Supply chain network toughness evaluation method under different attack strategies

Abstract

The invention requests to protect a supply chain network toughness evaluation method based on a complex network theory, and aims to evaluate and optimize the toughness of a supply chain network under different attack strategies (such as random attack and deliberate attack) by establishing an evolution model of the supply chain network. According to the method, a network model with enterprises as nodes and cooperation relations as edges is established, key parameters (such as node degree, shortest path length and clustering coefficient) in a network structure are analyzed, and the stability and recovery capability of a supply chain under external impact are evaluated. By simulating different risk scenes, the structural performance, the efficiency performance and the recovery capability of the network are calculated and evaluated. The toughness evaluation method provides a new theoretical basis and technical means for supply chain risk management, and is helpful for improving the toughness of the supply chain network.

Description

Technical Field

[0001] The present invention relates to the modeling and resilience assessment of supply chain networks, and in particular to a supply chain network resilience assessment method based on complex network theory. This method is mainly used in the risk management and optimization of supply chains, and can effectively assess the stability and resilience of supply chains in the face of different attack strategies, thereby improving the resilience of supply chains. Background Art

[0002] The supply chain is highly complex and dynamically evolving. The cooperative relationships between the nodes of the supply chain (such as raw material suppliers, manufacturers, distributors, etc.) are intricate, and under the influence of different external factors, node failures and broken cooperative relationships may occur, resulting in reduced operational efficiency of the entire supply chain. Therefore, how to evaluate and improve the resilience of the supply chain network in the face of emergencies such as natural disasters, policy changes, and market fluctuations has become the focus of current supply chain management research.

[0003] Traditional supply chain network resilience assessment methods focus on single-level risk assessment, such as node failure or supply chain disruption, but ignore the evolution of the supply chain network structure and the multi-dimensional evaluation of its overall resilience under complex and ever-changing attack strategies. With the expansion of the supply chain network scale and the complexity of the dependencies between nodes, how to conduct multi-dimensional resilience assessment from a holistic perspective has become a technical problem that needs to be solved urgently.

[0004] After searching, the application publication number CN117273428A, an industrial chain resilience assessment method and system based on multi-chain coupling, involves the technical field of manufacturing industry chain resilience assessment. First, it is necessary to determine the topological structure of the modern industrial chain through a complex network method, and measure the characteristic indicators of the complex network; secondly, identify key indicators that help evaluate the resilience of the modern industrial chain from four dimensions: value chain, technology chain, innovation chain and supply chain, and construct a modern industrial chain resilience evaluation index system from the perspective of multi-chain coupling collaboration; finally, construct and apply a multi-dimensional evaluation pyramid model to conduct a comprehensive assessment of the resilience of the modern industrial chain. The pyramid model can not only be used to evaluate the current status of the resilience of the modern industrial chain, but also to monitor the resilience of the industrial chain in real time by inputting real-time dynamic data, which is helpful for risk identification and early warning of the modern industrial chain.

[0005] Through the introduction of complex network theory, dynamic simulation analysis, and a comprehensive resilience evaluation index system, the present invention has successfully overcome multiple defects in the prior art. First, the traditional supply chain network model has a relatively high level of abstraction and fails to fully reflect the hierarchical nature, dynamic characteristics, and differences between nodes in the supply chain network. In contrast, the present invention adopts the BA scale-free network model, combines the hierarchical division of upstream, midstream, and downstream, as well as the weight assignment of nodes and edges, which more accurately reflects the complex characteristics of the industrial supply chain and significantly improves the accuracy and applicability of the model. Second, the existing supply chain resilience evaluation methods are one-sided and only focus on the risks in a single scenario. The present invention innovatively introduces three resilience indicators, namely structural performance, efficiency performance, and recovery ability, to comprehensively measure the risk resistance ability of the supply chain network under random attacks and deliberate attacks, and makes up for the deficiencies of traditional methods through multi-dimensional evaluation. Finally, traditional optimization designs often lack scientific verification. The present invention uses the MATLAB simulation platform to dynamically simulate the changes in the network topology after an attack, proposes targeted optimization solutions including the protection of critical nodes and the enhancement of edges, and quantitatively verifies the optimization effect to ensure its practical feasibility and scientific nature. While improving the level of supply chain model construction, resilience evaluation, and optimization design, the present invention also provides strong technical support for the stability and risk resistance ability of the supply chain. Summary of the Invention

[0006] The present invention aims to solve the above problems in the prior art. A method for evaluating the resilience of a supply chain network under different attack strategies is proposed. The technical solution of the present invention is as follows:

[0007] A method for evaluating the resilience of a supply chain network under different attack strategies, comprising the following steps:

[0008] a) Construct a complex network model based on the enterprises and cooperation relationships in the supply chain, where the nodes of the network represent enterprises and the edges represent the cooperation relationships between enterprises;

[0009] b) Calculate the key parameters of the supply chain network, including node degree, shortest path length, and clustering coefficient, to evaluate the stability of the initial supply chain network;

[0010] c) Introduce random attack and deliberate attack strategy models to simulate the loss of nodes or edges during the attack process;

[0011] d) Define the resilience measurement indicators of the supply chain network, including the structural performance, efficiency performance, and recovery ability of the network;

[0012] e) Evaluate the risk resistance ability and recovery efficiency of the network by simulating and analyzing the network structure changes under different attack strategies;

[0013] f) According to the simulation results, propose optimization suggestions to improve the risk resistance ability of the supply chain.

[0014] Further, the network model adopts the BA scale-free network model, in which new nodes are more inclined to establish cooperative relationships with existing nodes with high connectivity, following the "the rich get richer" mechanism, that is, the probability that a new node connects to node i is:

[0015]

[0016] where k i represents the degree of node i, and represents the sum of the degrees of nodes in the network.

[0017] Further, in step b), the key parameters of the supply chain network are calculated, including node degree, shortest path length, and clustering coefficient, specifically including:

[0018] Node degree distribution: Analyze the importance of different nodes and their connection strengths; the node degree distribution can be described by the degree distribution function P(k). From a statistical perspective, the node degree distribution in the network refers to the proportion of the number of nodes with degree k in the network to the total number of network nodes, that is:

[0019] n k represents the number of nodes with degree k, and N represents the total number of network nodes;

[0020] Shortest path length: Evaluate the transportation efficiency and response speed of the supply chain network; it represents the shortest distance length between any two node enterprises in the supply chain network;

[0021] Clustering coefficient: Analyze the collaboration and stability of the network; the clustering coefficient of node i measures the link density between its neighbors. The formula for the clustering coefficient Ci of node i is:

[0022]

[0023] where Ei represents the actual number of edges existing between the neighbors of node i, and K i (K i -1) / 2 represents the maximum possible number of edges that can be formed between the neighbor nodes of node i.

[0024] Further, in c), a random attack and deliberate attack strategy model is introduced to simulate the loss of nodes or edges during the attack process, specifically including;

[0025] (1) Node attack strategy

[0026] The present invention defines the random attack on nodes as randomly removing 10%, 20%, …, 100% of the node enterprises from the supply chain network; while the deliberate attack on nodes is to remove them successively according to the importance degree of the nodes in the supply chain network, where the importance degree of the nodes is measured by the degree value of the nodes. Thus, the changes in the resilience metric are analyzed when the node enterprises are removed from the network to different extents under the two modes of random and deliberate attacks.

[0027] (2) Link attack strategy

[0028] The random attack on links refers to randomly removing a certain proportion of the cooperation relationships (links) between the node enterprises in the supply chain network from the network; the way of the deliberate attack on links is to first calculate the betweenness of the network edges, and then delete the links between the nodes in the order of the betweenness size. Analyze the changes in the resilience indicators of the supply chain network under random and deliberate attacks

[0029] Furthermore, the resilience evaluation of the supply chain network includes the following indicators:

[0030] a) Structural performance indicator: Evaluate the change in connectivity of the network after being attacked. Define the degree of change in network connectivity as the ratio of the number of nodes in the largest connected subgraph after the network is attacked to the total number of nodes in the original network:

[0031]

[0032] where N' represents the scale of the largest connected subgraph, and N represents the scale of the original network;

[0033] b) Efficiency performance indicator: Measure the efficiency of network information flow; network efficiency reflects the efficiency of the cooperation relationship between the node enterprises in the supply chain network within a certain period of time, and the calculation method can be expressed as the average value of the reciprocal sum of the shortest paths between any two nodes in the network:

[0034]

[0035] where d ij is the shortest path between the two nodes, and 1 / d ij represents the turnover efficiency of the cooperation relationship between enterprise node i and node j; if the path between two nodes is longer, then if the distance required to transfer resources between two nodes in the supply chain network is longer, then d ij is larger, and the transfer process will take more time. Therefore, the information transfer efficiency of this network is lower; on the contrary, the network transmission efficiency is higher;

[0036] c) Recovery ability index: Evaluates the ability of the supply chain network to return to its normal state after an interruption; the recovery ability index is measured by the recovery time, which represents the time elapsed from the start of the impact on the supply chain network until all nodes in the network return to a healthy state and the network resumes its functions.

[0037] Furthermore, the specific implementation steps in the evaluation process are as follows:

[0038] a) Initialize the supply chain network, define the initial node set and its hierarchical division;

[0039] b) Through the growth and preferential attachment mechanism, iteratively evolve the supply chain network model until the total number of target nodes N is reached;

[0040] c) Introduce the risk diffusion probability (RDP) and recovery probability (RP) of nodes in the simulation, and simulate random and deliberate attacks, as well as the network recovery process, through MATLAB or other simulation tools;

[0041] d) Analyze and compare the changes in the resilience indicators under different attack strategies, and identify the vulnerable points and critical nodes of the supply chain network;

[0042] e) Based on the simulation results, optimize the supply chain network structure, add redundant nodes, enhance the risk resistance ability of core nodes, and optimize the redundant design and response mechanism of critical links.

[0043] An electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the supply chain network resilience evaluation method under different attack strategies as described in any one of the above.

[0044] A non-transitory computer-readable storage medium stores a computer program. When the computer program is executed by a processor, it implements the supply chain network resilience evaluation method under different attack strategies as described in any one of the above.

[0045] A computer program product includes a computer program. When the computer program is executed by a processor, it implements the supply chain network resilience evaluation method under different attack strategies as described in any one of the above. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is the supply chain network topology diagram obtained according to the evolution steps in the preferred embodiment provided by the present invention;

[0047] Figure 2 They are respectively the size distribution diagram of the degrees of each node in the network and the degree distribution diagram of the network nodes;

[0048] Figure 3 They are respectively the change diagrams of the network structure performance under different node and link attack ratios;

[0049] Figure 4 They are the change diagrams of the network efficiency performance under different node and edge attack ratios respectively;

[0050] Figure 5 They are the change diagrams of the network recovery ability under different node and edge attack ratios respectively.

[0051] Figure 6 It is the process schematic diagram of the present invention. Specific implementation manners

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and detailedly described in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only a part of the embodiments of the present invention.

[0053] The technical solution for the present invention to solve the above technical problems is:

[0054] The technical solution of the present invention is as follows:

[0055] Step 1), initialization. Construct an initial small-scale network of the manufacturing supply chain, representing the core structure of the supply chain in the initial stage. Define an initial node set m0, representing the initial network nodes, which are divided into s1, s2, and s3 by level at the same time. The total number satisfies m0 = s1 + s2 + s3, and the nodes between each level can be randomly connected.

[0056] Step 2), growth. This model assumes that the nodes arriving at the network follow a Poisson process with parameter λ. Each newly added node to the network has m (m < m0) edges, that is, the newly added node is connected to m original network nodes.

[0057] Step 3), preferential attachment. The new node will preferentially connect to the nodes with high importance (degree) in the existing network, reflecting the "the rich get richer" effect, that is, the core enterprise attracts more resources and cooperation. The probability that the new node connects to node i is:

[0058]

[0059] where k i represents the degree of node i, represents the sum of the degrees of the nodes in the network.

[0060] Step 4), iterative evolution. Repeat steps 2 to 3 until the total number of nodes N of the target supply chain network is reached.

[0061] Step 5), Node attack strategy. Random attack means randomly removing 10%, 20%, …, 100% of the node enterprises from the supply chain network; while the deliberate attack on nodes is to remove them one by one according to the importance of the nodes in the supply chain network, where the importance of the nodes is measured by the degree value. Analyze the changes in the resilience metric when node enterprises are removed from the network to different extents under the two methods of random and deliberate attacks.

[0062] Step 6), Edge attack strategy. Random attack on edges means randomly removing a certain proportion of the cooperation relationships (edges) between node enterprises in the manufacturing supply chain network from the network; the deliberate attack method on edges is to first calculate the betweenness of the network edges (the calculation method is the proportion of the number of shortest paths passing through this edge in all shortest paths), and then delete the edges between nodes in the order of betweenness. Analyze the changes in the resilience metrics of the supply chain network under random and deliberate attacks.

[0063] The present invention Figure 4 According to the supply chain network topology diagram obtained from the evolutionary steps, it includes the following steps:

[0064] 1. Construction of supply chain network

[0065] Assume that the node distribution and connection characteristics in the network conform to the characteristics of a scale-free network, and use the BA (Barabási-Albert) scale-free network model for modeling to construct a supply chain network model containing N nodes, where each node represents an enterprise (including suppliers, manufacturers, distributors, etc.). The edges between nodes represent the cooperation relationships between enterprises (such as supply relationships, production collaborations, or logistics services).

[0066] 2. Network layering and simplification

[0067] Divide the entire network into multiple levels, representing the upstream (raw material suppliers), manufacturing (production enterprises), and downstream (distribution and retail) of the supply chain respectively, to ensure the hierarchy of the model and the matching degree with the actual supply chain. After the level division, further optimize the network scale and complexity to ensure that the key characteristics can still be reflected after the model is simplified.

[0068] 3. Calculation of key parameters

[0069] Calculate the important topological parameters in the supply chain network, such as Figure 2 :

[0070] Node degree distribution: Analyze the importance of different nodes and their connection strengths. The node degree distribution can be described by the degree distribution function P(k). From a statistical perspective, the node degree distribution in the network refers to the proportion of the number of nodes with degree k in the total number of network nodes, that is:

[0071]

[0072] Shortest path length: Evaluate the transportation efficiency and response speed of the supply chain network. It represents the shortest distance length between any two node enterprises in the supply chain network.

[0073] Clustering coefficient: Analyze the collaboration and stability of the network. The clustering coefficient of node i measures the link density among its neighbors. The formula for the clustering coefficient Ci of node i is:

[0074]

[0075] Through these metrics, comprehensively evaluate the structural characteristics of the network, identify potential key nodes and the vulnerabilities of the network.

[0076] 4. Output the network model

[0077] Such as Figure 1 , generate a visualization diagram of the supply chain network, showing the relationships and hierarchical distributions among various nodes. Provide a detailed structural analysis report as the basic data for subsequent simulation and optimization.

[0078] Furthermore, based on Figure 1 , input different attack strategy parameters, including:

[0079] Step 1: Set the attack strategy

[0080] Random attack: Randomly select a certain proportion of network nodes or edges to make them fail, simulating the impact of emergencies (such as natural disasters or system failures) on the supply chain.

[0081] Targeted attack: Prioritize attacking the nodes with the highest degree or the edges with the highest betweenness in the network, simulating situations such as malicious competition, technology supply disruption, or loss of key resources.

[0082] Step 2: Simulation modeling

[0083] Based on the MATLAB simulation tool, construct the attack scenario:

[0084] Input parameters: The total number of nodes N, the initial number of connections m, the attack ratio P (i.e., the proportion of failed nodes or edges), and conduct attacks in sequence according to the ratios of 10%, 20%,..., 100%.

[0085] Output parameters: The connectivity of the network, the average shortest path, the number of nodes in the remaining sub-network, etc.

[0086] Introduce the risk diffusion probability (RDP) and recovery probability (RP) of nodes in the simulation, which represent the probability that a healthy node is infected and the probability that a failed node recovers to a healthy state, respectively.

[0087] Step 3: Calculation of Resilience Metrics

[0088] Structural Performance Metric: Calculate the ratio of the number of nodes in the largest connected subgraph of the remaining network after the attack to the total number of nodes in the original network, which represents the change in network connectivity.

[0089] Efficiency Performance Metric: Calculate the average shortest path length of the network after the attack to evaluate the information flow efficiency of the remaining network.

[0090] Recovery Ability Metric: Simulate the dynamic process of nodes from failure to recovery, and record the time when the network returns to the original connected state, which is used as an indicator to measure the recovery ability of the supply chain.

[0091] Step 4: Analysis of Simulation Results

[0092] Compare the differences in resilience metrics under random attacks and deliberate attacks, and analyze the weak points and critical nodes of the supply chain network in different scenarios.

[0093] According to the simulation results, optimize the supply chain network structure (such as adding redundant nodes and enhancing the risk resistance ability of core nodes).

[0094] In this embodiment, Figure 1 is the topological graph of the supply chain network obtained according to the evolution steps. The nodes in the graph represent the enterprises in the supply chain network, and the edges between the nodes represent the cooperation relationships between the enterprises. The larger the node, the higher the importance of the enterprise in the supply chain network. Figure 2 They are respectively the size distribution diagram of the degrees of each node in the network and the degree distribution diagram of the network nodes. Figure 3 They are respectively the change diagrams of the network structural performance under different node and edge attack ratios. Figure 4 They are respectively the change diagrams of the network efficiency performance under different node and edge attack ratios. Figure 5 They are respectively the change diagrams of the network recovery ability under different node and edge attack ratios.

[0095] From Figure 2 It can be seen that the degree values of the supply chain network nodes show an obvious right-skewed distribution (long-tailed distribution), that is, the degree values of most nodes are low, while the degree values of a few nodes are high. This distribution characteristic indicates that there are a few highly connected critical nodes in the supply chain network, and these nodes have an important impact on the stability and efficiency of the entire network. Therefore, identifying and optimizing these critical nodes is crucial for improving the robustness and efficiency of the supply chain network. In addition, this characteristic also indicates that the constructed supply chain network has the properties of a scale-free network, further verifying the rationality of the supply chain network constructed by the present invention.

[0096] Figure 3 and Figure 4 and Figure 5Respectively represent the comparison charts of the network resilience indicators under different node and edge attack ratios under random and deliberate attacks, and summarize from the following two aspects:

[0097] 1. Node attacks have a greater impact on the resilience of the supply chain network compared to edge attacks

[0098] Node attacks preferentially remove the core nodes in the network, and have a significant impact on various indicators of the supply chain network resilience (structural performance, efficiency performance, and recovery ability). In contrast, edge attacks have a relatively small impact on the network. Especially when the attack ratio is low, the network resilience shows stronger resistance. However, when the attack ratio exceeds 70%-80%, edge attacks will also cause a sharp decline in network performance or even collapse. The destruction of nodes directly affects the core structure and information flow of the network, which is the part that needs to be protected first in the supply chain network.

[0099] 2. Deliberate attacks cause a faster decline in the resilience indicators of the supply chain network than random attacks

[0100] Deliberate attacks usually target nodes and edges with larger degree values or in key positions first, significantly weakening the connectivity and collaborative efficiency of the supply chain network, resulting in a rapid decline in structural performance and efficiency performance, while significantly prolonging the recovery time and significantly reducing the recovery ability. Random attacks, due to the non-targeted nature of target selection, cause more gradual damage to the supply chain network, and the network resilience shows higher anti-disturbance ability. However, when the attack ratio approaches 100%, both attacks will cause the supply chain network to completely collapse.

[0101] Through the above implementation methods, the present invention proposes a systematic method for evaluating the resilience of the supply chain network. This method combines complex network theory to analyze the resilience of the supply chain under random and deliberate attacks. Through simulation verification, it is proved that the present invention can significantly evaluate the robustness and risk resistance ability of the supply chain network, providing effective technical support for supply chain management and optimization.

[0102] The resilience of the supply chain network depends on the protection of core nodes and key edges. In the face of random attacks, the network shows a certain degree of robustness, but deliberate attacks pose a greater threat to network resilience. Therefore, improving the resilience of the supply chain network needs to start from two aspects: one is to strengthen the protection of core nodes to reduce the destructiveness of node attacks; the other is to optimize the redundant design and response mechanism of key edges to enhance the network's ability to respond to deliberate attacks.

[0103] The main innovation of the present invention, the "method for evaluating the resilience of the supply chain network under different attack strategies", lies in combining complex network theory with supply chain management practice, and proposing a systematic and multi-dimensional framework for evaluating and optimizing supply chain resilience. The specific innovation points are as follows:

[0104] 1. Introduction of complex network theory: The BA scale-free network model is adopted to construct the supply chain network, which is a novel modeling method in the field of supply chain management. It not only captures the "the rich get richer" characteristic of the supply chain network but also takes into account the hierarchy and heterogeneity of the network, making the model closer to reality.

[0105] 2. Multi-strategy attack simulation: By simulating two scenarios of random attack and deliberate attack, the risk resistance and recovery capabilities of the supply chain network are comprehensively evaluated. This comprehensive simulation analysis method helps identify the vulnerable points of the supply chain network and provides data support for targeted optimization.

[0106] 3. Multi-dimensional resilience indicators: The present invention proposes a comprehensive resilience indicator system including structural performance, efficiency performance, and recovery capability, which makes the method for evaluating the resilience of the supply chain network more comprehensive and systematic and can reflect the stability and efficiency of the network from multiple perspectives.

[0107] Corresponding beneficial effects

[0108] 1. Enhancement of supply chain stability: Through the evaluation method of the present invention, enterprises can identify the key nodes and weak links in the supply chain, take preventive measures or optimization strategies to enhance the stability of the network structure, and reduce the risk of supply chain interruption caused by emergencies.

[0109] 2. Optimization of resource allocation: The multi-dimensional resilience indicator analysis helps enterprises allocate resources more reasonably, improve the resource utilization efficiency and risk resistance ability of the entire supply chain by enhancing the risk resistance ability of core nodes or optimizing the redundant design of key links.

[0110] 3. Acceleration of the recovery process: The proposal of the recovery capability indicator enables enterprises to evaluate the recovery time of the supply chain after being impacted, which provides a quantitative basis for enterprises to formulate emergency plans and recovery strategies and helps accelerate the speed of the supply chain's return to normal operation.

[0111] 4. Promotion of supply chain optimization: The comprehensive evaluation results can guide enterprises to optimize the structure of the supply chain network, including adding redundant nodes, optimizing logistics routes, etc., to enhance the overall robustness and efficiency of the supply chain.

[0112] Not easily thought of:

[0113] The reason why this invention is not easily conceived is mainly because the combination of supply chain management and complex network theory requires cross - field knowledge and innovative thinking. Supply chain management usually focuses on the optimization of logistics, information flow and capital flow, while complex network theory is mainly applied to the research of complex systems such as physics, society, and biology. Applying complex network theory to supply chain management not only requires understanding the operation mechanism of the supply chain, but also mastering the modeling methods and analysis techniques of complex networks. In addition, the simulation of multi - strategy attacks and the proposal of multi - dimensional resilience indicators require in - depth analysis of the response mechanism of the supply chain network under various extreme conditions, which is rarely considered in traditional supply chain management practices. Therefore, from theoretical integration to practical application, this invention embodies a high degree of innovation and practicality.

[0114] The systems, devices, modules or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0115] Computer - readable media includes both permanent and non - permanent, removable and non - removable media and can be implemented by any method or technology for information storage. The information can be computer - readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase - change memory (PRAM), static random - access memory (SRAM), dynamic random - access memory (DRAM), other types of random - access memory (RAM), read - only memory (ROM), electrically erasable programmable read - only memory (EEPROM), flash memory or other memory technologies, compact disc read - only memory (CD - ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non - transitory media that can be used to store information that can be accessed by a computing device. As defined herein, computer - readable media does not include transitory computer - readable media, such as modulated data signals and carrier waves.

[0116] It should also be noted that the term "comprise", "include" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising said element.

[0117] The above embodiments should be understood as being only for illustrative purposes of the present invention and not for limiting the protection scope of the present invention. After reading the content described in the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A method for evaluating the resilience of a supply chain network under different attack strategies, characterized in that It includes the following steps: a) Construct a complex network model based on the enterprises and cooperation relationships in the supply chain, where the nodes of the network represent enterprises and the edges represent the cooperation relationships between enterprises; b) Calculate the key parameters of the supply chain network, including node degree, shortest path length, and clustering coefficient, to evaluate the stability of the initial supply chain network; c) Introduce random attack and deliberate attack strategy models to simulate the loss of nodes or edges during the attack process; d) Define the resilience metrics of the supply chain network, including the structural performance, efficiency performance, and recovery ability of the network; e) Through simulation analysis, analyze the changes in the network structure under different attack strategies, and evaluate the risk resistance ability and recovery efficiency of the network; f) According to the simulation results, propose optimization suggestions to improve the risk resistance ability of the supply chain.

2. The supply chain network resilience evaluation method under different attack strategies according to claim 1, wherein, The network model adopts the BA scale-free network model, where new nodes are more inclined to establish cooperation relationships with existing nodes with high connectivity, following the "the rich get richer" mechanism. That is, the probability that a new node connects to node i is: Among them, k i represents the degree of node i, represents the sum of the degrees of nodes in the network.

3. The supply chain network resilience evaluation method under different attack strategies according to claim 1, characterized in that In step b), the key parameters of the supply chain network are calculated, including node degree, shortest path length, and clustering coefficient, specifically including: Node degree distribution: Analyze the importance of different nodes and their connection strengths; the node degree distribution can be described by the degree distribution function P(k). From a statistical perspective, the node degree distribution in the network refers to the proportion of the number of nodes with degree k in the network to the total number of network nodes, that is: n k represents the number of nodes with node degree k, and N represents the total number of nodes in the network; Shortest path length: Evaluate the transportation efficiency and response speed of the supply chain network; it represents the shortest distance length between any two node enterprises in the supply chain network; Clustering coefficient: Analyze the collaboration and stability of the network; the clustering coefficient of node i measures the link density between its neighbors. The formula for the clustering coefficient Ci of node i is: where Ei represents the number of edges actually existing among the neighbors of node i, and K i (K i - 1) / 2 represents the maximum number of edges that may be formed among the neighbor nodes of node i.

4. The supply chain network resilience evaluation method under different attack strategies according to claim 1, characterized in that In step c), random attack and deliberate attack strategy models are introduced to simulate the loss of nodes or edges during the attack process, specifically including: (1) Node attack strategy Define the random attack of nodes as randomly removing 10%, 20%,..., 100% of the node enterprises from the supply chain network; while the deliberate attack of nodes is to remove them in sequence according to the importance of the nodes in the supply chain network, where the degree value of the node is used to measure the importance of the node. In this way, analyze the changes in the resilience metrics when node enterprises are removed from the network to different extents under the two methods of random and deliberate attacks; (2) Edge attack strategy The random attack of edges refers to randomly removing a certain proportion of the cooperation relationships (edges) between the node enterprises in the supply chain network from the network; the deliberate attack method of edges is to first calculate the betweenness of the network edges, and then delete the edges between nodes in the order of betweenness size, and analyze the changes in the resilience indicators of the supply chain network under random and deliberate attacks.

5. The supply chain network resilience evaluation method under different attack strategies according to claim 1, characterized in that, The resilience assessment of the supply chain network includes the following indicators: a) Structural performance indicator: Evaluate the change in connectivity of the network after being attacked. Define the degree of change in network connectivity as the ratio of the number of nodes in the largest connected subgraph after the network is attacked to the total number of nodes in the original network: where N' represents the scale of the largest connected subgraph, and N represents the scale of the original network; b) Efficiency performance index: Measures the efficiency of network information flow; network efficiency reflects the efficiency of the cooperative relationship among the enterprise nodes in the supply chain network over a period of time, and can be calculated as the average of the sum of the reciprocals of the shortest paths between any two nodes in the network: Among them, d ij That is, the shortest path between two nodes, 1 / d ij represents the turnover efficiency of the cooperation relationship between enterprise node i and node j; if the path between two nodes is longer, then if the distance required to transfer resources between two nodes in the supply chain network is longer, then d ij will be larger, and more time will be required for the transfer process. Therefore, the information transfer efficiency of this network will be lower; on the contrary, the network transmission efficiency will be higher; c) Recovery ability index: Evaluates the ability of the supply chain network to recover to the normal state after interruption; the recovery ability index is measured by the recovery time, which represents the time elapsed from the moment the supply chain network is impacted until all nodes in the network recover to a healthy state and the network resumes its functions.

6. The supply chain network resilience evaluation method under different attack strategies according to claim 1, wherein The specific implementation steps in the evaluation process are as follows: a) Initialize the supply chain network and define the initial node set and its hierarchical division; b) Through the growth and preferential attachment mechanism, iteratively evolve the supply chain network model until the total number of target nodes N is reached; c) Introduce the risk diffusion probability (RDP) and recovery probability (RP) of nodes in the simulation, and simulate random and deliberate attacks, as well as the network recovery process through MATLAB or other simulation tools; d) Analyze and compare the changes in the resilience indicators under different attack strategies, and identify the vulnerable points and key nodes of the supply chain network; e) Based on the simulation results, optimize the supply chain network structure, add redundant nodes, enhance the risk resistance ability of core nodes, and optimize the redundant design and response mechanism of key links.

7. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the supply chain network resilience evaluation method under different attack strategies as described in any one of claims 1 to 6.

8. A non-transitory computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by the processor, it implements the supply chain network resilience evaluation method under different attack strategies as described in any one of claims 1 to 6.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the supply chain network resilience evaluation method under different attack strategies as described in any one of claims 1 to 6.

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