Industrial chain supply chain risk deduction method and system based on simulation method

Abstract

The invention provides an industrial chain supply chain risk deduction method and system based on a simulation method, and the method comprises the steps: constructing a main simulation network, enabling each enterprise in the main simulation network to process a preset risk event according to a preset decision rule, and carrying out the industrial chain supply chain risk deduction. Due to the fact that the subject simulation network can reflect the assembly relation and the upstream and downstream relation between the industries, the supply relation between the enterprises and the production relation between the enterprises in the target industry field, the heterogeneity between the different enterprises can be reflected, and the accuracy of risk deduction based on the subject simulation network is greatly improved. Moreover, by setting decision rules for different enterprises, the decision rules can simulate the decision process of the enterprises for coping with the risk events, that is, the autonomy of the enterprises is simulated in the risk deduction process, and the accuracy of risk deduction is further improved.

Description

Technical Field

[0001] The present invention relates to the field of simulation technology, and in particular to a method and system for deducing risks in an industrial chain and supply chain based on a simulation method. Background Art

[0002] Supply chain refers to the network structure formed by upstream and downstream enterprises involved in the production and circulation process, which provides products or services to end users, that is, the entire chain from merchants to consumers. Supply chain risk deduction is a method of systematically analyzing, evaluating and predicting potential risks in the supply chain. In related technologies, supply chain risk deduction is generally achieved through mathematical methods and complex network methods.

[0003] In mathematical methods, the supply chain is generally described as a control system based on Markov chains. That is, if there are N companies in the supply chain, the inventories of these companies can together constitute a system state vector containing N data. Assuming that each company's inventory can only select 2 values, it is also necessary to construct a 2-dimensional N 's matrix to calculate the changes in system inventory over time.

[0004] The complex network method generally models the supply chain as a complex network, in which each node is an enterprise, and the supply and demand quantities between nodes can be represented by the weights of the adjacency matrix. Through certain matrix calculations, the changes in the supply and demand relationship of the entire network can be simulated.

[0005] However, since both methods regard each enterprise in the supply chain as the same node, and in actual applications, each enterprise in the supply chain will have different decision-making logic when facing different situations, the above two methods cannot reflect this decision-making difference, which leads to poor accuracy of supply chain risk deduction results. Summary of the invention

[0006] In view of this, the present invention provides an industrial chain and supply chain risk deduction method and system based on a simulation method to improve the accuracy of industrial chain and supply chain risk deduction.

[0007] According to one aspect of the present invention, a method for deriving risks of an industrial chain and supply chain based on a simulation method is provided, the method comprising:

[0008] Based on the dependency relationship between industries in the target industry field, the supply relationship between enterprises, and the production relationship between the enterprises and industries, a subject simulation network is constructed, wherein the subject simulation network includes industry nodes and enterprise nodes, wherein the industry nodes are connected by a first directed edge, the enterprise nodes are connected by a second directed edge, and the enterprise nodes and industry nodes are connected by a third undirected edge, wherein the first directed edge is used to represent the dependency relationship between industry nodes, and the dependency relationship includes an assembly relationship and an upstream and downstream relationship; the second directed edge is used to represent the supply relationship between enterprise nodes; and the third undirected edge is used to represent the production relationship between the enterprise and the industry;

[0009] Inputting a preset risk event into the subject simulation network, so that each of the enterprise nodes in the subject simulation network processes the preset risk event based on a preset decision rule preset for each of the enterprise nodes, and obtaining a risk propagation path and a risk deduction result output by the subject simulation network;

[0010] When the risk deduction result is consistent with the risk treatment result corresponding to the preset risk event, the preset decision rule is determined as the target decision rule; when the risk deduction result does not meet the risk treatment result, the preset decision rule or other parameters in the subject simulation network are modified until the risk deduction result meets the risk treatment result.

[0011] In a possible embodiment, the subject simulation network includes an industrial chain network, a supply chain network, and a mapping relationship network. The subject simulation network is constructed based on the dependency relationship between industries in the target industry field, the supply relationship between enterprises, and the production relationship between each enterprise and industry, including:

[0012] Based on the dependency relationship between industries in the target industry field, construct an industrial chain network, wherein the industrial chain network includes industrial nodes, and the industrial nodes are connected by the first directed edges;

[0013] Based on the supply relationship between enterprises in the target industry field, a supply chain network is constructed, wherein the supply chain network includes enterprise nodes, and the enterprise nodes are connected by the second directed edges;

[0014] Based on the production relationship between each of the enterprises and each of the industries, the industry nodes in the industrial chain network and the enterprise nodes in the supply chain network are connected by using a third undirected edge to obtain a mapping relationship network.

[0015] In a possible embodiment, each of the enterprise nodes includes enterprise node attributes, and the enterprise node attributes include at least enterprise status, enterprise capacity, and inventory, and the enterprise status includes normal, risk impact, and disabled;

[0016] The enterprise nodes include key enterprise nodes and non-key enterprise nodes. One key enterprise node corresponds to one key enterprise, and one non-key enterprise node corresponds to multiple non-key enterprises. The enterprise node attributes of the non-key enterprise nodes are calculated by utilizing the system dynamics method based on industry stock, flow and preset auxiliary variables.

[0017] In a possible embodiment, the preset decision rules include: new supplier selection rules and supply request acceptance rules, wherein the new supplier selection rules at least include enterprise scale, and the supply request acceptance rules at least include existing supplier preferences and additional capacity.

[0018] The step of inputting a preset risk event into the subject simulation network so that each of the enterprise nodes in the subject simulation network processes the preset risk event based on a preset decision rule preset for each of the enterprise nodes, and obtaining a risk propagation path and a risk deduction result output by the subject simulation network, includes:

[0019] Input the preset risk event into the subject simulation network, so that the purchasing enterprise selects a candidate supplier according to the new supplier selection rule preset for the purchasing enterprise, wherein the purchasing enterprise is an enterprise that purchases the industry provided by the risk enterprise, and the risk enterprise is an enterprise where the preset risk event occurs, so that the candidate supplier determines whether to accept the supply request of the purchasing enterprise according to the supply request acceptance rule preset for the candidate supplier;

[0020] Based on the candidate supplier selection results of the purchasing enterprise and the supply request acceptance results of the candidate suppliers, the risk propagation path and the risk deduction results are output; wherein the risk propagation path includes enterprises and industries affected by the preset risk events, and the risk deduction results include whether the enterprise status is affected.

[0021] In a possible embodiment, inputting a preset risk event into the subject simulation network so that each of the enterprise nodes in the subject simulation network processes the preset risk event based on a preset decision rule preset for each of the enterprise nodes, and obtaining a risk propagation path and a risk deduction result output by the subject simulation network, includes:

[0022] Input the preset risk event into the subject simulation network, and run the subject simulation network multiple times, so that each of the enterprise nodes in the subject simulation network processes the preset risk event based on the preset decision rules preset for each of the enterprise nodes, and obtain multiple risk propagation paths and risk deduction results output by the subject simulation network;

[0023] When the multiple risk propagation paths and risk deduction results are inconsistent, modify the preset decision rules or other parameters in the subject simulation network until the multiple risk propagation paths and risk deduction results output by the subject simulation network are consistent.

[0024] In a possible embodiment, the method further includes: detecting the risk deduction accuracy of the subject simulation network according to a preset time period, and when the risk deduction accuracy is lower than a preset threshold, modifying the target decision rule or other parameters in the subject simulation network until the risk deduction accuracy reaches the preset threshold.

[0025] According to another aspect of the present invention, a system for deducing risks in an industrial chain and supply chain based on a simulation method is provided, the system comprising:

[0026] A network construction module, used to construct a subject simulation network based on the dependency relationship between industries in the target industry field, the supply relationship between enterprises, and the production relationship between each enterprise and industry, wherein the subject simulation network includes each industry node and each enterprise node, each industry node is connected by a first directed edge, each enterprise node is connected by a second directed edge, and the enterprise node and the industry node are connected by a third undirected edge, wherein the first directed edge is used to represent the dependency relationship between industry nodes, and the dependency relationship includes an assembly relationship and an upstream and downstream relationship; the second directed edge is used to represent the supply relationship between enterprise nodes; and the third undirected edge is used to represent the production relationship between the enterprise and the industry;

[0027] A risk deduction module is used to input a preset risk event into the subject simulation network, so that each of the enterprise nodes in the subject simulation network processes the preset risk event based on a preset decision rule preset for each of the enterprise nodes, and obtains a risk propagation path and a risk deduction result output by the subject simulation network;

[0028] The model verification module is used to determine the preset decision rule as the target decision rule when the risk deduction result conforms to the risk treatment result corresponding to the preset risk event; and to modify the preset decision rule or other parameters in the subject simulation network when the risk deduction result does not conform to the risk treatment result until the risk deduction result conforms to the risk treatment result.

[0029] According to another aspect of the present invention, there is provided an electronic device, comprising:

[0030] Processor; and

[0031] Memory for storing programs,

[0032] Among them, the program includes instructions, which, when executed by the processor, enable the processor to execute any of the above-mentioned industrial chain and supply chain risk deduction methods based on simulation methods.

[0033] According to another aspect of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute any of the above-mentioned industrial chain and supply chain risk deduction methods based on simulation methods.

[0034] One or more technical solutions provided in the embodiments of the present invention construct a subject simulation network, and enable each enterprise in the subject simulation network to handle preset risk events according to preset decision rules, thereby conducting risk deduction of the industrial chain and supply chain. Since the subject simulation network can reflect the assembly relationship, upstream and downstream relationship, supply relationship between enterprises, and production relationship between enterprises and industries in the target industry field, that is, it can reflect the heterogeneity between different enterprises, so that the accuracy of risk deduction based on the subject simulation network is greatly improved. Furthermore, by setting decision rules for different enterprises, the decision rules can simulate the decision-making process of enterprises in response to risk events, that is, the autonomy of enterprises is simulated in the risk deduction process, further improving the accuracy of risk deduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Further details, features and advantages of the invention are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0036] Figure 1 A schematic diagram of a process flow of an industrial chain and supply chain risk deduction method based on a simulation method provided in an embodiment of the present invention;

[0037] Figure 2 A structural schematic diagram of a subject simulation network in the industrial chain supply chain risk deduction method based on a simulation method provided by an embodiment of the present invention;

[0038] Figure 3 A schematic diagram of the enterprise decision-making process in the industrial chain supply chain risk deduction method based on the simulation method provided in an embodiment of the present invention;

[0039] Figure 4 A schematic diagram of a logical structure of an industrial chain and supply chain risk deduction system based on a simulation method provided by an embodiment of the present invention;

[0040] Figure 5 A block diagram of an exemplary electronic device that can be used to implement an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0041] Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0042] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0043] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0044] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0045] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes, and are not used to limit the scope of these messages or information.

[0046] As mentioned in the background technology, mathematical methods and complex network methods cannot take into account the decision-making logic of enterprises, resulting in low accuracy in the results of industrial chain and supply chain deduction. With the development of simulation technology, mainstream simulation technologies such as discrete event simulation and system dynamics have begun to be used in supply chain risk deduction.

[0047] Among them, the discrete event simulation method identifies and simulates various events in the supply chain, such as the arrival and delivery of orders, fluctuations in inventory levels, and the resource scheduling of enterprises, so as to study the impact of risk events on the supply chain. The system dynamics method reflects the interaction between various factors in the supply chain, such as inventory levels, production volume, and sales volume, and depicts the evolution and control of important indicators of the supply chain under the impact of risk events through changes in inventory and flow. However, these two simulation methods are also unable to reflect the decision-making differences of enterprises.

[0048] Based on this, the present invention provides an industrial chain supply chain risk deduction method and system based on a simulation method. The industrial chain supply chain risk deduction method based on a simulation method provided by an embodiment of the present invention can be applied to any electronic device with an industrial chain supply chain risk deduction function, and the electronic device can be a computer, a server or a mobile terminal, etc. Specifically, the present invention can perform risk deduction through a subject simulation method. The subject modeling simulation method models different types of subjects, such as suppliers, manufacturers, wholesalers and retailers, and simulates the response of the entire supply chain system to risk events by changing their behaviors. The following describes the scheme of the present invention with reference to the accompanying drawings:

[0049] Figure 1 A flow chart of a method for deriving risk of an industrial chain and supply chain based on a simulation method provided in an embodiment of the present invention may include the following steps:

[0050] S101. Based on the dependency relationship between industries in the target industry field, the supply relationship between enterprises, and the production relationship between the enterprises and industries, a subject simulation network is constructed, wherein the subject simulation network includes industry nodes and enterprise nodes, the industry nodes are connected by a first directed edge, the enterprise nodes are connected by a second directed edge, and the enterprise nodes and industry nodes are connected by a third undirected edge, wherein the first directed edge is used to represent the dependency relationship between industry nodes, and the dependency relationship includes an assembly relationship and an upstream and downstream relationship; the second directed edge is used to represent the supply relationship between enterprise nodes; and the third undirected edge is used to represent the production relationship between the enterprise and the industry;

[0051] S102, inputting a preset risk event into the subject simulation network, so that each of the enterprise nodes in the subject simulation network processes the preset risk event based on a preset decision rule preset for each of the enterprise nodes, and obtaining a risk propagation path and a risk deduction result output by the subject simulation network;

[0052] S103. When the risk deduction result is consistent with the risk handling result corresponding to the preset risk event, determine the preset decision rule as the target decision rule; when the risk deduction result does not meet the risk handling result, modify the preset decision rule or other parameters in the subject simulation network until the risk deduction result meets the risk handling result.

[0053] By applying the embodiment of the present invention, a subject simulation network is constructed, and each enterprise in the subject simulation network processes a preset risk event according to a preset decision rule, thereby performing risk deduction of the industrial chain and supply chain. Since the subject simulation network can reflect the assembly relationship, upstream and downstream relationship, supply relationship between enterprises, and production relationship between enterprises and industries in the target industry field, that is, it can reflect the heterogeneity between different enterprises, so that the accuracy of risk deduction based on the subject simulation network is greatly improved. Furthermore, by setting decision rules for different enterprises, the decision rules can simulate the decision-making process of enterprises in response to risk events, that is, the autonomy of enterprises is simulated in the risk deduction process, further improving the accuracy of risk deduction.

[0054] The above S101-S103 are exemplarily described below:

[0055] The target industry field in S101 can be selected according to the actual application scenario. The target industry field may include products or services, etc. For example, the target industry field may be smart driving cars, smart phones, product sales and after-sales services, etc., which are not specifically limited in the present invention. In the present invention, a subject simulation network can be constructed based on the dependency relationship between industries in the target industry field, the supply relationship between enterprises, and the production relationship between each enterprise and industry.

[0056] The industries included in the target industry refer to the components included in the target industry. For example, when the target industry is the field of smart driving cars, the above industries may include cars, engines, display screens, tires, etc. When the target industry is the field of smart phones, the above industries may include mobile phones, cameras, chips, display screens, etc. The dependency relationship of each industry can characterize the value flow in the target industry. Specifically, the dependency relationship may include the assembly relationship and upstream and downstream relationship between industries, where the assembly relationship can characterize the components of the industry. Based on the example of the above smart phone, the components of the mobile phone may include cameras, chips, and display screens. The upstream and downstream relationship is used to characterize the production dependency relationship between industries. The upstream industry usually refers to the industry that provides raw materials, which may include raw materials and parts, etc. The downstream industry usually refers to the products formed by processing the above raw materials.

[0057] The enterprises included in the target industry field refer to the enterprises used to produce the industries included in the target industry field, and the supply relationship between enterprises refers to the industrial flow between enterprises. For example, if enterprise A provides products to enterprise B, then there is a supply relationship between enterprise A and enterprise B, and enterprise B is the purchasing enterprise of enterprise A. The production relationship between the above-mentioned enterprises and industries is determined based on the products produced by the enterprises. Based on the above example, if enterprise A is an enterprise that produces cameras, then there is a production relationship between it and the camera industry.

[0058] The industries in the above-mentioned target industrial field, the dependencies between industries, the enterprises, the supply relationships between enterprises, and the production relationships between enterprises and industries can all be obtained through public data. For example, the above information can be obtained from public materials such as the Internet and books, or through field research, expert interviews, etc. The present invention does not make specific limitations on this.

[0059] Based on the dependency relationship between industries in the target industry field, the supply relationship between enterprises, and the production relationship between each enterprise and industry, the constructed subject simulation network includes multiple nodes, and each node is connected by an edge. Specifically, the above-mentioned nodes may include industry nodes and enterprise nodes, wherein an industry node (or enterprise node) represents an industry (or enterprise) in the target industry field. The industry nodes are connected by directed edges, and the directed edges are used to represent the dependency relationship between industry nodes. For example, the direction of the directed edge between industry node a and industry node b is from a to b, which means that industry node a is a component of industry node b, or industry node a is the upstream industry of industry node b. For the convenience of description, the directed edges between industry nodes are referred to as first directed edges in the present invention.

[0060] The above-mentioned enterprise nodes are also connected by directed edges, which are used to represent the supply relationship between enterprise nodes. For example, the directed edge between enterprise node a and enterprise node b is from a to b, which means that enterprise node a is the supplier of enterprise node b. For the convenience of description, the directed edge between enterprise nodes is called the second directed edge in the present invention.

[0061] The industry node and the enterprise node are connected by an undirected edge, which is used to represent the production relationship between the enterprise node and the industry node. For example, if there is an undirected edge between industry node a and enterprise node A, it means that enterprise node A produces the industry represented by industry node a.

[0062] In a possible embodiment, the subject simulation network includes an industrial chain network, a supply chain network, and a mapping relationship network. Exemplarily, the subject simulation network can be constructed by the following steps:

[0063] S111. Based on the dependency relationship between industries in the target industry field, construct an industrial chain network, wherein the industrial chain network includes industrial nodes, and each of the industrial nodes is connected by the first directed edge.

[0064] Based on the industries in the target industry and the dependencies between the industries, a directed acyclic network can be formed. For example, a directed acyclic industrial chain network G b (V b ,E b ), where V b represents the set of industry nodes in the target industry field, V b It can include multiple industry nodes v i ', each v i '∈V b Indicates an industry in the target industry sector. b It represents the set of first directed edges contained in the industrial chain network, and the first directed edges are used to represent the dependency relationship between industrial nodes.

[0065] S112. Based on the supply relationship between enterprises in the target industry field, a supply chain network is constructed, wherein the supply chain network includes enterprise nodes, and the enterprise nodes are connected by the second directed edges.

[0066] Based on the supply relationship between enterprises, a directed acyclic supply chain network can be formed. For example, the supply chain network G f (V f ,E f ), where V f represents the set of enterprise nodes in the target industry field, V f Multiple enterprise nodes can be included in v i , each enterprise node v i ∈V f represents an enterprise in the target industry, E f Represents a set of second directed edges included in the supply chain network, and the second directed edges are used to represent the supply relationship between enterprise nodes. Exemplarily, the second directed edges may include attribute information, and the second directed edge attribute information may include products supplied between enterprise nodes, and specifically, may include industries supplied between enterprise nodes. Exemplarily, the second directed edges e i,j,vi’ Represents enterprise node v i To the enterprise node v j , and the enterprise node v i To the enterprise node v j The products supplied belong to industry v i ', the industry i' is the industry represented by the industrial nodes included in the above industrial chain network.

[0067] S113. Based on the production relationship between each of the enterprises and each of the industries, the industry nodes in the industrial chain network and the enterprise nodes in the supply chain network are connected by using a third undirected edge to obtain a mapping relationship network.

[0068] In this step, the above industrial chain network and supply chain network can be mapped. Specifically, the corresponding enterprises and industries can be connected using undirected edges according to the production relationship between the enterprises and the industries. The undirected edges can represent the industries provided by the enterprises and the enterprises included in the industries. For example, the mapping relationship network can be an undirected acyclic network G m (V m ,E m ), where V m Represents the set of industry nodes and enterprise nodes contained in the target industry field, that is, the V m The industry node set V in the industrial chain network can be included b And the enterprise node set V in the supply chain network f . E m represents the third undirected edge set, each edge e in the third undirected edge set vi’,vi ∈E m One end is the industry node v i ', the other end is the enterprise node v i .

[0069] like Figure 2 As shown, Figure 2 A structural diagram of an industrial chain network, a supply chain network and a mapping relationship network in the field of smart phones constructed according to an embodiment of the present invention. The industrial chain network includes a mobile phone (1), a chip (1.1), a camera (1.2), a display screen (1.3), an image sensor (1.2.1), a motor (1.2.2) and a lens (1.2.3). The image sensor, the motor and the lens are components of the camera. Therefore, the image sensor, the motor and the lens are connected to the camera through directed edges. Similarly, the chip, the camera and the display screen are also connected to the mobile phone through directed edges.

[0070] The supply chain network includes related companies in the field of smartphones, specifically company nodes 1 to 8. Each company node is connected by a directed edge to indicate the supply relationship between companies, and each directed edge includes the industry identifier of the supply, which is consistent with the corresponding industry identifier in the industrial chain network. For example, the directed edges between company 1 and company 2 and company 5 are marked with 1.2.1, indicating that company 1 and company 2 both provide image sensors to company 5. Companies can also provide products to themselves. For example, Figure 2 In the supply chain network shown, enterprise 8 itself includes directed edges, and the directed edges are labeled 1.3, indicating that enterprise 5 itself produces and uses display screens.

[0071] The industry nodes in the industrial chain network are connected to the enterprise nodes in the supply chain network through undirected edges. There can be multiple undirected edges connected to the same industry node, and there can also be multiple undirected edges connected to the same enterprise node. For example, Figure 3 In the industry-enterprise mapping network shown, image sensors are connected to Enterprise 1 and Enterprise 2 through undirected edges, indicating that Enterprise 1 and Enterprise 2 both produce image sensors. Image sensors and motors are both connected to Enterprise 2 through undirected edges, indicating that Enterprise 2 produces image sensors and motors.

[0072] In a possible embodiment, each industry node and enterprise node contained in the above-mentioned industrial chain network, supply chain network and mapping relationship network may contain corresponding node attributes, and the node attributes may be flexibly set according to the actual application scenario. Exemplarily, the node attributes of the industrial node may include enterprise information related to the industry, the industry's production capacity and inventory, etc. The above-mentioned enterprise information may be an enterprise logo, and the logo may be an enterprise name, code, etc. The node attributes of the enterprise node may include the enterprise status, the industrial information produced by the enterprise, the production capacity and inventory of each industry in the enterprise, etc., wherein the above-mentioned enterprise status may include normal, risk impact and disability, wherein disability means that the enterprise has lost the production capacity of the corresponding industry, and risk impact means that the enterprise is affected by risk events occurring in itself or other enterprises. The above-mentioned node attributes further improve the integrity of the information contained in the constructed industrial chain network, supply chain network and mapping relationship network and the heterogeneity between the nodes, and further improve the accuracy of subsequent risk deduction based on the network.

[0073] In practical applications, the target industry may include multiple enterprises, but the required node attribute information is not available for all enterprises. Therefore, in a possible embodiment, these enterprises whose accurate attributes are difficult to obtain can be regarded as a virtual subject, and the virtual subject can be regarded as an enterprise node. The system dynamics simulation method is used to characterize the node attributes of the enterprise node through stock, flow and auxiliary variables. The above auxiliary variables can include average wholesale price, total output, etc. The description of the node attributes of the enterprise node using the system dynamics simulation method through stock, flow and auxiliary variables can refer to the description of the prior art, and the present invention will not elaborate on this.

[0074] In a possible embodiment, enterprises producing in the same industry may be regarded as the same virtual entity, and the properties of the virtual entity may be characterized by using stock, flow and auxiliary variables using system dynamics simulation to maximize the accuracy of the property information.

[0075] That is to say, the enterprise nodes include key enterprise nodes and non-key enterprise nodes. One key enterprise node corresponds to one key enterprise, and one non-key enterprise node corresponds to multiple non-key enterprises. The non-key enterprise refers to an enterprise whose production capacity, stock and other data are difficult to obtain. The key enterprise refers to an enterprise with high production capacity and stock, and whose production capacity, stock and other data are available.

[0076] In a possible embodiment, after the above networks are constructed, decision rules can be set for enterprise nodes in the network, and the decision rules are used to simulate the decision-making process of enterprises actually encountering risk events. As a possible implementation method, the preset decision rules include: new supplier selection rules and supply request acceptance rules, wherein the new supplier selection rules are used to control the enterprise to select new suppliers in the face of risk events, and the supply request acceptance selection rules are used to control whether the selected new supplier decision is accepted.

[0077] For example, the above new supplier selection rules may include enterprise scale, production capacity and / or inventory, etc., and the new supplier selection rules may set different contents for different enterprises or different industries. For example, enterprises with a scale of more than 500 people and an industrial production capacity of more than 100 pieces / month may be selected as new suppliers.

[0078] The supply request acceptance supply rules may include the size of the enterprise sending the new supplier selection request, the capacity requirements and / or other inventory requirements included in the supply request, the enterprise's own additional capacity, etc. The supply request acceptance rules may also set different contents for different enterprises or industries. For example, it may be set to accept new supplier selection requests from enterprises with a scale of more than 100 people and an industrial capacity of more than 100 pieces / month. The supply request acceptance rules may also include supplier preferences. For example, a new supplier may choose to accept supply requests sent by an enterprise with which it has established a supply relationship.

[0079] In a possible embodiment, the construction of the industrial chain network, supply chain network and mapping relationship network and the setting of decision rules can be realized through object-oriented languages ​​such as Python and Java. For the convenience of description, the networks and decision rules constructed in the present invention will become the main simulation model.

[0080] During the risk deduction process, we can first obtain multiple preset risk events and the risk handling results corresponding to the preset risk events. The preset risk time and the corresponding handling results can be obtained through existing data in literature, or can be obtained by relevant personnel based on preset risk events and subject simulation network reasoning. The above-mentioned risk events may include machine failures, weather factors, etc. that cause the company to be unable to produce or transport corresponding industries. The above-mentioned risk handling results may include the company's candidate supplier selection results, the company's production results, etc., which can be obtained according to the actual application scenario.

[0081] During the risk deduction process, the above-mentioned preset risk events can be input into the constructed network so that the purchasing enterprise can select candidate suppliers according to the new supplier selection rules preset for the purchasing enterprise, wherein the purchasing enterprise is an enterprise that purchases the industry provided by the risk enterprise, and the risk enterprise is an enterprise where the preset risk event occurs. There can be multiple candidate suppliers, and the candidate suppliers can determine whether to accept the selection according to the supply request reception selection rules preset for them.

[0082] Based on the decision-making results of each enterprise, the network can output risk propagation paths and risk deduction results. The risk propagation path may include enterprise link information and industry information that are affected by preset risk events and cannot produce normally. The type of the above deduction results can be selected according to the actual application scenario. The deduction results at least include the content included in the risk handling results corresponding to the above preset risk events, such as the candidate supplier selection results, whether the enterprise status is affected, etc.

[0083] like Figure 3 As shown, Figure 3 FIG. 1 is a flow chart of the enterprise making a decision according to a preset decision rule when the risk event is a supply interruption event in the present invention. After the risk event occurs, the risk enterprise ( Figure 3 Enterprises in [above]) and corresponding risk industries ( Figure 3 The mapping relationship between the products in [above]) is interrupted, and at the same time, the risk enterprise and the purchasing enterprise ( Figure 3 The supply relationship between enterprises in [China] is interrupted.

[0084] The mapping relationship of the risk industry related to the purchasing enterprise is disturbed. At this time, the purchasing enterprise searches for all candidate enterprises producing risk industries based on the supply chain network and determines whether to establish a supply relationship with the candidate enterprise, that is, whether there is an edge between the purchasing enterprise and the candidate enterprise in the supply chain network. If so, the existing supplier is selected as the candidate supplier first. If not, a new supplier is selected as the candidate supplier, and the candidate supplier is applied to supply the risk industry in descending order of the size of the candidate supplier.

[0085] The supply request acceptance rules that the candidate supplier can set determine whether to agree to supply the risk industry. The acceptance rules include supply preferences and additional capacity. If the supply of products is agreed, the supply relationship between the candidate supplier and the purchasing enterprise is established, so that the mapping relationship between the purchasing enterprise and the corresponding industry is normal. If the candidate supplier does not agree to supply products, it is determined whether the number of requests from the purchasing enterprise has reached the upper limit. If the upper limit has been reached, the mapping relationship between the industry and the purchasing enterprise is interrupted, and the risk event will be spread to more industries and enterprises. If the upper limit has not been reached, you can continue to apply for supply in descending order of the size of the candidate suppliers.

[0086] As a possible implementation method, after the preset risk event is output to the main simulation network, the reasoning process can be repeated multiple times, and the risk propagation path and risk deduction results can be obtained based on multiple runs. Specifically, if the risk propagation path or risk deduction results output multiple times are inconsistent or inconsistent with the risk handling results corresponding to the preset risk event, it means that the deduction results are inaccurate. Therefore, the decision rules of each enterprise or other parameters in the main simulation network can be adjusted until the same risk propagation path and risk deduction results can be output after running the same preset risk event multiple times. The risk handling results corresponding to the above-mentioned preset risk events can be obtained through literature, or they can be obtained by relevant personnel based on risk event reasoning. Other parameters in the above-mentioned main simulation network may include nodes, node attributes, edges, and edge attributes.

[0087] In a possible embodiment, after obtaining the risk deduction result output by the model, it can be compared with the risk processing result corresponding to the preset risk event. If they are consistent, the training can be terminated. If they are inconsistent, the rules or other parameters in the subject simulation network can be adjusted to reprocess each preset risk event. Among them, the above consistency refers to the basic consistency between the risk deduction result and the reasoning result of the literature or relevant personnel, such as the similarity between the risk deduction result and the actual risk processing result is higher than the threshold M1, and the above inconsistency refers to the obvious inconsistency between the risk deduction result and the reasoning result of the literature or relevant personnel, such as the similarity between the risk deduction result and the actual risk processing result is lower than the threshold M1.

[0088] In a possible embodiment, the risk deduction accuracy of the subject simulation network can be detected according to a preset time period, and when the risk deduction accuracy is lower than a preset threshold, the target decision rule is modified until the risk deduction accuracy reaches the preset threshold. The accuracy can be represented by the proportion of correct risk deduction results output by the subject simulation model for multiple risk events in each risk event.

[0089] By applying the embodiments of the present invention, by constructing an industrial chain network and a supply chain network and establishing a mapping relationship between the two, the complexity of the actual industrial chain and supply chain can be simulated more accurately. Specifically, this multi-layer network integration method can reflect the heterogeneity of key enterprises, which is conducive to simulating the behavior of actual key enterprises according to real data, thereby solving the homogeneity problem. By simulating the decision-making process of key enterprises facing risk events through the subject simulation method, the differences in decision-making of supply chain enterprises when facing risks can be reflected, thereby solving the autonomy problem.

[0090] Furthermore, the present invention allows the inventory and other attributes of key enterprises to take arbitrary values, avoiding the problem of computational scale caused by calculating the entire system state space, and effectively solving the problem of computational complexity. Moreover, by introducing virtual subjects to simulate the group behavior of a large number of non-key enterprises, the specific decision-making process of these enterprises can be ignored, and the important indicators of the entire group can be characterized by the stock, flow, auxiliary variables, etc. of the system dynamics, thereby solving the problem of slow calculation and improving the computational efficiency of the system.

[0091] Moreover, by setting the decision-making process of the supply chain entities facing risk events, the present invention can simulate the response strategy of enterprises when facing risk events such as supply interruption. This simulation not only includes the enterprise's request for supply from new suppliers, but also includes the decision of the new supplier to decide whether to accept the supply, making the risk management strategy more comprehensive and detailed. By repeatedly running the model multiple times, the risk propagation path and system deduction results can be obtained, providing enterprises with more accurate risk warnings and response strategies.

[0092] In addition, the technical solution of the present invention allows the rules to be adjusted according to actual conditions, making the model highly flexible and adaptable. Enterprises can flexibly modify the rule settings in the simulation model according to market changes and internal strategy adjustments to adapt to the ever-changing external environment and internal needs.

[0093] Based on the same inventive concept, the embodiment of the present invention also provides an industrial chain supply chain risk deduction system based on a simulation method, such as Figure 4 As shown, the system 400 may include:

[0094] The network construction module 401 is used to construct a subject simulation network based on the dependency relationship between industries in the target industry field, the supply relationship between enterprises, and the production relationship between the enterprises and industries. The subject simulation network includes industry nodes and enterprise nodes. The industry nodes are connected by a first directed edge, the enterprise nodes are connected by a second directed edge, and the enterprise nodes are connected to the industry nodes by a third undirected edge. The first directed edge is used to represent the dependency relationship between industry nodes, and the dependency relationship includes an assembly relationship and an upstream and downstream relationship; the second directed edge is used to represent the supply relationship between enterprise nodes; and the third undirected edge is used to represent the production relationship between the enterprise and the industry.

[0095] The risk deduction module 402 is used to input the preset risk event into the subject simulation network, so that each of the enterprise nodes in the subject simulation network processes the preset risk event based on the preset decision rules preset for each of the enterprise nodes, and obtain the risk propagation path and risk deduction results output by the subject simulation network;

[0096] The model verification module 403 is used to determine the preset decision rule as the target decision rule when the risk deduction result conforms to the risk treatment result corresponding to the preset risk event; when the risk deduction result does not conform to the risk treatment result, modify the preset decision rule or other parameters in the subject simulation network until the risk deduction result conforms to the risk treatment result.

[0097] In a possible embodiment, the subject simulation network includes an industrial chain network, a supply chain network, and a mapping relationship network. The subject simulation network is constructed based on the dependency relationship between industries in the target industry field, the supply relationship between enterprises, and the production relationship between each enterprise and industry, including:

[0098] Based on the dependency relationship between industries in the target industry field, construct an industrial chain network, wherein the industrial chain network includes industrial nodes, and the industrial nodes are connected by the first directed edges;

[0099] Based on the supply relationship between enterprises in the target industry field, a supply chain network is constructed, wherein the supply chain network includes enterprise nodes, and the enterprise nodes are connected by the second directed edges;

[0100] Based on the production relationship between each of the enterprises and each of the industries, the industry nodes in the industrial chain network and the enterprise nodes in the supply chain network are connected by using a third undirected edge to obtain a mapping relationship network;

[0101] Each of the enterprise nodes includes enterprise node attributes, and the enterprise node attributes include at least enterprise status, enterprise capacity and inventory, and the enterprise status includes normal, risk impact and disabled;

[0102] The enterprise nodes include key enterprise nodes and non-key enterprise nodes, one key enterprise node corresponds to one key enterprise, one non-key enterprise node corresponds to multiple non-key enterprises, and the enterprise node attributes of the non-key enterprise nodes are calculated by using the system dynamics method based on the industry stock, flow and preset auxiliary variables;

[0103] The preset decision rules include: new supplier selection rules and supply request acceptance rules, wherein the new supplier selection rules at least include enterprise scale, and the supply request acceptance rules at least include existing supplier preferences and additional capacity.

[0104] The step of inputting a preset risk event into the subject simulation network so that each of the enterprise nodes in the subject simulation network processes the preset risk event based on a preset decision rule preset for each of the enterprise nodes, and obtaining a risk propagation path and a risk deduction result output by the subject simulation network, includes:

[0105] Input the preset risk event into the subject simulation network, so that the purchasing enterprise selects a candidate supplier according to the new supplier selection rule preset for the purchasing enterprise, wherein the purchasing enterprise is an enterprise that purchases the industry provided by the risk enterprise, and the risk enterprise is an enterprise where the preset risk event occurs, so that the candidate supplier determines whether to accept the supply request of the purchasing enterprise according to the supply request acceptance rule preset for the candidate supplier;

[0106] Based on the candidate supplier selection result of the purchasing enterprise and the supply request acceptance result of the candidate supplier, a risk propagation path and a risk deduction result are output; wherein the risk propagation path includes enterprises and industries affected by the preset risk event, and the risk deduction result includes whether the enterprise status is affected;

[0107] The step of inputting a preset risk event into the subject simulation network so that each of the enterprise nodes in the subject simulation network processes the preset risk event based on a preset decision rule preset for each of the enterprise nodes, and obtaining a risk propagation path and a risk deduction result output by the subject simulation network, includes:

[0108] Input the preset risk event into the subject simulation network, and run the subject simulation network multiple times, so that each of the enterprise nodes in the subject simulation network processes the preset risk event based on the preset decision rules preset for each of the enterprise nodes, and obtain multiple risk propagation paths and risk deduction results output by the subject simulation network;

[0109] In the case where the multiple risk propagation paths and risk deduction results are inconsistent, modifying the preset decision rule or other parameters in the subject simulation network until the multiple risk propagation paths and risk deduction results output by the subject simulation network are consistent;

[0110] The model verification module is also used to detect the risk deduction accuracy of the main simulation network according to a preset time period, and when the risk deduction accuracy is lower than a preset threshold, modify the target decision rule or other parameters in the main simulation network until the risk deduction accuracy reaches the preset threshold.

[0111] Among them, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in the present invention are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0112] The exemplary embodiment of the present invention further provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication. The memory stores a computer program that can be executed by the at least one processor, and the computer program is used to enable the electronic device to perform a method according to an embodiment of the present invention when executed by the at least one processor.

[0113] Exemplary embodiments of the present invention also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform a method according to an embodiment of the present invention.

[0114] An exemplary embodiment of the present invention further provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor of a computer, the computer is used to enable the computer to perform a method according to an embodiment of the present invention.

[0115] refer to Figure 5, a block diagram of an electronic device 500 that can be used as a server or client of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0116] like Figure 5 As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0117] A plurality of components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. The input unit 506 may be any type of device capable of inputting information to the electronic device 500, and the input unit 506 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 507 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 508 may include, but is not limited to, a disk, an optical disk. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0118] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above. For example, in some embodiments, any of the above-described industrial chain supply chain risk deduction methods based on simulation methods may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via the ROM 502 and / or the communication unit 509. In some embodiments, the computing unit 501 may be configured to perform any of the above-described industrial chain supply chain risk deduction methods based on simulation methods by any other appropriate means (e.g., by means of firmware).

[0119] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0120] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0121] As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0122] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0123] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0124] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

Claims

1. A method for deducing industrial chain and supply chain risks based on simulation method, characterized in that: The method comprises: Based on the dependency relationship between industries in the target industry field, the supply relationship between enterprises, and the production relationship between the enterprises and industries, a subject simulation network is constructed, wherein the subject simulation network includes industry nodes and enterprise nodes, wherein the industry nodes are connected by a first directed edge, the enterprise nodes are connected by a second directed edge, and the enterprise nodes and industry nodes are connected by a third undirected edge, wherein the first directed edge is used to represent the dependency relationship between industry nodes, and the dependency relationship includes an assembly relationship and an upstream and downstream relationship; the second directed edge is used to represent the supply relationship between enterprise nodes; and the third undirected edge is used to represent the production relationship between the enterprise and the industry; Inputting a preset risk event into the subject simulation network, so that each of the enterprise nodes in the subject simulation network processes the preset risk event based on a preset decision rule preset for each of the enterprise nodes, and obtaining a risk propagation path and a risk deduction result output by the subject simulation network; When the risk deduction result is consistent with the risk treatment result corresponding to the preset risk event, the preset decision rule is determined as the target decision rule; when the risk deduction result does not meet the risk treatment result, the preset decision rule or other parameters in the subject simulation network are modified until the risk deduction result meets the risk treatment result.

2. The method according to claim 1, characterized in that The subject simulation network includes an industrial chain network, a supply chain network and a mapping relationship network. The subject simulation network is constructed based on the dependency relationship between industries in the target industry field, the supply relationship between enterprises and the production relationship between each enterprise and industry, including: Based on the dependency relationship between industries in the target industry field, construct an industrial chain network, wherein the industrial chain network includes industrial nodes, and the industrial nodes are connected by the first directed edges; Based on the supply relationship between enterprises in the target industry field, a supply chain network is constructed, wherein the supply chain network includes enterprise nodes, and the enterprise nodes are connected by the second directed edges; Based on the production relationship between each of the enterprises and each of the industries, the industry nodes in the industrial chain network and the enterprise nodes in the supply chain network are connected by using a third undirected edge to obtain a mapping relationship network.

3. The method according to claim 2, characterized in that Each of the enterprise nodes includes enterprise node attributes, and the enterprise node attributes include at least enterprise status, enterprise capacity and inventory, and the enterprise status includes normal, risk impact and disabled; The enterprise nodes include key enterprise nodes and non-key enterprise nodes. One key enterprise node corresponds to one key enterprise, and one non-key enterprise node corresponds to multiple non-key enterprises. The enterprise node attributes of the non-key enterprise nodes are calculated by utilizing the system dynamics method based on industry stock, flow and preset auxiliary variables.

4. The method according to claim 3, characterized in that The preset decision rules include: new supplier selection rules and supply request acceptance rules, wherein the new supplier selection rules at least include enterprise scale, and the supply request acceptance rules at least include existing supplier preferences and additional capacity; The step of inputting a preset risk event into the subject simulation network so that each of the enterprise nodes in the subject simulation network processes the preset risk event based on a preset decision rule preset for each of the enterprise nodes, and obtaining a risk propagation path and a risk deduction result output by the subject simulation network, includes: Input the preset risk event into the subject simulation network, so that the purchasing enterprise selects a candidate supplier according to the new supplier selection rule preset for the purchasing enterprise, wherein the purchasing enterprise is an enterprise that purchases the industry provided by the risk enterprise, and the risk enterprise is an enterprise where the preset risk event occurs; so that the candidate supplier determines whether to accept the supply request of the purchasing enterprise according to the supply request acceptance rule preset for the candidate supplier; Based on the candidate supplier selection results of the purchasing enterprise and the supply request acceptance results of the candidate suppliers, the risk propagation path and the risk deduction results are output; wherein the risk propagation path includes enterprises and industries affected by the preset risk events, and the risk deduction results include whether the enterprise status is affected.

5. The method according to claim 4, characterized in that The step of inputting a preset risk event into the subject simulation network so that each of the enterprise nodes in the subject simulation network processes the preset risk event based on a preset decision rule preset for each of the enterprise nodes, and obtaining a risk propagation path and a risk deduction result output by the subject simulation network, includes: Input the preset risk event into the subject simulation network, and run the subject simulation network multiple times, so that each of the enterprise nodes in the subject simulation network processes the preset risk event based on the preset decision rules preset for each of the enterprise nodes, and obtain multiple risk propagation paths and risk deduction results output by the subject simulation network; In the case where the multiple risk propagation paths and risk deduction results are inconsistent, modifying the preset decision rule or other parameters in the subject simulation network until the multiple risk propagation paths and risk deduction results output by the subject simulation network are consistent; or, When the multiple risk propagation paths and risk deduction results do not conform to the risk handling results, modify the preset decision rules or other parameters in the subject simulation network until the multiple risk propagation paths and risk deduction results output by the subject simulation network are consistent.

6. The method according to claim 1, characterized in that The method further comprises: The risk deduction accuracy of the subject simulation network is detected according to a preset time period, and when the risk deduction accuracy is lower than a preset threshold, the target decision rule or other parameters in the subject simulation network are modified until the risk deduction accuracy reaches the preset threshold.

7. An industrial chain and supply chain risk deduction system based on simulation method, characterized in that: The system comprises: A network construction module, used to construct a subject simulation network based on the dependency relationship between industries in the target industry field, the supply relationship between enterprises, and the production relationship between each enterprise and industry, wherein the subject simulation network includes each industry node and each enterprise node, each industry node is connected by a first directed edge, each enterprise node is connected by a second directed edge, and the enterprise node and the industry node are connected by a third undirected edge, wherein the first directed edge is used to represent the dependency relationship between industry nodes, and the dependency relationship includes an assembly relationship and an upstream and downstream relationship; the second directed edge is used to represent the supply relationship between enterprise nodes; and the third undirected edge is used to represent the production relationship between the enterprise and the industry; A risk deduction module is used to input a preset risk event into the subject simulation network, so that each of the enterprise nodes in the subject simulation network processes the preset risk event based on a preset decision rule preset for each of the enterprise nodes, and obtains a risk propagation path and a risk deduction result output by the subject simulation network; The model verification module is used to determine the preset decision rule as the target decision rule when the risk deduction result conforms to the risk treatment result corresponding to the preset risk event; and to modify the preset decision rule or other parameters in the subject simulation network when the risk deduction result does not conform to the risk treatment result until the risk deduction result conforms to the risk treatment result.

8. The system according to claim 7, characterized in that The subject simulation network includes an industrial chain network, a supply chain network and a mapping relationship network. The subject simulation network is constructed based on the dependency relationship between industries in the target industry field, the supply relationship between enterprises and the production relationship between each enterprise and industry, including: Based on the dependency relationship between industries in the target industry field, construct an industrial chain network, wherein the industrial chain network includes industrial nodes, and the industrial nodes are connected by the first directed edges; Based on the supply relationship between enterprises in the target industry field, a supply chain network is constructed, wherein the supply chain network includes enterprise nodes, and the enterprise nodes are connected by the second directed edges; Based on the production relationship between each of the enterprises and each of the industries, the industry nodes in the industrial chain network and the enterprise nodes in the supply chain network are connected by using a third undirected edge to obtain a mapping relationship network; Each of the enterprise nodes includes enterprise node attributes, and the enterprise node attributes include at least enterprise status, enterprise capacity and inventory, and the enterprise status includes normal, risk impact and disabled; The enterprise nodes include key enterprise nodes and non-key enterprise nodes, one key enterprise node corresponds to one key enterprise, one non-key enterprise node corresponds to multiple non-key enterprises, and the enterprise node attributes of the non-key enterprise nodes are calculated by using the system dynamics method based on the industry stock, flow and preset auxiliary variables; The preset decision rules include: new supplier selection rules and supply request acceptance rules, wherein the new supplier selection rules at least include enterprise scale, and the supply request acceptance rules at least include existing supplier preferences and additional capacity. The step of inputting a preset risk event into the subject simulation network so that each of the enterprise nodes in the subject simulation network processes the preset risk event based on a preset decision rule preset for each of the enterprise nodes, and obtaining a risk propagation path and a risk deduction result output by the subject simulation network, includes: Input the preset risk event into the subject simulation network, so that the purchasing enterprise selects a candidate supplier according to the new supplier selection rule preset for the purchasing enterprise, wherein the purchasing enterprise is an enterprise that purchases the industry provided by the risk enterprise, and the risk enterprise is an enterprise where the preset risk event occurs, so that the candidate supplier determines whether to accept the supply request of the purchasing enterprise according to the supply request acceptance rule preset for the candidate supplier; Based on the candidate supplier selection result of the purchasing enterprise and the supply request acceptance result of the candidate supplier, a risk propagation path and a risk deduction result are output; wherein the risk propagation path includes enterprises and industries affected by the preset risk event, and the risk deduction result includes whether the enterprise status is affected; The step of inputting a preset risk event into the subject simulation network so that each of the enterprise nodes in the subject simulation network processes the preset risk event based on a preset decision rule preset for each of the enterprise nodes, and obtaining a risk propagation path and a risk deduction result output by the subject simulation network, includes: Input the preset risk event into the subject simulation network, and run the subject simulation network multiple times, so that each of the enterprise nodes in the subject simulation network processes the preset risk event based on the preset decision rules preset for each of the enterprise nodes, and obtain multiple risk propagation paths and risk deduction results output by the subject simulation network; In the case where the multiple risk propagation paths and risk deduction results are inconsistent, modifying the preset decision rule or other parameters in the subject simulation network until the multiple risk propagation paths and risk deduction results output by the subject simulation network are consistent; The model verification module is also used to detect the risk deduction accuracy of the main simulation network according to a preset time period, and when the risk deduction accuracy is lower than a preset threshold, modify the target decision rule or other parameters in the main network until the risk deduction accuracy reaches the preset threshold.

9. An electronic device, comprising: processor; as well as Memory for storing programs, The program includes instructions, which, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to make a computer execute the method according to any one of claims 1-6.

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