A method and system for evaluating the cascading effect of local energy shortage risk based on Betweennesss algorithm

Abstract

The present invention discloses a method and system for evaluating the cascading effects of local energy shortage risks based on a Betweenness algorithm, which relates to the field of energy resource management. The method includes S1: obtaining the energy pressure index of each region and the energy dependence of each department, calculating the energy shortage probability based on the energy pressure index, and calculating the initial energy shortage risk based on the energy shortage probability and the energy dependence of each department; S2: combining the initial energy shortage risk with a multi-region input-output model to construct a virtual energy shortage risk transmission network; S3: evaluating and identifying key transmission nodes of the local energy shortage risk cascading effect in the virtual energy shortage risk transmission network based on the Betweenness algorithm. The present invention has a wide range of applications and an efficient calculation method, filling the gaps in previous research and providing effective methods and data support for improving the resilience of trade networks to energy shortage risks.

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 risks based on a Betweennesss algorithm. Background Art

[0002] Energy shortages not only directly impact the economies of certain regions and sectors, but can also spread risks to other regions and sectors through the supply chain, generating systemic risks. This means that initial risks can cascade, causing indirect economic losses to downstream regions and sectors, increasing the complexity and scope of the risks.

[0003] Previous research has focused solely on the starting and ending points of risk transmission pathways, failing to delve deeper into and identify the key risk transmission sectors located in the middle of these pathways. Identifying these key risk transmission sectors can help implement early intervention during risk transmission.

[0004] The Betweenness method is often used to measure the centrality of individual nodes in a network. The transmission of energy shortage risk within a socioeconomic system can be viewed as a directed, weighted complex network. Deconstructing the characteristics of risk networks and identifying key risk transmission nodes based on the Betweenness method can provide new insights for improving the resilience of socioeconomic systems to energy shortage risks.

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

[0006] To address these issues, this application proposes a method for evaluating the cascading effects 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, more effective strategies can be developed to enhance the resilience of the entire trade network to energy shortage risks. In particular, the identification of key intermediate nodes allows for proactive intervention in the transmission of risks, thereby avoiding more severe 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 sector, calculate the probability of energy shortage based on the energy stress index, and calculate the initial energy shortage risk based on the energy shortage probability and the energy dependence of each sector;

[0009] S2. Combining the initial energy shortage risk with the multi-region input-output model to construct 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 k-th 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 ) represents Y i The expected value of Y i Indicates the energy shortage in region i at each time and space unit, 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 The energy consumption intensity of department m in region i 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 ×x m,i ;

[0022] Where, IESR m,i represents the initial energy shortage risk of department m in region i; ESP i is the energy shortage probability of 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 for evaluating the propagation of virtual energy shortage risk using the Ghosh inverse matrix 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, where each element represents the initial energy shortage risk of each sector in each region, and the expression is 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 sector n in region j caused by the cumulative production of unit products of sector m in region i, B is the direct output coefficient matrix in the multi-region input-output model, and I is the identity 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 sector q within the region. The expression for 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 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 as follows:

[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 sector 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: n×n matrix T=GB is composed of Ghosh inverse matrix G and direct output coefficient matrix B. The elements t in the matrix ij represents the direct and indirect output of sector j resulting from 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 a multiple of b:

[0055] Where λ is the multiplication factor, 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 determined to be a key transmission node for the cascading effect of local energy shortage risk.

[0057] A local energy shortage risk cascade effect evaluation system based on the Betweenness algorithm, comprising: a data acquisition module, an energy shortage risk transmission network acquisition module, and a cascade 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 obtaining 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] 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. When the processor executes the computer program, the method for evaluating the cascading effects of local energy shortage risks is implemented.

[0066] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method for evaluating the cascading effect of energy shortage risks.

[0067] In summary, the proposed method for evaluating the cascading effects of local energy shortage risks, based on the Betweennesss algorithm, provides a superior approach to calculating energy shortage risk compared to traditional techniques. By constructing a virtual energy shortage risk transmission network, this method proposes a methodology for assessing the cascading effects of local energy shortage risks. This methodology can identify all key nodes where virtual energy shortage risks propagate within the supply chain, helping to enhance the resilience of the entire trade network in the face of energy shortages.

[0068] This paper proposes a monetizable indicator for initial energy shortage risk and deeply analyzes its cascading effects from a supply chain perspective. By constructing a virtual energy shortage risk transmission network, it identifies key nodes of virtual energy shortage risk in the supply chain. This not only helps prevent fluctuations at a single node from spreading to upstream and downstream regions and sectors, but also effectively reduces the systemic risks caused by energy shortages.

[0069] This invention has the advantages of wide applicability and efficient calculation. It fills the gaps in previous research and can provide effective methods and data support for improving the resilience of trade networks to energy shortage risks.

[0070] The technical method of the present invention is further described in detail below through the accompanying drawings and examples. 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 This 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 through the accompanying drawings and embodiments. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and numerical values described in these embodiments do not limit the scope of this 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 disclosure, its application, or uses.

[0075] Technologies, systems, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, they 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 same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0078] The main purpose of the present invention is to solve some defects existing in 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 sector, calculate the probability of energy shortage based on the energy stress index, and calculate the initial energy shortage risk based on the energy shortage probability and the energy dependence of each sector;

[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 k-th 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 type h described in this step is 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 ) represents Y i The expected value of Y i Indicates the energy shortage in region i at each time and space unit, 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,iThe energy consumption intensity of department m in region i 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 ×x m,i ;

[0093] Where, IESR m,i represents the initial energy shortage risk of department m in region i; ESP i is the energy shortage probability of 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. Combining the initial energy shortage risk with the multi-region input-output model to construct 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 an economic system consists of n production sectors. Define a 1×n row vector v to represent the initial input of each sector, an n×n matrix Z to represent inter-sectoral product transactions, an n×1 column vector x to represent the total input of each sector, and all elements of the n×1 column vector e to be 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 total output x and initial input v. Matrix (IB) -1 is the Ghosh inverse matrix, whose element gij It represents the cumulative (direct and indirect) output of department j caused by the 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, where each element represents the initial energy shortage risk of each sector in each region, and the expression is 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 sector n in region j caused by the cumulative production of unit products of sector m in region i, B is the direct output coefficient matrix in the multi-region input-output model, and I is the identity 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 sector q within the region. The expression for 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 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 as follows:

[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 sector 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: n×n matrix T=GB is composed of Ghosh inverse matrix G and direct output coefficient matrix B. The elements t in the matrix ijrepresents the direct and indirect output of sector j resulting from 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 a multiple of b:

[0132] Where λ is the multiplication factor, 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 determined to be a key transmission node for the cascading effect of local energy shortage risk.

[0134] like Figure 2 As shown in FIG, a 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. When the processor executes the computer program, the method for evaluating the cascading effects of local energy shortage risks is implemented.

[0143] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method for evaluating the cascading effect of energy shortage risks.

[0144] This example covers 31 provinces and municipalities across China. Based on 2017 input-output table data, each province and municipality includes 42 industrial sectors, with data current as of 2017. The cascading effect evaluation method for local energy shortage risk described in Example 1 was used to calculate the local energy shortage risk and its cascading effect, following 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), and 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 actual economic losses. They reflect the relative magnitude of potential economic losses. By comparing these relative values, we can identify the key transmission nodes where local energy shortage risk cascades.

[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); the 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 not only the donors and recipients of the energy shortage risk cascade effect, but also the key intermediate transmission nodes of the energy shortage risk cascade effect, 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] These results identify all key transmission nodes in the cascading energy shortage risk. This provides a foundation for a more systematic and comprehensive assessment of the cascading effects of energy shortage risk. This data supports the development of more effective strategies to enhance the resilience of the entire trade network to energy shortage risks. Identifying these critical intermediate nodes also helps us intervene early in the spread of risk, thereby preventing potentially severe impacts.

[0150] Specifically, regional authorities can implement the following measures to address key transmission nodes in the cascading energy shortage risk: First, improve energy efficiency and increase energy supply to ensure a stable supply chain even when supply and demand are imbalanced. Second, diversify upstream supply chains and adjust trade strategies to reduce reliance on a single energy source, thereby reducing vulnerability to energy shortages. Furthermore, strengthen cooperation and communication with key supply chain nodes, leveraging technology and information-sharing platforms to enhance resilience 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 sector, calculate the probability of energy shortage based on the energy stress index, and calculate the initial energy shortage risk based on the energy shortage probability and the energy dependence of each sector; S2. Combining the initial energy shortage risk with the multi-region input-output model to construct a virtual energy shortage risk transmission network; S3. Evaluate and identify key transmission nodes of local energy shortage risk cascade effects in a virtual energy shortage risk transmission network based on the Betweenness algorithm; Combining the initial energy shortage risk with the multi-region input-output model, the specific contents of constructing a virtual energy shortage risk transmission network are as follows: The expression for evaluating the propagation of virtual energy shortage risk using the Ghosh inverse matrix is: ； Where: Represents the propagation matrix of virtual energy shortage risk, where the elements Indicates area department For the region department cascading effects; is a row vector, each element of which represents the initial energy shortage risk of each department in each region. Is a vector The process of diagonalization; matrix It is called the Ghosh inverse matrix, whose elements Indicates area department The cumulative cost of producing unit products Regional Department The output, is the direct output coefficient matrix in the multi-region input-output model, is the identity matrix; After Taylor expansion of the Ghosh inverse matrix, the energy shortage risk is decomposed into different production levels: ； ； Supply chain path from In the region Department starts, through department , and in this area At the end of the department, the expression of the cascade effect generated by this path is: ； Where: Represents the supply chain path The weight of Representative area department initial energy shortage risk; element is an element in matrix B; The virtual energy shortage risk transmission network is composed of supply chain paths; 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: Indicates department Betweenness; represents the number of sectors in the transmission network with virtual energy shortage risk; Represents the supply chain path The department between the two ends Time of appearance; By Department The total weight of the supply chain path is defined as , The expression is: Where: Indicates department The number of upstream sectors, Indicates department The number of downstream sectors, and are all integers greater than or equal to 1; Indicates a A matrix with 1 at each element and zero at all other elements; Indicates a size of The unit column vector of , all its elements are equal to 1; ,department The expression for Betweenness is: ； Where: matrix By Ghosh inverse matrix And directly output the coefficient matrix Composition, elements in the matrix Indicated by department The single output of the department direct and indirect outputs; Set the Betweenness value of all nodes Add and average to get the mean : ； In the formula is the total number of nodes in the network; Set the screening threshold of key nodes by using the mean multiple method ,Right now Multiples of: ; In the formula is the multiplication factor, set to 2; Compare the Betweenness value of each regional department node with the threshold If the Betweenness value of a node , it is determined to be a key transmission node for the cascading effect of local energy shortage risks.

2. The method for evaluating the cascading effect of local energy shortage risk based on the Betweennesss algorithm according to claim 1 is characterized in that: The expression for calculating the energy pressure index is: ； in, , Indicates area Energy stress index, Indicates area No. The supply of energy, Indicates area No. Energy consumption, 1≤ ≤ , Indicates the type of energy, is the production of the k-th 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. The method for evaluating the cascading effect of local energy shortage risk based on the Betweennesss algorithm according to claim 2 is characterized in that: The expression of energy shortage probability is: ； in, , , , Indicates area The probability of energy shortage, express The expected value of Indicates that in each space-time unit, the area Energy shortages within The lognormal distribution indicates that the The ratio of energy supply to consumption in a region, for The variance of the parameter is the standard deviation, which is equal to 1.

4. The method for evaluating the cascading effect of local energy shortage risk based on the Betweennesss algorithm according to claim 3 is characterized in that: The expression of energy dependence of each sector is: ； in, , Indicates area Middle Department energy dependence, Indicates area Middle Department The consumption of the kth energy source, Indicates area Middle Department The energy intensity of the region is equal to the sum of the 10 energy consumptions of the sector. Divided by its total output , for The cutoff parameter is set to 2.

5. The method for evaluating the cascading effect of local energy shortage risk based on the Betweennesss algorithm according to claim 4 is characterized in that: The expression of initial energy shortage risk is: ； Where, Indicates area Middle Department initial energy shortage risk; For the region probability of energy shortage; For the region Middle Department energy dependence; Indicates area Middle Department total output.

6. A local energy shortage risk cascade effect evaluation system based on the Betweenness algorithm, characterized by: 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 obtaining 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; 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; Cascade effect evaluation module, which is used to determine the key transmission nodes of the cascade effect of local energy shortage risk based on the Betweenness algorithm; Combining the initial energy shortage risk with the multi-region input-output model, the specific contents of constructing a virtual energy shortage risk transmission network are as follows: The expression for evaluating the propagation of virtual energy shortage risk using the Ghosh inverse matrix is: ； Where: Represents the propagation matrix of virtual energy shortage risk, where the elements Indicates area department For the region department cascading effects; is a row vector, each element of which represents the initial energy shortage risk of each department in each region. Is a vector The process of diagonalization; matrix It is called the Ghosh inverse matrix, whose elements Indicates area department The cumulative amount of production units Regional Department The output, is the direct output coefficient matrix in the multi-region input-output model, is the identity matrix; After Taylor expansion of the Ghosh inverse matrix, the energy shortage risk is decomposed into different production levels: ； ； Supply chain path from In the region Department starts, through department , and in this area At the end of the department, the expression of the cascade effect generated by this path is: ； Where: Represents the supply chain path The weight of Representative area department initial energy shortage risk; element is an element in matrix B; The virtual energy shortage risk transmission network is composed of supply chain paths; 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: Indicates department Betweenness; represents the number of sectors in the transmission network with virtual energy shortage risk; Represents the supply chain path The department between the two ends Time of appearance; By Department The total weight of the supply chain path is defined as , The expression is: Where: Indicates department The number of upstream sectors, Indicates department The number of downstream sectors, and are all integers greater than or equal to 1; Indicates a A matrix with 1 at each element and zero at all other elements; Indicates a size of The unit column vector of , all its elements are equal to 1; ,department The expression for Betweenness is: ； Where: matrix By Ghosh inverse matrix And directly output the coefficient matrix Composition, elements in the matrix Indicated by department The single output of the department direct and indirect outputs; Set the Betweenness value of all nodes Add and average to get the mean : ； In the formula is the total number of nodes in the network; Set the screening threshold of key nodes by using the mean multiple method ,Right now Multiples of: ; In the formula is the multiplication factor, set to 2; Compare the Betweenness value of each regional department node with the threshold If the Betweenness value of a node , it is determined to be a key transmission node for the cascading effect of local energy shortage risks.

7. 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 effects of local energy shortage risks according to any one of claims 1 to 5 when executing the computer program.

8. 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 5.

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