A smart grid security risk data processing method and system

Abstract

The present invention discloses a method for processing security risk data of a smart grid, which relates to the technical field of smart grid data processing, including: obtaining multiple power distribution nodes, obtaining the interaction index of each power distribution node based on a flow calculation model, and summarizing multiple power distribution nodes into multiple levels based on a control level model; obtaining a target power distribution node, obtaining the first security risk data of the target power distribution node, and obtaining the first vulnerability value based on a vulnerability value calculation model; obtaining an adjacent power distribution node, obtaining the second security risk data of the adjacent power distribution node, and obtaining the second vulnerability value based on a vulnerability value calculation model; obtaining the first level and the second level corresponding to the target power distribution node and the adjacent power distribution node, and obtaining a weight value based on a control level model; issuing a risk warning according to the first vulnerability value, the second vulnerability value and the weight value. The present invention has the advantages of comprehensive data processing, accurate resource allocation, and timely and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grid data processing, and in particular to a smart grid security risk data processing method and system. Background Art

[0002] With the continuous advancement of science and technology, smart grid has become an important part of the modern power system. By integrating advanced information, communication and control technologies, smart grid has realized the intelligent management and optimized operation of the power grid. With the continuous deepening of the intelligent and interactive services of the power grid, the user side continues to extend to the information external network side, which will lead to the increasing security threats to the information system. The high integration of distribution network information and physical systems will further lead to network attacks on information systems, which will directly affect the safe and stable operation of physical systems while destroying their functions. In short, with the increase in the complexity and interconnectivity of smart grids, security risks are also rising. These risks may come from a variety of factors such as cyber attacks, data leakage, equipment failure, etc., which pose a serious threat to the stable operation and information security of the power grid. Summary of the invention

[0003] In view of the defects in the prior art, the present invention provides a smart grid security risk data processing method and system.

[0004] A method for processing security risk data of a smart grid comprises: S1, obtaining a plurality of power distribution nodes, analyzing the data interaction between the power distribution nodes based on a flow calculation model, and obtaining an interaction index of each power distribution node, and sorting and grading the interaction index based on a control level model, so that the plurality of power distribution nodes are classified into a plurality of levels; S2, obtaining any two power distribution nodes at the same level and recording them as target power distribution nodes, if security risk data interaction occurs between the two target power distribution nodes, obtaining the first security risk data of the target power distribution node, and grading the first security risk data of the target power distribution node based on a vulnerability value calculation model. The first and second levels corresponding to the target power distribution node and the adjacent power distribution node are obtained, and the first and second levels are processed based on the control level model to obtain the weight value; S5, and a risk warning is issued according to the first and second vulnerability values, the second vulnerability value and the weight value.

[0005] Preferably, S1 includes: S11, obtaining the data interaction frequency, data interaction string size and data interaction distance of the two interacting parties, and obtaining a first index based on this; S12, obtaining the average data interaction volume of the two interacting parties within a preset time window; S13, introducing the historical interaction stability index of the two interacting parties; S14, processing the first index, the average data interaction volume and the historical interaction stability through a flow calculation model, and obtaining an interaction index; S15, sorting and grading the interaction index based on a control level model, so that multiple power distribution nodes are summarized into multiple levels.

[0006] Preferably, the flow calculation model in S1 includes:

[0007] Among them, I i is the interaction index of the ith power distribution node, f ij is the data interaction frequency between node i and node j, v ij is the size of the data interaction string between node i and node j, d ij is the data interaction distance between node i and node j, T is the time window, D i (t) is a function of time and data interaction, S i It is the historical interaction stability index, and α, β, and γ are all weight coefficients.

[0008] Preferably, the vulnerability value calculation model includes: Among them, V i is the vulnerability value of the ith power distribution node, n represents the number of types of security risk data considered, and w k is the weight of the kth security risk, R k (d ik ) is the risk function associated with the kth security risk.

[0009] Preferably, S5 includes: S51, processing the first vulnerability value, the second vulnerability value and the weight value based on the risk assessment model, and obtaining a comprehensive score; S52, judging whether the comprehensive score exceeds a preset threshold, and if so, starting S53; S53, issuing a risk warning.

[0010] Preferably, the risk assessment model includes: S = W m ×(V 1 +V 2 )-r×|V 1 -V 2 |; where S is the comprehensive score, V 1 is the first fragile value, V 2 is the second fragile value, W is the weight value, m is an exponent greater than 1, and r is the correction coefficient.

[0011] Preferably, in S4, the control level model includes: Among them, W x is the weight value when the level is x, A, B, C are positive parameters, and x is a positive integer.

[0012] A smart grid security risk data processing system is also provided, the system is used to implement the smart grid security risk data processing method as described in any one of claims 1 to 7, the system includes: a level classification module, used to obtain multiple power distribution nodes, analyze the data interaction between each power distribution node based on the flow calculation model, and obtain the interaction index of each power distribution node, sort and grade the interaction index based on the control level model, so that the multiple power distribution nodes are summarized into multiple levels; a first vulnerability assessment module, used to obtain any two power distribution nodes at the same level and record them as target power distribution nodes, if security risk data interaction occurs between the two target power distribution nodes, then obtain the first vulnerability assessment module of the target power distribution node. A security risk data, processing the first security risk data based on a vulnerability value calculation model and obtaining a first vulnerability value; a second vulnerability assessment module, used to obtain power distribution nodes that interact with the target power distribution node and have different levels from the target power distribution node and record them as adjacent power distribution nodes, obtain the second security risk data of the adjacent power distribution node, process the second security risk data based on the vulnerability value calculation model and obtain a second vulnerability value; a weight calculation module, used to obtain the first level and the second level corresponding to the target power distribution node and the adjacent power distribution node, process the first level and the second level based on the control level model and obtain a weight value; an output module, used to issue a risk warning according to the first vulnerability value, the second vulnerability value and the weight value.

[0013] A non-temporary computer-readable storage medium is also provided, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned smart grid security risk data processing method is implemented.

[0014] An electronic device is also provided, comprising: a memory on which a computer program is stored; and a processor for executing the computer program in the memory to implement the above-mentioned smart grid security risk data processing method.

[0015] The beneficial effects of the present invention are embodied in:

[0016] By comprehensively analyzing the data interaction of power distribution nodes, it is possible to accurately identify the nodes that play a key role in the power grid, which is crucial for subsequent security risk management and resource optimization allocation. Through level division, power grid operators can focus more on high-level nodes, thereby achieving reasonable allocation and efficient management of resources; by processing the security risk data between nodes of the same level, it is possible to timely discover the potential safety hazards between these nodes, so as to take corresponding preventive measures. By calculating the vulnerability value, the power grid operator can intuitively understand the vulnerability of each node, providing data support for the formulation of targeted security protection strategies; considering the interaction of security risk data between nodes across levels is helpful to discover the possible risks between nodes of different levels Transmission and influence, thereby achieving a more comprehensive risk assessment. By identifying and handling security risks between nodes at different levels, the robustness of the entire power grid system can be improved, and the risk of the entire system collapsing due to the failure of a certain node can be reduced. By introducing weight values, the importance of nodes at different levels in the power grid security risk assessment can be more accurately reflected, thereby optimizing the assessment model and improving the accuracy of the assessment results. The determination of weight values ​​is helpful for power grid operators to formulate more reasonable risk management strategies and take different safety protection measures for nodes at different levels. By comprehensively evaluating the first vulnerability value, the second vulnerability value and the weight value, the system can monitor the security risk status of the power grid in real time, and issue timely warnings when high risks are found, thereby improving the security and stability of the smart grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0018] Figure 1 A schematic diagram of the steps of the smart grid security risk data processing method of the present invention;

[0019] Figure 2 A schematic diagram of the steps of the smart grid security risk data processing method S1 of the present invention;

[0020] Figure 3 A schematic diagram of the steps of the smart grid security risk data processing method S5 of the present invention;

[0021] Figure 4 The present invention is a block diagram of an electronic device according to an embodiment of the present invention.

[0022] Reference numerals:

[0023] 700 - electronic device, 701 - processor, 702 - memory, 703 - multimedia component, 704 - input / output (I / O) interface, 705 - communication component. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions 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 part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0025] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. 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.

[0026] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0027] like Figure 1 As shown, a method for processing smart grid security risk data is provided, comprising:

[0028] S1. Acquire multiple power distribution nodes, analyze the data interaction between the power distribution nodes based on the flow calculation model, and obtain the interaction index of each power distribution node, and sort and grade the interaction index based on the control level model, so that the multiple power distribution nodes are summarized into multiple levels;

[0029] S2. Obtain any two power distribution nodes at the same level and record them as target power distribution nodes. If security risk data interaction occurs between the two target power distribution nodes, obtain first security risk data of the target power distribution nodes, process the first security risk data based on the vulnerability value calculation model and obtain a first vulnerability value.

[0030] S3, obtaining power distribution nodes that interact with the target power distribution node and have different levels from the target power distribution node and recording them as adjacent power distribution nodes, obtaining second security risk data of the adjacent power distribution nodes, processing the second security risk data based on the vulnerability value calculation model and obtaining a second vulnerability value;

[0031] S4, obtaining the first level and the second level corresponding to the target power distribution point node and the adjacent power distribution point nodes, processing the first level and the second level based on the control level model and obtaining weight values;

[0032] S5. Issue a risk warning based on the first vulnerability value, the second vulnerability value and the weight value.

[0033] In this embodiment, it should be noted that in S1, the data interaction analysis and level division of power distribution point nodes are performed; first, the data interaction information of multiple power distribution point nodes in the smart grid is collected, which includes key parameters such as interaction frequency, data interaction string size, and data interaction distance; the flow calculation model is used to analyze these data and calculate the interaction index of each power distribution point node. This index is obtained after comprehensive consideration of multiple dimensions of data interaction and can accurately reflect the activity and importance of the node in the power grid; then, according to the control level model, the calculated interaction index is sorted and the power distribution point nodes are divided into different levels. The higher the level, the more frequent or important the data interaction of the node in the power grid is.

[0034] In S2, the security risk data between nodes of the same level are processed; any two power distribution nodes at the same level are selected and marked as target power distribution nodes; when security risk data interaction occurs between the two target nodes, these interaction data are collected; then, the vulnerability value calculation model is used to process these security risk data to obtain the first vulnerability value of each target node, which reflects the vulnerability of the node when facing security risks.

[0035] In S3, security risk data is processed across nodes of different levels; power distribution nodes that interact with the target power distribution node but are at different levels are identified and marked as adjacent power distribution nodes; security risk data of these adjacent nodes are collected and processed using a vulnerability value calculation model to obtain a second vulnerability value.

[0036] In S4, the level weight value is determined; the levels corresponding to the target power distribution point node and the adjacent power distribution point node are obtained; based on the control level model, these two levels are processed to obtain a weight value, which reflects the importance of nodes of different levels in the power grid security risk assessment.

[0037] In S5, a comprehensive security risk assessment is conducted by comprehensively considering the first vulnerability value, the second vulnerability value and the weight value; if the assessment result shows that there is a high security risk, a risk warning will be issued immediately to notify relevant personnel to take timely measures to ensure the safe and stable operation of the power grid.

[0038] In summary, through a comprehensive analysis of the data interaction of power distribution nodes, the nodes that play a key role in the power grid can be accurately identified, which is crucial for subsequent security risk management and resource optimization allocation. Through level division, power grid operators can focus more on high-level nodes, thereby achieving reasonable allocation and efficient management of resources; by processing the security risk data between nodes of the same level, the potential safety hazards between these nodes can be discovered in a timely manner, so as to take corresponding preventive measures. By calculating the vulnerability value, the power grid operator can intuitively understand the vulnerability of each node, providing data support for the formulation of targeted security protection strategies; considering the security risk data interaction between nodes across levels is helpful to discover possible The existing risk transmission and impact can achieve a more comprehensive risk assessment. By identifying and handling security risks between nodes at different levels, the robustness of the entire power grid can be improved, and the risk of the entire collapse caused by the failure of a certain node can be reduced. By introducing weight values, the importance of nodes at different levels in the power grid security risk assessment can be more accurately reflected, thereby optimizing the assessment model and improving the accuracy of the assessment results. The determination of weight values ​​can help power grid operators to formulate more reasonable risk management strategies and take different safety protection measures for nodes at different levels. By comprehensively evaluating the first vulnerability value, the second vulnerability value and the weight value, the security risk status of the power grid can be monitored in real time, and timely warnings can be issued when high risks are found, thereby improving the security and stability of the smart grid.

[0039] like Figure 2 As shown, in one implementation, S1 includes:

[0040] S11, obtaining the data interaction frequency, data interaction string size and data interaction distance of the two interacting parties, and obtaining a first index based on the frequency;

[0041] S12, obtaining the average data interaction volume between the interacting parties within a preset time window;

[0042] S13, introduce the historical interaction stability index of the interacting parties;

[0043] S14, processing the first index, the average data interaction volume, and the historical interaction stability through a flow calculation model, and obtaining an interaction index;

[0044] S15. Sort and grade the interaction indexes based on the control level model, so that multiple power distribution nodes are classified into multiple levels.

[0045] In this embodiment, it should be noted that S11, S12 and S13 take into account multiple aspects of data interaction and can more comprehensively reflect the data interaction between nodes. S14 can quantify complex interaction situations into a specific index value through the processing of mathematical models, which is convenient for subsequent analysis and comparison. S15 can clearly understand the importance and activity of each node in the power grid through level division, which is helpful to formulate targeted management strategies.

[0046] In one implementation, the flow calculation model in S1 includes:

[0047] in,

[0048] I i is the interaction index of the ith power distribution node, f ij is the data interaction frequency between node i and node j, v ij is the size of the data interaction string between node i and node j, d ij is the data interaction distance between node i and node j, T is the time window, D i (t) is a function of time and data interaction, S i It is the historical interaction stability index, and α, β, and γ are all weight coefficients.

[0049] In this embodiment, it should be noted that f ij and v ij These two factors directly reflect the activity and importance of data interaction between nodes. High-frequency and large-data-volume interactions mean that nodes play a key role in information transmission. In smart grids, advanced nodes such as data analysis centers and dispatching centers usually handle a large amount of data interaction, so these two indicators are crucial for evaluating the level of nodes.

[0050] d ij The ease of data interaction is taken into account. Interactions between nodes that are closer may be more frequent and efficient because the communication delay and cost between them are lower. In the model, by dividing by d ij , which can balance the weights of long-distance interaction and short-distance interaction in the index calculation.

[0051] It is used to measure the continuous activity of a node over a period of time. Data interaction in smart grids is dynamic, so considering the time factor can more accurately reflect the actual role of the node. Through integral calculation, the model can capture the average interaction level of the node over a period of time, avoiding the impact of instantaneous peaks or troughs on the evaluation results.

[0052] S iIt reflects the reliability and consistency of node data interaction. In smart grids, stable data interaction is essential to ensure normal operation. By considering the historical interaction stability index, the model is able to evaluate the performance of nodes in long-term operation, thereby gaining a more comprehensive understanding of their value in practical applications.

[0053] α, β, and γ are all weight coefficients. They are used to balance the influence of different components in the model. According to the specific needs and characteristics of the smart grid, these coefficients can be adjusted to optimize the evaluation effect of the model. For example, if you pay more attention to the instant interaction ability of the node, you can appropriately increase the weight of the coefficient related to frequency and data volume; if you pay more attention to the long-term stability and reliability of the node, you can increase the weight of stability and the average interaction volume within the time window.

[0054] In one implementation, the vulnerability value calculation model includes:

[0055] in,

[0056] Vi is the vulnerability value of the ith power distribution node, n represents the number of types of security risk data considered, and w k is the weight of the kth security risk, R k (d ik ) is the risk function associated with the kth security risk.

[0057] In this embodiment, it should be noted that w k It reflects the importance of k security risks in the overall vulnerability assessment. k (d ik ) is the risk function associated with the kth security risk, which is calculated based on the specific risk data d ik (such as the magnitude of load disturbance, duration of downtime, etc.) to calculate the corresponding risk value. For example: for load disturbance, the risk function R 1 (d i1 ) can be a function based on the magnitude and duration of the disturbance. For downtime, the risk function R 2 (d i2 ) can take into account the frequency of downtime and the duration of each downtime. For overload operation, the risk function R 3 (d i3 ) may be related to the percentage of overload and the length of overload operation. For abnormal start, the risk function R 4 (d i4 ) may consider the frequency of abnormal startups and the impact of each abnormal startup on .

[0058] In S2, when security risk data interaction occurs between two target power distribution nodes at the same level, we collect the first security risk data of these nodes (such as load disturbance, shutdown, etc.), and then use the above-mentioned vulnerability value calculation model to quantify these risks and obtain the first vulnerability value of each node.

[0059] Similarly, in S3, when the target power distribution point node interacts with the power distribution point nodes of the adjacent level, we will also collect the second security risk data of these nodes and use the vulnerability value calculation model to obtain the second vulnerability value of each node.

[0060] like Figure 3 As shown, in one implementation, S5 includes:

[0061] S51. Processing the first vulnerability value, the second vulnerability value, and the weight value based on the risk assessment model, and obtaining a comprehensive score;

[0062] S52, determining whether the comprehensive score exceeds a preset threshold, if so, starting S53;

[0063] S53. Issue risk warning.

[0064] In this embodiment, it should be noted that in S51, the risk assessment model comprehensively considers the first vulnerability value (reflecting the security risk between nodes of the same level), the second vulnerability value (reflecting the security risk between nodes across levels) and the weight value (reflecting the importance of nodes of different levels in risk assessment). In S52, the preset threshold is a safety standard set in the risk assessment model to determine when the risk reaches a level that requires action. This threshold is set based on historical data, industry standards and the risk tolerance of the grid operator. In S53, once the comprehensive score exceeds the threshold, a risk warning will be issued immediately. This warning can be sent to relevant grid operators and managers via email, text message, notification or other communication methods.

[0065] In one embodiment, the risk assessment model includes:

[0066] S=W m ×(V 1 +V 2 )-r×|V 1 -V 2 |; Among them,

[0067] S is the comprehensive score, V is 1 is the first fragile value, V 2 is the second fragile value, W is the weight value, m is an exponent greater than 1, and r is the correction coefficient.

[0068] In this embodiment, it should be noted that W m ×(V 1 +V 2 ) This part considers the sum of the vulnerability values ​​of the target power distribution node and the adjacent power distribution nodes (V 1 +V 2 ), and through the weight value m power W m Here, m is an exponent greater than 1, which amplifies the influence of the weight value W. When W is close to 1 (i.e. the node is high in rank and important), W m ×(V 1 +V 2 ) will increase significantly, making it easier for the comprehensive score S to exceed the preset threshold, which may trigger a risk warning. 1 -V 2 This part considers the absolute difference between the vulnerability values ​​of the target power distribution point node and the adjacent power distribution point nodes. 1 -V 2 |, and weighted by a correction factor r. This difference value reflects the potential inconsistency between the two nodes in terms of security risk. By subtracting this difference value, the expression somewhat penalizes those node pairs with large differences in vulnerability values, even though their average vulnerability values ​​may be high.

[0069] The final comprehensive score S is the difference between the above two parts. In this way, the expression takes into account both the sum of the node vulnerabilities and the differences between them. If the vulnerability values ​​of two nodes are both high and similar (i.e., the difference is small), then the comprehensive score may be high, indicating a significant security risk. On the contrary, if the vulnerability values ​​of two nodes are very different, even if their average vulnerability values ​​are high, the comprehensive score may be reduced because the difference will offset part of the effect of the sum.

[0070] In one implementation, in S4, the control level model includes:

[0071] in,

[0072] W x is the weight value when the level is x, A, B, C are positive parameters, and x is a positive integer.

[0073] In this embodiment, it should be noted that in this model, when x=1, the weight value W x will be A+C, which is the highest weight the model can give. As x increases, The weights of nodes at lower levels gradually decrease, so that nodes at lower levels have lower weights. Parameter B controls the rate at which weights decrease with level. If B is larger, the weights decrease faster; if B is smaller, the weights decrease slower. Parameters A and C can be adjusted to ensure a range and reasonableness of weight values.

[0074] A smart grid security risk data processing system is also provided, the system is used to implement the above-mentioned smart grid security risk data processing method, the system comprises:

[0075] A level classification module is used to obtain multiple power distribution point nodes, analyze the data interaction between each power distribution point node based on the flow calculation model, and obtain the interaction index of each power distribution point node, and sort and grade the interaction index based on the control level model, so that the multiple power distribution point nodes are classified into multiple levels;

[0076] A first vulnerability assessment module is used to obtain any two power distribution point nodes at the same level and record them as target power distribution point nodes. If security risk data interaction occurs between the two target power distribution point nodes, first security risk data of the target power distribution point nodes is obtained, and the first security risk data is processed based on the vulnerability value calculation model to obtain a first vulnerability value;

[0077] The second vulnerability assessment module is used to obtain power distribution nodes that interact with the target power distribution node and have different levels from the target power distribution node and record them as adjacent power distribution nodes, obtain second security risk data of the adjacent power distribution nodes, process the second security risk data based on the vulnerability value calculation model and obtain a second vulnerability value;

[0078] A weight calculation module, used to obtain the first level and the second level corresponding to the target power distribution point node and the adjacent power distribution point node, process the first level and the second level based on the control level model and obtain weight values;

[0079] The output module is used to issue a risk warning according to the first vulnerability value, the second vulnerability value and the weight value.

[0080] Regarding the big data-based information security protection system in the above-mentioned embodiment, the specific method of performing operations therein has been described in detail in the implementation method of the smart grid security risk data processing method, and will not be elaborated here.

[0081] Figure 4 is a block diagram of an electronic device for a method for processing smart grid security risk data according to an exemplary embodiment. Figure 4As shown, the electronic device 700 may include: a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input / output (I / O) interface 704, and a communication component 705.

[0082] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the above-mentioned smart grid security risk data processing method. The memory 702 is used to store various types of data to support the operation of the electronic device 700. These data may include, for example, instructions for any application or method used to operate on the electronic device 700, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 702 or sent through the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, and the other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 705 may include: Wi-Fi module, Bluetooth module, NFC module, etc.

[0083] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, referred to as ASIC), digital signal processors (Digital Signal Processor, referred to as DSP), digital signal processing devices (Digital Signal Processing Device, referred to as DSPD), programmable logic devices (Programmable Logic Device, referred to as PLD), field programmable gate arrays (Field Programmable Gate Array, referred to as FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned smart grid security risk data processing method.

[0084] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned smart grid security risk data processing method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 702 including program instructions, and the above-mentioned program instructions can be executed by the processor 701 of the electronic device 700 to complete the above-mentioned smart grid security risk data processing method.

[0085] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program that can be executed by a programmable device. The computer program has a code portion for executing the above-mentioned smart grid security risk data processing method when executed by the programmable device.

[0086] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0087] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0088] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for processing smart grid security risk data, characterized in that: include: S1. Acquire multiple power distribution nodes, analyze the data interaction between the power distribution nodes based on the flow calculation model, and obtain the interaction index of each power distribution node, and sort and grade the interaction index based on the control level model, so that the multiple power distribution nodes are summarized into multiple levels; S2. Obtain any two power distribution nodes at the same level and record them as target power distribution nodes. If security risk data interaction occurs between the two target power distribution nodes, obtain first security risk data of the target power distribution nodes, process the first security risk data based on the vulnerability value calculation model and obtain a first vulnerability value. S3, obtaining power distribution nodes that interact with the target power distribution node and have different levels from the target power distribution node and recording them as adjacent power distribution nodes, obtaining second security risk data of the adjacent power distribution nodes, processing the second security risk data based on the vulnerability value calculation model and obtaining a second vulnerability value; S4, obtaining the first level and the second level corresponding to the target power distribution point node and the adjacent power distribution point nodes, processing the first level and the second level based on the control level model and obtaining weight values; S5. Issue a risk warning based on the first vulnerability value, the second vulnerability value and the weight value.

2. The method for processing smart grid security risk data according to claim 1, characterized in that: The S1 includes: S11, obtaining the data interaction frequency, data interaction string size and data interaction distance of the two interacting parties, and obtaining a first index based on the frequency; S12, obtaining the average data interaction volume between the interacting parties within a preset time window; S13, introduce the historical interaction stability index of the interacting parties; S14, processing the first index, the average data interaction volume, and the historical interaction stability through a flow calculation model, and obtaining an interaction index; S15. Sort and grade the interaction indexes based on the control level model, so that multiple power distribution nodes are classified into multiple levels.

3. The method for processing smart grid security risk data according to claim 2, characterized in that: The flow calculation model in S1 includes: in, I i is the interaction index of the ith power distribution node, f ij is the data interaction frequency between node i and node j, v ij is the size of the data interaction string between node i and node j, d ij is the data interaction distance between node i and node j, T is the time window, D i (t) is a function of time and data interaction, S i It is the historical interaction stability index, and α, β, and γ are all weight coefficients.

4. The method for processing smart grid security risk data according to claim 3, characterized in that: The vulnerability value calculation model includes: in, V i is the vulnerability value of the ith power distribution node, n represents the number of types of security risk data considered, and w k is the weight of the kth security risk, R k (d ik ) is the risk function associated with the kth security risk.

5. The method for processing smart grid security risk data according to claim 1, characterized in that: The S5 includes: S51. Processing the first vulnerability value, the second vulnerability value, and the weight value based on the risk assessment model, and obtaining a comprehensive score; S52, determining whether the comprehensive score exceeds a preset threshold, if so, starting S53; S53. Issue risk warning.

6. The method for processing smart grid security risk data according to claim 5, characterized in that: The risk assessment model includes: S=W m ×(V1+V2)-r×|V1-V2|; where S is the comprehensive score, V1 is the first vulnerability value, V2 is the second vulnerability value, W is the weight value, m is an index greater than 1, and r is the correction coefficient.

7. The method for processing smart grid security risk data according to claim 1, characterized in that: In said S4, said control level model comprises: in, W x is the weight value when the level is x, A, B, C are positive parameters, and x is a positive integer.

8. A smart grid security risk data processing system, characterized in that: The system is used to implement the smart grid security risk data processing method according to any one of claims 1 to 7, and the system includes: A level classification module is used to obtain multiple power distribution point nodes, analyze the data interaction between each power distribution point node based on the flow calculation model, and obtain the interaction index of each power distribution point node, and sort and grade the interaction index based on the control level model, so that the multiple power distribution point nodes are classified into multiple levels; A first vulnerability assessment module is used to obtain any two power distribution point nodes at the same level and record them as target power distribution point nodes. If security risk data interaction occurs between the two target power distribution point nodes, first security risk data of the target power distribution point nodes is obtained, and the first security risk data is processed based on the vulnerability value calculation model to obtain a first vulnerability value; The second vulnerability assessment module is used to obtain power distribution nodes that interact with the target power distribution node and have different levels from the target power distribution node and record them as adjacent power distribution nodes, obtain second security risk data of the adjacent power distribution nodes, process the second security risk data based on the vulnerability value calculation model and obtain a second vulnerability value; A weight calculation module, used to obtain the first level and the second level corresponding to the target power distribution point node and the adjacent power distribution point node, process the first level and the second level based on the control level model and obtain weight values; The output module is used to issue a risk warning according to the first vulnerability value, the second vulnerability value and the weight value.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it implements the smart grid security risk data processing method described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, used to execute the computer program in the memory to implement the smart grid security risk data processing method as described in any one of claims 1 to 7.

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