Supply chain management and control platform based on collaborative network

Abstract

The invention discloses a supply chain management and control platform based on a collaborative network, and relates to the technical field of data processing, and the platform comprises an interactive supply chain management and control platform which obtains a supply chain collaborative network; recording supply participant attributes of each supply chain node in the supply chain collaborative network; constructing a supply chain redundant network; detecting a supply chain risk index of the supply chain collaborative network; constructing a supply chain combination network; establishing a combined optimization algorithm model; and updating the supply chain collaborative network by using the optimized to-be-updated supply participant to generate a supply chain optimization network. The technical problem of poor stability and continuity of the supply chain caused by the interruption risk of the supply chain due to the problem of a single supply chain network in the existing supply chain management and control is solved, information sharing and cooperative work among participants of the supply chain are realized, and the supply chain management and control efficiency is improved. The technical effect of improving the flexibility, stability and continuity of the supply chain is achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to data processing, and specifically to a supply chain management and control platform based on a collaborative network. Background Art

[0002] With the rapid development of global trade and increasingly fierce market competition, the complexity and dynamism of supply chain management are constantly increasing. Effective supply chain management requires not only that companies can accurately predict market demand, but also that they can quickly respond to market changes to ensure the stability, reliability and efficiency of the supply chain. However, traditional supply chain management methods often rely on a single supply chain network. Once a problem occurs in a link in the network, the entire supply chain may face the risk of interruption. Even with the support of a collaborative network, the supply chain still faces various risks due to insufficient redundancy of high-risk nodes, leading to supply chain interruptions and affecting the normal operations of the company. At the same time, traditional supply chain collaborative network management and control is difficult to optimize, update and control based on actual risks, affecting the stability and efficiency of the supply chain.

[0003] Therefore, at the current stage, there are technical problems in supply chain management and control related technologies, such as problems in a single supply chain network, which leads to the risk of supply chain interruption, and in turn causes poor stability and continuity of the supply chain. Summary of the Invention

[0004] This application provides a supply chain management and control platform based on a collaborative network, and adopts technical means such as building a redundant supply chain network and establishing an optimization algorithm model to solve the technical problems existing in existing supply chain management and control, such as the risk of supply chain interruption caused by problems in a single supply chain network, which in turn leads to poor stability and continuity of the supply chain. It realizes information sharing and collaborative work among all participants in the supply chain, and achieves the technical effect of improving the flexibility, stability and continuity of the supply chain.

[0005] The present application provides a supply chain management and control platform based on a collaborative network, the platform comprising: a supply chain collaborative network acquisition module, the supply chain collaborative network acquisition module is used to interact with the supply chain management and control platform to acquire a supply chain collaborative network, wherein the supply chain collaborative network comprises a plurality of supply chain nodes; a supply participant attribute recording module, the supply participant attribute recording module is used to record the supply participant attributes of each supply chain node in the supply chain collaborative network; a supply chain redundant network construction module, the supply chain redundant network construction module is based on the supply participant attributes, wherein the supply chain redundant network is a backup network of the supply chain collaborative network; a supply chain risk indicator detection module, the supply chain risk indicator detection module is used to detect the supply chain risk indicators of the supply chain collaborative network; a supply chain combination module. A network establishment module, the supply chain combination network establishment module is used to connect the supply chain redundant network with the supply chain collaborative network when the supply chain risk index is greater than the preset risk index to construct a supply chain combination network; a combination optimization algorithm model establishment module, the combination optimization algorithm model establishment module is used to establish a combination optimization algorithm model, the combination optimization algorithm model takes the preset risk index as the optimization target, performs optimization in the supply chain combination network, and obtains optimization results, wherein the optimization results include the supply participants to be updated obtained through optimization; a supply chain optimization network generation module, the supply chain optimization network generation module is used to update the supply chain collaborative network with the supply participants to be updated obtained through optimization, and generate a supply chain optimization network. The supply chain management and control platform performs supply management and control based on the supply chain optimization network.

[0006] In the implementation method of the present application, a supply chain redundant network is constructed based on the attributes of the supply participants, and the following processing is also performed: according to the supply management module of the supply chain control platform, a historical supply participant sample corresponding to each supply chain node is obtained; based on the attributes of the supply participants, matching is performed in the historical supply participant samples to obtain matching supply participants with a matching degree greater than a preset threshold; according to the matching supply participants corresponding to each supply chain node, a supply chain redundant network is constructed.

[0007] In the implementation method of the present application, the supply chain risk indicators of the supply chain collaborative network are detected, and the following processing is also performed: a risk identification model is established, and risk identification is performed on each supply chain node in the supply chain collaborative network according to the risk identification model to obtain a node risk value and an edge risk value; wherein, the node risk value is the node risk degree corresponding to each supply chain node, and the edge risk value is the edge risk degree corresponding to the dependency between each supply chain node; based on the node risk value and the edge risk value, the supply chain risk indicators of the supply chain collaborative network are detected.

[0008] In the implementation method of the present application, a supply chain redundant network is constructed based on the attributes of the supply participants, and the following processing is also performed: obtaining the node risk value corresponding to each supply chain node in the supply chain collaborative network; introducing a loss function to perform loss analysis on the node risk value corresponding to each supply chain node in the supply chain collaborative network, and obtaining the loss index corresponding to each supply chain node; according to the size of the loss index corresponding to each supply chain node, obtaining the key supply chain nodes whose loss index is greater than the preset loss index; and constructing a supply chain redundant network corresponding to the key supply chain nodes.

[0009] In the implementation method of the present application, the combined optimization algorithm model performs optimization in the supply chain combination network with the preset risk index as the optimization target, and also performs the following processing: defining the optimization probability corresponding to each supply chain node according to the node risk value corresponding to each supply chain node in the supply chain collaborative network; obtaining the redundant solution space corresponding to each supply chain node in the supply chain combination network; inputting the optimization probability corresponding to each supply chain node into the combined optimization algorithm model, and performing multiple rounds of iterative optimization in the redundant solution space corresponding to each supply chain node based on the optimization probability. If the preset risk index is met, the optimization ends, and the supply participants to be updated obtained by the optimization are output.

[0010] In the implementation method of the present application, the establishment of the combination optimization algorithm model also performs the following processing: configuring the first constraint condition, the first constraint condition includes the preset risk persistence stability, wherein the risk persistence stability characterizes the risk stability degree of each participant, which is obtained by calculating the standard deviation of the node risk value at different time points; using the first constraint condition as the constraint condition of the combination optimization algorithm model, and optimizing the supply participants to be updated obtained by optimization.

[0011] In the implementation method of the present application, the establishment of the combination optimization algorithm model also performs the following processing: configuring a second constraint condition, the second constraint condition includes a preset collaborative dependency stability, wherein the collaborative dependency stability characterizes the stability of the collaborative dependency relationship between each participant, and is obtained by calculating the historical collaboration frequency between each participant; using the second constraint condition as the constraint condition of the combination optimization algorithm model, and performing secondary optimization on the supply participants to be updated obtained by optimization.

[0012] In the implementation method of the present application, the supply chain collaborative network is updated by the supply participants to be updated obtained by optimal search, and the following processing is also performed: the number of supply chain nodes of the supply chain collaborative network is obtained; the number of supply chain nodes to be updated is obtained based on the supply participants to be updated obtained by optimal search; if the ratio of the number of supply chain nodes to be updated to the number of supply chain nodes is greater than a preset ratio, the retention instruction is activated, and the supply participants to be updated are optimized according to the retention instruction.

[0013] The collaborative network-based supply chain management and control platform proposed in this application is intended to be used to interact with the supply chain management and control platform to obtain a collaborative supply chain network; record the attributes of the supply participants of each supply chain node in the collaborative supply chain network; construct a redundant supply chain network; detect the supply chain risk index of the collaborative supply chain network; when the supply chain risk index is greater than the preset risk index, connect the redundant supply chain network with the collaborative supply chain network to construct a combined supply chain network; establish a combined optimization algorithm model; update the collaborative supply chain network with the updated supply participants obtained through optimization to generate a supply chain optimization network. This solves the technical problem of existing supply chain management and control, which is that problems with a single supply chain network cause the supply chain to be interrupted, leading to poor stability and continuity of the supply chain. It enables information sharing and collaborative work among all participants in the supply chain, achieving the technical effect of improving the flexibility, stability and continuity of the supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the platform according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. On the contrary, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0015] Figure 1 A schematic diagram of the structure of a collaborative network-based supply chain management and control platform provided in an embodiment of the present application;

[0016] Figure 2 A schematic diagram of the execution process of the data processing module in the collaborative network-based supply chain management and control platform provided in an embodiment of the present application.

[0017] Explanation of the accompanying symbols: supply chain collaborative network acquisition module 10, supply participant attribute recording module 20, supply chain redundant network construction module 30, supply chain risk indicator detection module 40, supply chain combination network establishment module 50, combination optimization algorithm model establishment module 60, supply chain optimization network generation module 70. DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, platform, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0021] The present application embodiment provides a supply chain management and control platform based on a collaborative network, such as Figure 1 As shown, the platform includes:

[0022] The supply chain collaborative network acquisition module 10 is used to interact with the supply chain management and control platform to acquire a supply chain collaborative network, wherein the supply chain collaborative network includes multiple supply chain nodes. The supply chain management and control platform is an enterprise-level system that integrates supply chain planning, collaboration, execution, monitoring, and optimization functions. It can help enterprises achieve transparency, visualization, and intelligence in their supply chains, improve supply chain responsiveness and flexibility, and reduce costs and risks. Through the platform, enterprises can access real-time data and information from the supply chain collaboration network, including multiple supply chain nodes. Specifically, supply chain nodes refer to the different entities or links in the supply chain that work together to complete the entire supply chain process, from raw material procurement to final product delivery to consumers. For example, suppliers, manufacturers, distributors, retailers, and end users. In the supply chain collaboration network, each node plays a specific role and is interconnected with other nodes. Supply chain collaboration refers to the realization of more efficient, flexible, and cost-effective operations through information sharing, collaborative decision-making, and process optimization among various nodes in the supply chain. Supply chain collaboration requires close cooperation among various supply chain nodes. Collaborative decision-making among these nodes is crucial in the supply chain collaboration network. Through the supply chain management and control platform, enterprises can achieve close communication and collaboration with partners such as suppliers and distributors to jointly formulate and adjust supply chain strategies, which helps improve the responsiveness and flexibility of the entire supply chain.

[0023] The supply participant attribute recording module 20 is used to record the supply participant attributes of each supply chain node in the supply chain collaborative network. Recording the supply participant attributes of each supply chain node in the supply chain collaborative network specifically refers to registering and recording the detailed information and characteristics of each supply chain node (such as suppliers, manufacturers, distributors, retailers, etc.) in the supply chain collaborative network. This helps to fully understand and evaluate the capabilities and performance of supply chain nodes, thereby optimizing the operation of the entire supply chain. Specifically, the supply participant attributes may include basic information (supply chain node name, etc.), role positioning (such as supplier, manufacturer, distributor, retailer, etc.), supply level (such as first-tier supplier, second-tier supplier, etc.), supply capacity (production capacity / supply capacity, inventory level, product quality), cooperation history (cooperation time with other supply chain nodes, on-time delivery rate and order cancellation rate, etc.), risk management (risk rating or classification such as high risk and low risk, potential risk nodes such as supply interruption and quality issues, etc.), relationship management (the closeness of the relationship with other supply chain nodes, communication mechanism and frequency, etc.), etc.

[0024] The supply chain redundant network construction module 30 constructs a supply chain redundant network based on the attributes of the supply participants, wherein the supply chain redundant network is a backup network of the supply chain collaborative network. Based on the original supply chain collaboration network, one or more backup networks are established based on the various attributes of the participants to ensure that in the event of a failure or interruption in the supply chain collaboration network, a rapid switchover to the backup network is possible, maintaining the continuity and stability of the supply chain. Specifically, the attributes of the supply participants are analyzed in detail, and the stability and reliability of each supply participant, as well as their influence on the entire supply chain, are assessed. Based on the attribute analysis results of the supply chain participants, key nodes in the supply chain are identified. These nodes are often bottlenecks or high-risk points in the supply chain. The risks these key nodes may face, such as supply disruptions, are assessed. Based on these key nodes and potential risks, the structure and layout of the redundant network are designed. The redundant network is designed based on operability and scalability, covering key nodes and providing sufficient backup resources and capabilities. Appropriate backup participants are identified and selected in the supply chain. These backup participants should have similar attributes and capabilities to the original participants to ensure the continuity and stability of the supply chain during the switchover. A cooperative relationship is established with the backup participants, and a corresponding cooperation agreement is signed to clarify the rights and obligations of both parties. An efficient switchover mechanism is designed and implemented, including information such as switchover conditions, processes, and time limits, to ensure a rapid switchover to the redundant network in the event of a failure or interruption in the original supply chain collaboration network.

[0025] Supply chain risk indicator detection module 40 is used to detect supply chain risk indicators of the supply chain collaborative network. Various risk factors that may arise in the supply chain collaborative network are quantified and evaluated to promptly identify potential risks and take appropriate measures to address them. Supply chain risk indicators may include, but are not limited to, inventory risk indicators, delivery risk indicators, quality risk indicators, and partnership risk indicators, such as inventory levels, inventory turnover rates, order completion rates, quality acceptance rates, quality stability, and partnership stability. By monitoring and analyzing these supply chain risk indicators in real time, potential risks can be promptly identified and appropriate measures can be taken to address them, thereby ensuring the stability and reliability of the supply chain. At the same time, supply chain management strategies can be continuously optimized based on changes in risk indicators to improve the efficiency and competitiveness of the supply chain.

[0026] The supply chain combination network establishment module 50 is configured to connect the supply chain redundant network with the supply chain collaborative network to construct a supply chain combination network when the supply chain risk indicator exceeds a preset risk indicator. The preset risk indicator is a critical value set to ensure that when certain supply chain nodes fail, there are still alternative paths to maintain network connectivity. If the monitored risk indicator exceeds the preset risk indicator threshold, a risk assessment process is initiated to conduct an in-depth analysis of potential risks, assess the likelihood and impact of the risk, and determine whether an emergency response mechanism needs to be activated. After the risk assessment confirms the need for response measures, the supply chain redundant network is activated to ensure that the backup participants in the redundant network are ready and can replace or supplement the failed or failed nodes in the collaborative network at any time. The supply chain redundant network is connected to the collaborative network, for example, through information technology means (such as API interfaces, EDI systems, etc.) to ensure seamless integration between the two networks, achieve real-time information sharing and collaborative work, and integrate the collaborative network and the redundant network into a combined network to address current or potential supply chain risks. Each node in the combined network (whether an original node in the collaborative network or a backup node in the redundant network) should be able to collaborate according to preset rules and processes.

[0027] A combination optimization algorithm model establishment module 60 is used to establish a combination optimization algorithm model. The combination optimization algorithm model uses the preset risk indicator as the optimization target, performs optimization in the supply chain combination network, and obtains optimization results, wherein the optimization results include the supply participants to be updated obtained through optimization. The combinatorial optimization algorithm model uses preset risk indicators as the optimization target and conducts optimization in the supply chain portfolio network. The preset risk indicators are quantitative standards used to measure potential risks in supply chain management. When these indicators exceed the preset threshold, it indicates that the supply chain has potential risks and needs to be optimized. Specifically, the combinatorial optimization algorithm model sets initial search conditions and parameters based on the current state of the supply chain portfolio network and the preset risk indicators, and adopts appropriate search strategies (such as genetic algorithms and particle swarm optimization algorithms) to search in the supply chain portfolio network. During the search process, an evaluation function is used to evaluate the quality of each potential solution. The evaluation function is usually calculated based on the preset risk indicators to quantify the risk level of the solution. Through continuous iteration and evolution, solutions with lower risk levels and better performance are gradually found. After each iteration, the algorithm will retain excellent individuals and adjust the search direction and parameters based on the characteristics of these individuals. After multiple iterations and optimizations, the algorithm will output the optimization results, including a set of supply participants to be updated (such as suppliers, distributors, etc.) and the optimal configuration of these participants in the supply chain portfolio network.

[0028] The supply chain optimization network generation module 70 is used to update the supply chain collaborative network with the supply participants to be updated obtained through optimization, generate a supply chain optimization network, and the supply chain management and control platform performs supply management and control based on the supply chain optimization network. Based on the supply participants to be updated obtained through optimization, the existing supply chain collaboration network is updated. For example, participants with higher risks or poor performance are replaced, new and better-quality participants are added, or the connection methods and cooperation strategies between participants are adjusted. The goals of updating the supply chain collaboration network are to reduce supply chain risks, improve supply chain efficiency, and optimize cost structures. Through the update, the supply chain collaboration network will be more robust and efficient, and better able to respond to market changes. Specifically, the updated supply chain collaboration network will form a new, optimized network structure, namely the supply chain optimization network. Based on the optimization results, this network will allocate resources, assign tasks, and coordinate the relationships between various links in an optimal manner. The supply chain optimization network will have higher flexibility, stronger adaptability, and lower risk levels. The supply chain management and control platform performs supply management and control based on the supply chain optimization network, realizing real-time monitoring, predictive analysis, decision-making, and other functions of the supply chain. Specifically, through the supply chain management and control platform, enterprises can obtain various data and information in the supply chain optimization network in real time, including inventory levels, order status, transportation status, etc., and use advanced data analysis technologies to analyze and mine this data, provide decision support and optimization suggestions, and achieve refined management and optimized control of the supply chain.

[0029] The collaborative network-based supply chain management and control platform according to an embodiment of the present invention is used to solve the technical problem of existing supply chain management and control, which is that problems with a single supply chain network cause the supply chain to be at risk of interruption, thereby leading to poor stability and continuity of the supply chain. It enables information sharing and collaborative work among all parties involved in the supply chain, achieving the technical effect of improving the flexibility, stability, and continuity of the supply chain. The collaborative network-based supply chain management and control platform includes: a supply chain collaborative network acquisition module 10, a supply participant attribute recording module 20, a supply chain redundant network construction module 30, a supply chain risk indicator detection module 40, a supply chain combination network establishment module 50, a combination optimization algorithm model establishment module 60, and a supply chain optimization network generation module 70.

[0030] The specific configuration of the supply chain redundant network building module 30 will be described in detail below. Figure 2As shown, the supply chain redundant network construction module 30 may further include: obtaining a sample of historical supply participants corresponding to each supply chain node based on the supply management module of the supply chain management and control platform. The supply management module is a key component of the supply chain management and control platform, responsible for managing supply activities within the supply chain, including supplier selection, procurement, inventory management, and other functions. It has data collection, storage, analysis, and reporting capabilities, and can provide enterprises with comprehensive supply chain information. Specifically, through the supply management module, enterprises can collect historical supply participant data corresponding to each supply chain node, including supplier name, location, supply history, product quality, delivery time, etc., and filter representative supply participant samples from the historical data to reflect the overall situation and characteristics of supply participants at different nodes in the supply chain. The module also includes matching within the sample of historical supply participants based on the attributes of the supply participants, obtaining matching supply participants with a matching degree greater than a preset threshold. Before matching, it is necessary to clarify the current demand or standards for supply participants. Based on these current demand or standards, a matching model is constructed. This model evaluates the degree of match between supply participants and current demand based on their attributes. The constructed matching model is then applied to a sample of historical supply participants, scoring or rating each sample to quantify its degree of match with current demand. Based on actual conditions and needs, a preset threshold for matching is set to determine which supply participants have a high enough match with current demand to be considered as potential qualified supply participants. Based on the matching model's scoring or rating results, supply participants with a matching degree exceeding the preset threshold are selected. This also involves constructing a redundant supply chain network based on the matched supply participants corresponding to each supply chain node. At each supply chain node, in addition to the primary supply participant, matched alternative supply participants are selected as backups. These alternative supply participants can provide alternative supply in the event of problems with the primary supply participant. Redundant links are established between multiple suppliers or distributors to ensure that if one link is interrupted, other links can quickly take over, ensuring supply chain continuity. Ultimately, a redundant supply chain network is constructed.

[0031] The specific configuration of the supply chain risk indicator detection module 40 will be described in detail below. The supply chain risk indicator detection module 40 may further include: establishing a risk identification model, performing risk identification on each supply chain node in the supply chain collaborative network based on the risk identification model, and obtaining a node risk value and an edge risk value. The risk identification model is a model used to identify, analyze, and quantify various risks in the supply chain collaborative network. Its purpose is to comprehensively identify potential risk sources through a systematic approach. Specifically, risks are classified into different categories based on their source, nature, and impact, such as supplier risk and logistics risk. Specific assessment indicators are defined for each risk category, such as supply stability, inventory levels, and production capacity. The risk identification model is used to comprehensively identify risks for each supply chain node (supplier, manufacturer, distributor, retailer, etc.) in the supply chain collaborative network, and the risk level is quantified to obtain a node risk value and an edge risk value. The node risk value is the node risk level corresponding to each supply chain node, and the edge risk value is the edge risk level corresponding to the dependency between each supply chain node. The node risk value refers to the size of the potential risk faced by a node in the supply chain collaborative network. The higher the value, the greater the risk. The edge risk value refers to the size of the potential risk of the connection between two nodes in the supply chain collaborative network (i.e., the supply chain relationship). For example, if the dependency between the two nodes is close and the risk value of one of the nodes is high, then the risk value of this edge will also be correspondingly high. It also includes detecting the supply chain risk index of the supply chain collaborative network based on the node risk value and the edge risk value. The supply chain risk index is detected based on the node risk value and the edge risk value to reflect the risk status of the entire supply chain collaborative network, which helps to improve the robustness and reliability of the supply chain and reduce the losses caused by risk events.

[0032] The specific configuration of the supply chain redundant network construction module 30 will be described in detail below. The supply chain redundant network construction module 30 may further include: obtaining the node risk value corresponding to each supply chain node in the supply chain collaborative network. It also includes introducing a loss function to perform loss analysis on the node risk value corresponding to each supply chain node in the supply chain collaborative network, and obtaining the loss index corresponding to each supply chain node. The loss function is a function that quantifies the difference between the predicted value and the actual value of the model. In supply chain risk analysis, the loss function can be used to quantify the potential loss corresponding to the node risk value. Specifically, according to the previous risk identification model and method, the risk value of each supply chain node in the supply chain collaborative network is determined, and the risk value of each node is used as input and applied to the loss function. The loss function will calculate the corresponding loss index based on these risk values. The size of the loss index reflects the size of the potential loss that may be caused by the node risk. For example, a simple loss function may be linear, that is, the loss index is equal to the risk value multiplied by a constant (representing the loss value per unit risk).

[0033] The supply chain redundancy network construction module 30 also includes, based on the size of the loss index corresponding to each supply chain node, obtaining the key supply chain node whose loss index is greater than the preset loss index. The preset loss index is a threshold value used to distinguish which nodes' loss index is considered to be high-risk. The loss index of each node is compared with the preset loss index. If the loss index of a node is greater than the preset loss index, the node is identified as a key supply chain node. It also includes building a supply chain redundancy network corresponding to the key supply chain node. An in-depth analysis is conducted on each key node to understand its position, role, dependency and possible risks in the supply chain. Based on the analysis results of the key nodes, the architecture of the redundant network is designed, including determining the number, location and connection method of the backup suppliers, backup production lines, backup warehouses and other resources that need to be added, and adding backup suppliers for the key nodes to ensure that when problems occur with the main supplier, it can quickly switch to the backup supplier to ensure the normal operation of the supply chain, and finally build a supply chain redundancy network.

[0034] The specific configuration of the combined optimization algorithm model building module 60 will be described in detail below. The combined optimization algorithm model building module 60 may further include: defining the probability of optimization for each supply chain node based on the node risk value corresponding to each supply chain node in the supply chain collaborative network. Based on the node risk value corresponding to each supply chain node in the supply chain collaborative network, a probability of optimization is defined for each node. The probability of optimization is a value between 0 and 1, indicating the likelihood of the node being selected during the optimization process. Generally speaking, the greater the node risk value, the higher the probability of optimization should be, to ensure that high-risk nodes are processed during the optimization process. For example, suppose we have a supply chain collaborative network containing N nodes, and the risk value of each node is R1, R2, .... The risk values of all nodes are normalized to obtain corresponding relative risk values r1, r2, .... The probability of optimization P1, P2, ... is defined based on the relative risk values. The probability of optimization can be directly set as the relative risk value, or a weighting factor can be introduced to more finely adjust the distribution of the probability of optimization.

[0035] The combinatorial optimization algorithm model building module 60 also includes obtaining the redundant solution space corresponding to each supply chain node in the supply chain combination network. For each key node, a series of redundant solutions are defined based on the characteristics and possible impact of potential risks, including backup suppliers, backup production lines, backup warehouses, alternative transportation methods, etc. Each redundant solution should have similar functions and performance to the main supply chain process to ensure seamless switching when needed. It also includes inputting the optimization probability corresponding to each supply chain node into the combinatorial optimization algorithm model, and performing multiple rounds of iterative optimization in the redundant solution space corresponding to each supply chain node based on the optimization probability. If the preset risk indicators are met, the optimization ends and the optimized supply participants to be updated are output. After identifying key supply chain nodes and calculating the probability of each node being optimized, these probability values are provided as input data to the combinatorial optimization algorithm model. Within the combinatorial optimization algorithm model, the redundant solution space of each node is iteratively optimized based on the input optimization probability. In each round of iteration, the algorithm prioritizes nodes with a higher optimization probability and attempts to select a better solution from their redundant solution space. This process is repeated until a preset stopping condition is met (such as reaching a preset number of iterations or finding a solution that meets the preset risk index). If a solution that meets the preset risk index is found during the iterative optimization process, the algorithm stops iterating and outputs the updated supply participants obtained through optimization. These updated supply participants may be new suppliers, new logistics routes, new production strategies, etc., which can reduce the overall risk level while maintaining the stability and continuity of the supply chain. The preset risk index is a standard used to evaluate the effectiveness of supply chain optimization. It may be a specific value (such as a risk reduction percentage) or a comprehensive evaluation indicator based on multiple factors.

[0036] The specific configuration of the combination optimization algorithm model establishment module 60 will be described in detail below. The combination optimization algorithm model establishment module 60 may further include: configuring a first constraint condition, wherein the first constraint condition includes a preset risk persistence stability, wherein the risk persistence stability represents the risk stability of each participant, and is obtained by calculating the standard deviation of the node risk values at different time points. The first constraint, i.e., the preset risk persistence and stability, is configured to ensure that the risk level of each participant in the supply chain collaborative network can remain relatively stable over a period of time to avoid drastic fluctuations. Risk persistence and stability refers to the ability of each participant in the supply chain (such as suppliers, manufacturers, distributors, etc.) to maintain a relatively stable risk level over a period of time, thereby reducing the uncertainty caused by risk fluctuations. Specifically, the risk value of each participant at different time points is determined. For each participant, the standard deviation of its risk value at different time points is calculated to measure the fluctuation of the participant's risk value. If the calculated standard deviation is small, it means that the participant's risk value has fluctuated less over the selected time period, i.e., the risk persistence and stability is high, which means that the participant's risk level is relatively stable. On the contrary, if the standard deviation is large, it means that the participant's risk value has fluctuated more, i.e., the risk persistence and stability is low, which may mean that the participant faces greater uncertainty or risk. It also includes using the first constraint as a constraint of the combined optimization algorithm model to optimize the supply participants to be updated obtained by optimization. The first constraint condition (i.e., the preset risk persistence stability) is used as the constraint condition of the combined optimization algorithm model, and the supply participants to be updated obtained by optimization are optimized. Specifically, the risk persistence stability is set as a constraint condition that must be met to ensure that the supply participants in the optimization result are stable in terms of risk control. The supply participants to be updated obtained by optimization are input into the combined optimization algorithm model as candidate solutions. According to the preset optimization objectives and constraints (including risk persistence stability), the optimal solution is found through iterative calculation. If the optimization result meets all conditions, the optimized list of supply participants to be updated is output.

[0037] The specific configuration of the combinatorial optimization algorithm model establishment module 60 will be described in detail below. The combinatorial optimization algorithm model establishment module 60 may further include: configuring a second constraint condition, wherein the second constraint condition includes a preset collaborative dependency stability, wherein the collaborative dependency stability characterizes the stability of the collaborative dependency relationship between each participant, and is obtained by calculating the historical collaboration frequency between each participant. Configuring the second constraint condition, i.e., the preset collaborative dependency stability, is to ensure the stability of the collaborative dependency relationship between each participant during the supply chain optimization process. Collaborative dependency stability characterizes the stability of the collaborative cooperation relationship between each participant in the supply chain. A stable collaborative dependency relationship means that the participants can cooperate effectively and frequently over a long period of time, reducing the supply chain risks caused by unstable cooperative relationships. Collaborative dependency stability can be obtained by calculating the historical collaboration frequency between each participant, wherein the historical collaboration frequency refers to the actual number or frequency of collaborations between the participants within a certain time range. The higher the collaboration frequency, the more stable the collaborative dependency relationship between the participants. It also includes using the second constraint condition as a constraint condition of the combinatorial optimization algorithm model to perform secondary optimization on the supply participants to be updated obtained through optimization. The second constraint (i.e., the preset collaborative dependency stability) is used as a constraint of the combinatorial optimization algorithm model, and the supply participants to be updated obtained through optimization are optimized for the second time. Specifically, based on the preliminary optimization results, the preset collaborative dependency stability is introduced into the algorithm model as a new constraint. For the combination of supply participants to be updated obtained through the preliminary optimization, the collaborative dependency stability between them is calculated. By collecting and analyzing historical collaboration data between the participants, such as collaboration frequency, collaboration success rate, etc., the stability of the collaborative dependency relationship between them is evaluated. Based on the calculation results of the collaborative dependency stability, the preliminary optimization results are optimized for the second time. If a combination of participants performs poorly in terms of collaborative dependency stability, some of the participants are replaced to improve the overall collaborative dependency stability. If multiple combinations of participants perform similarly in terms of collaborative dependency stability, the algorithm may conduct further screening based on other optimization objectives and constraints.

[0038] Below, the specific configuration of the supply chain optimization network generation module 70 will be described in detail. The supply chain optimization network generation module 70 may further include: obtaining the number of supply chain nodes of the supply chain collaborative network. It also includes, based on the supply participants to be updated obtained through optimization, obtaining the number of supply chain nodes to be updated. A group of optimized supply participants (such as suppliers, manufacturers, distributors, etc.) is obtained by applying a combined optimization algorithm model, and then the number of nodes that need to be updated or adjusted in the supply chain is determined based on this group of optimized participants. It also includes, if the ratio of the number of supply chain nodes to be updated to the number of supply chain nodes is greater than a preset ratio, activating a retention instruction, and optimizing the supply participants to be updated according to the retention instruction. Calculate the ratio of the number of supply chain nodes to be updated to the number of nodes in the entire supply chain, which reflects the scale and scope of the supply chain structure adjustment. Compare this ratio with a preset ratio (usually set based on factors such as the company's risk tolerance and supply chain stability requirements). If the proportion of nodes to be updated is greater than the preset ratio, it means that the supply chain structure adjustment is large, which may affect the stability or operational efficiency of the supply chain, and it is necessary to activate the retention instruction. The retention instruction usually contains a series of strategies and measures to minimize the potential risks brought about by large-scale node updates while maintaining the overall stability of the supply chain. For example, delay the update of some nodes; make special reservations or formulate alternative plans for key nodes to ensure the continuity of the supply chain; temporarily strengthen the supply chain to deal with possible risks, etc. After activating the retention instruction, further optimization is carried out on the supply participants to be updated.

[0039] Although this application makes various references to certain modules in the platform according to the embodiments of this application, any number of different modules can be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0040] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A supply chain management and control platform based on a collaborative network, characterized by: The platform is used to perform: A supply chain collaborative network acquisition module, wherein the supply chain collaborative network acquisition module is used to interact with the supply chain management and control platform to acquire a supply chain collaborative network, wherein the supply chain collaborative network includes multiple supply chain nodes; A supply participant attribute recording module, the supply participant attribute recording module is used to record the supply participant attributes of each supply chain node in the supply chain collaborative network; A supply chain redundant network construction module, wherein the supply chain redundant network construction module constructs a supply chain redundant network based on the attributes of the supply participants, wherein the supply chain redundant network is a backup network of the supply chain collaborative network; A supply chain risk indicator detection module, wherein the supply chain risk indicator detection module is used to detect the supply chain risk indicators of the supply chain collaborative network; a supply chain combination network establishment module, configured to connect the supply chain redundancy network with the supply chain collaboration network to construct a supply chain combination network when the supply chain risk indicator is greater than a preset risk indicator; a combination optimization algorithm model establishment module, the combination optimization algorithm model establishment module is used to establish a combination optimization algorithm model, the combination optimization algorithm model uses the preset risk indicator as the optimization target, performs optimization in the supply chain combination network, and obtains optimization results, wherein the optimization results include the supply participants to be updated obtained through optimization; A supply chain optimization network generation module is used to update the supply chain collaborative network with the supply participants to be updated obtained through optimization, generate a supply chain optimization network, and the supply chain management and control platform performs supply management and control based on the supply chain optimization network.

2. The collaborative network-based supply chain management and control platform according to claim 1, characterized in that: The supply chain redundancy network building module is used to perform: According to the supply management module of the supply chain management and control platform, a sample of historical supply participants corresponding to each supply chain node is obtained; Based on the attributes of the supply participant, matching is performed in the sample of historical supply participants to obtain matching supply participants with a matching degree greater than a preset threshold; Build a supply chain redundant network based on the matching supply participants corresponding to each supply chain node.

3. The collaborative network-based supply chain management and control platform according to claim 1, characterized in that: The supply chain risk indicator detection module is used to perform: Establishing a risk identification model, performing risk identification on each supply chain node in the supply chain collaborative network according to the risk identification model, and obtaining a node risk value and an edge risk value; The node risk value is the node risk degree corresponding to each supply chain node, and the edge risk value is the edge risk degree corresponding to the dependency between each supply chain node. The supply chain risk index of the supply chain collaborative network is detected according to the node risk value and the edge risk value.

4. The collaborative network-based supply chain management and control platform according to claim 3, characterized in that: The supply chain risk indicator detection module is used to perform: Obtaining a node risk value corresponding to each supply chain node in the supply chain collaborative network; Introducing a loss function to perform loss analysis on the node risk value corresponding to each supply chain node in the supply chain collaborative network, and obtaining the loss index corresponding to each supply chain node; According to the loss index size corresponding to each supply chain node, obtain the key supply chain node whose loss index is greater than the preset loss index; Construct a supply chain redundancy network corresponding to the key supply chain nodes.

5. The collaborative network-based supply chain management and control platform according to claim 1, characterized in that: The combined optimization algorithm model building module is used to execute: According to the node risk value corresponding to each supply chain node in the supply chain collaborative network, the probability of being optimized corresponding to each supply chain node is defined; Obtaining a redundant solution space corresponding to each supply chain node in the supply chain combination network; The optimized probability corresponding to each supply chain node is input into the combined optimization algorithm model, and multiple rounds of iterative optimization are performed in the redundant solution space corresponding to each supply chain node based on the optimized probability. If the preset risk index is met, the optimization is terminated, and the supply participants to be updated obtained by the optimization are output.

6. The collaborative network-based supply chain management and control platform according to claim 5, characterized in that: The combined optimization algorithm model building module is also used to execute: Configuring a first constraint condition, wherein the first constraint condition includes presetting risk persistence stability, wherein risk persistence stability represents the risk stability of each participant and is obtained by calculating the standard deviation of node risk values at different time points; The first constraint condition is used as the constraint condition of the combined optimization algorithm model, and the supply participants to be updated obtained by the optimization are optimized.

7. The collaborative network-based supply chain management and control platform according to claim 6, characterized in that: The combined optimization algorithm model building module is also used to execute: Configuring a second constraint condition, wherein the second constraint condition includes presetting collaborative dependency stability, wherein the collaborative dependency stability represents the stability of the collaborative dependency relationship between the participants, and is obtained by calculating the historical collaboration frequency between the participants; The second constraint condition is used as the constraint condition of the combined optimization algorithm model, and the supply participants to be updated obtained by the optimization are optimized twice.

8. The collaborative network-based supply chain management and control platform according to claim 1, characterized in that: The supply chain optimization network generation module is further used to perform: Obtaining the number of supply chain nodes in the supply chain collaboration network; Based on the supply participants to be updated obtained through optimization, the number of supply chain nodes to be updated is obtained; If the ratio of the number of supply chain nodes to be updated to the number of supply chain nodes is greater than a preset ratio, the reservation instruction is activated, and the supply participants to be updated are optimized according to the reservation instruction.

Images