Power distribution Internet of Things CPS-oriented multi-dimensional risk quantitative analysis method, system and device, and storage medium

Abstract

The invention discloses a power distribution Internet of Things CPS-oriented multi-dimensional risk quantitative analysis method, system and device, and a storage medium, and relates to the field of information side and physical side fusion, and the method comprises the steps: determining key factors affecting the CPS risk based on power distribution Internet of Things physical equipment and information system data, constructing a multi-level and multi-dimensional risk evaluation index system, and determining the risk of the CPS according to the key factors. Analyzing a system component correlation coupling effect and a risk conduction rule, and establishing a fault propagation model; quantitative calculation and comprehensive evaluation are carried out on system risks by means of a fault propagation model, and a targeted risk prevention and control strategy is made according to an evaluation result; the power distribution Internet of Things CPS risk can be accurately evaluated, operation and maintenance personnel are assisted in prevention and control in advance, the power failure risk is reduced, power grid planning is optimized, and the operation efficiency and economic benefits are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information side and physical side fusion, and particularly relates to a multi-dimensional risk quantification analysis method, system and device for power distribution Internet of Things CPS (Cyber-Physical Systems) and a storage medium. BACKGROUND

[0002] In today's era of deep integration of information and physics, the stable operation of the power grid faces unprecedented challenges. With the rapid development of power distribution Internet of Things, the coupling between the information domain and the physical domain is becoming increasingly close, which greatly expands the scope of power grid safety risks. The traditional safety guarantee mechanism has been difficult to meet the needs of the complex operating environment of modern power grids. How to comprehensively and accurately assess the risks of power distribution Internet of Things CPS (Cyber-Physical Systems) has become a technical problem to be solved.

[0003] At present, the risk analysis technology of power distribution Internet of Things mostly follows the traditional evaluation paradigm, such as Failure Mode and Effects Analysis (FMEA) and risk matrix. These methods can make basic qualitative discrimination and quantification of risks, but have many defects. On the one hand, the evaluation dimension is limited, the systematization degree is insufficient, and it is difficult to adapt to the dynamic characteristics of power distribution Internet of Things. On the other hand, the correlation and coupling effects between system components and the risk transmission law are not fully considered, resulting in poor accuracy and comprehensiveness of the evaluation results. Although some researches have made certain progress in the mechanism of cross-domain risk propagation of power CPS, such as constructing a dynamic diffusion model based on the principle of cellular automata and establishing a cascading failure analysis model using improved percolation theory, there are still significant deficiencies in constructing a system reliability index system considering the deep interaction between information and physics, a safety boundary quantization model and an efficient simulation algorithm, which cannot meet the demand for accurate risk assessment in practical applications. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is how to realize accurate quantification and comprehensive evaluation of the risk of power distribution Internet of Things CPS system.

[0006] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the present application provides a multi-dimensional risk quantification analysis method for power distribution Internet of Things CPS, comprising: Based on the physical device operation data and information system state data in power distribution Internet of Things, determining the key factors affecting the risk of power distribution Internet of Things CPS, and constructing a multi-level and multi-dimensional risk evaluation index system; Based on the risk evaluation index system, analyzing the correlation and coupling effects between components of power distribution Internet of Things CPS system and the risk transmission law, and establishing a fault propagation model; Based on the fault propagation model, the risk of the power distribution Internet of Things CPS system is quantitatively calculated, and the quantitative values of each risk index are obtained and comprehensively evaluated; According to the evaluation result, a targeted risk prevention and control strategy is formulated.

[0007] As a preferred scheme of the multi-dimensional risk quantitative analysis method for the power distribution Internet of Things CPS, wherein: The key factors affecting the risk of the power distribution Internet of Things CPS are determined based on the physical device operation data and information system state data in the power distribution Internet of Things, and a multi-level and multi-dimensional risk evaluation index system is constructed, including: Based on the operation condition of the power distribution network information system, a multi-dimensional risk scene is constructed by comprehensively considering information anomaly types, power grid operation parameters and potential attack types, and the key factors affecting the risk of the power distribution Internet of Things CPS are determined; For physical side risk, the impact effect of cross-regional fault on physical layer facilities is quantified by using relevant indicators.

[0008] As a preferred scheme of the multi-dimensional risk quantitative analysis method for the power distribution Internet of Things CPS, wherein: The key factors affecting the risk of the power distribution Internet of Things CPS are determined based on the physical device operation data and information system state data in the power distribution Internet of Things, and a multi-level and multi-dimensional risk evaluation index system is constructed, further including: Considering the information-physical interaction characteristics, the coupling strength is introduced to represent the coupling degree of the information layer and the physical layer; for information side risk, the possibility of information security event leading to information side device failure is evaluated by using relevant probability; a complete risk system including power supply interruption, voltage deviation, power transmission out-of-bound, information side and information-physical coupling risk is constructed.

[0009] As a preferred scheme of the multi-dimensional risk quantitative analysis method for the power distribution Internet of Things CPS, wherein: Based on the risk evaluation index system, the associated coupling effect and risk transmission law among components of the power distribution Internet of Things CPS system are analyzed, and a fault propagation model is established, including: The abnormal working condition is divided into device layer fault, information layer anomaly and cross-domain coupling fault; the fault propagation model adopts a double-layer structure including nodes and links, and the node layer represents the running state of physical devices and information nodes, and the link layer defines the coupling relationship matrix between nodes.

[0010] As a preferred scheme of the multi-dimensional risk quantitative analysis method for the power distribution Internet of Things CPS, wherein: Based on the risk evaluation index system, the associated coupling effect and risk transmission law among components of the power distribution Internet of Things CPS system are analyzed, and a fault propagation model is established, further including: The fault propagation model combines a cellular automaton and an improved percolation theory to realize risk propagation evolution, the cellular automaton depicts the iteration change of the node state over time, and the improved percolation model calculates the overall connectivity degradation of the network and the system reliability.

[0011] The preferred technical scheme has the beneficial effects that the cellular automaton and the improved percolation theory are combined to give full play to the advantages of both. The cellular automaton can dynamically depict the change of the node state over time and reflect the dynamic process of risk propagation. The improved percolation theory can be used to calculate the overall connectivity degradation of the network and the system reliability, thus providing an effective method for evaluating the overall risk of the system, and thus realizing accurate simulation and analysis of risk propagation evolution.

[0012] As an optimal scheme of the multi-dimensional risk quantification analysis method for the power distribution Internet of Things CPS, the following are included: The risk of the power distribution Internet of Things CPS system is quantitatively calculated based on the fault propagation model to obtain the quantitative values of various risk indicators and comprehensively evaluate the risk, including: Based on the fault propagation model, a unified multi-dimensional risk quantification evaluation model is established by comprehensively considering the physical side, information side risk and information-physical interaction characteristics. The risk indicators are normalized, the weight is determined by an improved method, the comprehensive risk quantification value is calculated, an interaction correction term is introduced, the correction term is obtained by mapping the information-physical coupling strength and the probability that the information layer event triggers the physical layer response, and finally the system risk indicator is obtained.

[0013] The preferred technical scheme has the beneficial effects that a unified multi-dimensional risk quantification evaluation model is established, the physical side, information side risk and information-physical interaction characteristics are comprehensively considered, the system risk can be more comprehensively and accurately evaluated, the risk indicators are normalized and the weight is improved, the accuracy and rationality of risk quantification are improved, and the interaction correction term is introduced, the influence of information-physical interaction is fully considered, and the final system risk indicator can better reflect the actual situation.

[0014] As an optimal scheme of the multi-dimensional risk quantification analysis method for the power distribution Internet of Things CPS, the following are included: The risk of the power distribution Internet of Things CPS system is quantitatively calculated based on the fault propagation model to obtain the quantitative values of various risk indicators and comprehensively evaluate the risk, including: According to the final system risk indicator, the risk level is set, the final system risk indicator is mapped to a specific interval and graded according to the set threshold, and the comprehensive risk level evaluation result is formed. The evaluation process is automatically updated with new data at each time step to realize rolling evaluation and online early warning.

[0015] The beneficial effects of the preferred technical solutions are as follows: through risk level setting and grading, the system risk condition can be intuitively displayed, and the system risk level can be quickly understood by the management personnel and corresponding measures can be taken; the rolling evaluation and online early warning function can update the evaluation results in real time according to new data, potential risks can be found in time, powerful support is provided for timely taking prevention and control measures, and the safety and reliability of the system are improved.

[0016] In a second aspect, the present application provides a multi-dimensional risk quantification analysis system for power distribution Internet of Things CPS, comprising: A risk factor mining and index construction module is configured to determine key factors affecting the risk of power distribution Internet of Things CPS based on physical device operation data and information system state data in the power distribution Internet of Things, and construct a multi-level and multi-dimensional risk evaluation index system. An association analysis and model establishment module is configured to analyze the association coupling effect and risk transmission law among components of the power distribution Internet of Things CPS system based on the risk evaluation index system, and establish a fault propagation model. A risk quantification and comprehensive evaluation module is configured to quantitatively calculate the risk of the power distribution Internet of Things CPS system based on the fault propagation model, obtain quantitative values of each risk index, and comprehensively evaluate. A prevention and control strategy formulation module is configured to formulate a targeted risk prevention and control strategy according to the evaluation results.

[0017] In a third aspect, the present application provides a computer device, comprising: A memory and a processor; The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, which realize the steps of the multi-dimensional risk quantification analysis method for power distribution Internet of Things CPS.

[0018] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer executable instructions, and the computer executable instructions realize the steps of the multi-dimensional risk quantification analysis method for power distribution Internet of Things CPS when executed by a processor.

[0019] The power distribution Internet of Things multi-dimensional risk quantification analysis method has significant practical landing effects, and can effectively solve many technical problems of the power distribution Internet of Things in operation. The traditional evaluation method is difficult to adapt to the dynamic characteristics of the power distribution Internet of Things, and does not fully consider the system component correlation coupling and risk transmission law, resulting in insufficient accuracy and comprehensiveness of the evaluation result. The more perfect and scientific risk quantification analysis method established by the present application can break through these limitations. The method integrates key elements and constructs a multi-level and multi-dimensional risk evaluation model. In practical application, the power distribution Internet of Things CPS system risk can be accurately quantified and comprehensively evaluated, helping grid operation and maintenance personnel to accurately master the safety status of the system. The accurate risk evaluation result can help the operation and maintenance personnel to find potential risk points in advance, avoid the expansion of the risk to cause power failure accidents, and protect the reliable power use of users. At the same time, considering the information-physical deep interaction, the risk brought by the increasingly close coupling of the information domain and the physical domain of the power distribution Internet of Things can be better coped with. Through the analysis of the correlation coupling effect and risk transmission law between the components of the system, targeted risk prevention and control strategies can be developed in advance to reduce the probability of catastrophic failure of the power grid. In addition, the method of the present application is helpful to optimize the planning and design of the power grid. According to the risk evaluation result, resources can be reasonably allocated to improve the risk resistance and reliability of the power grid, reduce unnecessary investment and maintenance costs, and improve the overall operation efficiency and economic benefits of the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0021] Figure 1 The present application provides a multi-dimensional risk quantification analysis method for power distribution Internet of Things CPS. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0023] Embodiment 1, refer to Figure 1 The first embodiment of the present application provides a multi-dimensional risk quantification analysis method for power distribution Internet of Things CPS, comprising: S1: Based on the physical device operation data and information system state data in the power distribution Internet of Things, determine the key factors affecting the power distribution Internet of Things CPS risk, and build a multi-level, multi-dimensional risk evaluation index system; S2: Based on the risk evaluation index system, analyze the correlation and coupling effect between the components of the power distribution Internet of Things CPS system and the risk transmission law, and establish a fault propagation model; S3: Based on the fault propagation model, the risk of the power distribution Internet of Things CPS system is quantitatively calculated, and the quantitative value of each risk index is obtained and comprehensively evaluated; S4: According to the evaluation results, formulate targeted risk prevention and control strategies.

[0024] It should be noted that through steps S1-S4, this method relies on the power grid resource business platform and the intelligent power distribution Internet of Things architecture, uses the platform to realize the standardized fusion and centralized management of multi-source data, and based on the comprehensive calculation of multi-dimensional risk quantitative indicators, the operation risk of the power distribution network CPS is quantitatively analyzed, and then the risk quantitative analysis function is provided for the CPS simulation environment, which ensures the collaborative interaction mechanism of the information layer and the physical layer in the power distribution Internet of Things, including power distribution network CPS risk scenario modeling and fault propagation mechanism analysis, risk quantitative index system construction, and operation state risk quantitative analysis.

[0025] Embodiment 2, refer to Figure 1 For an embodiment of the present application, a multi-dimensional risk quantitative analysis method for power distribution Internet of Things CPS is provided based on the previous embodiment, comprising: In this embodiment, the step S1 based on the physical device operation data and information system state data in the power distribution Internet of Things, determining the key factors affecting the power distribution Internet of Things CPS risk, and building a multi-level, multi-dimensional risk evaluation index system includes: Based on the operation condition of the power distribution network information system, considering the information abnormal type, power grid operation parameter and potential attack type, a multi-dimensional risk scenario of the power distribution network information physical system is constructed, and the key factors affecting the power distribution Internet of Things CPS risk are determined; Specifically, for the physical side risk, power supply interruption risk index, voltage offset risk index and power transmission out-of-bounds index are used to quantify the impact effect of cross-regional fault on physical layer facilities; In another possible implementation, the multi-dimensional risk scenario can be constructed by using the Monte Carlo simulation method, according to the probability distribution of the operation condition of the power distribution network information system, the information abnormal type, the power grid operation parameter and the potential attack type, etc. A large number of risk scenarios are randomly generated. For example, set the probability of information abnormality, the probability of different types of attacks, etc. Different risk scenario combinations are generated through multiple simulations, so as to comprehensively cover the possible risk situations.

[0026] In another possible implementation, constructing a multi-dimensional risk scenario can also be combined with historical event data, analyzing past power distribution network failures, information security incidents, etc. Cases are summarized to find out the risk scenario patterns caused by different factor combinations. On this basis, according to the current system state and environmental changes, these patterns are adjusted and expanded to generate multi-dimensional risk scenarios that conform to the actual situation.

[0027] For the information-physical interaction characteristics, the coupling strength between nodes and systems and between systems is introduced to represent the coupling degree of the information layer and the physical layer in the power distribution network. For information side risk, the probability of communication equipment being attacked is used to evaluate the possibility of information security incidents causing information measuring equipment failure.

[0028] Further, a complete risk system is constructed, including power distribution network CPS power supply interruption risk, power distribution network CPS voltage offset risk, power distribution network CPS power transmission boundary risk, power distribution network CPS information side risk, and information-physical coupling risk, which realizes the accurate description of information-physical complex risk. Finally, a multi-level and multi-dimensional risk evaluation index system of power distribution network CPS is formed, including the following risks: physical side risk, information-physical cross-domain coupling risk, information side risk, and power distribution network CPS global system risk.

[0029] In another possible implementation, when determining the key factors affecting the power distribution Internet of Things CPS risk, big data analysis technology can also be used to deeply mine massive physical device operation data and information system state data in the power distribution Internet of Things. By constructing a data mining algorithm model such as a decision tree algorithm, the characteristics and laws in the data are analyzed to find out factors with high correlation with risk as key factors. For example, by analyzing the fault frequency of equipment in different time periods, information transmission delay and other data, it is determined which factors have the most significant impact on risk.

[0030] In another possible implementation, when determining the key factors affecting the power distribution CPS risk, the principal component analysis (PCA) method can also be used: collect the physical device operation data and information system state data in the power distribution CPS, which may contain multiple variables such as the temperature, pressure, voltage, current of the device, the response time, throughput of the information system, etc. Preprocess these data, including data cleaning (remove missing values, outliers), standardization (make the data have zero mean and unit variance). Use the PCA algorithm to analyze the processed data. PCA will convert the original data into a set of new orthogonal variables, i.e. principal components, each of which is a linear combination of the original variables. The principal components are sorted by variance, and the principal component with larger variance contains more information of the original data. After calculating the principal components, analyze the coefficients of each original variable in each principal component. The original variable with a larger absolute coefficient value contributes more to the principal component, which means that these variables are more important in the data. By setting a threshold, select the original variables with absolute coefficient values greater than the threshold as the key factors. For example, if the coefficients of the device temperature and the information system response time in a principal component are large, then these two variables can be determined as the key factors affecting the risk.

[0031] In another possible implementation, when constructing a multi-level, multi-dimensional risk evaluation index system, the levels can be divided according to different types of physical devices and different functional modules of information systems. For physical devices, it can be divided into power generation devices, power transmission devices, power transformation devices, etc.; for information systems, it can be divided into data acquisition layer, transmission layer, processing layer, etc. In each level, evaluation indexes are set from multiple dimensions such as device performance, operation stability, safety, etc., such as power loss of physical devices, network bandwidth utilization of information systems, etc.

[0032] In another possible implementation, when constructing a multi-level, multi-dimensional risk evaluation index system, the levels and dimensions can also be constructed according to the sources and impact ranges of risks. The sources of risks are divided into internal risks (such as device aging, software failure) and external risks (such as natural disasters, network attacks), and different dimensions are further divided under each source. For example, for the device aging dimension in internal risks, evaluation indexes such as device service life and maintenance frequency can be set; for the network attack dimension in external risks, evaluation indexes such as attack frequency and attack intensity can be set.

[0033] In this embodiment, the step S2 of analyzing the correlation coupling effect and risk transmission law between components of the power distribution CPS system based on the risk evaluation index system includes: Based on the risk evaluation index system, the correlation and coupling effect between each component of the distribution Internet of Things CPS system and the risk transmission law are analyzed, and a CPSDN (Cyber-Physical System of Distribution Network) fault propagation model under the centralized feeder automation architecture is constructed. Specifically, the abnormal working conditions are divided into three types: equipment layer failure, information layer anomaly, and cross-domain coupling failure. The fault propagation model adopts a "node-link" double-layer structure: the node layer represents the running state of physical devices and information nodes, and defines the state vector wherein, represents the number of nodes, and each element represents the state of a node, which reflects the three running states of the node, i.e. normal (value 0), degraded (value 0.5), and failure (value 1); the link layer defines the coupling relationship matrix between nodes and nodes , which is constructed according to the electrical connection relationship and communication topology.

[0034] In another possible implementation, when a double-layer structure including nodes and links is adopted, in the node layer, for physical device nodes, sensors can be used to monitor the running parameters of the devices in real time, such as temperature, current, voltage, etc., and these parameters can be used as the state information of the nodes. For information nodes, the transmission rate, error rate, etc. of data can be recorded. In the link layer, the coupling relationship matrix between nodes can be determined through the network topology structure and data flow direction, for example, according to the path and bandwidth allocation of information transmission, the weight and direction of the link can be determined.

[0035] In another possible implementation, when a double-layer structure including nodes and links is adopted, the concepts of virtual nodes and virtual links can also be introduced. For some logically related but not directly connected devices or information modules, virtual nodes and virtual links can be set to represent the coupling relationship between them. For example, different regional power distribution devices can be associated through remote monitoring systems, at which time virtual nodes and virtual links can be set to reflect the information interaction and risk transmission relationship between them. At the same time, blockchain technology is used to ensure the security and non-tamperability of node and link information.

[0036] In terms of algorithms, the fault propagation model combines Cellular Automata and improved Percolation Theory to realize the evolution of risk propagation; wherein, Cellular Automata is used to depict the change of node state over time, and the state update function is: wherein, is a node corresponding node fault tolerance threshold, state update function According to the needs, the threshold type, the probability infection type or the continuous state type is selected.

[0037] The improved percolation model based on the improved percolation theory is used for calculating network overall connectivity degradation and system reliability. The improved percolation model is input with real-time power grid operation state data (voltage, current, power flow, communication link delay, packet loss rate, etc.), and is output with: according to the real-time state and failure probability vector of each node, each type of risk index is calculated, wherein the voltage deviation, power supply interruption type risk is defined as the physical node loss expectation, and the communication intrusion risk is defined as the information equipment failure probability weighted sum.

[0038] It should be noted that the constructed fault propagation model can dynamically simulate the interaction and conduction law between the physical layer fault and the information layer anomaly, and realize the quantitative evolution analysis from the local anomaly to the system level risk.

[0039] In the embodiment, the risk of the power distribution Internet of Things CPS system is quantitatively calculated based on the fault propagation model in the above step S3, and the quantitative values of each risk index are obtained and comprehensively evaluated, including: Based on the fault propagation model, the physical side risk, the information side risk and the information-physical interaction characteristics are comprehensively considered, a unified multi-dimensional risk quantitative evaluation model is established, the risk of the power distribution Internet of Things CPS system is quantitatively calculated, and the quantitative values of each risk index are obtained and comprehensively evaluated.

[0040] Specifically, the index normalization and weight determination are performed, and after the various risk indexes (such as voltage deviation risk , power supply interruption risk , communication intrusion risk , coupling strength , etc.) are normalized by Min-Max, the improved entropy weight method is used to calculate the weight vector . The comprehensive risk quantitative value is defined as: wherein, denotes the comprehensive risk quantitative value, is the risk quantitative value of each sub-index.

[0041] An interaction correction term is introduced, wherein, denotes the probability that the information layer event triggers the physical layer response, and in the formula is biased to the system static structure layer, and represents the long-term inherent attribute, is biased to the system dynamic behavior layer, and changes with the real-time changes of working conditions and attack behaviors. This represents the information-physical coupling strength. The probability of an information layer event triggering a physical layer response Mapped to interactive correction items The function.

[0042] Multidimensional risk quantification assessment model through Reflecting the amplification effect of information-side events on the physical side, the final system risk indicator is expressed as: in, This represents the final system risk indicator.

[0043] When the degree of cyber-physical coupling is high (e.g.) When >0.7), it can be based on Will Mapped to the interval [0.1, 0.3]; when weakly coupled, .

[0044] Risk level setting and visualization output based on final system risk indicators: Mapped to the [0,1] interval, and classified according to set thresholds (e.g., low risk <0.3, medium risk 0.3–0.6, high risk >0.6), a comprehensive risk level assessment result for the distribution network CPS is generated. The risk assessment process is automatically updated at each time step based on new data, realizing rolling assessment and online early warning.

[0045] In this embodiment, step S4 above, which involves formulating targeted risk prevention and control strategies based on the evaluation results, includes: Regarding physical-side risks, for power outage risks, redundant power lines and backup power sources can be established, such as equipping important substations or load centers with emergency generators, which can quickly switch to ensure basic power supply when the main power line fails. At the same time, regular inspections and maintenance of physical layer facilities should be strengthened to address potential faults promptly. For voltage deviation risks, voltage regulation equipment such as automatic voltage regulators and reactive power compensation devices should be installed to monitor and adjust voltage in real time; the grid topology and power flow distribution should be optimized to avoid voltage deviations caused by excessive local loads or excessively long lines. For power transmission over-limit risks, the grid's power transmission capacity should be monitored and assessed in real time, loads should be allocated reasonably to prevent line or equipment overload; a power transmission early warning mechanism should be established to adjust promptly when power approaches or exceeds safe limits.

[0046] Regarding information-side risk prevention and control, to address the risk of intrusion into communication equipment, we will strengthen the security protection of communication equipment, adopt encryption technology to encrypt the transmission of communication data, establish intrusion detection and intrusion prevention systems, monitor abnormal behavior in real time and block and alarm in a timely manner, and regularly scan and repair security vulnerabilities in communication equipment and update security patches.

[0047] To address the risks of information-physical cross-domain coupling, an isolation mechanism should be established between the information layer and the physical layer, such as using firewalls, network gateways, and other devices to isolate the information network and the physical network; the monitoring and management of the information-physical interaction process should be strengthened, and the data collected by sensors should be verified and validated to avoid erroneous information leading to malfunctions of physical devices.

[0048] When implementing risk-based prevention and control, in low-risk situations, maintain the existing monitoring and maintenance plan, regularly inspect and evaluate the system, and strengthen monitoring of key equipment and nodes. In medium-risk situations, increase the frequency of system monitoring, closely monitor changes in risk indicators, conduct in-depth analysis of factors that may trigger risks, and formulate targeted corrective measures, such as repairing or replacing equipment with safety hazards. In high-risk situations, immediately activate the emergency plan, take emergency measures to reduce risks, such as power outages for maintenance in some high-risk areas; organize professional technicians to comprehensively investigate and repair the system, and restore normal operation as soon as possible.

[0049] In terms of risk propagation path and critical node prevention and control, key nodes and links along the risk propagation path are monitored and protected, and isolation devices are set up to block risk propagation; the system topology is optimized to reduce risk propagation paths. Redundancy design is implemented for critical nodes, and methods such as dual-machine hot standby and multi-node parallelism are used to improve reliability and fault tolerance; security protection of critical nodes is strengthened by adopting multiple security measures such as encryption, authentication, and access control.

[0050] Example 3 illustrates the multi-dimensional risk quantification analysis method for distribution network IoT CPS described in this embodiment. It should be noted that the technical solution of the multi-dimensional risk quantification analysis system for distribution network IoT CPS is based on the same concept as the technical solution of the multi-dimensional risk quantification analysis method for distribution network IoT CPS described above. Details not described in detail in the technical solution of the multi-dimensional risk quantification analysis system for distribution network IoT CPS in this embodiment can be found in the description of the technical solution of the multi-dimensional risk quantification analysis method for distribution network IoT CPS described above.

[0051] This embodiment also provides a multi-dimensional risk quantification analysis system for distribution network IoT CPS, including: The risk factor mining and indicator construction module is used to identify key factors affecting the risks of the distribution IoT CPS based on the operation data of physical equipment and the status data of information systems in the distribution IoT, and to construct a multi-level, multi-dimensional risk assessment indicator system. The correlation analysis and model building module is used to analyze the correlation and coupling effects and risk transmission patterns among the components of the distribution Internet of Things (CPS) system based on the risk assessment index system, and to build a fault propagation model. The risk quantification and comprehensive assessment module is used to quantify the risks of the distribution Internet of Things (CPS) system based on the fault propagation model, obtain the quantitative values ​​of each risk indicator, and conduct a comprehensive assessment. The risk prevention and control strategy development module is used to develop targeted risk prevention and control strategies based on the assessment results.

[0052] This embodiment also provides an electronic device suitable for multi-dimensional risk quantification analysis methods for distribution IoT CPS, including: The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement the multi-dimensional risk quantification analysis method for distribution IoT CPS proposed in the above embodiments.

[0053] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the multi-dimensional risk quantification analysis method for distribution IoT CPS proposed in the above embodiments.

[0054] The storage medium proposed in this embodiment and the multi-dimensional risk quantification analysis method for distribution IoT CPS proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0055] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A multi-dimensional risk quantification analysis method for distribution network Internet of Things (CPS), characterized in that, include: Based on the operation data of physical equipment and the status data of information systems in the distribution Internet of Things (IoT), the key factors affecting the risks of distribution IoT CPS are identified, and a multi-level, multi-dimensional risk assessment indicator system is constructed. Based on the risk assessment index system, the correlation and coupling effects and risk transmission laws among the components of the distribution Internet of Things (CPS) system are analyzed, and a fault propagation model is established. Based on the fault propagation model, the risks of the distribution Internet of Things (CPS) system are quantitatively calculated, and the quantitative values ​​of each risk indicator are obtained and comprehensively evaluated. Based on the assessment results, develop targeted risk prevention and control strategies.

2. The multi-dimensional risk quantification analysis method for distribution network IoT CPS as described in claim 1, characterized in that, Based on the operational data of physical devices and the status data of information systems in the distribution IoT, the key factors affecting the risks of distribution IoT CPS are identified, and a multi-level, multi-dimensional risk assessment indicator system is constructed, including: Based on the operating conditions of the distribution network information system, and considering the types of information anomalies, power grid operating parameters, and potential attack types, a multi-dimensional risk scenario is constructed to identify the key factors affecting the risks of the distribution Internet of Things (CPS). To address physical-side risks, relevant indicators are used to quantify the impact of cross-regional failures on physical layer facilities.

3. The multi-dimensional risk quantification analysis method for distribution network IoT CPS as described in claim 2, characterized in that, The process of identifying key factors affecting the risks of the Distribution Internet of Things (CPS) based on the operational data of physical devices and the status data of information systems in the distribution IoT, and constructing a multi-level, multi-dimensional risk assessment indicator system, also includes: Considering the characteristics of information-physical interaction, coupling strength is introduced to characterize the degree of coupling between the information layer and the physical layer; for information-side risks, relevant probability is used to assess the possibility of information security events causing information-side equipment failure; a complete risk system is constructed that includes power outages, voltage deviations, power transmission overruns, information-side risks, and information-physical coupling risks.

4. The multi-dimensional risk quantification analysis method for distribution network IoT CPS as described in claim 3, characterized in that, The aforementioned analysis of the correlation and coupling effects and risk transmission patterns among various components of the distribution IoT CPS system based on the risk assessment index system, and the establishment of a fault propagation model, includes: Abnormal operating conditions are categorized into equipment-level faults, information-level faults, and cross-domain coupling faults. The fault propagation model adopts a two-layer structure including nodes and links. The node layer represents the operating status of physical devices and information nodes, while the link layer defines the coupling relationship matrix between nodes.

5. The multi-dimensional risk quantification analysis method for distribution network IoT CPS as described in claim 4, characterized in that, The analysis of the correlation and coupling effects and risk transmission patterns among various components of the distribution network IoT CPS system based on the risk assessment index system, and the establishment of a fault propagation model, also includes: The fault propagation model combines cellular automata and improved seepage theory to realize the risk propagation evolution. The cellular automata characterize the iterative changes of node states over time, while the improved seepage model calculates the overall network connectivity degradation and system reliability.

6. The multi-dimensional risk quantification analysis method for distribution network IoT CPS as described in claim 5, characterized in that, The method based on the fault propagation model quantifies the risks of the distribution network IoT CPS system, derives quantitative values ​​for each risk indicator, and conducts a comprehensive assessment, including: Based on the fault propagation model, a unified multidimensional risk quantification assessment model is established by integrating physical and information-side risks and information-physical interaction characteristics. Various risk indicators are normalized, and weights are determined using an improved method to calculate the comprehensive risk quantification value. An interaction correction term is introduced, which is derived by mapping the information-physical coupling strength with the probability of information layer events triggering physical layer responses, thereby obtaining the final system risk index.

7. A multi-dimensional risk quantification analysis method for distribution network IoT CPS as described in claim 6, characterized in that, The method for quantifying the risks of the distribution network IoT CPS system based on the fault propagation model, obtaining quantitative values ​​for each risk indicator, and conducting a comprehensive assessment also includes: Risk levels are set based on the final system risk indicators, which are then mapped to specific ranges and graded according to set thresholds to form a comprehensive risk level assessment result. The assessment process is automatically updated at each time step with new data, enabling rolling assessment and online early warning.

8. A multi-dimensional risk quantification analysis system for distribution network Internet of Things (CPS), using the method described in any one of claims 1 to 7, characterized in that, include: The risk factor mining and indicator construction module is used to identify key factors affecting the risks of the distribution IoT CPS based on the operation data of physical equipment and the status data of information systems in the distribution IoT, and to construct a multi-level, multi-dimensional risk assessment indicator system. The correlation analysis and model building module is used to analyze the correlation and coupling effects and risk transmission patterns among the components of the distribution Internet of Things (CPS) system based on the risk assessment index system, and to build a fault propagation model. The risk quantification and comprehensive assessment module is used to quantify the risks of the distribution Internet of Things (CPS) system based on the fault propagation model, obtain the quantitative values ​​of each risk indicator, and conduct a comprehensive assessment. The risk prevention and control strategy development module is used to develop targeted risk prevention and control strategies based on the assessment results.

9. An electronic device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer-executable instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

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