Energy coupling system risk identification method and device based on deduction theory

Abstract

The invention provides an energy coupling system risk identification method and device based on a deduction theory, equipment and a medium, and the method comprises the steps: constructing a coupling model of an energy-economy-environment multi-layer network complex system; determining a network interaction relationship; constructing an evolutionary game model; performing energy strategy deduction simulation; according to the method, a more accurate and systematic energy strategy deduction tool can be provided, a decision maker is helped to evaluate potential influences under different policy scenarios, and the energy policy making process is optimized; wherein the scientificity, systematicness and real-time performance of energy policy making are expected to be remarkably improved through energy strategy deduction and policy making simulation, and the coordination and comprehensiveness of the energy strategy are enhanced by comprehensively considering the interaction of energy, economy and environment; the dynamic evaluation capability of the system can enable decision makers to quickly respond to changes of market and social demands and simulate demands and expectations of different interested parties, so that the social acceptance of policies is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of energy planning, and in particular to a method and device for identifying risks of an energy coupling system based on deduction theory. Background Art

[0002] Energy, as an important production material, is the cornerstone of modern industrialized society. Almost all production activities rely on energy supply, which plays a basic supporting role in the normal operation of the economic system. The sufficient, stable and efficient use of energy is crucial to the stability and sustainable development of the economy, because the research on energy strategy helps to optimize resource allocation, improve energy utilization efficiency, and thus promote national economic growth.

[0003] However, in existing research, the formulation of energy strategies and policy simulations mostly rely on qualitative analysis and empirical judgment, and lack systematic quantitative analysis tools. In terms of energy system modeling methods, research often adopts "top-down" and "bottom-up" modeling methods. For example, Li Chenguang et al. [1] simulated and analyzed the low-carbon transformation policy tools and paths of the steel industry based on the CGE model. However, these models usually focus on the transformation of a single industry and lack the discussion of cross-industry energy and carbon emission transfer issues. In terms of risk assessment and management, existing technologies have made certain progress in the risk assessment of energy systems. For example, Xie Hua et al. [2] proposed an integrated energy system operation risk assessment method based on improved K-means clustering technology and semi-invariant method for the electric-gas interconnected integrated energy system formed by the coupling of power systems and natural gas systems. However, there is often a lack of systematic risk identification and management methods, especially in the analysis of adaptability and resilience in the face of major events such as extreme climate.

[0004] Specifically, the prior art has the following problems:

[0005] (1) On the one hand, existing research focuses on a single energy type or a single industry, and fails to fully consider the interactions among energy, economy, and environment. At the same time, existing assessment methods are difficult to adapt to the rapid changes in the market and lack real-time and flexibility.

[0006] (2) Existing technologies generally fail to fully consider the complex interactions within the energy system and its extensive connections with the economic and environmental systems, resulting in information silos and decision-making blind spots in the policy-making process. There is a significant lag in real-time monitoring and response to energy market dynamics, policy changes, and social needs, making it difficult to provide timely and accurate decision-making support for policymakers.

[0007] (3) Existing technologies are insufficient in assessing the long-term impact of energy policies, including but not limited to comprehensive consideration of environmental sustainability, social justice, and economic feasibility. In particular, in the context of globalization, the international impact of energy policies and their impact on international trade and investment flows have not been fully assessed, which limits the ability of policymakers to make strategic arrangements in global energy governance.

[0008] In view of this, there is an urgent need to provide an accurate, flexible and user-friendly energy strategy simulation and policy-making method to support policy makers in making scientific and reasonable decisions in a complex and changing energy environment.

[0009] [1] Li Chenguang, Wang Shuai, Guo Yuhui. Analysis of policy tools and paths for low-carbon transformation and development of the steel industry under the background of carbon neutrality - a simulation study based on a dynamic CGE model [J]. Exploration of Economic Issues, 2023(01): 34-59.

[0010] [2] Xie Hua, Chen Hao, Deng Xiaoyang, et al. Operation risk assessment method for electric-gas integrated energy system based on improved K-means clustering technology and semi-invariant method [J]. Proceedings of the CSEE, 2020, 40(01): 59-69+374. Summary of the invention

[0011] In order to overcome the problems existing in the relevant technologies, the present disclosure provides an energy coupling system risk identification method, device, equipment and medium based on deduction theory to solve a series of technical difficulties encountered in the existing energy strategy deduction and policy formulation process, including weak dynamic assessment capabilities, insufficient analysis of cross-industry energy coupling and transformation paths, and shallow technical problems in energy system risk assessment and management.

[0012] One or more embodiments of this specification provide a method for identifying energy coupling system risks based on deduction theory, including the following steps:

[0013] Construct a coupling model of energy-economy-environment multi-layer network complex system;

[0014] According to the complex network analysis method, the node degree centrality, closeness centrality, betweenness centrality and clustering coefficient are used to characterize the node importance of each country and region in the multi-layer network;

[0015] Considering the evolutionary game model of economic and transportation factors and the evolutionary game benefit indicators, the strategic interaction process of each node in the energy-economy-environment network is simulated, and the game strategy and update strategy of each node are set to enable each node to trigger the corresponding strategy according to the benefit indicator value of itself and other related nodes, and update the node's own benefit value based on the adopted strategy, so as to enable the node to play the game according to its own energy demand and supply status;

[0016] According to the initial load and capacity of each node and edge in the energy-economy-environment network, and the distribution law between nodes, based on the coupling model and evolutionary game model, an improved cascading failure analysis method is used to simulate and deduce the energy strategy deduction under the conditions of random energy changes, determine the risk nodes in the coupling model, and thus formulate corresponding energy strategic policies.

[0017] One or more embodiments of this specification provide a risk identification device for an energy coupling system based on deduction theory, including:

[0018] Model building module, used to build coupled models of energy-economy-environment multi-layer network complex systems;

[0019] The network interaction relationship determination module is used to characterize the node importance indicators of various countries and regions in the multi-layer network through node degree centrality, closeness centrality, betweenness centrality and clustering coefficient according to the complex network analysis method;

[0020] The module for building an evolutionary game model is used to consider the evolutionary game model of economic and transportation factors and the evolutionary game benefit indicators, simulate the strategic interaction process of each node in the energy-economy-environment network, set the game strategy and update strategy of each node, and enable each node to trigger the corresponding strategy according to the benefit indicator value of itself and other related nodes, and update the node's own benefit value based on the adopted strategy, so as to enable the node to play the game according to its own energy demand and supply status;

[0021] The energy strategy deduction simulation module is used to simulate the initial load and capacity of each node and edge in the energy-economy-environment network, the distribution law between nodes, based on the coupling model and evolutionary game model, and use the improved cascading failure analysis method to realize energy strategy deduction under the conditions of random energy changes, determine the risk nodes in the coupling model, and thus formulate corresponding energy strategic policies.

[0022] One or more embodiments of the present specification provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the energy coupling system risk identification method based on deduction theory as described above when executing the computer program.

[0023] One or more embodiments of the present specification provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the energy coupling system risk identification method based on deduction theory as described above.

[0024] The present disclosure provides a method, device, equipment and medium for identifying risks in energy coupling systems based on deduction theory. The advantage is that it can provide a more accurate and systematic energy strategy deduction tool to help decision makers evaluate the potential impacts under different policy scenarios and optimize the energy policy making process. Among them, through energy strategy deduction and policy making simulation, it is expected that the scientificity, systematicness and real-timeness of energy policy making will be significantly improved. Taking into account the interaction between energy, economy and environment, this method can provide a comprehensive analysis tool to enhance the coordination and comprehensiveness of energy strategy. Its dynamic evaluation capability allows decision makers to quickly respond to changes in market and social needs, and simulate the needs and expectations of different stakeholders, helping policy makers to achieve a balance of interests, enhance the social acceptance of policies, and promote the coordination of multiple interests. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 A flow chart of a method for identifying energy coupling system risks based on deduction theory provided for one or more embodiments of this specification;

[0027] Figure 2 An example diagram of energy coupling system risk based on energy-economy-environment network characteristics provided for one or more embodiments of this specification;

[0028] Figure 3 An example diagram of the energy-economy-environment network coupling relationship provided for one or more embodiments of this specification;

[0029] Figure 4 A block diagram of a risk identification device for an energy coupling system based on deduction theory provided for one or more embodiments of this specification; and

[0030] Figure 5 A schematic diagram of the structure of a computer device provided for one or more embodiments of this specification. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below in conjunction with the drawings in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0032] The present invention is described in detail below in conjunction with specific implementation methods and the accompanying drawings.

[0033] Method Embodiment

[0034] According to an embodiment of the present invention, a method for identifying energy coupling system risks based on deduction theory is provided. Figure 1 As shown, it is a flow chart of the energy coupling system risk identification method based on deduction theory provided in this embodiment. According to the energy coupling system risk identification method based on deduction theory in this embodiment of the present invention, the steps include:

[0035] Step S1, constructing a coupling model of energy-economy-environment multi-layer network complex system.

[0036] In this embodiment, based on the complex network analysis method, according to the distribution pattern of energy, economic and environmental networks, key nodes and driving / evolution relationships between network layers, an energy-economy-environment coupling network is constructed to characterize the transportation relationship, economic and trade activity relationship and carbon emission transfer between the energy network, economic network and environmental network; wherein, in the energy network and economic network, the nodes represent import and export countries and regions, and the edges represent their trade levels; in the environmental network, the nodes represent the main virtual carbon transfer countries and regions, and the edges represent the virtual carbon transfer amount; the energy network and the economic network form network flow through import and export trade, and the environmental network forms transfer along with the energy and economic flow.

[0037] In this embodiment, the characteristics of a complex system covering the multi-layer network of "energy-economy-environment" are analyzed. The network takes the transportation relationship of energy resources, the connection of economic and trade activities, and the transfer of carbon emissions as representative characteristics, identifies the mutual interactions, and then determines the distribution pattern, evolution characteristics, and key nodes (risk nodes) of each layer of the network for multi-dimensional energy strategy analysis.

[0038] The driving relationship analysis between the multi-layer networks in this embodiment shows that the energy network itself has an energy resource transportation relationship between nodes. From the perspective of the transmission chain between the energy, economic, and environmental network layers, energy, as an important economic sector, supports the production of almost all other sectors. The driving relationship between the energy and economic networks is to embed the energy system into the economic network as an economic sector to realize the driving relationship between the two. The quantification of the driving relationship is based on the input-output relationship between regions. Carbon emissions are mainly the result of the association of various economic activities, among which the activities of the energy network bring about the main energy carbon emissions, and other sectors also generate a certain amount of carbon emissions, and energy carbon emissions generate carbon transfer through the operation of the economic network.

[0039] For reference Figure 2 and Figure 3 As shown, there are respectively an example diagram of energy coupling system risk based on energy-economy-environment network characteristics and an example diagram of energy-economy-environment network coupling relationship provided in this embodiment. At the energy network layer, by constructing transportation networks for coal, oil and natural gas respectively, the production and transportation of energy, as well as the mutual substitution process between oil, natural gas and coal are considered, and the main international energy transmission channels are analyzed. The key nodes and transmission channels of energy are identified, the main risk nodes and transmission paths of the current energy network are revealed, prevention strategies are proposed, and the energy consumption process is further considered in combination with the economic network.

[0040] The economic network layer constructs the economic and trade network relations between international and domestic regions and sectors, reveals the structural evolution characteristics of the international economic and trade network, analyzes the stability and weak nodes of the economic and trade network structure and its impact on my country, identifies the role of key industrial sectors, and proposes targeted industrial strategies to resist systemic risks.

[0041] The environmental network layer is characterized by carbon emissions that are highly related to energy production and consumption. On the one hand, carbon emissions are mainly affected by the energy network, and its total amount is decisively affected; on the other hand, carbon emissions will be transferred through the indirect effect of the economic network. Therefore, the environmental network is the result of the joint action of the energy network and the economic network, and is also directly affected by the above two layers of networks in subsequent risk assessments.

[0042] Step S2, determining the network interaction relationship; according to the complex network analysis method, the node degree centrality, closeness centrality, betweenness centrality and clustering coefficient are used to characterize the node importance indicators of each country and region in the multi-layer network;

[0043] In this embodiment, degree centrality indicates the number of countries or regions with which a country or region has trade relations, betweenness centrality indicates the proportion of the number of times any two countries or regions with trade relations pass through a specified node to the number of all shortest paths; closeness centrality indicates the average distance from a node to other nodes.

[0044] In this embodiment, the energy layer, driven by the demand of the economic sector of the economic layer, forms a physical flow through energy trade. In an international energy trade network, various countries and regions constitute the nodes in the network, and the energy import and export volume between countries and regions constitutes the edges between nodes in the network. In the economic layer, nodes represent countries and regions, and edges represent product trade volume. The risk resistance and robustness of the network system are analyzed. The weighted degree index of node degree centrality and the weighted index of node betweenness centrality are used as the initial load of the node, respectively. The changes in the economic network under the attack of the nodes with the highest and lowest initial loads in each year are observed, so as to evaluate the evolution characteristics of the international economic and trade network and its impact on China's economic security, and provide support for the formulation of my country's economic and energy security foreign strategy. The carbon emission layer is the result of the combined effect of the total amount of carbon emissions of the energy network and the economic network.

[0045] In this embodiment, node degree centrality and betweenness centrality are indicators that characterize the importance of nodes of various countries and regions in the network. First, node degree centrality mainly characterizes the number of countries or regions with which a specified country or region has trade relations. Second, node betweenness centrality mainly characterizes the proportion of the number of times any two countries or regions with trade relations pass through a specified node to the number of times of all shortest paths. This is an indicator to measure the importance of a node as a bridge for information transmission in the network, and it reflects the measure of the frequency of occurrence of a node in all shortest paths in the network. A high betweenness centrality of a node means that it plays an important intermediary role in the network, which is crucial to the connectivity and information flow of the network. In summary, since trade relations are key indicators in energy networks, the scale of energy trade cannot be ignored, and the amount of energy trade is considered as the weight of the edge in the network. Therefore, this method uses the weighted degree index of node degree centrality and the weighted index of node betweenness centrality to jointly characterize the importance of various countries or regions in the energy trade network.

[0046] In this embodiment, the above key indicators are quantified as follows:

[0047] Node degree centrality refers to the position of a node in the network relative to other nodes, reflecting the importance of the node. The higher the strength value, the more important the node is, and it is a key node. This study uses weighted degree to express it, and the calculation formula is as follows:

[0048]

[0049] in, represents the input weight of node i on day t, represents the influence strength of node j on node i on day t; represents the output weight of node i on day t, Represents the influence strength of node i on node j on day t.

[0050] In this embodiment, node medium centrality is a centrality indicator used to measure the degree to which a node acts as a medium or bridge in a network. Node medium centrality measures the key role a node plays as a bridge for information flow or dissemination in a network. Its calculation formula is as follows:

[0051]

[0052] Among them, σ st represents the number of shortest paths from node s to node t; σ st (i) represents the number of paths passing through node i in the shortest path from node s to node t.

[0053] Closeness centrality is a centrality indicator used in complex networks to measure the degree to which a node is close to other nodes in the network. The closeness centrality of a node depends on its average distance to other nodes. The distance can be calculated as the length of the shortest path or other metrics. Its calculation formula is as follows:

[0054]

[0055] In actual networks, there are often some groups with closer connections, which are called communities. In complex energy governance networks, communities are often formed. Modularity is an index for evaluating the density of connections between and within communities. There are many ways to divide communities. This study uses a detection algorithm to divide the community structure. The formula is as follows:

[0056]

[0057] Among them, W ij represents the edge weight between node i and node j, A i and Aj represent the point weights of node i and node j respectively, C i represents the community to which node i belongs, C j represents the community to which node j belongs. When C i and C j When there are overlapping nodes, σ is 1.

[0058] Step S3, constructing an evolutionary game model; considering the evolutionary game model of economic and transportation factors and the evolutionary game benefit indicators, simulating the strategic interaction process of each node in the energy-economy-environment network, setting the game strategy and update strategy of each node, so that each node triggers the corresponding strategy according to the benefit indicator value of itself and other related nodes, and updates the benefit value of the node itself based on the adopted strategy, so as to enable the node to play the game according to its own energy demand and supply status.

[0059] In this embodiment, the evolutionary game profit index is first constructed, and the comprehensive profit value is calculated, the game strategy and update strategy of each node are set, and the corresponding strategy is adopted according to the numerical value of the profit index of the node itself and other related nodes, and the profit value of each node is updated based on the strategy adopted by each node, so as to provide a basis for the next step of strategy adoption. In the evolutionary game of the energy network, each node will adopt a different trade strategy, that is, different energy trade volumes are imported and exported, and the edges represent the trade relationship between the nodes. The interaction between the nodes is characterized by the game, and each node obtains profit according to the adopted strategy and the corresponding profit matrix; the state of the node is updated according to the current profit strategy to achieve continuous evolution of the entire system.

[0060] This embodiment constructs a global energy network evolutionary game model that can realize Python and NetLogo coupled input and output. It focuses on model deduction, including the construction and application of evolutionary game models, and the design and implementation of network deduction simulation. This section first describes the composition and attribute setting of the game subject, the construction of the evolutionary game network, and the construction and calculation of the evolutionary game benefit index. Then, the specific implementation of program design assistance and network deduction simulation is further discussed, including the design of simulation, data-driven evolutionary pattern, and the evolution of carbon emission network. These studies provide an important theoretical and practical basis for understanding and analyzing the interaction of energy, economic and environmental networks.

[0061] In this embodiment, the evolutionary game model can comprehensively consider the various factors that affect cooperation and more comprehensively reflect the complexity between the game nodes (countries / provinces and cities). It is particularly important to note that this model can be used to deduce the development path of energy systems at both the international and domestic levels. When used in an international network, the game nodes (countries / provinces and cities) are major countries in the world. The main game behavior of these countries lies in the import and export choices of energy trade. By identifying and analyzing the energy import and export behaviors of these countries, we can gain insight into their strategies and dynamics in the global energy market. For example, some countries may adjust their import and export strategies according to changes in the international market to optimize their economic interests. When used in a domestic network, the game nodes (countries / provinces and cities) are provinces in China. These provinces play games in the domestic energy market based on their respective energy demand and supply conditions. For example, resource-rich provinces may choose to export more energy, while resource-poor provinces may seek to import energy from other provinces. By analyzing the energy import and export behaviors of these provinces, we can understand the operating mode of the domestic energy market and the energy flow between provinces.

[0062] On this basis, the model also considers the interactive influence between energy networks and economic networks and environmental networks to form an energy coupling system, which can analyze the interaction and strategy adjustment of different nodes (i.e. different countries or provinces) in the energy coupling network. This method provides a new perspective for understanding the complexity and dynamics of energy coupling systems, helping policymakers to deal with energy industry development decision-making issues more effectively.

[0063] In this embodiment, step S3 specifically includes the following steps:

[0064] Step S31: Based on the construction according to the evolutionary game benefit index, an energy trade benefit evaluation system between nodes of the energy-economy-environment network is established. An energy trade benefit evaluation system between nodes of the energy-economy-environment network is established.

[0065] Energy trade game is a complex game involving multiple levels, and its benefits cannot simply consider economic benefits, so the entropy method is used to evaluate the benefits of a country's oil energy trade. Considering economic, transportation, and carbon emission factors, an energy trade benefit evaluation system between nodes in the energy-economy-environment network is established. For details, refer to the benefit evaluation system table shown in Table 1 below, where the evolutionary game benefit indicators include the trade volume, energy reserves, energy reserve-production ratio, production and consumption between nodes involving energy factors, unit GDP, logistics performance index, transportation distance and number of transportation channels for economic factors, and the total national carbon emissions, atmospheric pollutant emission standards and the number of important environmental monitoring points for environmental factors.

[0066] Table 1. Evolutionary game profit indicators

[0067]

[0068]

[0069] In this embodiment, the energy trade game is a complex game involving multiple levels, and its benefits cannot simply consider economic benefits, so the entropy method is used to evaluate the benefits of a country's oil energy trade. Considering economic and transportation factors, an evaluation system for the benefits of energy trade in importing and exporting countries is established. Among them, the logistics performance index is used to reflect the efficiency of customs clearance procedures, the quality of infrastructure related to trade and transportation quality, the difficulty of arranging freight with competitive prices, the quality of logistics services, the ability to track and query goods, and the frequency of goods arriving at the consignee within the scheduled time. It is used to evaluate the quality of a country's logistics and is divided into 5 levels according to its comprehensive score, namely 1. Very low, 2. Low, 3. Normal, 4. High, and 5. Very high.

[0070] According to the meaning of the logistics performance index, it can be seen that the logistics performance index can reflect the crude oil transportation capacity in the process of crude oil import and export. Similarly, the logistics performance index can also affect the revenue. The transportation distance mainly refers to the sea transportation distance between countries. The specific data selects the distance between the important oil ports of two countries, and the national distance is divided into different levels according to the interval. If two countries are adjacent, the national sea transportation distance is ignored and defined as the first level of national distance. If the sea transportation distance is 0-5000km, the national distance is defined as level 2. If the sea transportation distance is 5000-10000km, the national distance is defined as level 3. If the sea transportation distance is 10000-15000km, the national distance is defined as level 4. If the sea transportation distance exceeds 15000km, the national distance is defined as level 5.

[0071] Step S32, calculation of the evolutionary game benefit index of each node, including positive correlation index, negative correlation index, the proportion of the node under the corresponding index, the entropy value of the index, the weight of each index and the benefit index of each node (country / province / city).

[0072] For the analysis of energy coupling system, i represents the node (country / province / city) (as mentioned above, it can be for the country in international analysis, or for the provinces and cities in my country's analysis), j represents each benefit indicator corresponding to the node, refer to Table 1. The essence of evolutionary game analysis is the deduction of the dynamic behavior of each node (country / province / city), so it is necessary to construct a data matrix. Assume that the value of the jth benefit indicator of the i-th node is x ij. Normalization of indicators. The measurement units of various indicators are not unified, which leads to the need to normalize the numerical values ​​before the entropy method is calculated to eliminate the influence of different units on the results and convert the absolute value of the data into a relative value. At the same time, according to the correlation of the data, it is divided into positive correlation indicators and negative correlation indicators. The calculation is as follows:

[0073] Positive correlation indicators:

[0074]

[0075] Negative correlation indicators:

[0076]

[0077] Calculate the proportion of the i-th node (country / province / city) under the j-th indicator:

[0078]

[0079] Calculate the entropy value of the j-th indicator:

[0080]

[0081] Calculate the weight of each indicator:

[0082]

[0083] Calculate the revenue index of each node (country / province / city):

[0084]

[0085] Step S33, the evolutionary game strategy setting of each node is used to adjust the game strategy of each node (country / province / city) during the deduction process according to the profit index to adjust the various indicators to achieve the adjustment of the energy industry development strategy.

[0086] The deduction of the future horizontal development of the energy coupling system is an iterative calculation process. In each round of calculation, the game strategy of each node (country / province / city) will adjust the energy industry development strategy according to the profit index. These adjustments are reflected in the above-mentioned indicators.

[0087] Step S34, update of evolutionary game strategy. Each round of national or regional trade strategy will form a new indicator factor of inter-national or inter-regional trade volume, and it is necessary to recalculate the profit indicator in the next round to form a feedback mechanism, so as to achieve the update of the game strategy.

[0088] According to the game strategy and update strategy set by each node, each node triggers the corresponding strategy according to the numerical value of its own and other related nodes' profit indicators, and updates the node's own profit value based on the adopted strategy, so that the node can play the game according to its own energy demand and supply situation.

[0089] Step S4, energy strategy simulation; set the evolutionary game strategy update mechanism, the update mechanism is: each round of national or regional trade strategy will form a new indicator factor of inter-national or inter-regional trade volume, and it is necessary to recalculate the benefit indicator in the next round to form a feedback mechanism, so as to realize the update of the game strategy. In this embodiment, based on the energy-economy-environment coupling network constructed in steps S1-S3, the evolutionary game benefit indicator, the game strategy and the update strategy, according to the initial load of each node and edge in the energy-economy-environment network and the upper and lower limits of the capacity, and the initial load ratio of the interaction relationship between the nodes with interactive relationships, the random network failure is simulated based on the cascading failure analysis method and the network state is updated, and the real-time online prediction results are output, and the failure nodes under the random network failure are simulated and determined as risk nodes; in this way, the impact of major events on the energy, economy and environment coupling system can be analyzed. Through the scenario simulation of typical scenarios, the potential impact of major economic and social events on energy, economy and environment networks can be simulated, and the corresponding policy impact and response strategies are explored. Through these studies, a deeper understanding and preparation of possible future events and their impact on the global energy network are provided.

[0090] In one embodiment, the idea of ​​the improved cascading failure analysis method (hereinafter referred to as "OLUCF") provided in this embodiment is: in the actual energy system, each country has its upper limit of production and consumption. Once the actual load exceeds the upper limit, they have the ability to self-regulate the trade volume instead of destroying themselves. However, in the context of economic globalization, once the actual load is lower than the lower limit requirement, as the basic supply department in the economic system, the lack of sufficient energy will destroy the national economy and cause a cascading failure process. Therefore, the existing "overload failure" cascading failure analysis method is not suitable for the energy network risk deduction of this embodiment, so a "underload failure" cascading failure analysis method is proposed. The failure law of the OLUCF method is specifically as follows:

[0091] Based on the initial load, upper and lower capacity limits of each node and edge, that is, during the deduction process, overload limit will not cause node failure. When a target node is disturbed, the corresponding disturbance variable is allocated to other edges and nodes that have an interactive relationship with the target node. Once the load of an edge exceeds its upper capacity limit, the remaining part is allocated from other edges to other nodes. When the target node load is lower than its lower capacity limit, the remaining capacity of other nodes is allocated to the target node. If the target node load is still lower than its lower capacity limit, an overload failure (cascade destruction process) is triggered, and the node is a failed node, that is, a risk node.

[0092] In this embodiment, the specific implementation process of the improved cascading failure analysis is as follows.

[0093] Initial load, capacity, distribution law and failure law are the four basic components of the improved complex network cascading failure model mentioned above. In the OLUCF-based analysis, the initial load, capacity of nodes and edges, and distribution law between related nodes are first determined according to the actual situation, and then the target node (country / province / city) under analysis is simulated for disturbance operation, and then for other nodes that have trade relations with the target node, the corresponding disturbance variables are allocated to other edges and nodes according to the initial load ratio and distribution law. Once the load of an edge exceeds its upper capacity limit, the remaining part will be allocated to other nodes from other edges; when the target node load is lower than its lower capacity limit, the remaining capacity of other nodes will be allocated to the target node. If the target node load is still lower than its lower capacity limit, an overload failure is triggered; iterative calculations are performed based on the above failure laws until the cascading failure fault rule is triggered and the risk node is determined. The iterative calculation process is as follows.

[0094] In the first step, we determine the upper and lower capacity of each node and edge, as well as the initial load of each node.

[0095] L0 i =Export i

[0096] NC i(max) =αL0 i , α≥1

[0097]

[0098] Among them, NC i(max) represents the upper capacity limit of node i, NC i(min) represents the lower capacity limit of node i, L0 i represents the initial load of node i, Export i represents the energy export of node i, Import i represents the energy import amount of node i, α represents the fixed upper limit parameter of node i, and β represents the fixed lower limit parameter of node i.

[0099] In the second step, once the target node is disturbed, its exports will be distributed from the relevant edges to other nodes that have trade relations with the target node, and the scale is proportional to their initial load.

[0100]

[0101] Where n represents the target node, t represents the node that imports energy from the target node, k represents the node that exports energy to node t, b represents the number of nodes from node k to node t, EW kt0 represents the initial edge load from node k to node t, EW nt represents the initial edge load from node n to node t, represents the sum of the initial edge loads from all nodes j to node t, EW kt represents the edge load from node k to node t after load redistribution.

[0102] The third step is to perform secondary load distribution on the target node according to the following rules.

[0103]

[0104] Among them, EC kt represents the load upper limit from node k to node t, EC mt represents the upper limit of the load from any other node m to node t, EW mt Represents the upper limit of the load from any other node m to node t.

[0105] Finally, after allocating the remaining capacity of other available nodes, if the export amount of the export node in the energy network or the import amount of the import node is lower than the limit of this node, the failure rule will be triggered and the failed node will be prohibited from importing or exporting energy.

[0106] In this embodiment, the deduction process is displayed through the pyecharts tool. The pyecharts tool can build a visual energy network analysis platform, with the energy network as the main map, supplemented by the energy import and export structure and the trend map of the total world energy trade. The network analysis platform can dynamically display the global energy flow. Each block can be displayed in linkage. By clicking on the timeline on the right, the energy trade flow of the corresponding year can be displayed at the same time, including the flow of coal, oil and natural gas, as well as the corresponding annual trends of each energy type, and the situation of the country with the largest energy import and export trade in the corresponding year.

[0107] The energy coupling system risk identification method based on deduction theory provided in this embodiment has the following beneficial effects compared with the prior art:

[0108] The method of this embodiment can provide a more accurate and systematic energy strategy deduction tool to help decision makers evaluate the potential impacts under different policy scenarios and optimize the energy policy-making process. Among them, through energy strategy deduction and policy-making simulation, it is expected that the scientificity, systematicness and real-time nature of energy policy-making will be significantly improved. By comprehensively considering the interaction between energy, economy and environment, this method can provide a comprehensive analysis tool to enhance the coordination and comprehensiveness of energy strategy. Its dynamic evaluation capability allows decision makers to quickly respond to changes in market and social needs, and simulate the needs and expectations of different stakeholders, helping policy makers achieve a balance of interests, enhance the social acceptance of policies, and promote the coordination of multiple interests.

[0109] In addition, the method of this embodiment deepens the risk assessment and management of the energy system, improves the resilience and adaptability of the system through advanced risk assessment methods, and the user-friendly decision support tool design enables non-professional users to obtain intuitive decision-making basis, thereby improving decision-making efficiency. The comprehensive evaluation framework incorporates cost, environmental and social benefits, providing a comprehensive analysis of the economic feasibility of energy transformation technology paths.

[0110] Device Embodiment

[0111] According to an embodiment of the present invention, a risk identification device for energy coupling system based on deduction theory is provided, such as Figure 4 As shown, it is a block diagram of the energy coupling system risk identification device based on the deduction theory provided in this embodiment. The energy coupling system risk identification device based on the deduction theory according to the embodiment of the present invention includes:

[0112] The model building module 10 is used to build a coupling model of the energy-economy-environment multi-layer network complex system.

[0113] The model building module 10 is based on the complex network analysis method. According to the distribution pattern of energy, economic and environmental networks, the driving / evolution relationship between key nodes and network layers, it constructs an energy-economy-environment coupling network that characterizes the transportation relationship, economic and trade activity relationship and carbon emission transfer between the energy network, economic network and environmental network. Among them, in the energy network and economic network, the nodes represent import and export countries and regions, and the edges represent their trade levels; in the environmental network, the nodes represent the main virtual carbon transfer countries and regions, and the edges represent the virtual carbon transfer amount. The energy network and the economic network form network flow through import and export trade, and the environmental network forms transfer along with the energy and economic flow.

[0114] The network interaction relationship determination module 20 is used to characterize the node importance indicators of various countries and regions in the multi-layer network through node degree centrality, closeness centrality, betweenness centrality and clustering coefficient according to the complex network analysis method.

[0115] In this embodiment, degree centrality indicates the number of countries or regions with which a country or region has trade relations, betweenness centrality indicates the proportion of the number of times any two countries or regions with trade relations pass through a specified node to the number of all shortest paths; closeness centrality indicates the average distance from a node to other nodes.

[0116] In this embodiment, the above key indicators are quantified as follows:

[0117] Node degree centrality refers to the position of a node in the network relative to other nodes, reflecting the importance of the node. The higher the strength value, the more important the node is, and it is a key node. This study uses weighted degree to express it, and the calculation formula is as follows:

[0118]

[0119] in, represents the input weight of node i on day t, represents the influence strength of node j on node i on day t; represents the output weight of node i on day t, Represents the influence strength of node i on node j on day t.

[0120] In this embodiment, node medium centrality is a centrality indicator used to measure the degree to which a node acts as a medium or bridge in a network. Node medium centrality measures the key role a node plays as a bridge for information flow or dissemination in a network. Its calculation formula is as follows:

[0121]

[0122] Among them, σ st represents the number of shortest paths from node s to node t; σ st (i) represents the number of paths passing through node i in the shortest path from node s to node t.

[0123] Closeness centrality is a centrality indicator used in complex networks to measure the degree to which a node is close to other nodes in the network. The closeness centrality of a node depends on its average distance to other nodes. The distance can be calculated as the length of the shortest path or other metrics. Its calculation formula is as follows:

[0124]

[0125] In actual networks, there are often some groups with closer connections, which are called communities. In complex energy governance networks, communities are often formed. Modularity is an index for evaluating the density of connections between and within communities. There are many ways to divide communities. This study uses a detection algorithm to divide the community structure. The formula is as follows:

[0126]

[0127] Among them, W ij represents the edge weight between node i and node j, A i and Aj represent the point weights of node i and node j respectively, C i represents the community to which node i belongs, C j represents the community to which node j belongs. When C i and C j When there are overlapping nodes, σ is 1.

[0128] The evolutionary game model construction module 30 is used to consider the evolutionary game model of economic and transportation factors and the evolutionary game profit index, simulate the strategy interaction process of each node in the energy-economy-environment network, set the game strategy and update strategy of each node, and enable each node to trigger the corresponding strategy according to the profit index value of itself and other related nodes, and update the node's own profit value based on the adopted strategy, so as to enable the node to play the game according to its own energy demand and supply status.

[0129] In this embodiment, the evolutionary game model construction module 30 is specifically configured to perform the following steps:

[0130] Step S31: Based on the construction of evolutionary game benefit indicators, an energy trade benefit evaluation system between nodes in the energy-economy-environment network is established.

[0131] Energy trade game is a complex game involving multiple levels. Its benefits cannot simply consider economic benefits. Therefore, the entropy method is used to evaluate the benefits of a country's oil energy trade. Considering economic and transportation factors, an energy trade benefit evaluation system for importing and exporting countries is established. Please refer to the benefit evaluation system table shown in Table 1 below. Among them, the game benefit indicators include the trade volume between nodes, energy reserves, energy reserve-production ratio, production and consumption for energy factors, unit GDP, logistics performance index, transportation distance and number of transportation channels for economic factors, and national carbon emissions, atmospheric pollutant emission standards and the number of important environmental monitoring points for environmental factors.

[0132] Step S32, calculation of the evolutionary game benefit index of each node, including positive correlation index, negative correlation index, the proportion of the node under the corresponding index, the entropy value of the index, the weight of each index and the benefit index of each node (country / province / city), the calculation is as follows:

[0133] Positive correlation indicators:

[0134]

[0135] Negative correlation indicators:

[0136]

[0137] Calculate the proportion of the i-th node (country / province / city) under the j-th indicator:

[0138]

[0139] Calculate the entropy value of the j-th indicator:

[0140]

[0141] Calculate the weight of each indicator:

[0142]

[0143] Calculate the revenue index of each node (country / province / city):

[0144]

[0145] Among them, i represents the node (country / province / city) and j represents the indicator.

[0146] Step S33, setting the evolutionary game strategy of each node, realizing that the game strategy of each node in the deduction process adjusts each indicator according to the profit indicator, thereby realizing the adjustment of the energy industry development strategy.

[0147] Step S34, setting an evolutionary game strategy update mechanism, the update mechanism is: each round of national or regional trade strategy will form a new indicator factor of inter-national or inter-regional trade volume, and it is necessary to recalculate the profit indicator in the next round to form a feedback mechanism, so as to realize the update of the game strategy.

[0148] The energy strategy deduction simulation module 40 is used to simulate and deduce the initial load and capacity of each node and edge in the energy-economy-environment network, the distribution law between nodes, based on the coupling model and the evolutionary game model, and use the improved cascading failure analysis method to realize energy strategy deduction under the conditions of random energy changes, determine the risk nodes in the coupling model, and thus formulate corresponding energy strategic policies.

[0149] In this embodiment, the failure rule of the OLUCF method is specifically as follows:

[0150] Based on the initial load, upper and lower capacity limits of each node and edge, that is, during the deduction process, overload limit will not cause node failure. When a target node is disturbed, the corresponding disturbance variable is allocated to other edges and nodes that have an interactive relationship with the target node. Once the load of an edge exceeds its upper capacity limit, the remaining part is allocated from other edges to other nodes. When the target node load is lower than its lower capacity limit, the remaining capacity of other nodes is allocated to the target node. If the target node load is still lower than its lower capacity limit, an overload failure (cascade destruction process) is triggered, and the node is a failed node, that is, a risk node.

[0151] In this embodiment, the specific implementation process of the improved cascading failure analysis is as follows.

[0152] Initial load, capacity, distribution law and failure law are the four basic components of the improved complex network cascading failure model mentioned above. In the OLUCF-based analysis, the initial load, capacity of nodes and edges, and distribution law between related nodes are first determined according to the actual situation, and then the perturbation operation of the analyzed target node (country / province / city) is simulated, and then the corresponding perturbation variables are allocated to other edges and nodes for other nodes that have trade relations with the target node according to the initial load ratio and distribution law. Once the load of an edge exceeds its upper capacity limit, the remaining part will be allocated to other nodes from other edges; when the target node load is lower than its lower capacity limit, the remaining capacity of other nodes will be allocated to the target node. If the target node load is still lower than its lower capacity limit, an overload failure is triggered; iterative calculations are performed based on the above failure laws until the cascading failure fault rule is triggered and the risk node is determined. The iterative calculation process is as follows.

[0153] In the first step, we determine the upper and lower capacity of each node and edge, as well as the initial load of each node.

[0154] L0 i =Export i

[0155] NC i(max) =αL0 i ,α≥1

[0156]

[0157] Among them, NC i(max) represents the upper capacity limit of node i, NC i(min) represents the lower capacity limit of node i, L0 i represents the initial load of node i, Export i represents the energy export of node i, Import i represents the energy import amount of node i, α represents the fixed upper limit parameter of node i, and β represents the fixed lower limit parameter of node i.

[0158] In the second step, once the target node is disturbed, its exports will be distributed from the relevant edges to other nodes that have trade relations with the target node, and the scale is proportional to their initial load.

[0159]

[0160] Where n represents the target node, t represents the node that imports energy from the target node, k represents the node that exports energy to node t, b represents the number of nodes from node k to t, EW kt0 represents the initial edge load from node k to node t, EW nt represents the initial edge load from node n to t, represents the sum of the initial edge loads from all nodes j to node t, EW kt represents the edge load from node k to node t after load redistribution.

[0161] The third step is to perform secondary load distribution on the target node according to the following rules.

[0162]

[0163] Among them, EC kt represents the load upper limit from node k to node t, EC mt represents the load upper limit from node m to node t, EW mt Indicates the upper limit of the load from node m to node t.

[0164] Finally, after allocating the remaining capacity of other available nodes, if the export amount of the export node in the energy network or the import amount of the import node is lower than the limit of this node, the failure rule will be triggered and the failed node will be prohibited from importing or exporting energy.

[0165] The embodiment of the present invention is an apparatus embodiment corresponding to the above-mentioned method embodiment. The specific operations of the processing steps of each module can be understood by referring to the description of the method embodiment, which will not be repeated here.

[0166] like Figure 5 As shown, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the energy coupling system risk identification method based on deduction theory in the above-mentioned embodiment is implemented.

[0167] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the energy coupling system risk identification method based on deduction theory in the above-mentioned embodiment is implemented; or when the computer program is executed by a processor, the energy coupling system risk identification method based on deduction theory in the above-mentioned embodiment is implemented.

[0168] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0169] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0170] 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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above 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 the contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

Claims

1. A method for identifying energy coupling system risks based on deduction theory, characterized in that: include: Construct a coupling model of energy-economy-environment multi-layer network complex system; According to the complex network analysis method, the node degree centrality, closeness centrality, betweenness centrality and clustering coefficient in the coupling model are used to characterize the node importance indicators of each country and region in the multi-layer network; The evolutionary game model and evolutionary game benefit indicators of economic and transportation factors are considered to simulate the strategic interaction process of each node in the energy-economy-environment network, set the game strategy and update strategy of each node, and realize that each node triggers the corresponding strategy according to the benefit indicator value of itself and other related nodes, and updates the benefit value of the node itself based on the adopted strategy, so that the node can play the game according to its own energy demand and supply situation; according to the initial load and capacity of each node and edge in the energy-economy-environment network, the distribution law between nodes, based on the coupling model and the evolutionary game model, the improved cascading failure analysis method is used to simulate and deduce the energy strategy deduction under the conditions of random energy changes, determine the risk nodes in the coupling model, and thus formulate corresponding energy strategic policies.

2. The energy coupling system risk identification method based on deduction theory according to claim 1 is characterized in that: The construction of the evolutionary game model considering economic and transportation factors includes the following steps: Step S31: Based on the construction of the evolutionary game benefit index, an energy trade benefit evaluation system between nodes of the energy-economy-environment network is established; Step S32, calculating the evolutionary game benefit index of each node, including the positive correlation index, the negative correlation index, the proportion of the node under the corresponding index to the index, the entropy value of the index, the weight of each index and the benefit index of each node; Step S33, setting the evolutionary game strategy of each node, so that the game strategy and behavior of each node in the deduction process will be adjusted according to its benefit index, thereby realizing the change of the development evolution state of the energy coupling system; Step S34: setting an evolutionary game strategy update mechanism, the update mechanism is: each round of national or regional trade strategy will form a new indicator factor of inter-national or inter-regional trade volume, and it is necessary to recalculate the profit indicator in the next round to form a feedback mechanism, so as to realize the update of the game strategy; According to the game strategy and update strategy of each node, each node triggers the corresponding strategy according to the profit indicator value of itself and other related nodes, and updates the profit value of the node itself based on the adopted strategy, so that the node can play the game according to its own energy demand and supply status.

3. The energy coupling system risk identification method based on deduction theory as claimed in claim 2 is characterized in that: The evolutionary game benefit indicators include inter-node trade volume, energy reserves, energy reserve-production ratio, output and consumption related to energy factors; unit GDP, logistics performance index, transportation distance and number of transportation channels related to economic factors; and national total carbon emissions, atmospheric pollutant emission standards and the number of important environmental monitoring points related to environmental factors.

4. The energy coupling system risk identification method based on deduction theory as claimed in claim 2 is characterized in that: The calculation of the evolutionary game profit index is as follows: Positive correlation indicators: Negative correlation indicators: Calculate the proportion of the i-th node under the j-th index: Calculate the entropy value of the jth indicator: Calculate the weight of each indicator: Calculate the profit index of each node: Among them, i is the node, j is each profit indicator corresponding to the node, and x ij is the value of the jth profit indicator of the ith node.

5. The energy coupling system risk identification method based on deduction theory according to claim 1 is characterized in that: The failure law of the improved cascading failure analysis method is as follows: Based on the initial load, upper and lower capacity limits of each node and edge, that is, overload limit will not cause node failure during the deduction process. When a target node is disturbed, the corresponding disturbance variable is allocated to other edges and nodes that have interactive relationships with the target node. When the load of an edge exceeds its upper capacity limit, the remaining part is allocated from other edges to other nodes. When the target node load is lower than its lower capacity limit, the remaining capacity of other nodes is allocated to the target node. If the target node load is still lower than its lower capacity limit, an overload failure is triggered, and the node is a failed node, that is, a risk node.

6. A risk identification device for energy coupling system based on deduction theory, characterized in that: include: The model building module is used to build a coupling model of the energy-economy-environment multi-layer network complex system; the network interaction relationship determination module is used to characterize the node importance indicators of various countries and regions in the multi-layer network through the node degree centrality, closeness centrality, betweenness centrality and clustering coefficient in the coupling model according to the complex network analysis method; The module for building an evolutionary game model is used to consider the evolutionary game model of economic and transportation factors and the evolutionary game benefit indicators, simulate the strategic interaction process of each node in the energy-economy-environment network, set the game strategy and update strategy of each node, and enable each node to trigger the corresponding strategy according to the benefit indicator value of itself and other related nodes, and update the node's own benefit value based on the adopted strategy, so as to enable the node to play the game according to its own energy demand and supply status; The energy strategy deduction simulation module is used to simulate and deduce the initial load and capacity of each node and edge in the energy-economy-environment network, the distribution law between nodes, based on the coupling model and evolutionary game model, and use the improved cascading failure analysis method to realize energy strategy deduction under the conditions of random energy changes, so as to determine the risk nodes in the coupling model and formulate corresponding energy strategic policies.

7. The energy coupling system risk identification device based on deduction theory according to claim 6 is characterized in that: The evolutionary game model construction module configuration executes the following steps to realize the construction of the evolutionary game model. The specific steps are as follows: Step S31: Based on the construction of the evolutionary game benefit index, an energy trade benefit evaluation system between nodes of the energy-economy-environment network is established; Step S32, calculating the evolutionary game benefit index of each node, including the positive correlation index, the negative correlation index, the proportion of the node under the corresponding index to the index, the entropy value of the index, the weight of each index and the benefit index of each node; Step S33, setting the evolutionary game strategy of each node, realizing that the game strategy of each node in the deduction process adjusts each indicator according to the benefit indicator, thereby realizing the adjustment of the energy industry development strategy; Step S34: setting an evolutionary game strategy update mechanism, the update mechanism is: each round of national or regional trade strategy will form a new indicator factor of inter-national or inter-regional trade volume, and it is necessary to recalculate the profit indicator in the next round to form a feedback mechanism, so as to realize the update of the game strategy; According to the game strategy and update strategy of each node, each node triggers the corresponding strategy according to the profit indicator value of itself and other related nodes, and updates the profit value of the node itself based on the adopted strategy, so that the node can play the game according to its own energy demand and supply status.

8. The energy coupling system risk identification device based on deduction theory according to claim 6 is characterized in that: The evolutionary game benefit indicators include inter-node trade volume, energy reserves, energy reserve-production ratio, output and consumption related to energy factors; unit GDP, logistics performance index, transportation distance and number of transportation channels related to economic factors; and national total carbon emissions, atmospheric pollutant emission standards and the number of important environmental monitoring points related to environmental factors.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the energy coupling system risk identification method based on deduction theory as described in any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for identifying risks of an energy coupling system based on deduction theory as described in any one of claims 1 to 5 is implemented.

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