Method and system for evaluating cascade effect of local energy shortage risk based on Beweennss algorithm

Abstract

The invention discloses a Betweennss algorithm-based local energy shortage risk cascade effect evaluation method and system, and relates to the field of energy resource management, and the method comprises the steps: S1, obtaining the energy pressure index of each region and the energy dependence degree of each department, carrying out the calculation of an energy shortage probability according to the energy pressure index, and carrying out the calculation of the energy shortage probability according to the energy pressure index; calculating an initial energy shortage risk according to the energy shortage probability and the energy dependence degree of each department; s2, combining the initial energy shortage risk with a multi-region input-output model, and constructing a virtual energy shortage risk transmission network; and S3, based on a Beweenness algorithm, evaluating and identifying a key transmission node of a local energy shortage risk cascade effect in the virtual energy shortage risk transmission network. The method is wide in application range and efficient in calculation mode, fills the blank of previous research, and can provide an effective method and data support for improving the toughness of the trade network to cope with the energy shortage risk.

Description

Technical Field

[0001] The present invention relates to the field of energy resource management, and in particular to a method and system for evaluating the cascading effect of local energy shortage risk based on a Betweennesss algorithm. Background Art

[0002] Energy shortages will not only directly cause severe impacts on the economies of certain regional sectors, but may also spread risks to other regional sectors through the supply chain, generating systemic risks. That is, the initial risk will have a cascading effect, causing indirect economic losses to downstream regional sectors, increasing the complexity of the risk and the scope of impact.

[0003] Previous studies have only focused on the starting point and end point of the risk transmission path, and have failed to conduct in-depth research and identify the key risk transmission sectors in the middle of the path. Identifying key risk transmission sectors is helpful for implementing early intervention in the risk transmission process.

[0004] The Betweenness method is often used to measure the centrality of each node in the network. The transmission process of energy shortage risk in the social and economic system can be regarded as a directed weighted complex network. Deconstructing the characteristics of the risk network based on the Betweenness method and identifying key risk transmission nodes can provide a new focus for improving the resilience of the social and economic system to energy shortage risks.

[0005] Therefore, in order to solve these problems, in the context of the current highly interconnected global economy, there is an urgent need for an evaluation method for the cascading effect of local energy shortage risks based on the Betweennesss algorithm. Summary of the invention

[0006] To solve the above problems, this application proposes a method for evaluating the cascading effect of local energy shortage risks based on the Betweennesss algorithm. By systematically identifying all key nodes where local energy shortage risks are transmitted through the supply chain, a more effective strategy is formulated to enhance the resilience of the entire trade network to energy shortage risks. In particular, the identification of intermediate key nodes helps us intervene in the transmission of risks in advance, thereby avoiding more serious impacts.

[0007] The evaluation method of the cascading effect of local energy shortage risk based on the Betweennesss algorithm includes the following steps:

[0008] S1. Obtain the energy stress index of each region and the energy dependence of each department, calculate the probability of energy shortage based on the energy stress index, and calculate the initial energy shortage risk based on the probability of energy shortage and the energy dependence of each department;

[0009] S2. Combine the initial energy shortage risk with the multi-region input-output model to build a virtual energy shortage risk transmission network;

[0010] S3. Based on the Betweenness algorithm, the key transmission nodes of the local energy shortage risk cascade effect are evaluated and identified in the virtual energy shortage risk transmission network.

[0011] Preferably, the expression for calculating the energy pressure index is:

[0012]

[0013] in, EPI i represents the energy pressure index of region i, represents the supply of the kth energy in region i, represents the consumption of the kth energy in region i, 1≤k≤h, h represents the type of energy, is the production of the kth energy source in region i, is the import volume of the kth energy source in region i, is the export volume of the kth energy source in region i.

[0014] Preferably, the expression of energy shortage probability is:

[0015] ESP i =f ESP (EPI i ;σ)=E(Y i );

[0016] in, ESP i represents the probability of energy shortage in region i, E(Y i ) indicates Y i The expected value of Y i In each time and space unit, the energy shortage in region i, X i The log-normal distribution represents the ratio of energy supply and consumption in region i within each space-time unit, μ i For X i The parameter σ is the standard deviation and is equal to 1.

[0017] Preferably, the expression of energy dependence of each department is:

[0018]

[0019] in, EDL m,i represents the energy dependence of sector m in region i, represents the consumption of the kth energy by department m in region i, EIm,i It represents the energy consumption intensity of department m in region i, which is equal to the sum of the 10 energy consumptions of the department in the region. Divide by its total output x m,i , α is ED m,i The cutoff parameter is set to 2.

[0020] Preferably, the expression of initial energy shortage risk is:

[0021] IESR m,i =ESP i ×EDL m,i × m,i ;

[0022] Where IESR m,i represents the initial energy shortage risk of sector m in region i; ESP i is the probability of energy shortage in region i; EDL m,i is the energy dependence of sector m in region i; x m,i represents the total output of sector m in region i.

[0023] Preferably, the initial energy shortage risk is combined with the multi-region input-output model to construct a virtual energy shortage risk transmission network. The specific contents are as follows:

[0024] The expression of using Ghosh inverse matrix to evaluate the propagation of virtual energy shortage risk is:

[0025]

[0026] Where: U represents the propagation matrix of virtual energy shortage risk, where the elements represents the cascading effect of department m in region i on department n in region j;

[0027] E is a row vector, each element of which represents the initial energy shortage risk of each department in each region. It refers to the process of diagonalizing the vector E;

[0028] Matrix (IB) -1 Often called the Ghosh inverse matrix, its elements It represents the output of department n in region j caused by the cumulative production of unit products in department m in region i, B is the direct output coefficient matrix in the multi-region input-output model, and I is the unit matrix;

[0029] After Taylor expansion of the Ghosh inverse matrix, the energy shortage risk is decomposed into different production levels:

[0030] G=(IB) -1 =I+B+B 2 +B3 +…;

[0031]

[0032] The supply chain path starts from department m in region i and passes through department k (r1, r2, ... r k ), and ends in the q sector within the region. The expression of the cascade effect generated by this path is:

[0033]

[0034] Where: P(m,q|r1,r2,…r k ) represents the supply chain path (m→r1→r2→…→r k →q) weight;

[0035] E m,i represents the initial energy shortage risk of sector m in region i;

[0036] element is an element in matrix B;

[0037] The virtual energy shortage risk transmission network consists of supply chain paths.

[0038] Preferably, the specific content of evaluating and identifying the key transmission nodes of the local energy shortage risk cascade effect in the virtual energy shortage risk transmission network based on the Betweenness algorithm is:

[0039]

[0040] Where: b i represents the Betweenness of department i;

[0041] n represents the number of sectors in the virtual energy shortage risk transmission network;

[0042] t k represents the supply chain path (m→r1→r2→…→r k →q) the appearance time of department i between the two ends;

[0043] The total weight of the supply chain path through department i is defined as b i (l1,l2),b i The expression of (l1,l2) is:

[0044]

[0045] Where: l1 represents the number of upstream departments of department i, and l2 represents the number of downstream departments of department i. Both l1 and l2 are integers greater than or equal to 1;

[0046] J i represents a matrix with 1 at element (i,i) and all other elements are zero;

[0047] e represents a unit column vector e of size n×1, all elements of which are equal to 1;

[0048] T=GB=BG=B+B 2 +B 3 +…, the Betweenness expression of department i is:

[0049]

[0050] Where: The n×n matrix T=GB consists of the Ghosh inverse matrix G and the direct output coefficient matrix B. The elements t in the matrix ij represents the direct and indirect output of sector j generated by the single output of sector i;

[0051] Set the Betweenness value b of all nodes i Add and average to get the mean b:

[0052]

[0053] Where n is the total number of nodes in the network;

[0054] By using the mean multiple method, the screening threshold T of key nodes is set, which is the multiple of b:

[0055] Where λ is the multiplication factor, which is set to 2;

[0056] Compare the Betweenness value of each regional department node with the threshold T. If the Betweenness value of a node is b i ≥T, it is judged as a key transmission node for the cascading effect of local energy shortage risk.

[0057] A local energy shortage risk cascading effect evaluation system based on a Betweenness algorithm, comprising: a data acquisition module, an energy shortage risk transmission network acquisition module and a cascading effect evaluation module;

[0058] The data acquisition module includes an energy pressure index acquisition unit, an energy shortage probability acquisition unit, an energy dependence acquisition unit, and an initial energy shortage risk acquisition unit;

[0059] An energy pressure index acquisition unit, which is used to calculate the energy pressure index of each area;

[0060] An energy shortage probability acquisition unit, which is used to calculate the energy shortage probability of each area;

[0061] An energy dependence acquisition unit, which is used to calculate the energy dependence of each sector in each region;

[0062] An initial energy shortage risk acquisition unit, which is used to calculate the energy shortage risk of each department in each region;

[0063] A virtual energy shortage risk transmission network acquisition module, which is used to construct a virtual energy shortage risk transmission network;

[0064] The cascade effect evaluation module is used to determine the key transmission nodes of the local energy shortage risk cascade effect based on the Betweenness algorithm.

[0065] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for evaluating the cascading effect of local energy shortage risk when executing the computer program.

[0066] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for evaluating the cascading effect of energy shortage risk is implemented.

[0067] In summary, the method for evaluating the cascading effect of local energy shortage risk based on the Betweennesss algorithm of the present invention provides a method for calculating energy shortage risk compared with traditional technologies. By constructing a virtual energy shortage risk transmission network, a methodology for evaluating the cascading effect of local energy shortage risk is proposed. The methodology can identify all key nodes where virtual energy shortage risk propagates in the supply chain, which helps to enhance the resilience of the entire trade network in the face of energy shortages.

[0068] This paper proposes a monetization indicator for the initial energy shortage risk and deeply analyzes its cascading effect from the perspective of the supply chain. By constructing a virtual energy shortage risk transmission network, the key nodes of virtual energy shortage risk in the supply chain are identified. This not only helps prevent the fluctuation of a certain node from spreading to upstream and downstream regions and departments, but also effectively reduces the systemic risks caused by energy shortages.

[0069] The present invention has the advantages of wide application range and efficient calculation method, which fills the gaps in previous research and can provide effective methods and data support for improving the resilience of trade networks in coping with energy shortage risks.

[0070] The technical method of the present invention is further described in detail below through the drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1This is a step diagram of a method for evaluating the cascading effect of local energy shortage risk based on the Betweennesss algorithm of the present invention;

[0072] Figure 2 It is a schematic diagram of a local energy shortage risk cascade effect evaluation system based on the Betweennesss algorithm of the present invention. DETAILED DESCRIPTION

[0073] The technical method of the present invention is further described below by means of the accompanying drawings and embodiments. It should be noted that unless otherwise specifically stated, the relative arrangement of the components and steps, numerical expressions and numerical values ​​described in these embodiments do not limit the scope of the present application.

[0074] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present application, its application, or uses.

[0075] Technologies, systems, and devices known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, systems, and devices should be considered part of the specification.

[0076] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0077] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0078] The main purpose of the present invention is to solve some defects existing in the existing image compression technology, especially the problem that when compressing images in machine vision applications, the relationship between the image volume after compression and the retention of effective visual data information of the image is not fully considered, and then compressing the image according to a fixed compression ratio leads to the loss of effective visual information after image compression.

[0079] like Figure 1 As shown, the present invention provides a method for evaluating the cascading effect of local energy shortage risk based on the Betweennesss algorithm, comprising the following steps:

[0080] S1. Obtain the energy stress index of each region and the energy dependence of each department, calculate the probability of energy shortage based on the energy stress index, and calculate the initial energy shortage risk based on the probability of energy shortage and the energy dependence of each department;

[0081] Preferably, the expression for calculating the energy pressure index is:

[0082]

[0083] in, EPI i represents the energy pressure index of region i, represents the supply of the kth energy in region i, represents the consumption of the kth energy in region i, 1≤k≤h, h represents the type of energy, is the production of the kth energy source in region i, is the import volume of the kth energy source in region i, is the export volume of the kth energy source in region i.

[0084] The energy types h described in this step are limited to 10 categories: coal, coke, crude oil, gasoline, kerosene, diesel, fuel oil, liquefied petroleum gas, natural gas and electricity.

[0085] Preferably, the expression of energy shortage probability is:

[0086] ESP i =f ESP (EPI i ;σ)=E(Y i );

[0087] in, X i ~Lognormal(μ i ,σ 2 ), ESP i represents the probability of energy shortage in region i, E(Y i ) indicates Y i The expected value of Y i In each time and space unit, the energy shortage in region i, X i The log-normal distribution represents the ratio of energy supply and consumption in region i within each space-time unit, μ i For X i The variance of i for The parameter σ is the standard deviation and is equal to 1.

[0088] Preferably, the expression of energy dependence of each department is:

[0089]

[0090] in, EDL m,i represents the energy dependence of sector m in region i, represents the consumption of the kth energy by department m in region i, EI m,iIt represents the energy consumption intensity of department m in region i, which is equal to the sum of the 10 energy consumptions of the department in the region. Divide by its total output x m,i , α is ED m,i The cutoff parameter is set to 2.

[0091] Preferably, the expression of initial energy shortage risk is:

[0092] IESR m,i =ESP i ×EDL m,i × m,i ;

[0093] Where IESR m,i represents the initial energy shortage risk of sector m in region i; ESP i is the probability of energy shortage in region i; EDL m,i is the energy dependence of sector m in region i; x m,i represents the total output of sector m in region i.

[0094] S2. Combine the initial energy shortage risk with the multi-region input-output model to build a virtual energy shortage risk transmission network;

[0095] Preferably, the initial energy shortage risk is combined with the multi-region input-output model to construct a virtual energy shortage risk transmission network. The specific contents are as follows:

[0096] Assume that an economic system includes n production sectors. Define 1×n row vector v to represent the initial input of each sector, n×n matrix Z to represent inter-sector product transactions, n×1 column vector x to represent the total input of each sector, and all elements of n×1 column vector e are 1. The column balance expression of the input-output model is:

[0097] x=eZ+v;

[0098] Suppose there is an n×n matrix B, defined as The symbol “^” indicates that the vector is converted into a diagonal matrix, and the symbol “-1” indicates the inverse of the matrix. Matrix B is the direct output coefficient matrix, and its element b ij It means that the direct output of department j is generated by one unit of output produced by department i. The transformation formula of the column balance expression of the input-output model is:

[0099] x=v(IB) -1 ;

[0100] The transformation of the column balance expression of the input-output model establishes the relationship between the total output x and the initial input v. Matrix (IB) -1 is the Ghosh inverse matrix, whose element gij It represents the output of department j caused by the cumulative (direct and indirect) production of unit products of department i.

[0101] We use the Ghosh inverse matrix to evaluate the propagation of virtual energy shortage risk. The expression for using the Ghosh inverse matrix to evaluate the propagation of virtual energy shortage risk is:

[0102]

[0103] Where: U represents the propagation matrix of virtual energy shortage risk, where the elements represents the cascading effect of department m in region i on department n in region j;

[0104] E is a row vector, each element of which represents the initial energy shortage risk of each department in each region. It refers to the process of diagonalizing the vector E;

[0105] Matrix (IB) -1 Often called the Ghosh inverse matrix, its elements It represents the output of department n in region j caused by the cumulative production of unit products in department m in region i, B is the direct output coefficient matrix in the multi-region input-output model, and I is the unit matrix;

[0106] After Taylor expansion of the Ghosh inverse matrix, the energy shortage risk is decomposed into different production levels:

[0107] G=(IB) -1 =I+B+B 2 +B 3 +…;

[0108]

[0109] The supply chain path starts from department m in region i and passes through department k (r1, r2, ... r k ), and ends in the q sector within the region. The expression of the cascade effect generated by this path is:

[0110]

[0111] Where: P(m,q|r1,r2,…r k ) represents the supply chain path (m→r1→r2→…→r k →q) weight;

[0112] E m,i represents the initial energy shortage risk of sector m in region i;

[0113] element is an element in matrix B;

[0114] The virtual energy shortage risk transmission network consists of supply chain paths.

[0115] S3. Based on the Betweenness algorithm, the key transmission nodes of the local energy shortage risk cascade effect are evaluated and identified in the virtual energy shortage risk transmission network.

[0116] Preferably, the specific content of evaluating and identifying the key transmission nodes of the local energy shortage risk cascade effect in the virtual energy shortage risk transmission network based on the Betweenness algorithm is:

[0117]

[0118] Where: b i represents the Betweenness of department i;

[0119] n represents the number of sectors in the virtual energy shortage risk transmission network;

[0120] t k represents the supply chain path (m→r1→r2→…→r k →q) the appearance time of department i between the two ends;

[0121] The total weight of the supply chain path through department i is defined as b i (l1,l2),b i The expression of (l1,l2) is:

[0122]

[0123] Where: l1 represents the number of upstream departments of department i, and l2 represents the number of downstream departments of department i. Both l1 and l2 are integers greater than or equal to 1;

[0124] J i represents a matrix with 1 at element (i,i) and all other elements are zero;

[0125] e represents a unit column vector e of size n×1, all elements of which are equal to 1;

[0126] T=GB=BG=B+B 2 +B 3 +…, the Betweenness expression of department i is:

[0127]

[0128] Where: The n×n matrix T=GB consists of the Ghosh inverse matrix G and the direct output coefficient matrix B. The elements t in the matrix ijrepresents the direct and indirect output of sector j generated by the single output of sector i;

[0129] Set the Betweenness value b of all nodes i Add and average to get the mean

[0130] Where n is the total number of nodes in the network;

[0131] By using the mean multiple method, the screening threshold T of key nodes is set, which is the multiple of b:

[0132] Where λ is the multiplication factor, which is set to 2;

[0133] Compare the Betweenness value of each regional department node with the threshold T. If the Betweenness value of a node is b i ≥T, it is judged as a key transmission node for the cascading effect of local energy shortage risk.

[0134] like Figure 2 As shown, the local energy shortage risk cascade effect evaluation system based on the Betweenness algorithm includes a data acquisition module, an energy shortage risk transmission network acquisition module and a cascade effect evaluation module.

[0135] Among them, the data acquisition module includes an energy pressure index acquisition unit, an energy shortage probability acquisition unit, an energy dependence acquisition unit, and an initial energy shortage risk acquisition unit.

[0136] Energy pressure index acquisition unit, which is used to calculate the energy pressure index of each area:

[0137] The energy shortage probability acquisition unit is used to calculate the energy shortage probability of each area.

[0138] The energy dependence acquisition unit is used to calculate the energy dependence of each department in each region.

[0139] The initial energy shortage risk acquisition unit is used to calculate the initial energy shortage risk of each department in each region.

[0140] A virtual energy shortage risk transmission network acquisition module is used to construct a virtual energy shortage risk transmission network.

[0141] The cascade effect evaluation module is used to determine the key transmission nodes of the local energy shortage risk cascade effect based on the Betweenness algorithm.

[0142] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for evaluating the cascading effect of local energy shortage risk when executing the computer program.

[0143] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for evaluating the cascading effect of energy shortage risk is implemented.

[0144] This example takes various provinces and cities in China as the research object, covering 31 provinces and cities. Based on the input-output table data of 2017, each province and city contains 42 industrial sectors, and the data is up to date as of 2017. The cascading effect evaluation method of local energy shortage risk described in Example 1 is used to calculate the local energy shortage risk and its cascading effect according to the algorithms and formulas specified in each step. The results are as follows:

[0145] The five regional sectors with the greatest risk of local energy shortages are: Hebei-metal smelting and rolling products (140.8 billion yuan), Jiangsu-metal smelting and rolling products (89.4 billion yuan), Sichuan-transportation, warehousing and postal services (64.2 billion yuan), Sichuan-metal smelting and rolling processing industry (60.7 billion yuan), Guangdong-transportation, warehousing and postal services (59.2 billion yuan).

[0146] Key transmission nodes of the cascading effect of local energy shortage risks: Hebei-metal smelting and rolling products (109.3 billion yuan), Jiangsu-metal smelting and rolling products (107 billion yuan), Shandong-metal smelting and rolling products (96.1 billion yuan), Sichuan-chemical products (70.5 billion yuan), Henan-metal smelting and rolling products (70.4 billion yuan).

[0147] The energy shortage risk values ​​in brackets in the above results are relative values, not the actual economic losses. They reflect the relative amount of potential economic losses. By comparing the sizes of these relative values, the key transmission nodes of the local energy shortage risk cascade effect can be determined.

[0148] Previous studies have identified the donors and recipients of the cascading effects of energy shortage risks, including the main donors: Hebei-metal smelting and rolling products (272.9 billion yuan), Jiangsu-metal smelting and rolling products (181.9 billion yuan), Henan-metal smelting and rolling products (118.3 billion yuan), Sichuan-metal smelting and rolling products (107.6 billion yuan), Guangxi-metal smelting and rolling products (93.3 billion yuan); main recipients: Sichuan-water production and supply (92.8 billion yuan), Yunnan-water production and supply (59.6 billion yuan), Chongqing-water production and supply (52.6 billion yuan), Hunan-water production and supply (52.6 billion yuan), Jiangsu-electrical machinery and equipment (50.7 billion yuan). Compared with previous studies, this study identified the key intermediate transmission nodes of the energy shortage risk cascade effect in addition to the donors and recipients, mainly including: Shandong-metal smelting and rolling products (96.1 billion yuan), Sichuan-chemical products (70.5 billion yuan), Jiangsu-electrical machinery and equipment (69.2 billion yuan), Shandong-chemical products (65.7 billion yuan), and Jiangsu-metal products (64.8 billion yuan).

[0149] The above results identify all the key transmission nodes in the cascading effect of energy shortage risk, which provides a basis for a more systematic and comprehensive assessment of the cascading effect of energy shortage risk. It provides data support for the formulation of more effective strategies to enhance the resilience of the entire trade network in the face of energy shortage risk. The role of identifying intermediate key nodes is also to help us intervene in risk transmission early, thereby preventing potential serious impacts.

[0150] Specifically, for key transmission nodes in the cascading effect of energy shortage risks, regional departments can take the following measures: First, improve energy utilization efficiency and increase energy supply to ensure that a stable supply chain can be maintained when there is an imbalance between supply and demand. Second, strengthen diversified reliance on the upstream of the supply chain and adjust trade strategies to reduce reliance on a single energy source, thereby reducing vulnerability to energy shortages. In addition, strengthen cooperation and communication with key supply chain nodes, use technology and information sharing platforms and other methods to enhance the ability to respond to supply chain disruptions and achieve more efficient risk management and emergency response.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical method of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical method to deviate from the spirit and scope of the technical method of the present invention.

Claims

1. A method for evaluating the cascading effect of local energy shortage risk based on the Betweennesss algorithm, characterized in that: The following steps are involved: S1. Obtain the energy stress index of each region and the energy dependence of each department, calculate the probability of energy shortage based on the energy stress index, and calculate the initial energy shortage risk based on the probability of energy shortage and the energy dependence of each department; S2. Combine the initial energy shortage risk with the multi-region input-output model to build a virtual energy shortage risk transmission network; S3. Based on the Betweenness algorithm, the key transmission nodes of the local energy shortage risk cascade effect are evaluated and identified in the virtual energy shortage risk transmission network.

2. According to claim 1, a method for evaluating the cascading effect of local energy shortage risk based on the Betweennesss algorithm is characterized in that: The expression for calculating the energy pressure index is: in, EPI i represents the energy pressure index of region i, represents the supply of the kth energy in region i, represents the consumption of the kth energy in region i, 1≤k≤h, h represents the type of energy, is the production of the kth energy source in region i, is the import volume of the kth energy source in region i, is the export volume of the kth energy source in region i.

3. According to claim 2, a method for evaluating the cascading effect of local energy shortage risk based on the Betweennesss algorithm is characterized in that: The expression of energy shortage probability is: ESP i =f ESP (EPI i ;σ)=E(Y i ); in, X i ~Lognormal(μ i ,σ 2 ), ESP i represents the probability of energy shortage in region i, E(Y i ) indicates Y i The expected value of Y i In each time and space unit, the energy shortage in region i, X i The log-normal distribution represents the ratio of energy supply and consumption in region i within each space-time unit, μ i For X i The parameter σ is the standard deviation and is equal to 1.

4. According to claim 3, a method for evaluating the cascading effect of local energy shortage risk based on the Betweennesss algorithm is characterized in that: The expression of energy dependence of each sector is: in, EDL m,i represents the energy dependence of sector m in region i, represents the consumption of the kth energy by department m in region i, EI m,i It represents the energy consumption intensity of department m in region i, which is equal to the sum of the 10 energy consumptions of the department in the region. Divide by its total output x m,i , α is ED m,i The cutoff parameter is set to 2.

5. According to claim 4, a method for evaluating the cascading effect of local energy shortage risk based on the Betweennesss algorithm is characterized in that: The expression of initial energy shortage risk is: IESR m,i =ESP i ×EDL m,i ×x m,i ; Where IESR m,i represents the initial energy shortage risk of sector m in region i; ESP i is the probability of energy shortage in region i; EDL m,i is the energy dependence of sector m in region i; x m,i represents the total output of sector m in region i.

6. According to claim 4, a method for evaluating the cascading effect of local energy shortage risk based on the Betweennesss algorithm is characterized in that: The specific contents of combining the initial energy shortage risk with the multi-regional input-output model to construct a virtual energy shortage risk transmission network are as follows: The expression of using Ghosh inverse matrix to evaluate the propagation of virtual energy shortage risk is: Where: U represents the propagation matrix of virtual energy shortage risk, where the elements represents the cascading effect of department m in region i on department n in region j; E is a row vector, each element of which represents the initial energy shortage risk of each department in each region. It refers to the process of diagonalizing the vector E; Matrix (IB) -1 Often called the Ghosh inverse matrix, its elements It represents the output of department n in region j caused by the cumulative production of unit products in department m in region i, B is the direct output coefficient matrix in the multi-region input-output model, and I is the unit matrix; After Taylor expansion of the Ghosh inverse matrix, the energy shortage risk is decomposed into different production levels: G=(I-B) -1 =I+B+B 2 +B 3 +…; The supply chain path starts from department m in region i and passes through department k (r1, r2, ... r k ), and ends in the q sector within the region. The expression of the cascade effect generated by this path is: Where: P(m,qr1,r2,…r k ) represents the supply chain path (m→r1→r2→…→r k →q) weight; E m,i represents the initial energy shortage risk of sector m in region i; element is an element in matrix B; The virtual energy shortage risk transmission network consists of supply chain paths.

7. According to claim 4, a method for evaluating the cascading effect of local energy shortage risk based on the Betweennesss algorithm is characterized in that: The specific contents of evaluating and identifying the key transmission nodes of the local energy shortage risk cascade effect in the virtual energy shortage risk transmission network based on the Betweenness algorithm are as follows: Where: b i represents the Betweenness of department i; n represents the number of sectors in the virtual energy shortage risk transmission network; t k represents the supply chain path (m→r1→r2→…→r k →q) the appearance time of department i between the two ends; The total weight of the supply chain path through department i is defined as b i (l1,l2),b i The expression of (l1,l2) is: Where: l1 represents the number of upstream departments of department i, l2 represents the number of downstream departments of department i, and both l1 and l2 are integers greater than or equal to 1; J i represents a matrix with 1 at element (i,i) and all other elements are zero; e represents a unit column vector e of size n×1, all elements of which are equal to 1; T=GB=BG=B+B 2 +B 3 +…, the Betweenness expression of department i is: Where: The n×n matrix T=GB consists of the Ghosh inverse matrix G and the direct output coefficient matrix B. The elements t in the matrix ij represents the direct and indirect output of sector j generated by the single output of sector i; Set the Betweenness value b of all nodes i Add and average to get the mean Where n is the total number of nodes in the network; By using the mean multiple method, the screening threshold T of key nodes is set, that is, Multiples of: Where λ is the multiplication factor, which is set to 2; Compare the Betweenness value of each regional department node with the threshold T. If the Betweenness value of a node is b i ≥T, it is judged as a key transmission node for the cascading effect of local energy shortage risk.

8. A local energy shortage risk cascade effect evaluation system based on Betweenness algorithm, characterized in that: include: Data acquisition module, virtual energy shortage risk transmission network acquisition module and cascade effect evaluation module; The data acquisition module includes an energy pressure index acquisition unit, an energy shortage probability acquisition unit, an energy dependence acquisition unit, and an initial energy shortage risk acquisition unit; An energy pressure index acquisition unit, which is used to calculate the energy pressure index of each area; An energy shortage probability acquisition unit, which is used to calculate the energy shortage probability of each area; An energy dependence acquisition unit, which is used to calculate the energy dependence of each sector in each region; An initial energy shortage risk acquisition unit, which is used to calculate the initial energy shortage risk of each department in each region; A virtual energy shortage risk transmission network acquisition module, which is used to construct a virtual energy shortage risk transmission network; The cascade effect evaluation module is used to determine the key transmission nodes of the local energy shortage risk cascade effect based on the Betweenness algorithm.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for evaluating the cascading effect of local energy shortage risks as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method for evaluating the cascading effect of energy shortage risk according to any one of claims 1 to 7.

Images