is represented as To ensure consistent convergence, the leader node and the quantum nodes with which it has communication connections use quantum encryption interaction;

[0040] Follow node s j Based on the Q network, construct the state-action evaluation function Q(Φ j (k),s j′ ,y j′,j (k)); Q(Φ j (k),s j′ ,y j′,j (k)=1) means that in state Φ j (k) next s j With s j′ The value of quantum encryption interaction between j (k),s j′ ,y j′,j (k) = 0) represents the value of using the classic encryption interaction method; therefore, the encryption interaction method decision is expressed as:

[0041] y j′,j (k) = Ξ{Q(Φ j (k),s j′ ,1)>Q(Φ j (k),s j′ ,0)} (9);

[0042] Based on the hierarchical collaborative DQN algorithm, the quantum key distribution rate is dynamically adjusted according to interaction needs, the node's own distribution rate and upper limit, and the key inventory. First, the leader node selects the key distribution rate based on the key distribution value function in the Q network. Second, the follower nodes make further adjustments based on the leader node's adjusted distribution rate and their own network status to ensure that the distribution rate is consistent with that of the interacting neighboring nodes. Finally, through multiple iterations and feedback optimization, the optimal strategy is gradually approached.

[0043] kth iteration leader node s j and follower node s j′ The initial key distribution rate a between j,j′ (k) is expressed as:

[0044]

[0045] Where, Distribute value functions for keys; Represents the leader node s j To all adjacent follower nodes s j′ The total value corresponding to key distribution;

[0046] Specify the node s to follow in the kth iteration j′ The key distribution rate Expressed as:

[0047]

[0048] Where, for The distribution rate and upper limit, Represents There is an interactive leader node; the first part in brackets is the remaining available distribution rate; in the numerator, express In state Next, select j′ The Q value corresponding to key distribution; the denominator is The sum of the Q values ​​of other follower nodes that interact;

[0049] Construct a loss function to evaluate the joint optimization performance of encryption interaction mode and key distribution rate; define the k-th iteration leader node s jThe loss variable is R j (k), to evaluate the number of encryption failures caused by key resource waste and insufficient quantum key, and construct the loss function L j for:

[0050]

[0051] Where K is the number of iterations at convergence, γ j is a discount factor used to evaluate the impact of future rewards on current decisions; Indicates that in the k+1th iteration, the leader node s j In state Φ j Select the best follower node under (k+1) The maximum Q value corresponding to key distribution;

[0052] After each iteration, the evaluation results of the loss function are fed back to each node to update its Q value and quantum key distribution rate strategy. Through continuous iteration and optimization, dynamic adjustment of the quantum key distribution rate strategy is achieved.

[0053] Furthermore, based on the serial correlation of encrypted communication delay and attack perception, the importance weights of consistency variables are constructed, normalized, and the consistency variables are updated. The integrated optimization of power communication coupled smart grid sensing, transmission and control is performed until the entire optimization cycle is completed. The specific method for outputting the optimal output power is as follows:

[0054] An electrical consistency control optimization method that considers communication delay and attack perception is proposed. After detecting an FDI attack, the importance weights of consistency variables based on encryption communication delay and sequence correlation are constructed according to the received message delay and encryption interaction mode. Using encryption interaction mode switching and elastic step size, false signals are prevented from spreading to the global network through communication bridges and causing cascading oscillations.

[0055] Considering the encryption communication delay and attack perception, construct the kth iteration s j With s j′ The importance weight of the consistency variable between is expressed as:

[0056]

[0057] Where Δ(k) is the elastic step size of the kth iteration, which gradually approaches 0 as the number of iterations increases, ensuring that the adjustment amplitude is large in the initial stage of the algorithm and becomes smaller in the later stages of the iteration to ensure consistent convergence; φ τ With φ F are adjustment factors, which are used to adjust the impact of encryption communication delay and sequence correlation on importance weight;

[0058] The weights are normalized and expressed as:

[0059]

[0060] When s j′ to s j When there is an FDI attack, the consistency variables detected during the FDI attack iteration will be discarded to ensure that the update of the consistency variables will not be affected by false data. j The consistency variable λ j (k+1), the update process at the kth iteration is expressed as:

[0061]

[0062] An integrated device for sensing, transmission and control of power communication coupled smart grid, comprising:

[0063] Modeling unit: This unit is used to first propose an electrical control model based on a distributed consensus algorithm to reduce the control's reliance on central nodes. Secondly, in response to FDI attacks in the system, a communication control model that integrates quantum encryption and classical encryption is proposed. The encryption method and key distribution rate are dynamically adjusted to ensure data security while reducing the transmission delay of the consistency variables of quantum encryption. Next, a communication-electrical collaborative control model under FDI attacks is proposed. Based on FDI attack perception, the encryption interaction method and key distribution rate in the communication domain are dynamically adjusted, as well as the importance weights of the consistency variables in the power domain. This prevents the injection of false data from spreading through the communication network and achieves rapid and accurate convergence of the consistency variables.

[0064] The FDI attack perception unit based on time series Kalman filtering is used to accurately perceive FDI attacks by comparing the correlation between the optimal estimation sequence of consistency variables and the measurement value sequence based on the three models mentioned above, ensuring the authenticity and reliability of consistency variable data.

[0065] A joint optimization unit for encryption interaction mode and quantum key distribution rate based on hierarchical collaborative DQN: In terms of communication control, when an FDI attack is detected, the system switches the encryption interaction mode to quantum encryption based on the state-action evaluation function in the Q network. Furthermore, through the collaborative work of the leader and follower nodes, the system selects and dynamically adjusts the quantum key distribution rate of each node.

[0066] Electrical consistency control optimization unit considering communication delay and attack perception: In terms of electrical control, it is used to construct consistency variable importance weights based on the serial correlation of encrypted communication delay and attack perception, and perform normalization processing, update consistency variables, and perform integrated optimization of power communication coupled smart grid sensing, transmission and control until the end of the entire optimization cycle and output the optimal output power.

[0067] Furthermore, the FDI attack perception unit based on temporal Kalman filtering includes:

[0068] Estimated value time series calculation module: uses the Kalman filter method to calculate the time series of the estimated value of the consistency variable and transmits the result to the correlation calculation module;

[0069] Measurement value time series calculation module: uses the Kalman filter method to calculate the consistency variable measurement value time series and transmits the results to the correlation calculation module;

[0070] Correlation calculation module: uses cosine similarity to measure the correlation between the optimal estimation value sequence and the measurement value sequence, and transmits the result to the FDI attack perception module;

[0071] FDI attack perception module: FDI attack perception is performed by comparing the correlation between two sequences and comparing them with the threshold, and the results are transmitted to the consistency variable discarding module.

[0072] Furthermore, the encryption interaction mode based on hierarchical collaborative DQN and quantum key distribution rate joint optimization unit includes:

[0073] DQN network module: Based on the loss function calculation results, it performs agent learning and updates the Q value and quantum key distribution rate strategy. Based on the current state information, it calculates the state-action evaluation function and transmits the result to the encryption interaction mode decision module, the leader node key distribution rate decision module, and the follower node key distribution rate decision module.

[0074] Encrypted interaction mode decision module: Based on the DQN network module, it calculates and compares the state-action evaluation function size, makes a node encryption interaction mode decision, and transmits the result to the loss function calculation module and the electrical consistency control optimization unit considering communication delay and attack perception;

[0075] Leader node key distribution rate decision module: Based on the DQN network module, it determines the leader node key distribution rate according to the key distribution value function in the Q network, and transmits the result to the follower node key distribution rate decision module, loss function calculation module, and electrical consistency control optimization unit considering communication delay and attack perception;

[0076] Follower Node Key Distribution Rate Decision Module: Based on the DQN network module, it determines the follower node key distribution rate according to the leader node's distribution rate and its own network status, and transmits the result to the loss function calculation module and the electrical consistency control optimization unit that considers communication delay and attack perception;

[0077] Loss function calculation module: The loss function is calculated based on the number of encryption failures caused by key resource waste and insufficient quantum keys. After each iteration, the evaluation result of the loss function is fed back to the DQN network module to update its Q value and quantum key distribution rate strategy. Through continuous iteration and optimization, dynamic adjustment of the quantum key distribution rate strategy is achieved.

[0078] Furthermore, the electrical consistency control optimization unit considering communication delay and attack perception includes:

[0079] Importance weight calculation module: Calculates the importance weight of the consistency variable based on the encryption communication delay and serial correlation, normalizes the importance weight, and transmits the result to the consistency variable update module based on the importance weight and attack perception;

[0080] Consistency variable discarding module: Based on the FDI attack perception results, the consistency variables detected by FDI attacks are discarded to ensure that the update of consistency variables will not be affected by false data, and the results are transmitted to the consistency variable updating module based on importance weight and attack perception;

[0081] Consistency variable update module based on importance weight and attack perception: updates the consistency variables of each node based on the consistency variable importance weight and the consistency variable discard results of FDI attack perception, and transmits the results to the output power calculation module;

[0082] Output power calculation module: Calculates the output power of each node based on the update results of the consistency variables.

[0083] An integrated power communication-coupled smart grid sensing, transmission, and control system, comprising an electrical control layer, a classical channel transmission layer, a quantum key distribution layer, and a communication control layer;

[0084] The electrical control layer includes multiple distribution substations, each of which uses a fusion terminal to optimize electrical control decisions and meet energy supply and demand balance by controlling the output of distributed power sources;

[0085] The classical channel transmission layer is composed of 5G channels and is used to transmit data encrypted with quantum keys or classical keys;

[0086] The quantum key distribution layer is composed of quantum channels and is used to distribute quantum keys.

[0087] By means of the above solution, the present invention has at least the following advantages:

[0088] 1. The present invention proposes an integrated control architecture for sensing, transmission and control of smart grids. First, considering the complexity and dynamic nature of the power grid, an electrical control model based on a distributed consensus algorithm is proposed to reduce the dependence of control on central nodes. Secondly, in response to possible FDI attacks in the system, a communication control model that integrates quantum encryption and classical encryption is proposed to dynamically adjust the encryption method and key distribution rate to ensure data security while reducing the transmission delay of consistency variables in quantum encryption. Finally, a communication-electrical collaborative control model under FDI attacks is proposed. Based on FDI attack perception, the encryption interaction method and key distribution rate of the communication domain and the importance weight of the consistency variables in the power domain are dynamically adjusted to avoid the spread of injected false data through the communication network and achieve rapid and accurate convergence of consistency variables.

[0089] 2. The present invention proposes an integrated optimization algorithm for sensing, transmission and control of smart grids based on communication-power domain performance deviation. First, in response to the possible FDI attacks in the system, a time series Kalman filtering method is used to compare the correlation between the optimal estimation sequence of the consistency variable and the measurement value sequence to achieve accurate perception of the FDI attack and ensure the authenticity and reliability of the consistency variable data. Secondly, in terms of communication control, when an FDI attack is perceived, the encryption interaction mode is switched to the quantum encryption mode based on the state-action evaluation function in the Q network, and the respective quantum key distribution rates are selected and dynamically adjusted through the collaborative work of the leader node and the follower node. Finally, in terms of electrical control, the importance weight of the consistency variable is constructed based on the encryption communication delay and the serial correlation of the attack perception, and normalized processing is performed to update the consistency variable.

[0090] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate a certain embodiment of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0092] Figure 1 This is a schematic diagram of the structure of the power communication coupled smart grid sensing, transmission and control integrated system of the present invention;

[0093] Figure 2 This is a flow chart of the integrated method of sensing, transmission and control of power communication coupled smart grid according to the present invention;

[0094] Figure 3 It is a structural schematic diagram of the power communication coupled smart grid sensing, transmission and control integrated device of the present invention. DETAILED DESCRIPTION

[0095] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0096] The present invention includes three parts: a power communication coupled smart grid sensing, transmission and control integrated system, a method and a device, which are described in detail as follows.

[0097] The present invention proposes a power communication coupled smart grid sensing, transmission and control integrated system. Figure 1 As shown, it includes an electrical control layer, a classical channel transmission layer, a quantum key distribution layer, and a communication control layer. The electrical control layer includes multiple distribution substations. Each substation uses a fusion terminal to optimize electrical control decisions and control the output of distributed power sources to meet energy supply and demand balance. The classical channel transmission layer is composed of 5G channels and is used to transmit quantum keys or data encrypted with classical keys. The quantum key distribution layer is composed of quantum channels and is used to distribute quantum keys. Due to the high cost of quantum communication, only some fusion terminals support quantum communication, referred to as quantum nodes, while other terminals are referred to as classical nodes. The fusion terminal optimizes the output of distributed power sources based on consistency control. Specifically, each terminal exchanges information with adjacent terminals to iteratively converge the consistency state variables and adjust the power output based on power deviations.

[0098] The integrated sensing, transmission, and control system for power-communication-coupled smart grids achieves integrated sensing, transmission, and control for smart grids. In terms of perception, the fusion terminal detects and locates FDI attacks based on the consistency variables received from neighboring terminals. In terms of transmission, the system dynamically adjusts the encryption interaction method and quantum key distribution rate based on the FDI attack detection results, reducing the transmission latency of consistency variables in quantum encryption. In terms of electrical control, the importance weights of consistency variables are adjusted based on the encryption communication latency and attack detection results to accelerate convergence.

[0099] The present invention proposes a method for integrating sensing, transmission and control of power communication coupled smart grid. The specific process is as follows: Figure 2 shown.

[0100] S1 smart grid sensing, transmission and control integrated control architecture

[0101] The present invention proposes an integrated control architecture for sensing, transmission and control of smart grids. First, considering the complexity and dynamic nature of the power grid, an electrical control model based on a distributed consensus algorithm is proposed to reduce the dependence of control on central nodes. Secondly, in response to possible FDI attacks in the system, a communication control model that integrates quantum encryption and classical encryption is proposed to dynamically adjust the encryption method and key distribution rate to ensure data security while reducing the transmission delay of consistency variables in quantum encryption. Finally, a communication-electrical collaborative control model under FDI attacks is proposed. Based on FDI attack perception, the encryption interaction method and key distribution rate of the communication domain and the importance weight of the consistency variables in the power domain are dynamically adjusted to prevent the injected false data from spreading through the communication network and achieve rapid and accurate convergence of consistency variables.

[0102] S1.1 Electrical control model based on distributed consensus algorithm

[0103] Assume that the number of fusion terminals is J, and its set is At the jth fusion terminal s j The cost function F of distributed generation output control in the controlled area j is j (P j ) is expressed as

[0104]

[0105] Where, α j , γ j , β j They represent the quadratic term coefficient, linear term coefficient, and constant term coefficient of the distributed generation output cost function in the grid area j, respectively. j is the output power of distributed generation in substation j. To achieve economic control of distributed generation, the optimization goal is to minimize the total control cost of distributed generation.

[0106] Based on the Lagrange multiplier method and KKT conditions, the consistency variable λ is introduced j , indicating that at the jth fusion terminal s j The Lagrange multiplier of the controlled region is adjusted by λ j Explore the optimal output power control scheme. Considering the complexity and dynamics of the power grid, the present invention adopts distributed control to adjust λ j Specifically, each terminal exchanges information with its neighboring terminals and uses a subgradient method to calculate the consistency variable λ. j Perform iterative updates and adjust the power output according to the power deviation. Assuming that the consistency control process can be divided into K iterations, the consistency variable λ of the jth fusion terminal j (k+1), the update process at the kth iteration is expressed as

[0107]

[0108] Where, ξ j (k) represents the λ in the kth iteration j (k) is the update step size. j′,j (k) is the number of s in the kth iteration j′ to s j Communication delay. j′,j (k) is the kth iteration s j′ to s j The importance weight of the consistency variable reflects the contribution of the consistency variable to the update of the consistency variable. j′ (k-τ j′,j (k)) represents the consideration of communication delay τ j′,j (k) next, s j From s j′ Received consistency variables. The j-th fusion terminal s j Control the consistency variable λ of the controlled area j The convergence value of the optimal distributed power output power P corresponding to the area j j , expressed as

[0109]

[0110] S1.2 Communication Control Model Integrating Quantum Encryption and Classical Encryption

[0111] To achieve FDI attack protection, quantum encryption and classical encryption are integrated. Definition It is the encrypted interaction mode between fusion terminals. j,j′ (k) = 1 means s j With s j′ Quantum encryption is used to interact with each other; j,j′ (k) = 0 means that the classic encryption method is used for interaction. Definition and are the quantum node set and the classical node set respectively. Define τ j,j′ (k) is the number of s in the kth iteration j With s j′ The encryption communication delay between

[0112]

[0113] Where, and Respectively represent the k-th iteration s j With s j′ The communication delay between classical encryption and quantum encryption.

[0114] S1.3 Communication-Electrical Coordinated Control Model under FDI Attack

[0115] During the consistency control process, the FDI attacker intercepts s j With s j′ The classical channel data between them is injected with false data to destroy the convergence of the consistency variables and reduce the control performance. is the attacker's number in s in the kth iteration j With s j′ The false data injected into the time, the consistency variable after tampering is expressed as

[0116]

[0117] Where, Θ j′,j (k) represents the kth iteration of the attacker in s j With s j′ Inter-FDI attack indicator variable, Θ j′,j (k) = 1 means s j With s j′ The communication data between them is attacked by FDI, otherwise Θ j′,j (k)=0.

[0118] Considering the impact of FDI attack and encryption delay on consistency control, a communication-electrical cooperative control model under FDI attack is proposed. The consistency variable λ of the jth fusion terminal is j (k+1), the update process at the kth iteration is expressed as

[0119]

[0120] S2 Smart Grid Sensing, Transmission and Control Integrated Optimization Algorithm Based on Communication-Power Domain Performance Deviation

[0121] The present invention proposes an integrated optimization algorithm for sensing, transmission and control of smart grids based on communication-power domain performance deviation. First, in response to the possible FDI attacks in the system, the present invention adopts a time series Kalman filtering method to achieve accurate perception of FDI attacks by comparing the correlation between the optimal estimation sequence of consistency variables and the measurement value sequence, thereby ensuring the authenticity and reliability of consistency variable data. Secondly, in terms of communication control, when an FDI attack is perceived, the system switches the encryption interaction mode to quantum encryption mode based on the state-action evaluation function in the Q network, and selects and dynamically adjusts the respective quantum key distribution rates through the collaborative work of the leader node and the follower node. Finally, in terms of electrical control, the importance weight of the consistency variable is constructed based on the serial correlation of the encryption communication delay and attack perception, and normalized processing is performed to update the consistency variable.

[0122] S2.1 FDI Attack Perception Based on Time-Series Kalman Filtering

[0123] The Kalman filter is a linear, unbiased, minimum mean square error (MSSE) estimation method that iteratively estimates the measured values ​​of consistency variables. It detects FDI attacks based on the similarity between the estimated and measured time series. Specifically, in the absence of an FDI attack, the estimated and measured values ​​of the consistency variable should be highly correlated. However, an FDI attack modifies the measured values ​​by injecting false data, causing them to become inconsistent with the estimated values. Consistency variables are updated sequentially and iteratively, and attack location is achieved based on attack perception.

[0124] Construct s in the kth iteration j With s j′ The optimal estimate of the consistency variable between time series X j′,j (k) = [X j′,j (kk * ),…,X j′,j (k-1),X j′,j (k)] and the measurement value time series Y j′,j (k)=[Y j′,j (kk * ),…,Y j′,j (k-1),Y j′,j (k)] FDI attack perception is performed by comparing the correlation between the two sequences and comparing them with the threshold. The cosine similarity is used to measure the correlation between the optimal estimation value sequence and the measurement value sequence. In the kth iteration, s j With s j′ The correlation between j′,j (k) and FDI attack perception are expressed as

[0125]

[0126]

[0127] In the formula, ||.|| represents the Euclidean norm. X j′,j (k) = [X j′,j (kk * ),…,X j′,j (k-1),X j′,j (k)] represents the optimal estimate sequence, whose elements include the (kk * ) iterations to the optimal estimate of the kth iteration. j′,j (k)=[Y j′,j (kk * ),…,Y j′,j (k-1),Y j′,j (k)] represents the measurement value sequence. ΔF represents the FDI attack perception threshold. Indicates the kth iteration sj With s j′ The indicator variable of FDI attack perception between the two groups is . Indicates the perception of s j With s j′ There is an FDI attack between. On the contrary, S2.2 Joint optimization of encryption interaction mode and quantum key distribution rate based on hierarchical collaborative DQN

[0128] This paper proposes a method for jointly optimizing the encryption interaction mode and quantum key distribution rate based on a hierarchical collaborative DQN. First, nodes are divided into leader nodes and follower nodes according to their positional importance, and the encryption interaction mode decision is made based on the state-action evaluation function in the Q network. Second, through the collaboration between the leader node and follower nodes, the quantum key distribution rate of nodes at all levels is formulated and dynamically adjusted. Through the mutual collaboration of Q networks at all levels, the proposed method prioritizes ensuring that the leader node has a sufficient number of keys to correctly and unbiasedly transmit critical control information to the follower nodes, preventing the large-scale spread of false data from the leader node and improving the accuracy of consistency convergence.

[0129] S2.2.1 Optimization of encryption interaction method

[0130] Each quantum node needs to dynamically adjust the encryption interaction mode according to the consistency variable gap and FDI attack perception. The fusion terminal that directly interacts with the distribution network control center is defined as the leader node, and the set is represented as Other terminals that cannot directly interact with information are follower nodes, and the set is represented as To ensure consistent convergence, the leader node and the quantum nodes with which it has communication connections use quantum encryption interaction.

[0131] Follow node s j Based on the Q network, construct the state-action evaluation function Q(Φ j (k),s j′ ,y j′,j (k)). Q(Φ j (k),s j′ ,y j′,j (k)=1) means that in state Φ j (k) next s j With s j′ The value of quantum encryption interaction between j (k),s j′ ,y j′,j (k) = 0) represents the value of using the classic encryption interaction method. Therefore, the encryption interaction method decision is expressed as

[0132] y j′,j (k) = Ξ{Q(Φ j(k),s j′ ,1)>Q(Φ j (k),s j′ ,0)} (9)

[0133] S2.2.2 Quantum Key Distribution Rate Optimization

[0134] The proposed hierarchical collaborative DQN algorithm dynamically adjusts the quantum key distribution rate based on interaction needs, the node's own distribution rate and upper limit, and the key inventory. First, the leader node selects the key distribution rate based on the key distribution value function in the Q network. Second, follower nodes further adjust the key distribution rate based on the leader node's adjusted distribution rate and their own network status to ensure consistency with the distribution rates of neighboring nodes. Finally, through multiple iterations and feedback optimization, the optimal strategy is gradually approached.

[0135] kth iteration leader node s j and follower node s j′ The initial key distribution rate a between j,j′ (k) is expressed as

[0136]

[0137] Where, Distribute value functions for keys. Represents the leader node s j To all adjacent follower nodes s j′ The total value corresponding to key distribution.

[0138] Specify the node s to follow in the kth iteration j′ The key distribution rate Expressed as

[0139]

[0140] Where, for The distribution rate and upper limit, Represents There is an interactive leader node. The first part in brackets is the remaining available distribution rate. In the numerator, express In state Next, select j′ The Q value corresponding to the key distribution. The denominator is The sum of the Q values ​​of other follower nodes that interact.

[0141] Construct a loss function to evaluate the joint optimization performance of encryption interaction mode and key distribution rate. Define the k-th iteration leader node s j The loss variable is R j(k), to evaluate the number of encryption failures caused by key resource waste and insufficient quantum key, and construct the loss function L j for

[0142]

[0143] Where K is the number of iterations at convergence, γ j is a discount factor used to evaluate the impact of future rewards on current decisions. Indicates that in the k+1th iteration, the leader node s j In state Φ j Select the best follower node under (k+1) The maximum Q value corresponding to key distribution.

[0144] After each iteration, the loss function evaluation results are fed back to each node to update its Q value and quantum key distribution rate strategy. Through continuous iteration and optimization, the quantum key distribution rate strategy can be dynamically adjusted.

[0145] S2.3 Electrical consistency control optimization considering communication delay and attack perception

[0146] Traditional consistency control methods typically assume that all nodes in the network have the same importance weight, failing to consider differences in node importance and state. This can slow or even prevent convergence. To address this issue, an electrical consistency control optimization method that considers communication latency and attack awareness is proposed. After detecting an FDI attack, the method constructs consistency variable importance weights based on encrypted communication latency and sequence correlation, taking into account received message latency and encryption interaction methods. Using encryption interaction method switching and elastic step size, the method prevents false signals from spreading across the communication bridge and into the global network, potentially causing cascading oscillations.

[0147] Considering the encryption communication delay and attack perception, construct the kth iteration s j With s j′ The importance weight of the consistency variable between

[0148]

[0149] Where Δ(k) is the elastic step size of the kth iteration, which gradually approaches 0 as the number of iterations increases, ensuring that the adjustment amplitude is large in the initial stage of the algorithm and becomes smaller in the later stages of the iteration to ensure consistent convergence. τ and φ are adjustment factors, which are used to adjust the impact of encryption communication delay and sequence correlation on the importance weight respectively.

[0150] The weights are normalized and expressed as

[0151]

[0152] When s j′ to s j When there is an FDI attack, the consistency variables detected during the FDI attack iteration will be discarded to ensure that the update of the consistency variables will not be affected by false data. j The consistency variable λ j (k+1), the update process at the kth iteration is expressed as

[0153]

[0154] Combining the smart grid sensing, transmission and control integrated control architecture proposed in this invention, and the encryption interaction mode based on hierarchical collaborative DQN and the quantum key distribution rate joint optimization method, the proposed power communication coupled smart grid sensing, transmission and control integrated method process is as follows: Figure 2 The specific steps are as follows:

[0155] Step 1: Use the Kalman filter method to construct the time series X of the consistent variable estimate j′,j (k) and the measurement value time series Y j′,j (k);

[0156] Step 2: Calculate X according to formula (7) j′,j (k) and Y j′,j (k) relevance;

[0157] Step 3: Perform FDI attack perception according to formula (8);

[0158] Step 4: Make an encryption interaction decision based on formula (9);

[0159] Step 5: Formulate the key distribution rate strategy of the leader node according to formula (10);

[0160] Step 6: Formulate the key distribution rate strategy of the follower node according to formula (11);

[0161] Step 7: Calculate the loss function according to formula (12), feed back the evaluation result of the loss function to each node, and update its Q value and quantum key distribution rate strategy;

[0162] Step 8: Calculate the consistency variable importance weight based on encryption communication delay and sequence correlation according to formula (13);

[0163] Step 9: Calculate the normalized importance weight according to formula (14);

[0164] Step 10: Based on the FDI attack detection results, discard the attacked consistency variables;

[0165] Step 11: Update the consistency variable according to formula (15);

[0166] Step 12: Repeat steps 1 to 11 to perform integrated optimization of power communication coupled smart grid sensing, transmission and control until the entire optimization cycle is completed, and output the optimal output power P according to formula (3) j .

[0167] The present invention proposes a power communication coupled smart grid sensing, transmission and control integrated device such as Figure 3 As shown in the figure, the power communication coupled smart grid sensing, transmission and control integrated device includes an FDI attack perception unit based on time-series Kalman filtering, a joint optimization unit for encryption interaction mode and quantum key distribution rate based on hierarchical collaborative DQN, and an electrical consistency control optimization unit considering communication delay and attack perception.

[0168] First, the FDI attack perception unit based on the time series Kalman filter includes an estimation time series calculation module, a measurement time series calculation module, a correlation calculation module, and an FDI attack perception module. The specific functions and functional interactions of each module are shown below.

[0169] Estimated value time series calculation module: uses the Kalman filtering method to calculate the estimated value time series of the consistency variable and transmits the results to the correlation calculation module.

[0170] Measurement value time series calculation module: uses the Kalman filtering method to calculate the consistency variable measurement value time series and transmits the results to the correlation calculation module.

[0171] Correlation calculation module: uses cosine similarity to measure the correlation between the optimal estimation value sequence and the measurement value sequence, and transmits the result to the FDI attack perception module.

[0172] FDI attack perception module: FDI attack perception is performed by comparing the correlation between two sequences and comparing them with the threshold, and the results are transmitted to the consistency variable discarding module.

[0173] Secondly, the unit for jointly optimizing the encryption interaction method and quantum key distribution rate based on the hierarchical collaborative DQN includes a DQN network module, an encryption interaction method decision module, a leader node key distribution rate decision module, a follower node key distribution rate decision module, and a loss function calculation module. The specific functions and functional interactions of each module are shown below.

[0174] DQN Network Module: Based on the loss function calculation results, it performs agent learning and updates the Q value and quantum key distribution rate strategy. Based on the current state information, it calculates the state-action evaluation function and transmits the result to the encryption interaction mode decision module, the leader node key distribution rate decision module, and the follower node key distribution rate decision module.

[0175] Encrypted interaction mode decision module: Based on the DQN network module, it calculates and compares the state-action evaluation function size, makes a node encryption interaction mode decision, and transmits the result to the loss function calculation module and the electrical consistency control optimization unit considering communication delay and attack perception.

[0176] Leader node key distribution rate decision module: Based on the DQN network module, it formulates the leader node key distribution rate according to the key distribution value function in the Q network, and transmits the result to the follower node key distribution rate decision module, the loss function calculation module and the electrical consistency control optimization unit considering communication delay and attack perception.

[0177] Follower node key distribution rate decision module: Based on the DQN network module, it determines the follower node key distribution rate according to the leader node's distribution rate and its own network status, and transmits the result to the loss function calculation module and the electrical consistency control optimization unit considering communication delay and attack perception.

[0178] The loss function calculation module calculates a loss function based on the number of encryption failures caused by key resource waste and insufficient quantum keys. After each iteration, the loss function evaluation result is fed back to the DQN network module to update its Q value and quantum key distribution rate strategy. Through continuous iteration and optimization, the quantum key distribution rate strategy can be dynamically adjusted.

[0179] Finally, the electrical consistency control optimization unit, which considers communication latency and attack awareness, includes an importance weight calculation module, a consistency variable discarding module, a consistency variable update module based on importance weights and attack awareness, and an output power calculation module. The specific functions and functional interactions of each module are shown below.

[0180] Importance weight calculation module: Based on the encrypted communication delay and serial correlation, it calculates the importance weight of the consistency variable, normalizes the importance weight, and transmits the result to the consistency variable update module based on the importance weight and attack perception.

[0181] Consistency variable discarding module: Based on the FDI attack perception results, the consistency variables detected in the FDI attack are discarded to ensure that the update of the consistency variables will not be affected by false data, and the results are transmitted to the consistency variable updating module based on importance weight and attack perception.

[0182] Consistency variable update module based on importance weight and attack perception: updates the consistency variable of each node according to the consistency variable importance weight and FDI attack perception consistency variable discarding results, and transmits the results to the output power calculation module.

[0183] Output power calculation module: Calculates the output power of each node based on the update results of the consistency variables.

[0184] The above device is used to perform integrated optimization of sensing, transmission and control of power communication coupled smart grid. The specific steps are as follows.

[0185] Step 0: Initialize each module.

[0186] Step 1: The estimated value time series calculation module and the measured value time series calculation module use the Kalman filter method to construct the consistent variable estimated value time series X j′,j (k) and the measurement value time series Y j′,j (k);

[0187] Step 2: Correlation calculation module, calculate X j′,j (k) and Y j′,j (k) relevance;

[0188] Step 3: FDI attack perception module, performs FDI attack perception based on the formula correlation calculation results;

[0189] Step 4: The encryption interaction mode decision module calculates and compares the state-action evaluation function size and makes a node encryption interaction mode decision;

[0190] Step 5: The leader node key distribution rate decision module determines the leader node key distribution rate based on the key distribution value function in the Q network;

[0191] Step 6: The follower node key distribution rate decision module determines the follower node key distribution rate based on the leader node's distribution rate and its own network status;

[0192] Step 7: The loss function calculation module calculates the loss function based on the number of encryption failures caused by key resource waste and insufficient quantum keys. After each iteration, the loss function evaluation result is fed back to the DQN network module.

[0193] Step 8: The DQN network module updates its Q value and quantum key distribution rate strategy based on the loss function. Through continuous iteration and optimization, the quantum key distribution rate strategy is dynamically adjusted.

[0194] Step 9: The importance weight calculation module calculates the importance weight of the consistency variable based on the encryption communication delay and sequence correlation, and normalizes the importance weight;

[0195] Step 10: Calculate the normalized importance weight according to formula (14);

[0196] Step 11: Based on the FDI attack detection results, discard the attacked consistency variables;

[0197] Step 12: The consistency variable discarding module discards the consistency variables detected to be FDI attacks based on the FDI attack perception results, ensuring that the update of consistency variables will not be affected by false data;

[0198] Step 13: Based on the importance weight and attack-aware consistency variable update module, the consistency variable of each node is iteratively updated according to the consistency variable importance weight and the FDI attack-aware consistency variable discarding result;

[0199] Step 13: The output power calculation module calculates the output power of each node based on the update result of the consistency variable;

[0200] Step 14: Repeat steps 1 to 13 to optimize the integration of power communication coupled smart grid sensing, transmission and control until the entire optimization cycle is completed and the optimal output power P is output. j .

[0201] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for integrating sensing, transmission and control of power communication coupled smart grid, characterized by: The specific steps are: First, an electrical control model based on a distributed consensus algorithm is proposed to reduce the dependence of control on central nodes. Secondly, to address the FDI attacks in the system, a communication control model that integrates quantum encryption and classical encryption is proposed. The encryption method and key distribution rate are dynamically adjusted to ensure data security while reducing the consistent variable transmission delay of quantum encryption. Next, a communication-electrical collaborative control model under FDI attacks is proposed. Based on FDI attack awareness, the encryption interaction mode and key distribution rate in the communication domain, as well as the importance weights of consistency variables in the power domain, are dynamically adjusted to prevent the injection of false data from spreading through the communication network and achieve rapid and accurate convergence of consistency variables. Based on the three models mentioned above, the time series Kalman filter method is used to achieve accurate perception of FDI attacks by comparing the correlation between the optimal estimation sequence of consistency variables and the measurement value sequence, ensuring the authenticity and reliability of consistency variable data; In terms of communication control, when an FDI attack is detected, the system switches the encryption interaction mode to quantum encryption based on the state-action evaluation function in the Q network, and through the collaborative work of the leader node and follower nodes, selects and dynamically adjusts their respective quantum key distribution rates; Finally, in terms of electrical control, the importance weights of consistency variables are constructed based on the serial correlation of encrypted communication delay and attack perception, and normalized, and the consistency variables are updated to perform integrated optimization of power communication coupled smart grid sensing, transmission and control until the entire optimization cycle is completed and the optimal output power is output.

2. The method for integrating sensing, transmission and control of power communication coupled smart grid according to claim 1, characterized in that: The electrical control model based on the distributed consensus algorithm is specifically: Define the number of fusion terminals as J, and its set is At the jth fusion terminal s j The cost function F of distributed generation output control in the controlled area j is j (P j )for: Where, α j , γ j , β j They represent the quadratic term coefficient, linear term coefficient, and constant term coefficient of the distributed generation output cost function in the grid area j, respectively. j is the output power of distributed power generation in area j; Based on the Lagrange multiplier method and KKT conditions, the consistency variable λ is introduced j , represents the Lagrange multiplier of the control area controlled by the j-th fusion terminal sj; The definition of consistency control process can be divided into K iterations, and the consistency variable λ of the j-th fusion terminal j (k+1), the update process at the kth iteration is expressed as: Where, ξ j (k) represents the λ in the kth iteration j (k) update step size; τ j′,j (k) is the number of s in the kth iteration j′ to s j Communication delay; ω j′,j (k) is the kth iteration s j′ to s j The importance weight of the consistency variable; λ j′ (k-τ j′,j (k)) represents the consideration of communication delay τ j′,j (k) next, s j From s j′ Received consistency variable; j-th fusion terminal s j Control the consistency variable λ of the controlled area j The convergence value of the optimal distributed power output power P corresponding to the area j j , expressed as 3. The method for integrating sensing, transmission and control of power communication coupled smart grid according to claim 1, characterized in that: The communication control model that integrates quantum encryption and classical encryption is specifically: definition is the encrypted interaction mode between fusion terminals; j,j′ (k) = 1 means s j With s j′ Quantum encryption is used to interact with each other; j,j′ (k) = 0 means using classic encryption method for interaction; definition and are the quantum node set and the classical node set respectively; define τ j,j′ (k) is the number of s in the kth iteration j With s j′ The encryption communication delay between is expressed as: Where, and Respectively represent the k-th iteration s j With s j′ The communication delay between classical encryption and quantum encryption.

4. The method for integrating sensing, transmission and control of power communication coupled smart grid according to claim 1, characterized in that: The communication-electrical coordinated control model under the FDI attack is specifically as follows: During the consistency control process, the FDI attacker intercepts s j With s j′ The classical channel data between them is injected with false data to destroy the convergence of the consistency variables and reduce the control performance; define is the attacker's number in s in the kth iteration j With s j′ The false data injected into the tampered consistency variable is expressed as: Where, Θ j′,j (k) represents the kth iteration of the attacker in s j With s j′ Inter-FDI attack indicator variable, Θ j′,j (k) = 1 means s j With s j′ The communication data between them is attacked by FDI, otherwise Θ j′,j (k) = 0; Communication-electrical cooperative control model under FDI attack, consistency variable λ of the j-th fusion terminal j (k+1), the update process at the kth iteration is expressed as:

5. The method for integrating sensing, transmission and control of power communication coupled smart grid according to claim 1, characterized in that: The specific method of using the time series Kalman filtering method to achieve accurate perception of FDI attacks by comparing the correlation between the optimal estimation sequence of the consistency variable and the measurement value sequence is as follows: Construct s in the kth iteration j With s j′ The optimal estimate of the consistency variable between time series X j′,j (k) = [X j′,j (kk * ),…,X j′,j (k-1),X j′,j (k)] and the measurement value time series Y j′,j (k)=[Y j′,j (kk * ),…,Y j′,j (k-1),Y j′,j (k)]; FDI attack perception is performed by comparing the correlation of the two sequences and comparing them with the threshold; the cosine similarity is used to measure the correlation between the optimal estimation value sequence and the measurement value sequence; in the kth iteration, s j With s j′ The correlation between j′,j (k) and FDI attack perception are expressed as: Where, ||.|| represents the Euclidean norm; X j′,j (k) = [X j′,j (kk * ),…,X j′,j (k-1),X j′,j (k)] represents the optimal estimate sequence, whose elements include the (kk * ) iterations to the optimal estimate of the kth iteration; Y j′,j (k)=[Y j′,j (kk * ),…,Y j′,j (k-1),Y j′,j (k)] represents the measurement value sequence; ΔF represents the FDI attack perception threshold; Indicates the kth iteration s j With s j′ FDI attack perception indicator variable; Indicates the perception of s j With s j′ There is an FDI attack; conversely, 6. The method for integrating sensing, transmission and control of power communication coupled smart grid according to claim 1, characterized in that: The system switches the encryption interaction mode to quantum encryption mode based on the state-action evaluation function in the Q network, and through the collaborative work of the leader node and the follower node, selects and dynamically adjusts the specific method of each quantum key distribution rate as follows: The fusion terminal that directly interacts with the distribution network control center is defined as the leader node, and the set is represented as Other terminals that cannot directly interact with information are follower nodes, and the set is represented as To ensure consistent convergence, the leader node and the quantum nodes with which it has communication connections use quantum encryption interaction; Follow node s j Based on the Q network, construct the state-action evaluation function Q(Φ j (k),s j′ ,y j′,j (k)); Q(Φ j (k),s j′ ,y j′,j (k)=1) means that in state Φ j (k) next s j With s j′ The value of quantum encryption interaction between j (k),s j′ ,y j′,j (k) = 0) represents the value of using the classic encryption interaction method; therefore, the encryption interaction method decision is expressed as: y j′,j (k)=Ξ{Q(Φ j (k),s j′ ,1)>Q(Φ j (k),s j′ ,0)} (9) Based on the hierarchical collaborative DQN algorithm, the quantum key distribution rate is dynamically adjusted according to interaction needs, the node's own distribution rate and upper limit, and the key inventory. First, the leader node selects the key distribution rate based on the key distribution value function in the Q network. Second, the follower nodes make further adjustments based on the leader node's adjusted distribution rate and their own network status to ensure that the distribution rate is consistent with that of the interacting neighboring nodes. Finally, through multiple iterations and feedback optimization, the optimal strategy is gradually approached. kth iteration leader node s j and follower node s j′ The initial key distribution rate a between j,j′ (k) is expressed as: Where, Distribute value functions for keys; Represents the leader node s j To all adjacent follower nodes s j′ The total value corresponding to key distribution; Specify the node s to follow in the kth iteration j′ The key distribution rate Expressed as: Where, for The distribution rate and upper limit, Represents There is an interactive leader node; the first part in brackets is the remaining available distribution rate; in the numerator, express In state Next, select j′ The Q value corresponding to key distribution; the denominator is The sum of the Q values ​​of other follower nodes that interact; Construct a loss function to evaluate the joint optimization performance of encryption interaction mode and key distribution rate; define the k-th iteration leader node s j The loss variable is R j (k), to evaluate the number of encryption failures caused by key resource waste and insufficient quantum key, and construct the loss function L j for: Where K is the number of iterations at convergence, γ j is a discount factor used to evaluate the impact of future rewards on current decisions; Indicates that in the k+1th iteration, the leader node s j In state Φ j Select the best follower node under (k+1) The maximum Q value corresponding to key distribution; After each iteration, the evaluation results of the loss function are fed back to each node to update its Q value and quantum key distribution rate strategy. Through continuous iteration and optimization, dynamic adjustment of the quantum key distribution rate strategy is achieved.

7. The method for integrating sensing, transmission and control of power communication coupled smart grid according to claim 1, characterized in that: Based on the serial correlation of encrypted communication delay and attack perception, the importance weight of consistency variables is constructed, normalized, and consistency variables are updated. The integrated optimization of power communication coupled smart grid sensing, transmission and control is performed until the end of the entire optimization cycle. The specific method for outputting the optimal output power is as follows: An electrical consistency control optimization method that considers communication delay and attack perception is proposed. After detecting an FDI attack, the importance weights of consistency variables based on encryption communication delay and sequence correlation are constructed according to the received message delay and encryption interaction mode. Using encryption interaction mode switching and elastic step size, false signals are prevented from spreading to the global network through communication bridges and causing cascading oscillations. Considering the encryption communication delay and attack perception, construct the kth iteration s j With s j′ The importance weight of the consistency variable between is expressed as: Where Δ(k) is the elastic step size of the kth iteration, which gradually approaches 0 as the number of iterations increases, ensuring that the adjustment amplitude is large in the initial stage of the algorithm and becomes smaller in the later stages of the iteration to ensure consistent convergence; φ τ With φ F are adjustment factors, which are used to adjust the impact of encryption communication delay and sequence correlation on importance weight; The weights are normalized and expressed as: When s j′ to s j When there is an FDI attack, the consistency variables detected during the FDI attack iteration will be discarded to ensure that the update of the consistency variables will not be affected by false data. j The consistency variable λ j (k+1), the update process at the kth iteration is expressed as:

8. An integrated device for sensing, transmission and control of power communication coupled smart grid, characterized by: include: Modeling unit: used to first propose an electrical control model based on a distributed consensus algorithm to reduce the control's dependence on the central node; Secondly, to address the FDI attacks present in the system, a communication control model that integrates quantum encryption and classical encryption is proposed. The encryption method and key distribution rate are dynamically adjusted to ensure data security while reducing the transmission delay of the consistency variables of quantum encryption. Next, a communication-electricity collaborative control model under FDI attacks is proposed. Based on FDI attack perception, the encryption interaction method and key distribution rate in the communication domain are dynamically adjusted, as well as the importance weights of the consistency variables in the power domain. This prevents the injection of false data from spreading through the communication network and achieves rapid and accurate convergence of the consistency variables. The FDI attack perception unit based on time series Kalman filtering is used to accurately perceive FDI attacks by comparing the correlation between the optimal estimation sequence of consistency variables and the measurement value sequence based on the three models mentioned above, ensuring the authenticity and reliability of consistency variable data. A joint optimization unit for encryption interaction mode and quantum key distribution rate based on hierarchical collaborative DQN: In terms of communication control, when an FDI attack is detected, the system switches the encryption interaction mode to quantum encryption based on the state-action evaluation function in the Q network. Furthermore, through the collaborative work of the leader and follower nodes, the system selects and dynamically adjusts the quantum key distribution rate of each node. Electrical consistency control optimization unit considering communication delay and attack perception: In terms of electrical control, it is used to construct consistency variable importance weights based on the serial correlation of encrypted communication delay and attack perception, and perform normalization processing, update consistency variables, and perform integrated optimization of power communication coupled smart grid sensing, transmission and control until the end of the entire optimization cycle and output the optimal output power.

9. The power communication coupled smart grid sensing, transmission and control integrated device according to claim 8, characterized in that: The FDI attack perception unit based on temporal Kalman filtering includes: Estimated value time series calculation module: uses the Kalman filter method to calculate the time series of the estimated value of the consistency variable and transmits the result to the correlation calculation module; Measurement value time series calculation module: uses the Kalman filter method to calculate the consistency variable measurement value time series and transmits the results to the correlation calculation module; Correlation calculation module: uses cosine similarity to measure the correlation between the optimal estimation value sequence and the measurement value sequence, and transmits the result to the FDI attack perception module; FDI attack perception module: FDI attack perception is performed by comparing the correlation between two sequences and comparing them with the threshold, and the results are transmitted to the consistency variable discarding module.

10. The power communication coupled smart grid sensing, transmission and control integrated device according to claim 8, characterized in that: The encryption interaction mode and quantum key distribution rate joint optimization unit based on the hierarchical collaborative DQN includes: DQN network module: Based on the loss function calculation results, it performs agent learning and updates the Q value and quantum key distribution rate strategy. Based on the current state information, it calculates the state-action evaluation function and transmits the result to the encryption interaction mode decision module, the leader node key distribution rate decision module, and the follower node key distribution rate decision module. Encrypted interaction mode decision module: Based on the DQN network module, it calculates and compares the state-action evaluation function size, makes a node encryption interaction mode decision, and transmits the result to the loss function calculation module and the electrical consistency control optimization unit considering communication delay and attack perception; Leader node key distribution rate decision module: Based on the DQN network module, it determines the leader node key distribution rate according to the key distribution value function in the Q network, and transmits the result to the follower node key distribution rate decision module, loss function calculation module, and electrical consistency control optimization unit considering communication delay and attack perception; Follower Node Key Distribution Rate Decision Module: Based on the DQN network module, it determines the follower node key distribution rate according to the leader node's distribution rate and its own network status, and transmits the result to the loss function calculation module and the electrical consistency control optimization unit that considers communication delay and attack perception; Loss function calculation module: The loss function is calculated based on the number of encryption failures caused by key resource waste and insufficient quantum keys. After each iteration, the evaluation result of the loss function is fed back to the DQN network module to update its Q value and quantum key distribution rate strategy. Through continuous iteration and optimization, dynamic adjustment of the quantum key distribution rate strategy is achieved.

11. The power communication coupled smart grid sensing, transmission and control integrated device according to claim 8, characterized in that: The electrical consistency control optimization unit considering communication delay and attack perception includes: Importance weight calculation module: Calculates the importance weight of the consistency variable based on the encryption communication delay and serial correlation, normalizes the importance weight, and transmits the result to the consistency variable update module based on the importance weight and attack perception; Consistency variable discarding module: Based on the FDI attack perception results, the consistency variables detected by FDI attacks are discarded to ensure that the update of consistency variables will not be affected by false data, and the results are transmitted to the consistency variable updating module based on importance weight and attack perception; Consistency variable update module based on importance weight and attack perception: updates the consistency variables of each node based on the consistency variable importance weight and the consistency variable discard results of FDI attack perception, and transmits the results to the output power calculation module; Output power calculation module: Calculates the output power of each node based on the update results of the consistency variables.

12. An integrated power communication coupled smart grid sensing, transmission and control system, characterized by: Including electrical control layer, classical channel transmission layer, quantum key distribution layer, and communication control layer; The electrical control layer includes multiple distribution substations, each of which uses a fusion terminal to optimize electrical control decisions and meet energy supply and demand balance by controlling the output of distributed power sources; The classical channel transmission layer is composed of 5G channels and is used to transmit data encrypted with quantum keys or classical keys; The quantum key distribution layer is composed of quantum channels and is used to distribute quantum keys.

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