Power distribution network data asset vulnerability identification method, device and system, and storage medium

Abstract

The invention discloses a power distribution network data asset vulnerability identification method, device and system, and a storage medium, and the method comprises the steps: S1, obtaining a data asset set according to original data in a power distribution network, and dividing the data asset set into a training set and a test set; s2, constructing a graph according to the data asset training set, the mutual relation between the data assets and the feature matrix; s3, fusing the extracted vulnerability features into the graph through a graph neural network and a vulnerability fusion strategy to obtain a GNN model containing vulnerability fusion features; s4, training and optimizing a GNN model of the vulnerability fusion features; and S5, inputting the test set into the trained GNN model containing the vulnerability fusion features to carry out power distribution network data asset vulnerability identification. By adopting the technical scheme of the invention, the accuracy and efficiency of vulnerability identification are improved.

Description

Technical Field

[0001] The present invention belongs to the field of power distribution network data asset security technology, and in particular relates to a power distribution network data asset vulnerability identification method and device, system, and storage medium. Background Art

[0002] With the development of smart grid technology, the data generated by smart meters, sensors and control devices in the distribution network are becoming increasingly valuable due to their key role in grid operation monitoring, energy optimization and efficiency improvement. These data not only record key information such as the real-time status of the grid, user electricity consumption behavior and equipment operation, but also play an important role in the management and decision-making of the grid. Therefore, they are regarded as data assets of the power system. However, the increase in the value of these data assets also makes them targets of potential attackers, especially in the circulation and processing of data. Once attacked or leaked, it may have a serious impact on the safe and stable operation of the grid. Therefore, there is an urgent need for a vulnerability identification method for distribution network data assets, which is of great significance for maintaining grid security and promoting the sustainable development of smart grids. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and device, system and storage medium for identifying the vulnerability of distribution network data assets.

[0004] To achieve the above object, the present invention adopts the following technical solution:

[0005] A method for identifying the vulnerability of distribution network data assets, comprising:

[0006] Step S1: obtain a data asset set based on the original data in the distribution network, and divide the data asset set into a training set and a test set; wherein the original data includes: user electricity consumption information of smart meters, grid status monitoring data of sensors, and grid control instructions of control devices;

[0007] Step S2: construct a graph based on the data asset training set, the relationships between data assets, and the feature matrix;

[0008] Step S3, fusing the extracted vulnerability features into the graph through the graph neural network and the vulnerability fusion strategy to obtain a GNN model containing the vulnerability fusion features;

[0009] Step S4, training and optimizing the GNN model of vulnerability fusion features;

[0010] Step S5: input the test set into the trained GNN model containing vulnerability fusion features to identify the vulnerability of distribution network data assets.

[0011] Preferably, step S1 comprises:

[0012] According to the original data in the distribution network, a data asset set is obtained;

[0013] Perform data cleaning on the data asset set;

[0014] Divide the cleaned data asset set into a training set and a test set;

[0015] Extract key information from the training set to obtain basic features of each type of data asset; wherein the basic features together constitute a feature matrix of the data asset.

[0016] Preferably, the vulnerability fusion strategy is: defining a feature fusion strategy for distribution network data assets for data leakage, service denial, data integrity, data availability and data dependency vulnerability types.

[0017] The present invention also provides a distribution network data asset vulnerability identification device, comprising:

[0018] The preprocessing module is used to obtain a data asset set based on the original data in the distribution network, and divide the data asset set into a training set and a test set; wherein the original data includes: user electricity consumption information of smart meters, grid status monitoring data of sensors, and grid control instructions of control devices;

[0019] A construction module for constructing a graph based on a training set of data assets, relationships between data assets, and a feature matrix;

[0020] The fusion module is used to fuse the extracted vulnerability features into the graph through the graph neural network and the vulnerability fusion strategy to obtain a GNN model containing the vulnerability fusion features;

[0021] Training module, used to train and optimize the GNN model of vulnerability fusion features;

[0022] The identification module is used to input the test set into the trained GNN model containing vulnerability fusion features to identify the vulnerability of distribution network data assets.

[0023] Preferably, the preprocessing module comprises:

[0024] A processing unit, used for obtaining a data asset set based on raw data in the distribution network;

[0025] A cleaning unit, used for performing data cleaning on a data asset set;

[0026] A partitioning module is used to divide the data asset set after data cleaning into a training set and a test set;

[0027] The extraction unit is used to extract key information from the training set to obtain the basic features of each type of data asset; wherein the basic features together constitute a feature matrix of the data asset.

[0028] Preferably, the vulnerability fusion strategy is: defining a feature fusion strategy for distribution network data assets for data leakage, service denial, data integrity, data availability and data dependency vulnerability types.

[0029] An embodiment of the present invention also provides a distribution network data asset vulnerability identification system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a distribution network data asset vulnerability identification method when executed by the processor.

[0030] An embodiment of the present invention further provides a storage medium, on which a computer program is stored, and the computer program executes a method for identifying the vulnerability of distribution network data assets when running.

[0031] The present invention first collects the raw data in the distribution network, forms a data asset set after cleaning, and then extracts basic features to form a feature matrix. Secondly, a graph is constructed based on the data asset training set, the relationship between data assets, and the feature matrix, and the extracted vulnerability features are integrated into the graph through the graph neural network and vulnerability fusion strategy. The present invention takes into account the multi-dimensional characteristics of distribution network data assets, and improves the accuracy and efficiency of vulnerability identification through graph neural network and machine learning technology, providing strong support for the stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0033] Figure 1 This is a flow chart of a method for identifying vulnerability of distribution network data assets according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention.

[0035] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] Embodiment 1:

[0037] like Figure 1 As shown, an embodiment of the present invention provides a method for identifying the vulnerability of distribution network data assets, including:

[0038] Step S1: According to the original data in the distribution network, a data asset set is obtained, and the data asset set is divided into a training set and a test set.

[0039] Collect raw data from the distribution network, including user electricity consumption information from smart meters, grid status monitoring data from various sensors (such as temperature and current sensor readings), and grid control instructions from control devices (such as circuit breakers and relay status information). After preliminary classification and organization of these raw data, a data asset set D = {d1, d2, …, d N}, where d i Represents the i-th type of data assets.

[0040] These data asset sets are cleaned to ensure the quality and accuracy of the data. The cleaning process mainly includes processing missing values ​​(by interpolating or deleting missing data points) and outlier detection (identifying and processing abnormal situations such as sudden jumps in sensor readings). Through this process, the reliability and effectiveness of data assets are ensured, providing a solid data foundation for subsequent vulnerability analysis and model building. Such processing not only improves the availability of data assets, but also provides the necessary prerequisite for vulnerability identification of distribution network data assets. The data asset set after data cleaning is divided into a training set and a test set.

[0041] Extract key information from the training set to form a comprehensive basic feature profile of each type of data asset. Basic features include: technical specifications, such as the model of the smart meter and the measurement range of the sensor; operating status, covering the online / offline status of the device and historical fault records; security configuration, including access control levels and encryption measures; network traffic, involving the size and direction of data traffic; and physical and environmental factors, such as the geographical location of the device and environmental conditions, such as temperature and humidity. Together, the basic features constitute the feature matrix of the data asset. (N is the number of data assets, d is the characteristic dimension of each type of data asset):

[0042]

[0043] Among them, x ijis the value of the jth feature of the i-th data asset, i ranges from 1 to N, and j ranges from 1 to d.

[0044] Step S2: Construct a graph based on the data asset training set, the relationships between data assets, and the feature matrix

[0045] 1) Node definition: Each type of data asset is a node in the graph and contains its basic feature vector. N} represents a set of graph nodes, where v i Represents the i-th type of data asset node.

[0046] 2) Edge definition: It is to determine the interaction relationship between data assets, and these relationships are used as edges of the graph. Interaction relationships may include data flow (such as sensor data sent to the control center), control signals (such as circuit breakers receiving signals from the control center), physical connections (such as devices directly connected by cables) and logical connections (such as communication between devices achieved through network protocols). Let E∈V×V be the set of edges, then it describes the connection relationship between any two nodes in the node set V. In other words, node v i and v j If there is an edge between them, then (v i ,v j )∈E.

[0047] 3) Graph attributeization: It is the process of combining the feature matrix X with the graph structure to form a complete graph representation G. Specifically, the graph G consists of three main parts: the node set V, the edge set E, and the node feature matrix X. At this time, the graph can be represented as:

[0048] G=(V,E,X) (2)

[0049] In this process, not only nodes and edges are defined, but also additional attribute information is introduced, which directly enriches the structure and semantics of the graph G. For example, the network topology, as an attribute of the graph, describes the physical or logical connection layout between devices in the distribution network, which is reflected by the edge set E. At the same time, the dependency between devices indicates that a device may rely on the data or services of another device to perform its function. This dependency can be represented by the features in the node feature matrix X, or further refined by the attributes of the edge. This attribute information not only greatly enriches the context of the graph G, but also enables the graph neural network (GNN) to more accurately capture and analyze the interaction patterns and potential vulnerabilities between data assets. In this way, the graph G is not just a structured graph, but a complex system containing rich semantic information, which provides a solid foundation for the vulnerability analysis of distribution network data assets.

[0050] Step S3: The extracted vulnerability features are integrated into the graph through the graph neural network and the vulnerability fusion strategy to obtain a GNN model containing the vulnerability fusion features.

[0051] Fragility feature fusion is to integrate different types of fragility features into the feature representation of graph nodes. These features include not only the properties of the nodes themselves, but also the information of the edges related to the nodes. The following is the process of integrating these fragility features into the graph G using the GNN model:

[0052] First, in the GNN model, for the lth layer (l=1,2,…,L), the node v i The feature update can be expressed as:

[0053]

[0054] in, is node v i In the feature representation at layer l, is node v i The set of neighbor nodes, W (l) is the learnable weight matrix of the lth layer, and τ is a nonlinear activation function, such as Relu or Sigmoid function.

[0055] Secondly, for vulnerability types such as data leakage, service denial, data integrity, data availability and data dependency, a set of feature fusion strategies are defined for distribution network data assets. These strategies effectively integrate features related to various vulnerabilities by introducing weight factors and customized feature functions. This not only enhances the model's ability to identify different vulnerability features, but also improves the accuracy of analyzing potential risks in the distribution network.

[0056] ① Data leakage vulnerability: Emphasize the data encryption characteristics and access control of nodes. First, define a function to quantify the node v i The data encryption strength. This function can be calculated based on the encryption algorithm, key length and encryption protocol used:

[0057] encrypt(x i )=log2(key_length(x i ))+log2(algorithm_strength(x i )) (4)

[0058] Here, key_length(x i ) is the node v i The length of the encryption key used, algorithm_strength(x i ) is the node v iThe strength of the encryption algorithm used. This function reflects the encryption security level of the node. Secondly, the access control characteristics can be defined by quantifying the access rights and control policies of the node, such as defining a function to evaluate the access control strength of the node:

[0059]

[0060] Among them, U is the set of all users, is the set of nodes that user u is authorized to access. This function reflects the strictness of the node access control. Finally, the data encryption and access control features are fused with the original node features to enhance the GNN model's ability to identify data leakage vulnerabilities. The fused features are expressed as:

[0061]

[0062] x i is node v i The original eigenvectors, β and are weight factors used to adjust the importance of encryption features and access control features, encrypt(x i ) and access_control(x i ) represent the nodes v i Data encryption and access control features.

[0063] ② Denial of service vulnerability: Focus on the traffic patterns and traffic anomaly characteristics of the nodes. On the one hand, the long short-term memory network (LSTM) is used to extract the features of the traffic data patterns of the nodes to capture the long-term dependence and periodic changes of the traffic. The update formula of LSTM is as follows:

[0064] h t =LSTM(x t ,h t-1 ) (7)

[0065] Among them, h t is the hidden state at time step t, x t is the input feature at time step t, h t-1 is the hidden state at time step t-1. On the other hand, the attention mechanism is introduced to enhance the feature extraction capability of LSTM, so as to complete the identification of abnormal traffic behavior. The output vector z after attention weighting is t It can be expressed as:

[0066] z t =Attention(H) (8)

[0067] H is the set of hidden states at all time steps. Finally, the features extracted by LSTM and attention mechanism are fused with the original node features to enhance the GNN model’s ability to identify service denial vulnerabilities. The fused features are expressed as:

[0068]

[0069] Among them, x i is node v i The original feature vector of traffic(x) is i )=z t .

[0070] ③ Data integrity vulnerability: Integrate the monitoring and anomaly detection features of data processing activities. The present invention uses time series analysis methods to monitor data processing activities. For example, an exponential smoothing algorithm is used to generate dynamic upper and lower bounds to detect data anomalies:

[0071] Upper Bound=μ+M·σ (10)

[0072] Lower Bound=μ-M·σ (11)

[0073] Where μ is the mean, σ is the standard deviation, and M is a constant used to determine the width of the upper and lower bounds. i If the data value of a feature exceeds these limits, it indicates that the integrity of the data asset may be threatened. In addition, an anomaly detection method based on GLR (Generalized Likelihood Ratio) is used to find anomalies. This method identifies anomalies by comparing predicted values ​​with actual values:

[0074]

[0075] Among them, p t (y t |x 1:t-1 ) is given the historical data x 1:t-1 The predicted probability under t (y t |x 1:t ,θ) is given the complete data x 1:t and the predicted probability under the model parameter θ. Finally, these features are fused with the original node features to enhance the GNN model's ability to identify data integrity vulnerabilities. The fused features are expressed as:

[0076]

[0077] Among them, x i is node v iThe original feature vector of , δ is the weight factor used to adjust the importance of integrity features; integrity(x i ) represents node v i The data asset integrity monitoring characteristics can be expressed as:

[0078]

[0079] It can be seen that integrity(x i ) is a binary feature, indicating that node v i Whether the characteristic data is within the normal range.

[0080] ④ Data availability vulnerability: Analyze the redundancy and backup mechanism characteristics of the nodes. Redundancy refers to the degree of replication of key components or functions in the system to improve the reliability of the system. In this invention, the redundancy characteristic R(x i )for:

[0081] R(x i )=(2N r -N) / N (15)

[0082] Among them, N r is node v i The number of redundant components, N is the total number of components. This indicator reflects the redundancy of the node in the face of a single point of failure. The backup mechanism reflects the ability to quickly recover when data assets are lost, damaged or unavailable. Here we define the backup mechanism feature B(x i )for:

[0083]

[0084] Among them, RTO(x i ) is the measure of node v i The index of data recovery speed (the closer its value is to 1, the stronger the recovery capability is), and λ is the weight parameter for adjusting the impact of data recovery time on the backup mechanism score, that is, this binary feature indicates the impact of the backup mechanism on data availability. The redundancy and backup mechanism features are integrated into the feature vector of the node, which can be expressed as:

[0085] availability(x i )=R(x i )+B(x i ) (17)

[0086] Visible availability(x i ) is a comprehensive indicator that not only considers the redundancy of the node, but also the backup mechanism of the node. Finally, this comprehensive availability feature is fused with the original feature vector to obtain the fused feature vector:

[0087]

[0088] Among them, x i is node v i is the original feature vector of , and ξ is the weight factor used to adjust the importance of availability features.

[0089] ⑤Data dependency vulnerability: Evaluate the dependency of nodes on external systems or components. Dependency vulnerability can be evaluated by quantifying the degree of dependency of nodes on external systems or components. First, define a dependency index D(x i ), which reflects the node v i Degree of dependence on external resources:

[0090]

[0091] in, is node v i The set of neighbor nodes, w ij is node v i With v j The edge weights between them represent the connection strength or data interaction frequency between them, dependency(x j ) is the node v j The dependency feature vector of . Secondly, the dependency index is fused with the original node features to enhance the GNN model's ability to identify dependency vulnerability. The fused features are expressed as:

[0092]

[0093] Among them, x i is node v i The original eigenvector of is a weight factor used to adjust the importance of dependent features, D(x i ) represents node v i Dependency index on external systems or components. It should be noted that in order to further evaluate the impact of dependency on data availability, an impact metric I(x i ), which reflects the i The impact of dependencies on its normal operation:

[0094]

[0095] It is a tuning parameter that controls the degree to which dependencies affect availability.

[0096] Finally, the fusion features Substituting into the update formula of the GNN model, we can get:

[0097]

[0098] Through GNN, different types of vulnerability features can be fused into the graph G to improve the ability of the present invention to identify the vulnerability of distribution network data assets. The feature fusion strategy for each type of vulnerability adjusts the importance of the feature through a learnable weight factor, so that the present invention can adaptively focus on the most relevant characteristics of each vulnerability. This method not only improves the accuracy of vulnerability identification, but also provides direct guidance for subsequent security measures.

[0099] Step S4: Training and optimizing the GNN model of vulnerability fusion features

[0100] The purpose of training the GNN model of vulnerability fusion features is to adjust the model parameters to minimize the difference between the model prediction and the true vulnerability label. The following is the detailed model training and optimization process:

[0101] First, set vulnerability category labels for the training set of the distribution network data asset dataset D. is the label matrix, where C is the total number of categories; this also means that for each type of data asset node v i Each is marked with a specific vulnerability category label.

[0102] Secondly, the cross entropy loss function is used to measure the difference between the vulnerability class predicted by the model and the true label. For multi-class classification problems, the cross entropy loss function is defined as:

[0103]

[0104] Among them, y ic is the category c for node v i The true label (usually one-hot encoded), is the node v predicted by the model i The probability of belonging to category c. This loss function encourages the model to increase the probability of predicting the correct category while reducing the probability of predicting the wrong category.

[0105] Again, in each iteration, the gradient of the loss function with respect to the model parameters is calculated, and the parameters are updated to reduce the loss by the following formula:

[0106]

[0107] Among them, θ is the model parameter, η is the learning rate, is the gradient of the loss function with respect to the parameters.

[0108] Finally, to prevent overfitting, a regularization term can be added to the loss function:

[0109] ψ regularized =ψ+λ∑ l ||w (l) || 2 (25)

[0110] Where λ is the regularization coefficient and ||W (l) || 2 is the Frobenius norm of the weight matrix of the lth layer. In order to make the model converge faster, it is usually necessary to make the input features distributed in a similar range, that is, to perform normalization:

[0111]

[0112] Here, μ and σ are the mean and standard deviation of the feature, respectively.

[0113] During the training process, the present invention uses graph batches to process data of multiple graphs in parallel; at the same time, the model performance is monitored on the validation set, and training is stopped when the performance no longer improves to avoid overfitting; and an independent validation set is used to adjust the model parameters and hyperparameters.

[0114] Through the above process, the GNN model can be effectively trained and its performance in the distribution network data asset vulnerability identification task can be optimized. These processes not only involve minimizing the loss function, but also include the optimization of model parameters, regularization, feature normalization, and adjustment of training strategies to ensure the generalization ability and prediction accuracy of the model.

[0115] Step S5: input the test set into the trained GNN model containing vulnerability fusion features to identify the vulnerability of distribution network data assets.

[0116] The present invention constructs a comprehensive distribution network data asset feature matrix, integrating features of multiple dimensions such as technical specifications, operating status, security configuration, network traffic, and physical and environmental factors. This multi-dimensional feature fusion technology provides rich contextual information for the GNN model, significantly improving the model's ability to capture and analyze interactions and potential vulnerabilities between data assets. When constructing a graph, the present invention implements the attributeization of the graph by defining nodes and edges and adding attribute information such as network topology and dependencies between devices. It not only enhances the GNN model's understanding of the complex interactions between devices in the distribution network, but also provides an effective multi-source heterogeneous data processing and fusion solution, solving the technical problem of extracting key information from multi-source heterogeneous data and effectively fusing them. For the various vulnerability types of distribution network data assets, the present invention defines their respective feature fusion strategies, and adjusts the importance of features through learnable weight factors, so that the model can adaptively focus on the most relevant features for each vulnerability. This customized feature fusion strategy directly solves how to use graph neural networks to process the complex interactions between devices in the distribution network, and how to enhance the model's understanding of dependencies between devices and network topology through the attributeization of the graph. The present invention adopts a variety of strategies in the model training and optimization process, including cross entropy loss function, regularization, feature normalization, and graph batch parallel processing, etc., to ensure the generalization ability and prediction accuracy of the model. By monitoring the model performance on the validation set and adjusting the model parameters and hyperparameters, and using the test set to evaluate the final performance of the model, the present invention effectively avoids overfitting and optimizes the performance of the model in the distribution network data asset vulnerability identification task.

[0117] Embodiment 2:

[0118] The embodiment of the present invention further provides a distribution network data asset vulnerability identification device, comprising:

[0119] The preprocessing module is used to obtain a data asset set based on the original data in the distribution network, and divide the data asset set into a training set and a test set; wherein the original data includes: user electricity consumption information of smart meters, grid status monitoring data of sensors, and grid control instructions of control devices;

[0120] A construction module for constructing a graph based on a training set of data assets, relationships between data assets, and a feature matrix;

[0121] The fusion module is used to fuse the extracted vulnerability features into the graph through the graph neural network and the vulnerability fusion strategy to obtain a GNN model containing the vulnerability fusion features;

[0122] Training module, used to train and optimize the GNN model of vulnerability fusion features;

[0123] The identification module is used to input the test set into the trained GNN model containing vulnerability fusion features to identify the vulnerability of distribution network data assets.

[0124] As an implementation of an embodiment of the present invention, the preprocessing module includes:

[0125] A processing unit, used for obtaining a data asset set based on raw data in the distribution network;

[0126] A cleaning unit, used for performing data cleaning on a data asset set;

[0127] A partitioning module is used to partition the data asset set after data cleaning into a training set and a test set;

[0128] The extraction unit is used to extract key information from the training set to obtain the basic features of each type of data asset; wherein the basic features together constitute a feature matrix of the data asset.

[0129] As an implementation method of an embodiment of the present invention, the vulnerability fusion strategy is: defining a feature fusion strategy for distribution network data assets for data leakage, service denial, data integrity, data availability and data dependency vulnerability types.

[0130] Embodiment 3:

[0131] An embodiment of the present invention also provides a distribution network data asset vulnerability identification system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a distribution network data asset vulnerability identification method when executed by the processor.

[0132] Embodiment 4:

[0133] An embodiment of the present invention further provides a storage medium, on which a computer program is stored, and the computer program executes a method for identifying the vulnerability of distribution network data assets when running.

[0134] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for identifying the vulnerability of distribution network data assets, characterized in that: include: Step S1: obtain a data asset set based on the original data in the distribution network, and divide the data asset set into a training set and a test set; wherein the original data includes: user electricity consumption information of smart meters, grid status monitoring data of sensors, and grid control instructions of control devices; Step S2: construct a graph based on the data asset training set, the relationships between data assets, and the feature matrix; Step S3, fusing the extracted vulnerability features into the graph through the graph neural network and the vulnerability fusion strategy to obtain a GNN model containing the vulnerability fusion features; Step S4, training and optimizing the GNN model of vulnerability fusion features; Step S5: input the test set into the trained GNN model containing vulnerability fusion features to identify the vulnerability of distribution network data assets.

2. The method for identifying the vulnerability of distribution network data assets according to claim 1, characterized in that: Step S1 includes: According to the original data in the distribution network, a data asset set is obtained; Perform data cleaning on the data asset set; Divide the cleaned data asset set into a training set and a test set; Extract key information from the training set to obtain basic features of each type of data asset; wherein the basic features together constitute a feature matrix of the data asset.

3. The method for identifying the vulnerability of distribution network data assets according to claim 2, characterized in that: The vulnerability fusion strategy is: to define a feature fusion strategy for distribution network data assets for data leakage, service denial, data integrity, data availability and data dependency vulnerability types.

4. A distribution network data asset vulnerability identification device, characterized in that: include: The preprocessing module is used to obtain a data asset set based on the original data in the distribution network, and divide the data asset set into a training set and a test set; wherein the original data includes: user electricity consumption information of smart meters, grid status monitoring data of sensors, and grid control instructions of control devices; A construction module for constructing a graph based on a training set of data assets, relationships between data assets, and a feature matrix; The fusion module is used to fuse the extracted vulnerability features into the graph through the graph neural network and the vulnerability fusion strategy to obtain a GNN model containing the vulnerability fusion features; Training module, used to train and optimize the GNN model of vulnerability fusion features; The identification module is used to input the test set into the trained GNN model containing vulnerability fusion features to identify the vulnerability of distribution network data assets.

5. The device for identifying vulnerability of data assets in a distribution network according to claim 4, characterized in that: The preprocessing modules include: A processing unit, used for obtaining a data asset set based on raw data in the distribution network; A cleaning unit, used for performing data cleaning on a data asset set; A partitioning module is used to partition the data asset set after data cleaning into a training set and a test set; The extraction unit is used to extract key information from the training set to obtain the basic features of each type of data asset; wherein the basic features together constitute a feature matrix of the data asset.

6. The device for identifying vulnerability of data assets in a distribution network as claimed in claim 5, characterized in that: The vulnerability fusion strategy is: to define a feature fusion strategy for distribution network data assets for data leakage, service denial, data integrity, data availability and data dependency vulnerability types.

7. A distribution network data asset vulnerability identification system, characterized in that: include: A memory and a processor, wherein the memory stores a computer program executed by the processor, and when the computer program is executed by the processor, the method for identifying the vulnerability of distribution network data assets as described in any one of claims 1 to 3 is executed.

8. A storage medium, characterized in that: The storage medium stores a computer program, which executes the distribution network data asset vulnerability identification method according to any one of claims 1 to 3 when running.

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