Supply chain system key element identification method and related device

Abstract

The invention discloses a supply chain system key element identification method and a related device, and the method comprises the steps: obtaining logistics data between enterprises, determining the cooperation and competition relation between the enterprises according to the logistics data between the enterprises, and building a supply chain network according to the cooperation and competition relation between the enterprises; obtaining a risk propagation process of a supply chain network by combining external and internal disturbance factors based on a flow relationship of products among enterprises, and obtaining a risk propagation chain of the enterprises; according to the risk propagation chain of the enterprise, key nodes and key edges in the supply chain network are determined, the method and the related device can effectively prevent and control cascade faults, and network stability is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of supply chain, and relates to a method for identifying key elements of a supply chain system and related devices. Background Art

[0002] The resilience of a supply chain system refers to the ability of the supply chain to resist external or internal disturbances and recover from disruptions, generally including robustness and recoverability. Since the 21st century, due to the rapid development of global economic integration and the continuous improvement of consumer personalization levels, the relationship between enterprises has transformed into cooperation and competition among supply chains. The supply chain has penetrated into fields such as military, economy, and people's livelihood. How to construct a reasonable supply chain network model is the premise of supply chain management. In 2020, the McKinsey report pointed out that enterprises are facing a series of risks, from external uncertainties to attacks on their own internal supply chains, which have caused major adjustments in the entire supply chain and triggered a series of secondary risk propagation problems, resulting in social and economic problems such as product shortages, enterprise bankruptcies, food and energy crises, posing more severe challenges to supply chain management.

[0003] The methods for identifying key elements of a supply chain system generally fall into four categories: (1) structured methods; (2) vector-based methods; (3) multi-criteria decision-making-based methods; (4) machine learning-based methods. There are the following deficiencies: the network dynamics of the supply chain system are inadequately considered, and the importance of nodes is evaluated based on static topologies or single indicators; the role in its network dynamics and cascading failure propagation is ignored, making it difficult to effectively prevent and control cascading failures and ensure network stability. Summary of the Invention

[0004] The purpose of the present invention is to overcome the above-mentioned shortcomings of the prior art, and provide a method for identifying key elements of a supply chain system and related devices, which can effectively prevent and control cascading failures and ensure network stability.

[0005] To achieve the above object, the present invention discloses a method for identifying key elements of a supply chain system, including:

[0006] Obtain the logistics data between enterprises, determine the cooperation and competition relationships between enterprises according to the logistics data between enterprises, and establish a supply chain network according to the cooperation and competition relationships between enterprises;

[0007] Based on the flow relationship of products between enterprises, combine external and internal disturbance factors to obtain the risk propagation process of the supply chain network, and obtain the risk propagation chain of enterprises;

[0008] According to the risk propagation chain of the enterprises, determine the key nodes and key edges in the supply chain network.

[0009] A further improvement of the method for identifying key elements of the supply chain system according to the present invention lies in:

[0010] Furthermore, the logistics data between enterprises includes at least one of suppliers, purchasers, middlemen, flow directions, transaction amounts, and years.

[0011] Furthermore, the process of obtaining the risk propagation process of the supply chain network based on the product flow relationship between enterprises and combining external and internal disturbance factors to determine the risk propagation chain of enterprises is as follows:

[0012] Based on the product flow relationship between enterprises, combining external and internal disturbance factors to obtain the risk propagation process of the supply chain network, and introducing a cascading failure model to determine the risk propagation chain of enterprises.

[0013] Furthermore, it also includes:

[0014] Based on the network structure of the supply chain network and the dynamic process of risk propagation, determine the average steady-state behavior value, maximum clustering, and critical point of the supply chain network.

[0015] Furthermore, the process of determining the key nodes in the supply chain network according to the risk propagation chain of the enterprises is as follows:

[0016] According to the risk propagation chain of the enterprises, calculate the steady-state behavior values of each node in the supply chain network;

[0017] Select key nodes according to the magnitudes of the steady-state behavior values of each node.

[0018] Furthermore, the steady-state behavior value of node i is:

[0019]

[0020] where k i is the degree of node i, and x eff = <k·x> / <k>is the nearest neighbor weighted enterprise infection rate, <k>is the network average degree.

[0021] Further, the process of determining the critical edges in the supply chain network is as follows:

[0022] Determine the evaluation value E of each edge in the supply chain network;

[0023]

[0024] where and respectively represent the steady-state behavior values of node i and node j.

[0025] Determine the critical edges according to the magnitudes of the evaluation values E of each edge in the supply chain network.

[0026] The present invention discloses a system for identifying key elements of a supply chain system, including:

[0027] A building module, configured to obtain the logistics data between enterprises, determine the cooperation and competition relationships between enterprises according to the logistics data between enterprises, and build a supply chain network according to the cooperation and competition relationships between enterprises;

[0028] A simulation module, configured to obtain the risk propagation process of the supply chain network based on the product flow relationship between enterprises and in combination with external and internal disturbance factors, and obtain the risk propagation chain of enterprises;

[0029] A determination module, configured to determine the critical nodes and critical edges in the supply chain network according to the risk propagation chain of the enterprises.

[0030] The present invention discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for identifying key elements of the supply chain system are implemented.

[0031] The present invention discloses a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the method for identifying key elements of the supply chain system are implemented.

[0032] The present invention has the following beneficial effects:

[0033] When the method for identifying key elements of the supply chain system and related devices according to the present invention are specifically operated, based on the product flow relationship between enterprises, the risk propagation process of the supply chain network is obtained in combination with external and internal disturbance factors, and the risk propagation chain of enterprises is obtained, so as to characterize the network dynamics of the supply chain network. Then, according to the risk propagation chain of the enterprises, the critical nodes and critical edges in the supply chain network are determined, thereby playing a role in network dynamics and cascade failure propagation, effectively preventing cascade failures, and ensuring network stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings forming a part of this invention are used to provide a further understanding of the invention. The schematic embodiments and descriptions thereof of the invention are used to explain the invention and shall not unduly limit the invention. In the drawings:

[0035] Figure 1 is a flowchart of the method of the present invention;

[0036] Figure 2 is a structural diagram of the effectiveness evaluation module;

[0037] Figure 3 is a schematic diagram of cascading fault propagation;

[0038] Figure 4 is a schematic structural diagram of an electronic device for implementing the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] In the description of the present invention, it should be understood that the terms "include" and "comprise" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0041] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0042] It should be further understood that the term " / and / " as used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the related listed items, and includes these combinations. For example, "A and / or B" can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present invention generally represents an "or" relationship between the contextually related objects.

[0043] It should be understood that although terms such as first, second, and third may be used in the embodiments of the present invention to describe preset ranges, etc., these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0044] Depending on the context, the word "if" as used herein can be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detected (stated condition or event)" can be interpreted as "when determined" or "in response to determining" or "when detected (stated condition or event)" or "in response to detecting (stated condition or event)".

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and shown in the accompanying drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0046] Various schematic structural diagrams according to the disclosed embodiments of the present invention are shown in the drawings. These figures are not drawn to scale, where for the purpose of clear expression, some details are enlarged and some details may be omitted. The shapes of various regions and layers shown in the figures and their relative sizes and positional relationships are merely exemplary, and may actually deviate due to manufacturing tolerances or technical limitations, and those skilled in the art can additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0047] Embodiment 1

[0048] Refer to Figure 1 , the method for identifying key elements of the supply chain system described in the present invention includes:

[0049] S101, Obtain the logistics data between enterprises, determine the cooperation and competition relationships between enterprises according to the logistics data between enterprises, and establish a supply chain network according to the cooperation and competition relationships between enterprises.

[0050] It should be noted that the logistics data includes at least one of the supplier, purchaser, middleman, flow direction, transaction amount, and year, and can be obtained from the existing logistics database.

[0051] The specific process is as follows:

[0052] Preprocess the logistics data, delete the problem of duplicate data, and supplement and improve the problem of missing data in the data by converting the inflow and outflow data. Secondly, mark the suppliers or purchasers of the same type of goods in the logistics data as a competitive relationship, and mark a single enterprise in the process from parts to finished products in the logistics data as a cooperative relationship to improve the accuracy of supply chain network modeling.

[0053] Based on the processed logistics data, establish the cooperation and competition relationships between enterprises according to the inflow and outflow relationships of logistics. For example, if an enterprise has an inflow of logistics, it means that there is an edge pointing from other enterprises to itself. If an enterprise has an outflow of logistics, it means that there is an edge pointing from itself to other enterprises. The weight of the edge is determined after standardizing the trade volume between enterprises. For the marked cooperation and competition relationships, construct the network adjacency matrix A ij , when there is a connection between enterprise j and enterprise i, then A ij = 1, otherwise, then A ij = 0.

[0054] Establish a supply chain network according to the logistics information of different finished products, such as a semiconductor supply chain network, an agricultural product supply chain network, a new energy vehicle supply chain, etc., so as to improve the pertinence of the construction of the supply chain network matrix and facilitate the subsequent analysis of the resilience of the supply chain and the formulation of corresponding early warning measures according to different supply chain networks.

[0055] S102, Based on the product flow relationship between enterprises, combine external and internal disturbance factors to obtain the risk propagation process of the supply chain network, and determine the risk propagation chain according to the risk propagation algorithm.

[0056] Specifically, the supply chain network contains the flow information of goods between enterprises, and the propagation of supply chain risks can be carried out through the supply chain network. When a risk occurs in an upstream enterprise, it will further spread to downstream enterprises. It can be understood that when a risk occurs in an upstream enterprise, it is the infected state in the epidemic model, and the downstream enterprise is in the susceptible state. The downstream enterprise is affected by the upstream enterprise and has a risk, which is the process from the susceptible state to the infected state. And the upstream enterprise can eliminate the crisis by increasing suppliers, actively seeking strategic investments, etc., that is, the process from the infected state to the susceptible state in the epidemic model. In this embodiment, an epidemic is used to map the risk propagation in the supply chain system, and the basic form is:

[0057]

[0058] The above formula characterizes the ability of nodes (enterprises) in the supply chain system to be infected by risks over time. The right side of the formula consists of two parts. The first part is the evolution process of the nodes (enterprises) themselves, representing the rate at which nodes (enterprises) change from the risk-infected state to the risk-susceptible state. B is the risk resistance ability of nodes (enterprises) in the supply chain network, and the larger its value, the higher the risk resistance ability of the nodes (enterprises) themselves. The second part is the process of mutual coupling between nodes (enterprises), representing the rate at which susceptible nodes (enterprises) are infected by infected nodes (enterprises). R is the rate at which nodes (enterprises) in the supply chain network are infected by risks, and the larger its value, the more easily the nodes (enterprises) are affected by risks; x i represents the infection rate of enterprise i at time t..

[0059] Introduce the cascading failure model to simulate the risk propagation chain of enterprises.

[0060] Let the node capacity ρ be the dynamic threshold. When the node activity exceeds its capacity, it is regarded as a failure. When removing some nodes according to a certain attack strategy, the cascading failure process is as follows:

[0061] 21) Initial state, the network reaches a steady state through the dynamic equation.

[0062] 22) Remove some nodes based on the attack strategy.

[0063] 23) Run the Runge-Kutta method until the network reaches a perturbed steady state.

[0064] 24) Calculate the steady-state activity change rate T=(1 - x *' / x*) of each node according to, and find the nodes with T>ρ to determine the cascading failure scale as:

[0065]

[0066] S103. Based on the network structure of the supply chain network and the dynamic process of risk propagation, determine the average steady-state behavior value, maximum clustering, and critical point of the supply chain network to evaluate the effectiveness of the key structure identification method.

[0067] The evaluation process of the average steady-state behavior value is as follows:

[0068] Calculate the average steady-state behavior value of the surviving nodes after cascading failure to evaluate the network recovery ability. Update the node degree distribution according to the node removal ratio as:

[0069]

[0070] Get the weighted average nearest neighbor degree β e ' ff as:

[0071]

[0072] Furthermore, the average steady-state behavior value <x * > is:

[0073]

[0074] The calculation process of the largest cluster is as follows: The largest cluster is calculated using the generating function method, which is used to evaluate the network robustness. First, calculate the generating function

[0075]

[0076] where P(k) is the probability that the node degree is k. By taking the derivative of G′(ξ) and setting ξ = 1, the average degree of the network is obtained <k>;

[0077] The construction of the branch process generating function H(ζ) is as follows:

[0078]

[0079] Combined with the cascading failure scale Calculate the maximum clustering after randomly removing some nodes as:

[0080]

[0081] Among them, g(f′) = 1 - G(1 - f′(1 - μ(f′))), p(f′) = H(f′μ(f′)) + 1 - f′) can be obtained through the generating function.

[0082] The evaluation process of the critical removal ratio is as follows:

[0083] The critical removal ratio refers to the situation where when the system removal rate reaches a critical value, the nodes in the system become isolated nodes that are not connected to each other. It means that when the proportion of removed nodes exceeds the critical value, the network will collapse. As known from the previous section, deleting 1 - f proportion of nodes will ultimately lead to the failure size Update the degree distribution of the system. Update the degree distribution parameters according to the node removal ratio and the cascading failure scale, and calculate the new average degree and the new second moment For the new degree distribution, the critical removal ratio can be defined as:

[0084]

[0085] Among them, β eff Is calculated from the weighted average degree of the nearest neighbor.

[0086] Compare the critical removal ratios under different attack strategies. A smaller ratio value means that the network is more likely to collapse under this strategy, thereby evaluating the effectiveness of the key node identification method in identifying the key nodes affecting network stability.

[0087] According to the network performance evaluation results under different attack strategies, it is determined that the attack strategy with the greatest impact on the network is the steady-state behavior value centrality. Therefore, it is considered that the steady-state behavior value centrality can effectively identify key nodes. These key nodes have a greater impact on stability in the network and can be monitored, protected, or optimized key points in network management to improve the overall stability of the network.

[0088] S104. Determine the key structure identification methods, which are the steady-state behavior value centrality for identifying key nodes and the identification method for edges respectively, and evaluate their effectiveness with evaluation indicators.

[0089] In a supply chain system, the criticality of a node means that it plays a crucial role in the stability and efficiency of the entire supply chain in multiple aspects. From the perspective of production operations, critical nodes often possess strong production capabilities or reasonable inventory management capabilities. In terms of information transmission, critical nodes are the concentration and transfer points of information, capable of integrating and accurately transmitting information from multiple upstream nodes to downstream nodes.

[0090] Using numerical calculation methods to solve the dynamic model, making the network reach a steady state, the steady-state risk of each enterprise is obtained as:

[0091]

[0092] Among them, k i is the degree of node i, x eff = <k·x> / <k>is the nearest neighbor weighted enterprise infection rate, <k>is the average network degree, and this indicator is used as a method for identifying key nodes.

[0093] In the supply chain system, assume there are nodes i and j, which represent different enterprises or links in the supply chain respectively, such as suppliers, producers, distributors, etc., and there is an edge between them, indicating that there is business interaction or logistics connection between them. The method for identifying key edges is based on the multiplication of the steady-state behavior values of these two nodes, that is, the method for identifying key edges can be expressed as:

[0094]

[0095] where and represent the steady-state behavior values of node i and node j respectively.

[0096] By this method, the key degree values of each edge in the supply chain network are calculated. In the supply chain system, the higher the key degree value of an edge, it means that this "relationship chain" connecting these two enterprises or links is more important for the stable operation of the entire supply chain. For example, when the connection edge between two enterprises with relatively high key degrees is interrupted (such as due to natural disasters, business disputes, etc.), it may have a significant impact on the goods flow, information transmission, and capital turnover of the entire supply chain, and may even lead to the paralysis of the entire supply chain system or a significant decrease in efficiency.

[0097] It should be noted that the present invention has the following characteristics:

[0098] The present invention has significant advantages in identifying the key structure of the supply chain system under uncertain environments. It fully considers the network dynamics, uses the epidemic dynamics model, combines the dynamic behavior of cascading failures, and closely fits the actual dynamics of the supply chain. In enterprise operations, it can accurately capture the changing trends such as the birth and exit of enterprises, risk propagation, and resource allocation. This enables enterprises to respond to problems in advance, such as preventing risks in a timely manner when risks first appear, improving the system performance, and helping enterprises take effective prevention and control measures in advance. Moreover, it can clearly show the dynamic association of nodes, and the changes in node roles and interactions in different scenarios are clear at a glance.

[0099] Multi - index Evaluation Optimization Identification and Management: The present invention uses three indicators, namely the average steady - state behavior value, the maximum clustering, and the critical point, for comprehensive evaluation. The average steady - state behavior value can reflect the activity level of nodes in the stable state of the system. In the supply chain, it can reflect the operation efficiency and resource utilization of enterprises. For example, for an enterprise with a high average steady - state behavior value, the connection between its production and sales links is tight, and resource waste is less. The maximum clustering index is used to evaluate the overall connectivity and stability of the network. In the supply chain, it can reflect the degree of close cooperation among enterprises. If the maximum clustering is large, it indicates that the connection between enterprises is close, information and resource circulation is smooth, which is conducive to quickly responding to market demands. The critical - point index clarifies the vulnerability of the system when under attack. Enterprises can know in advance the proportion of nodes to be removed when the system may collapse, so as to protect key nodes targeted and enhance the system's risk - resistance ability. By synthesizing these three indicators, enterprises can comprehensively and accurately identify the key structures in the supply - chain system.

[0100] Example Two

[0101] The key - element identification system of the supply - chain system described in the present invention includes:

[0102] A building module, used to obtain the logistics data between enterprises, determine the cooperation and competition relationships between enterprises according to the logistics data between enterprises, and establish a supply - chain network according to the cooperation and competition relationships between enterprises;

[0103] A simulation module, used to obtain the risk - propagation process of the supply - chain network based on the product - flow relationship between enterprises, combined with external and internal disturbance factors, and obtain the risk - propagation chain of enterprises;

[0104] An effectiveness - evaluation module, used to determine the average steady - state behavior value, the maximum clustering, and the critical point of the supply - chain network based on the network structure of the supply - chain network and the dynamic process of risk propagation;

[0105] A determination module, used to determine the key nodes and key edges in the supply - chain network according to the risk - propagation chain of the enterprises.

[0106] The division of modules in the embodiments of the present application is schematic, only a logical - function division. In actual implementation, there may be other division methods. In addition, in each embodiment of the present application, each functional module can be integrated in a processor, or can exist physically alone, or two or more modules can be integrated in one module. The above - integrated modules can be implemented in the form of hardware or in the form of software - functional modules.

[0107] Figure 2 It is a structural diagram of the effectiveness evaluation module. This device can be implemented by hardware and / or software, execute the supply chain network performance evaluation method proposed in the embodiments of the present invention, and has multi-functional modules to achieve the beneficial effects of evaluation. The device includes four main modules: First, the average stable behavior value calculation module obtains the data of surviving nodes after cascading failures, updates the node degree distribution, and calculates the average stable behavior value to provide basic data support for evaluating stability; the maximum clustering calculation module determines the maximum clustering probability of random node and edge links by calculating the generating function and the generating function of the branching process, and calculates the maximum clustering to evaluate the clustering characteristics of the network; the critical removal ratio analysis module updates the node degree distribution according to the removal ratio and calculates the critical removal ratio to quantify the invulnerability of the network under different node failure conditions; the comprehensive evaluation module summarizes the evaluation results of each module, verifies the effectiveness of the stable behavior value as a centrality index, and each module works together to complete the comprehensive evaluation of the supply chain network performance.

[0108] Figure 3 It is a schematic diagram of cascading failure propagation. It shows the failure propagation process of the network after being attacked. First, the attack targets specific nodes (such as key nodes) in the network, causing some nodes to fail; then, the failure of the attacked nodes further triggers cascading failures, gradually spreading to other nodes and connections, resulting in the functional failure of more nodes; finally, the failure spreads to a large area of the network, and some nodes fail and completely lose their functions, forming a significant degradation of the network structure. In the figure, the evolution of the node state during the failure propagation process is visually presented through the changes in the size and color of the nodes, illustrating the importance of key nodes in the network and the impact of their failures on the overall network stability.

[0109] Embodiment III

[0110] A computer device includes 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 steps of the key element identification method for the supply chain system are implemented. For example, it includes: obtaining logistics data between enterprises, determining the cooperation and competition relationships between enterprises based on the logistics data between enterprises, and establishing a supply chain network according to the cooperation and competition relationships between enterprises; based on the product flow relationship between enterprises, combining external and internal disturbance factors to obtain the risk propagation process of the supply chain network, and obtaining the risk propagation chain of enterprises; according to the risk propagation chain of the enterprises, determining the key nodes and key edges in the supply chain network. Among them, the memory may include internal memory, such as high-speed random access memory, and may also include non-volatile memory, such as at least one disk memory, etc.; the processor, network interface, and memory are interconnected through an internal bus, and this internal bus can be an Industry Standard Architecture bus, a Peripheral Component Interconnect standard bus, an Extended Industry Standard Architecture bus, etc., and the bus can be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the program can include program code, and the program code includes computer operation instructions. The memory can include internal memory and non-volatile memory, and provide instructions and data to the processor.

[0111] Figure 4 It is a schematic structural diagram of an electronic device for implementing the embodiments of the present invention. This device can be applied to various forms of supply chain network management, and the types include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), an artificial intelligence computing chip, a processor running machine learning algorithms, and other digital signal processors (DSPs), controllers, or microcontrollers. The processor supports the identification and analysis of key structures in the supply chain network by executing a computer program.

[0112] Embodiment Four

[0113] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the key element identification method of the supply chain system are implemented. For example, it includes: obtaining logistics data between enterprises, determining the cooperation and competition relationships between enterprises based on the logistics data between enterprises, and establishing a supply chain network according to the cooperation and competition relationships between enterprises; based on the product flow relationship between enterprises, combining external and internal disturbance factors to obtain the risk propagation process of the supply chain network, and obtaining the risk propagation chain of enterprises; according to the risk propagation chain of the enterprises, determining the key nodes and key edges in the supply chain network. Specifically, the computer-readable storage medium includes but is not limited to, for example, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disc, magnetic disk, etc.

[0114] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0116] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the functions specified in one process or a plurality of processes and / or blocks Figure 1 in one block or a plurality of blocks Figure 1 in the steps of the method.

[0118] Other embodiments of the present invention will be readily apparent to those skilled in the art from consideration of the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice in the art to which the invention pertains. The specification and examples are to be considered exemplary only, with the true scope and spirit of the invention being indicated by the following claims.

[0119] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and various modifications and changes may be made without departing from its scope. The scope of the present invention is limited only by the appended claims.

[0120] The above are only the preferred embodiments of the present invention, and do not limit the present invention in any way. Any simple modifications, changes, and equivalent structural changes made to the above embodiments according to the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.< / k> < / k> < / k> < / k> < / k>

Claims

1. A method for identifying key elements of a supply chain system, characterized in that: include: Obtain logistics data between enterprises, determine the cooperation and competition relationship between enterprises based on the logistics data between enterprises, and establish a supply chain network based on the cooperation and competition relationship between enterprises; Based on the flow relationship of products between enterprises, the risk propagation process of the supply chain network is obtained by combining external and internal disturbance factors, and the risk propagation chain of the enterprise is obtained; According to the risk propagation chain of the enterprise, the key nodes and key edges in the supply chain network are determined.

2. The method for identifying key elements of a supply chain system according to claim 1, characterized in that: The inter-enterprise logistics data includes at least one of suppliers, buyers, intermediaries, flow directions, transaction amounts and years.

3. The method for identifying key elements of a supply chain system according to claim 1, characterized in that: The process of obtaining the risk propagation process of the supply chain network based on the flow relationship of products between enterprises and combining external and internal disturbance factors to determine the risk propagation chain of the enterprise is as follows: Based on the flow relationship of products between enterprises, the risk propagation process of the supply chain network is obtained by combining external and internal disturbance factors, and a cascading failure model is introduced to determine the risk propagation chain of the enterprise.

4. The method for identifying key elements of a supply chain system according to claim 1, characterized in that: Also includes: Based on the network structure of the supply chain network and the dynamic process of risk propagation, the average steady-state behavior value, maximum clustering and critical point of the supply chain network are determined.

5. The method for identifying key elements of a supply chain system according to claim 1, characterized in that: The process of determining the key nodes in the supply chain network according to the risk propagation chain of the enterprise is as follows: According to the risk propagation chain of the enterprise, the steady-state behavior value of each node in the supply chain network is calculated; Select key nodes based on the size of the steady-state behavior value of each node.

6. The method for identifying key elements of a supply chain system according to claim 5, characterized in that: The steady-state behavior value of the node i for: Among them, k i is the degree of node i, x eff =<k·x> / <k>is the nearest neighbor weighted enterprise infection rate, k> is the average degree of the network.< / k> 7. The method for identifying key elements of a supply chain system according to claim 5, characterized in that: The process of determining the key edges in the supply chain network is as follows: Determine the evaluation value E of each edge in the supply chain network; in, and Represent the steady-state behavior values ​​of node i and node j respectively; The key edges are determined based on the evaluation value E of each edge in the supply chain network.

8. A supply chain system key element identification system, characterized in that: include: Establish a module for obtaining logistics data between enterprises, determine the cooperation and competition relationship between enterprises based on the logistics data between enterprises, and establish a supply chain network based on the cooperation and competition relationship between enterprises; The simulation module is used to obtain the risk propagation process of the supply chain network based on the flow relationship of products between enterprises and combined with external and internal disturbance factors to obtain the risk propagation chain of the enterprise; The determination module is used to determine the key nodes and key edges in the supply chain network according to the risk propagation chain of the enterprise.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for identifying key elements of a supply chain system as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for identifying key elements of a supply chain system as described in any one of claims 1 to 7 are implemented.

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