Safety deployment method of terminal equipment in power system, electronic equipment, storage medium and program product

Abstract

The invention discloses a security deployment method for terminal equipment in a power system, electronic equipment, a storage medium and a program product, and the method comprises the steps: collecting equipment associated data of a plurality of terminal equipment in the power system; wherein the equipment association data comprises equipment state data, network data, equipment configuration data and network topology data; determining equipment description data of each piece of terminal equipment and an association relationship model among the plurality of pieces of terminal equipment based on the equipment association data, and determining a preliminary security deployment strategy of the plurality of pieces of terminal equipment based on the equipment description data and the association relationship model; and extracting a plurality of target device features from the device associated data, determining a device risk point through the plurality of target device features and the security identification model, and determining a target security deployment strategy of the plurality of terminal devices according to the device risk point and the preliminary security deployment strategy. The safety of the terminal equipment in a continuously changing safety environment is improved, and the safety and stability of a power system are improved.

Description

Technical Field

[0001] The present invention relates to the field of power grid security technology, and in particular to a method for securely deploying terminal equipment in a power system, electronic equipment, a storage medium, and a program product. Background Art

[0002] With the rapid development of the power industry, smart grid systems have become an important part of modern power grids. Smart grid systems have achieved intelligence, automation and efficiency of power grids through information technology, communication technology, automation technology and other means. This also puts forward higher requirements for the security of smart grid systems.

[0003] In the power grid system, smart terminal devices are an important part of the power grid system, and their security is one of the key factors affecting the security of the entire power grid. Once a smart terminal device has a security problem, it may affect the stability of the entire power grid. However, the security strategies in related technologies usually need to rely on human experience to set up, which is inefficient. Its deployment method is often unable to adapt to the complex and ever-changing security environment of the power grid system, and the security protection effect in the power grid system is also relatively unstable. Summary of the invention

[0004] The present invention provides a method for secure deployment of terminal equipment in an electric power system, an electronic device, a storage medium and a program product to solve the problem in the related art that the determination efficiency of the secure deployment mode of the electric power terminal is low and the effect is unstable.

[0005] According to one aspect of the present invention, a method for securely deploying terminal equipment in a power system is provided, the method comprising:

[0006] Collecting device-related data of multiple terminal devices in the power system; wherein the device-related data includes device status data, network data, device configuration data and network topology data;

[0007] Determine device description data of each terminal device and an association relationship model between multiple terminal devices based on the device association data, and determine a preliminary security deployment strategy for the multiple terminal devices based on the device description data and the association relationship model;

[0008] Multiple target device features are extracted from device association data, device risk points are determined through multiple target device features and security identification models, and target security deployment strategies for multiple terminal devices are determined based on device risk points and preliminary security deployment strategies.

[0009] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0010] at least one processor; and

[0011] a memory communicatively connected to at least one processor; wherein,

[0012] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the method for secure deployment of terminal equipment in the power system in any embodiment of the present invention.

[0013] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions, and the computer instructions are used to enable a processor to implement the method for secure deployment of terminal equipment in a power system according to any embodiment of the present invention when executed.

[0014] According to another aspect of the present invention, an embodiment of the present disclosure further provides a computer program product, including a computer program, which, when executed by a processor, implements a method for secure deployment of terminal devices in a power system as described in any one of the embodiments of the present disclosure.

[0015] The technical solution of the embodiment of the present invention collects device association data of multiple terminal devices in the power system; wherein the device association data includes device status data, network data, device configuration data and network topology data, and can obtain data related to the safe operation of the equipment from multiple dimensions, providing comprehensive data support for the analysis and processing of subsequent tasks; then, by determining the device description data of each terminal device and the association relationship model between the multiple terminal devices based on the device association data, determining the preliminary security deployment strategy of the multiple terminal devices based on the device description data and the association relationship model, and more accurately determining the self description data of each terminal device and the association relationship between the terminal devices through the device association data, so that the generated preliminary security deployment strategy can be adapted to the multiple terminal devices, that is, obtaining the preliminary A security deployment strategy that meets the needs; then by extracting multiple target device features from the device-related data, determining the device risk points through multiple target device features and security identification models, and determining the target security deployment strategies for multiple terminal devices based on the device risk points and the preliminary security deployment strategy, it can effectively identify the risk points in the equipment, and on the basis of the preliminary security deployment strategy, make targeted adjustments to the device risk points, so as to obtain a more complete target security deployment strategy, which solves the problem of low efficiency and unstable effect in determining the security deployment method of power terminals in related technologies, realizes the automatic generation of security deployment methods for power terminals, and supports dynamic adjustment of power terminals, thereby improving the compatibility of security deployment strategies with multiple power terminals in the power grid system and improving the safety and stability of the power system.

[0016] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 is a flow chart of a method for securely deploying terminal equipment in a power system provided by Embodiment 1 of the present invention;

[0019] Figure 2 is a flow chart of a method for securely deploying terminal equipment in a power system provided by Embodiment 2 of the present invention;

[0020] Figure 3 It is a structural schematic diagram of a secure deployment device for terminal equipment in a power system provided by Embodiment 3 of the present invention;

[0021] Figure 4 It is a structural schematic diagram of an electronic device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0025] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0026] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0027] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0028] As an optional but non-limiting implementation, in response to receiving an active request from the user, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0029] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet the relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0030] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0031] Embodiment 1

[0032] Figure 1 It is a flowchart of a method for secure deployment of terminal equipment in an electric power system provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where the security of terminal equipment in an electric power system is improved. The method can be executed by a secure deployment device for terminal equipment in an electric power system. The secure deployment device for terminal equipment in the electric power system can be implemented in the form of hardware and / or software, and optionally, can be implemented by an electronic device, which can be a mobile terminal, a PC, a server, etc.

[0033] like Figure 1 As shown, the method may specifically include:

[0034] S110. Collect device-related data of multiple terminal devices in the power system; wherein the device-related data includes device status data, device network data, device configuration data, and network topology data.

[0035] In this embodiment, the terminal device can be understood as a device that performs power services such as power production, power conversion, and power distribution in the power system. Exemplarily, the power equipment can include but is not limited to smart meters, substation equipment, distribution automation equipment, sensors, and the like. Equipment-related data can be understood as data that has an association relationship with the terminal device, and can include the device model of the terminal device, the connection relationship with other terminal devices, and the like. Equipment status data can be understood as data that characterizes the operating status of the terminal device during operation, and can include data of normal operation and data of abnormal operation. Equipment network data can be understood as data that characterizes the network status of the terminal device, which can be determined by the network speed of the terminal device, wherein the faster the network speed of the terminal device, the better the network status of the terminal device. Equipment configuration data can be understood as data that characterizes the parameters and attributes of the terminal device, including but not limited to the model, unique identification code, and rated voltage of the terminal device. Network topology data can be understood as data that characterizes the layout of the terminal device in the power system, and the connection method between the terminal devices.

[0036] In the power system, the device-associated data of the terminal device can be collected through sensors. Different types of terminal devices can be configured with different types of sensors to collect different types of device-associated data. For example, when the device-associated data is current, the current sensor can be used to collect the device-associated data, and when the device-associated data is voltage, the voltage sensor can be used to collect the device-associated data.

[0037] In some embodiments, after collecting device-associated data of multiple terminal devices in the power system, preprocessing operations can be performed on the device-associated data of the terminal devices, for example, data cleaning, denoising and normalization can be included to obtain preprocessed device-associated data, and the preprocessed device-associated data is used for subsequent analysis and processing.

[0038] S120: Determine device description data of each terminal device and an association relationship model between multiple terminal devices based on the device association data, and determine preliminary security deployment strategies for the multiple terminal devices based on the device description data and the association relationship model.

[0039] In this embodiment, the device description data can be understood as data that characterizes the weight of the device association data of the terminal device, which can be determined based on one or more of the device status data, device network data, device configuration data, and network topology data of the terminal device, and different terminal devices may include different device description data. The association relationship model can be understood as a model that characterizes the association relationship between multiple terminal devices, for example, it may include a model that characterizes the dependency relationship between multiple terminal devices. For example, the association relationship model between multiple terminal devices can be determined based on the device configuration data and network topology data of multiple terminal devices.

[0040] The preliminary security deployment strategy can be understood as a strategy deployed to ensure the security of the terminal device, which can be determined based on the device description data and the association model. For example, the operator can manually determine the preliminary security deployment strategy of multiple terminal devices based on the device description data and the association model. The preliminary security deployment strategy of multiple terminal devices can also be determined by matching, for example, the device description data and the association model of any terminal device are input into the server for matching, and the server stores the device description data association model and the preliminary security deployment strategy of different terminal devices. In the case of successful matching, the matched preliminary security deployment strategy is determined as the preliminary security deployment strategy of the terminal device; in the case of failed matching, the confidence between the device description data and the association model of different terminal devices in the server and the device description data and the association model of the terminal device is calculated and summed, the confidence of different terminal devices is compared, the corresponding device description data and the association model in the server are determined according to the maximum confidence, the preliminary security deployment strategy corresponding to the corresponding device description data and the association model in the server is determined, and the preliminary security deployment strategy corresponding to the device description data and the association model in the server is adjusted according to the device description data association model of the terminal device to obtain the preliminary security deployment strategy corresponding to the terminal device.

[0041] Optionally, determining device description data for each terminal device based on device association data includes: obtaining multiple device safety indicators corresponding to the terminal device, wherein the device safety indicators include at least one of device operating time, number of device failures, device update frequency, device operation information, and device network traffic; constructing a judgment matrix based on multiple device safety indicators, and determining the eigenvalues ​​and eigenvectors of the judgment matrix, determining a weight vector of the device safety indicator based on the eigenvector corresponding to the largest eigenvalue, and determining the device description data of the terminal device based on the device association data and the weight vector.

[0042] Specifically, the device safety index can be understood as an indicator for measuring the security of the terminal device, which can be determined by one or more of the device status data, device network data, device configuration data and network topology data in the device-associated data. The device operating time can be understood as the operating time of the terminal device within a preset time period. The number of device failures can be understood as the number of times the terminal device fails within a preset time period. The device update frequency can be understood as the number of times the terminal device is updated within a preset time period. The device operation information can be understood as the information generated by operating the terminal device, which may include, for example, the operation time, the number of operations and the operation location. The device network traffic can be understood as the terminal device network throughput. The judgment matrix can be understood as the matrix corresponding to the device description data of the terminal device.

[0043] The multiple device security indicators corresponding to the terminal device can be read through the server. The server stores multiple device security indicators of multiple terminal devices. The unique identification code corresponding to the terminal device is matched with the unique identification codes of multiple terminal devices in the server. When the match is successful, the multiple device security indicators corresponding to the matched terminal device are read through the server. The multiple device security indicators corresponding to the terminal device can also be collected through different types of sensors. For example, the device operation time can be collected through the operation time sensor, the number of device failures can be obtained through the failure count sensor, the device update frequency can be obtained through the update frequency count sensor, the device operation information can be collected through the operation information sensor, and the device network traffic can be collected through the network traffic sensor.

[0044] By acquiring multiple device safety indicators corresponding to the terminal device, a judgment matrix is ​​constructed according to the multiple device safety indicators corresponding to the terminal device. The judgment matrix can be constructed through a judgment matrix construction model. For example, the multiple device safety indicators corresponding to the terminal device can be input into a pre-trained judgment matrix construction model for processing to obtain a judgment matrix. The judgment matrix construction model includes but is not limited to a neural network model, for example, it can include a convolutional neural network model and a transformer model. The judgment matrix can also be constructed by manual input. For example, a preset input area for multiple device safety indicators is set in the judgment matrix. The multiple device safety indicators are input into the corresponding preset input area to obtain a complete judgment matrix. The eigenvalues ​​and eigenvectors of the judgment matrix can be calculated. For example, the eigenvalue method can be used to calculate the eigenvalues ​​and eigenvectors of the judgment matrix. The eigenvalues ​​and eigenvectors of the judgment matrix can also be obtained through an eigenvalue and eigenvector extraction model. For example, the judgment matrix can be input into a pre-trained eigenvalue and eigenvector extraction model for processing to obtain the eigenvalues ​​and eigenvectors of the judgment matrix. The eigenvalue and eigenvector extraction model includes but is not limited to a neural network model. Compare the sizes of multiple eigenvalues ​​of the judgment matrix, determine the largest eigenvalue of the judgment matrix, calculate the corresponding eigenvector according to the largest eigenvalue of the judgment matrix, normalize the eigenvector corresponding to the largest eigenvalue of the judgment matrix, obtain the weight vector of the device security index, calculate the product between the device association data and the weight vector, and obtain the device description data of the terminal device. The calculation formula of the device description data of the terminal device is as follows:

[0045]

[0046] Among them, S i Represents the device description data of the i-th terminal device, w j represents the weight vector of the jth device security index; x ij represents the preprocessed data of the j-th device security indicator of the ith terminal device, and m is the number of device security indicators.

[0047] By acquiring multiple device safety indicators corresponding to the terminal device, wherein the device safety indicators include at least one of the device operating time, the number of device failures, the device update frequency, the device operation information and the device network traffic, rich and comprehensive data support is provided for the analysis and processing of subsequent tasks; a judgment matrix is ​​constructed based on multiple device safety indicators, and the eigenvalues ​​and eigenvectors of the judgment matrix are determined, the weight vector of the device safety indicator is determined according to the eigenvector corresponding to the largest eigenvalue, and the device description data of the terminal device is determined according to the device association data and the weight vector, thereby improving the accuracy and authenticity of the device description data of the terminal device.

[0048] In some embodiments, the judgment matrix may be subjected to a consistency check to determine whether the consistency of the judgment matrix is ​​acceptable. For example, the consistency index CI and the consistency ratio CR may be calculated, and the calculation formula is as follows:

[0049]

[0050]

[0051] Among them, λ max Indicates the maximum eigenvalue of the judgment matrix; n indicates the number of equipment safety indicators; RI indicates the average random consistency index, which can be determined by looking up the preset table; if CR<0.1, it means that the consistency of the judgment matrix is ​​acceptable; if CR≥0.1, the consistency of the judgment matrix does not meet the requirements, and the judgment matrix is ​​adjusted until it meets the requirements. By performing a consistency test on the judgment matrix, it is determined whether the consistency of the judgment matrix is ​​acceptable, which improves the accuracy of the judgment matrix and thus improves the accuracy of the equipment description data.

[0052] S130. Extract multiple target device features from the device association data, determine device risk points through the multiple target device features and the security identification model, and determine target security deployment strategies for multiple terminal devices based on the device risk points and the preliminary security deployment strategy.

[0053] In this embodiment, the target device feature can be understood as a feature in the device association data of the terminal device used to determine the device risk point of the terminal device, which can be obtained through a feature extraction model. For example, the device association data can be sequentially input into a pre-trained feature extraction model for feature extraction, and multiple target device features can be obtained. The feature extraction model includes but is not limited to a neural network model, and can include a random forest model and a convolutional neural network model. The device risk point can be understood as a location where the terminal device has a risk, which can be determined by multiple target device features and a security identification model. For example, multiple target device features can be respectively input into a pre-trained security identification model for security identification, and the device risk points corresponding to multiple terminal devices can be obtained. The security identification model can be understood as a model for security identification of multiple target device features, which can include a neural network model, for example, a convolutional neural network model and a transformer model. The device risk point can be understood as a location where the terminal device has a risk. The target security deployment strategy can be understood as a security deployment strategy for multiple terminal devices determined according to the device risk point and the preliminary security deployment strategy. For example, it can be obtained by adjusting the preliminary security deployment strategy according to the device risk point on the basis of the preliminary security deployment strategy. Targeted treatment of equipment risk points makes the target security deployment strategy of terminal equipment more complete and targeted, thereby improving the security of terminal equipment in a constantly changing security environment and improving the safety and stability of the power system.

[0054] Optionally, target security deployment strategies for multiple terminal devices are determined based on device risk points and preliminary security deployment strategies, including: determining a device security score for the terminal device based on a risk point score corresponding to each device risk point and a risk point weight corresponding to the device risk point; if the device security score does not meet a preset score threshold, adjusting the preliminary security deployment strategy to obtain target security deployment strategies for multiple terminal devices.

[0055] Specifically, the risk point score can be understood as data used to assess the risk of the device risk point in the evaluation, which can be obtained through a risk point scoring model. For example, each device risk point can be input into a pre-trained risk point scoring model for processing to obtain a risk point score corresponding to each device risk point. The risk point scoring model includes but is not limited to a neural network model. The device security score can be understood as data that characterizes the security of the terminal device, which can be determined by the risk point score corresponding to each device risk point and the risk point weight corresponding to the device risk point. For example, the product between the risk point score corresponding to each device risk point and the risk point weight corresponding to the device risk point can be calculated, and the product between the risk point scores corresponding to all device risk points and the risk point weights corresponding to the device risk points can be summed to obtain the device security score of the terminal device. The higher the device security score of the terminal device, the greater the possibility of the device risk point of the terminal device to have a risk. The device security score does not meet the preset score threshold, which may include the device security score exceeding the preset score threshold, indicating that the device risk point of the terminal device has a high possibility of risk. Adjusting the preliminary security deployment strategy may include enhancing the security configuration of the terminal device, restricting the network access of the terminal device, and triggering one or more of the alarm system.

[0056] The device security score of the terminal device is determined based on the risk point score corresponding to each device risk point and the risk point weight corresponding to the device risk point, which increases the scientific nature of the device security score of the terminal device and provides data support for the adjustment of the preliminary security deployment strategy; when the device security score does not meet the preset score threshold, the preliminary security deployment strategy is adjusted to obtain the target security deployment strategy for multiple terminal devices, which improves the authenticity and rationality of the target security deployment strategy for multiple terminal devices.

[0057] Optionally, the target device characteristics include at least one of central processing unit usage characteristic data, memory usage characteristic data, disk usage characteristic data, network traffic characteristic data, fault characteristic data, system update characteristic data, and device operation characteristic data.

[0058] Specifically, the CPU usage characteristic data can be understood as the proportion of the time occupied by the CPU of the terminal device within a preset time interval. The memory usage characteristic data can be understood as the proportion of the memory usage of the terminal device to the total memory within a preset time interval. The disk usage characteristic data can be understood as the proportion of the disk usage of the terminal device to the total disk within a preset time interval. The network traffic characteristic data can be understood as the data throughput of the terminal device within a preset time interval. The fault characteristic data can be understood as the number of times the terminal device fails within a preset time interval. The system update characteristic data can be understood as the number of system updates of the terminal device within a preset time interval. The device operation characteristic data can be understood as the characteristic data generated by operating the terminal device, which may include the operation time, the number of operations and the operation location.

[0059] The technical solution of the embodiment of the present invention collects device association data of multiple terminal devices in the power system; wherein the device association data includes device status data, network data, device configuration data and network topology data, and can obtain data related to the safe operation of the equipment from multiple dimensions, providing comprehensive data support for the analysis and processing of subsequent tasks; then, by determining the device description data of each terminal device and the association relationship model between the multiple terminal devices based on the device association data, determining the preliminary security deployment strategy of the multiple terminal devices based on the device description data and the association relationship model, and more accurately determining the self description data of each terminal device and the association relationship between the terminal devices through the device association data, so that the generated preliminary security deployment strategy can be adapted to the multiple terminal devices, that is, obtaining the preliminary A security deployment strategy that meets the needs; then by extracting multiple target device features from the device-related data, determining the device risk points through multiple target device features and security identification models, and determining the target security deployment strategies for multiple terminal devices based on the device risk points and the preliminary security deployment strategy, it can effectively identify the risk points in the equipment, and on the basis of the preliminary security deployment strategy, make targeted adjustments to the device risk points, so as to obtain a more complete target security deployment strategy, which solves the problem of low efficiency and unstable effect in determining the security deployment method of power terminals in related technologies, realizes the automatic generation of security deployment methods for power terminals, and supports dynamic adjustment of power terminals, thereby improving the compatibility of security deployment strategies with multiple power terminals in the power grid system and improving the safety and stability of the power system.

[0060] Embodiment 2

[0061] Figure 2 1 is a flowchart of a method for securely deploying terminal equipment in a power system provided by Embodiment 2 of the present invention. This embodiment is an optimization of the above embodiment. Figure 2 As shown, the method may specifically include:

[0062] S210. Collect device-related data of multiple terminal devices in the power system; wherein the device-related data includes device status data, network data, device configuration data, and network topology data.

[0063] S220. Determine the dependency relationship between multiple terminal devices according to the network topology data, and construct a dependency relationship model with the terminal devices as nodes and the dependency relationships as edges.

[0064] In this embodiment, the dependency relationship between multiple terminal devices can be determined based on the network topology data of the multiple terminal devices. For example, the network topology data of terminal device one includes the connection between terminal device one and terminal device two and the data transmission of terminal device one to terminal device two, the network topology data of terminal device two includes the connection between terminal device two and terminal device three and the data transmission of terminal device two to terminal device three, and the dependency relationship between terminal device one, terminal device two and terminal device three can be terminal device three depends on terminal device two and terminal device two depends on terminal device one. The dependency model can be understood as a model that characterizes the dependency relationship between multiple terminal devices, and can be determined by a dependency determination model. For example, the network topology data is input into a pre-trained dependency determination model to determine the dependency relationship, and a dependency model can be obtained. The dependency determination model includes but is not limited to a neural network model, and can include, for example, a convolutional neural network model and a transformer model.

[0065] In some embodiments, the dependency model may be a directed graph model. Taking the directed graph model as the dependency model as an example, the dependency model construction process is as follows: taking each terminal device as a node, determining a node set, wherein the node set includes multiple terminal devices, and the node set is represented as follows:

[0066] V={v 1 , v 2 , ..., v i ,...v n};

[0067] Among them, V represents the node set, v i Represents the i-th terminal device. Determine the dependency relationship between multiple terminal devices according to the network topology data, determine the directed edges, and determine the directed edge set according to the multiple directed edges. The determination of the directed edge set is as follows:

[0068] E={(v i , v j )|v i Depends on v j};

[0069] Where E represents a set of directed edges, (v i , v j) represents a directed edge from terminal device i to terminal device j. A directed graph model is determined according to the node set and the directed edge set. The directed graph model can be expressed as G = (V, E).

[0070] S230. Determine device description data of each terminal device based on the device association data, determine device similarity between terminal devices based on the device description data of the terminal devices, and construct a similarity relationship model with the terminal devices as nodes and the device similarities as edges.

[0071] In this embodiment, device similarity can be understood as the degree of similarity between terminal devices, which can be calculated through the device description data of multiple terminal devices. Device similarity can also be obtained through a similarity calculation model, for example, by inputting device association data into a pre-trained similarity calculation model for calculation, the device similarity can be obtained, and the similarity calculation model includes but is not limited to a neural network model, for example, a convolutional neural network model and a transformer model.

[0072] Optionally, determining device description data for each terminal device based on device association data includes: obtaining multiple device safety indicators corresponding to the terminal device, wherein the device safety indicators include at least one of device operating time, number of device failures, device update frequency, device operation information, and device network traffic; constructing a judgment matrix based on multiple device safety indicators, and determining the eigenvalues ​​and eigenvectors of the judgment matrix, determining a weight vector of the device safety indicator based on the eigenvector corresponding to the largest eigenvalue, and determining the device description data of the terminal device based on the device association data and the weight vector.

[0073] In some embodiments, the similarity relationship model may be an undirected graph model. Taking the undirected graph model as an example of the similarity relationship model, the similarity relationship model construction process is as follows: taking each terminal device as a node, determining a node set V, wherein the node set includes multiple terminal devices, calculating the device similarity between the terminal devices according to the device description data of the multiple terminal devices, for example, calculating the cosine similarity between the terminal devices, and determining the cosine similarity between the terminal devices as the device similarity between the terminal devices, and the calculation formula of the device similarity is as follows:

[0074]

[0075]

[0076]

[0077] Among them, Sim(i, j) represents the device similarity between terminal device i and terminal device j, S i and S jRepresents the device description data of terminal device i and terminal device j respectively, ‖S i ‖ and ‖S j ‖ respectively represent S i and S j The Euclidean distance, s ik Represents the data corresponding to the kth device security index of terminal device i and terminal device j, and Represent the average value of the device description data of terminal device i and terminal device j respectively; m represents the number of device security indicators. Taking device similarity as the edge, determine the undirected edge set, and the determination of the undirected edge set is as follows:

[0078] E′={(v i , v j )′|Sim(i,j)>θ};

[0079] Where E′ represents the set of undirected edges, (v i , v j )′ represents an undirected edge between terminal device i and terminal device j, θ represents a preset device similarity threshold, and when the device similarity Sim(i, j) between terminal device i and terminal device j exceeds the preset device similarity threshold θ, an undirected edge (v i , v j )′. An undirected graph model is determined according to a node set and an undirected edge set. The undirected graph model can be expressed as G′=(V, E′).

[0080] S240: Determine a preliminary security deployment strategy for multiple terminal devices based on the device description data, the dependency relationship model, and the similarity relationship model.

[0081] In this embodiment, the preliminary security deployment strategy of multiple terminal devices can be determined by device description data, dependency model and similarity model. For example, the operator can manually determine the preliminary security deployment strategy of multiple terminal devices based on device description data, dependency model and similarity model. The preliminary security deployment strategies for multiple terminal devices can also be determined by matching, for example, the device description data, dependency model and similarity model of any terminal device are input into the server for matching, and the server stores different device description data, dependency models, similarity models and their corresponding preliminary security deployment strategies. When the match is successful, the matched preliminary security deployment strategy is determined as the preliminary security deployment strategy for the terminal device; when the match fails, the confidences between the device description data, dependency models and similarity models of different terminal devices in the server and the device description data, dependency model and similarity model of the terminal device are calculated and summed, the confidences of different terminal devices in the server are compared, and the corresponding device description data, dependency model and similarity model in the server are determined according to the maximum confidence, and the preliminary security deployment strategy corresponding to the device description data, dependency model and similarity model with the maximum confidence in the server is determined, and the above preliminary security deployment strategy is adjusted according to the device description data, dependency model and similarity model of the terminal device to obtain the preliminary security deployment strategy corresponding to the terminal device.

[0082] S250, extracting multiple target device features from the device association data, determining device risk points through the multiple target device features and the security identification model, and determining target security deployment strategies for multiple terminal devices based on the device risk points and the preliminary security deployment strategy.

[0083] In this embodiment, the security identification model adopts a support vector machine model, and the security identification model includes an input layer, a kernel function layer, a subtask layer, and an output layer connected in sequence. The input layer is used to receive the pre-processed device association data of multiple terminal devices, the kernel function layer is used to map the pre-processed device association data of multiple terminal devices received by the input layer to a feature space of a preset dimension to obtain feature mapping data, and the subtask layer includes multiple classifiers, which can be three or five classifiers, and can be set according to requirements, without limitation here. The subtask layer is used to obtain the device risk points of multiple terminal devices, and the output layer is used to output the device risk points of multiple terminal devices.

[0084] In some embodiments, each subtask in the subtask layer of the security identification model corresponds to a classifier, each subtask has an independent hyperplane, and the training process of each subtask corresponding to a classifier is as follows: the feature mapping data obtained by the kernel function layer is input into the classifier corresponding to each subtask in the subtask layer, and the classifier corresponding to each subtask is trained by the Lagrangian function. The expression of the Lagrangian function is as follows:

[0085]

[0086] Where L(α) represents the Lagrangian function; α represents the Lagrangian multiplier vector, and α represents the support vector; i represents the Lagrange multiplier vector corresponding to the i-th feature map data; y represents the class label vector, which contains the vector of class labels of all feature map data; the class label y i Identifies the class label vector corresponding to the i-th feature map data; x i K represents the feature vector of the i-th feature map data; task Represents the kernel function of the t-th subtask. After the classifier training for each subtask is completed, the decision boundary is obtained. The calculation formula of the decision boundary is as follows:

[0087]

[0088] Among them, f task (x) represents the kernel function output of the t-th subtask; α i represents the Lagrange multiplier of the i-th support vector; b task Represents the preset threshold of the tth subtask. The hyperplane corresponding to each subtask can be determined based on the decision boundary.

[0089] In some embodiments, the device risk points are determined through multiple target device features and a security identification model, including: receiving multiple target device features as input through the input layer of the security identification model, and mapping the target device features to a feature space of a preset dimension through the kernel function layer of the security identification model to obtain feature mapping data; inputting the feature mapping data into the classifier corresponding to each subtask in the subtask layer of the security identification model to obtain the device risk points corresponding to each subtask, and outputting the device risk points through the output layer of the security identification model. Outputting the device risk points through the security identification model improves the output efficiency and accuracy of the device risk points, improves the efficiency of determining the security deployment method in the power system, and thereby improves the safety and stability of the power system.

[0090] In some embodiments, the kernel function of the kernel function layer of the security identification model is:

[0091] K(x i , xj ) = exp(-γ||x i -x j || 2 );

[0092]

[0093] Among them, x i and x j represents the input i-th and j-th target device features; γ represents the parameter of the kernel function, which is used to control the degree of spatial mapping; ||x i -x j || 2 It represents the Euclidean distance between the i-th and j-th target device features; Feature Variance represents the variance of multiple target device features. It realizes the dynamic adjustment of kernel function parameters and improves the generalization ability of security identification model.

[0094] Optionally, target security deployment strategies for multiple terminal devices are determined based on device risk points and preliminary security deployment strategies, including: determining a device security score for the terminal device based on a risk point score corresponding to each device risk point and a risk point weight corresponding to the device risk point; if the device security score does not meet a preset score threshold, adjusting the preliminary security deployment strategy to obtain target security deployment strategies for multiple terminal devices.

[0095] Optionally, the target device characteristics include at least one of central processing unit usage characteristic data, memory usage characteristic data, disk usage characteristic data, network traffic characteristic data, fault characteristic data, system update characteristic data, and device operation characteristic data.

[0096] The technical solution of the embodiment of the present invention determines the dependency relationship between multiple terminal devices according to network topology data, takes the terminal devices as nodes and the dependency relationship as edges, and constructs a dependency relationship model, which can realize in-depth analysis of network topology data, is conducive to comprehensive analysis of the dependency relationship between multiple terminal devices, and provides a basis for determining the preliminary security deployment strategy; by determining the device description data of each terminal device based on the device association data, determining the device similarity between the terminal devices according to the device description data of the terminal devices, and constructing a similarity relationship model with the terminal devices as nodes and the device similarity as edges, the similarity between multiple terminal devices can be accurately determined through the device description data, and then similar terminal devices can be determined, so as to classify and manage multiple terminal devices, which provides a basis for determining the preliminary security deployment strategy; and then by determining the preliminary security deployment strategy of multiple terminal devices based on the device description data, the dependency relationship model and the similarity relationship model, combined with the terminal devices' own characteristics and the dependency relationship and similarity relationship between multiple terminal devices, the comprehensiveness and scenario fit of the preliminary security deployment strategy are improved. On this basis, a more complete target security deployment strategy can be obtained according to the preliminary security deployment strategy, and the safety and stability of the power system are further improved.

[0097] Embodiment 3

[0098] Figure 3 Schematic diagram of a secure deployment device for a terminal device in a power system provided by Embodiment 3 of the present invention. Figure 3 As shown, the device includes: a device association data acquisition module 310, which is used to collect device association data of multiple terminal devices in the power system; wherein the device association data includes device status data, device network data, device configuration data and network topology data; a preliminary safety deployment strategy determination module 320, which is used to determine the device description data of each terminal device and the association relationship model between multiple terminal devices based on the device association data, and determine the preliminary safety deployment strategy of multiple terminal devices based on the device description data and the association relationship model; a target safety deployment strategy determination module 330, which is used to extract multiple target device features from the device association data, determine the device risk points through the multiple target device features and the safety identification model, and determine the target safety deployment strategies of multiple terminal devices according to the device risk points and the preliminary safety deployment strategy.

[0099] Optionally, the preliminary security deployment strategy determination module 320 is also used to: obtain multiple device safety indicators corresponding to the terminal device, wherein the device safety indicators include at least one of device operating time, number of device failures, device update frequency, device operation information and device network traffic; construct a judgment matrix based on multiple device safety indicators, and determine the eigenvalues ​​and eigenvectors of the judgment matrix, determine the weight vector of the device safety indicator according to the eigenvector corresponding to the largest eigenvalue, and determine the device description data of the terminal device according to the device association data and the weight vector.

[0100] Optionally, the association relationship model includes a dependency relationship model and a similarity relationship model.

[0101] Optionally, the preliminary security deployment strategy determination module 320 is also used to: determine the dependency relationship between multiple terminal devices based on the network topology data, and construct a dependency model with the terminal devices as nodes and the dependency relationships as edges; and determine the device similarity between the terminal devices based on the device description data of the terminal devices, and construct a similarity model with the terminal devices as nodes and the device similarity as edges.

[0102] Optionally, the security identification model adopts a support vector machine model.

[0103] Optionally, the security identification model includes an input layer, a kernel function layer, a subtask layer and an output layer.

[0104] Optionally, the target security deployment strategy determination module 330 is also used to: receive multiple target device features as input through the input layer of the security identification model, and map the target device features to a feature space of preset dimensions through the kernel function layer of the security identification model to obtain feature mapping data; input the feature mapping data into the classifier corresponding to each subtask in the subtask layer of the security identification model to obtain the device risk points corresponding to each subtask, and output the device risk points through the output layer of the security identification model.

[0105] Optionally, the kernel function of the kernel function layer is:

[0106] K(x i , x j ) = exp(-γ||x i -x j || 2 );

[0107]

[0108] Among them, x i and x j represents the input i-th and j-th target device features; γ represents the parameter of the kernel function, which is used to control the degree of spatial mapping; ||x i -xj || 2 It represents the Euclidean distance between the i-th and j-th target device features; Feature Variance represents the variance of multiple target device features.

[0109] Optionally, the target security deployment strategy determination module 330 is also used to: determine the device security score of the terminal device based on the risk point score corresponding to each device risk point and the risk point weight corresponding to the device risk point; when the device security score meets a preset score threshold, adjust the preliminary security deployment strategy to obtain the target security deployment strategy for multiple terminal devices.

[0110] Optionally, the target device characteristics include at least one of central processing unit usage characteristic data, memory usage characteristic data, disk usage characteristic data, network traffic characteristic data, fault characteristic data, system update characteristic data, and device operation characteristic data.

[0111] The apparatus for secure deployment of terminal equipment in an electric power system provided by an embodiment of the present invention can execute the method for secure deployment of terminal equipment in an electric power system provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0112] Embodiment 4

[0113] Figure 4 : is a structural schematic diagram of an electronic device provided in Embodiment 4 of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0114] like Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0115] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0116] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a secure deployment method for terminal devices in a power system.

[0117] In some embodiments, the method for secure deployment of terminal devices in the power system may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for secure deployment of terminal devices in the power system described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for secure deployment of terminal devices in the power system in any other appropriate manner (e.g., by means of firmware).

[0118] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0119] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0120] Embodiment 5

[0121] Embodiment 5 of the present invention further provides a computer-readable storage medium, the computer-readable storage medium storing computer instructions, the computer instructions being used to cause a processor to execute a method for securely deploying a terminal device in a power system, the method comprising:

[0122] Collect device association data of multiple terminal devices in the power system; wherein the device association data includes device status data, device network data, device configuration data and network topology data; determine the device description data of each terminal device and the association relationship model between multiple terminal devices based on the device association data, and determine the preliminary security deployment strategy of the multiple terminal devices based on the device description data and the association relationship model; extract multiple target device features from the device association data, determine the device risk points through the multiple target device features and the security identification model, and determine the target security deployment strategy of the multiple terminal devices according to the device risk points and the preliminary security deployment strategy.

[0123] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0124] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0125] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0126] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0127] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0128] Embodiment 6

[0129] Embodiment 6 of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the secure deployment of terminal devices in a power system according to any embodiment of the present invention.

[0130] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for secure deployment of terminal equipment in a power system, characterized in that: include: Collecting device-related data of multiple terminal devices in the power system; wherein the device-related data includes device status data, network data, device configuration data and network topology data; Determine device description data of each terminal device and an association relationship model between the plurality of terminal devices based on the device association data, and determine preliminary security deployment strategies for the plurality of terminal devices based on the device description data and the association relationship model; Extract multiple target device features from the device association data, determine device risk points through the multiple target device features and a security identification model, and determine target security deployment strategies for the multiple terminal devices based on the device risk points and the preliminary security deployment strategy.

2. The method for secure deployment of terminal equipment in a power system according to claim 1, characterized in that: The determining of the device description data of each of the terminal devices based on the device association data includes: Acquire a plurality of device safety indicators corresponding to the terminal device, wherein the device safety indicator includes at least one of device operation time, number of device failures, device update frequency, device operation information, and device network traffic; A judgment matrix is ​​constructed based on multiple device security indicators, and the eigenvalues ​​and eigenvectors of the judgment matrix are determined. The weight vector of the device security indicator is determined according to the eigenvector corresponding to the largest eigenvalue. The device description data of the terminal device is determined according to the device association data and the weight vector.

3. The method for secure deployment of terminal equipment in a power system according to claim 1, characterized in that: The association relationship model includes a dependency relationship model and a similarity relationship model; the determining of the association relationship model between the plurality of terminal devices based on the device association data includes: Determine the dependency relationships among multiple terminal devices according to the network topology data, and construct a dependency relationship model with the terminal devices as nodes and the dependency relationships as edges; and The device similarity between the terminal devices is determined according to the device description data of the terminal devices, and a similarity relationship model is constructed with the terminal devices as nodes and the device similarities as edges.

4. The method for secure deployment of terminal equipment in a power system according to claim 3, characterized in that: The security identification model adopts a support vector machine model; the security identification model includes an input layer, a kernel function layer, a subtask layer and an output layer; the device risk points are determined by using multiple target device features and the security identification model, including: Receive multiple input target device features through the input layer of the security identification model, and map the target device features to a feature space of a preset dimension through the kernel function layer of the security identification model to obtain feature mapping data; The feature mapping data is input into the classifier corresponding to each subtask in the subtask layer of the security identification model to obtain the equipment risk point corresponding to each subtask, and the equipment risk point is output through the output layer of the security identification model.

5. The method for secure deployment of terminal equipment in a power system according to claim 4, characterized in that: The kernel function of the kernel function layer is: K(x i ,x j )=exp(-γ||x i -x j || 2 ); Among them, x i and x j represents the input i-th and j-th target device features; γ represents the parameter of the kernel function, which is used to control the degree of spatial mapping; ||x i -x j || 2 represents the Euclidean distance between the i-th and j-th target device features; Feature Variance represents the variance of multiple target device features.

6. The method for secure deployment of terminal equipment in a power system according to claim 1, characterized in that: The step of determining target security deployment strategies for the plurality of terminal devices according to the device risk points and the preliminary security deployment strategy includes: Determine a device security score for the terminal device according to the risk point score corresponding to each of the device risk points and the risk point weight corresponding to the device risk point; When the device security score meets a preset score threshold, the preliminary security deployment strategy is adjusted to obtain a target security deployment strategy for the plurality of terminal devices.

7. The method for secure deployment of terminal equipment in a power system according to claim 1, characterized in that: The target device characteristics include at least one of central processing unit usage characteristic data, memory usage characteristic data, disk usage characteristic data, network traffic characteristic data, fault characteristic data, system update characteristic data and device operation characteristic data.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for secure deployment of terminal equipment in the power system according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for secure deployment of terminal equipment in a power system according to any one of claims 1 to 7 when executed.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the method for secure deployment of terminal equipment in a power system according to any one of claims 1 to 7.

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