Artificial intelligence-based supply chain logistics efficiency coordination optimization method and system

Abstract

The present application relates to the field of supply chain optimization, and more particularly to a supply chain logistics efficiency collaborative optimization method and system based on artificial intelligence. The method comprises the following steps: acquiring real-time supply chain logistics data flow, performing node coupling density analysis and adaptive hierarchical mapping, and constructing a multi-layer logistics collaboration network; performing path node load response backstepping on the multi-layer logistics collaboration network, and performing nonlinear distortion evaluation, thereby obtaining the response curve nonlinear distortion degree; performing sensitivity quantification evaluation according to the response curve nonlinear distortion degree, and performing inter-node topology correlation reconstruction, and constructing a supply chain logistics topology optimization network. According to the real-time supply chain logistics data flow, future load demand pre-play is performed, and calling operation path evolution analysis is performed to generate calling operation path evolution data. The present application optimizes the configuration of supply chain logistics resources, and improves the overall operation efficiency and response capability of supply chain logistics.

Description

Technical Field

[0001] This invention relates to the field of supply chain optimization, and in particular to a method and system for collaborative optimization of supply chain logistics efficiency based on artificial intelligence. Background Technology

[0002] With the continued advancement of global economic integration and digital transformation, supply chain logistics, as a core system connecting production, warehousing, transportation, and sales, is undergoing a profound transformation from traditional management models to intelligent and collaborative ones. Logistics, as a crucial link in supply chain operations, directly impacts a company's operating costs, customer satisfaction, and market responsiveness. Especially against the backdrop of rapid e-commerce development and constantly changing consumption patterns, supply chain logistics systems are facing multiple challenges, including order fragmentation, demand uncertainty, and complex transportation routes, placing higher demands on logistics efficiency and supply chain logistics collaboration capabilities.

[0003] Traditional supply chain logistics management typically relies on rule-driven and experience-based decision-making, making it difficult to effectively address dynamic, changing, and complex business scenarios. Existing optimization methods are mostly based on static data analysis and linear model solving, lacking the ability to process and intelligently mine massive amounts of heterogeneous data in real time, hindering collaborative optimization across nodes and processes. Furthermore, information silos are prevalent among different enterprises, resulting in insufficient transparency and responsiveness in the overall supply chain logistics, further restricting the improvement of logistics efficiency and the optimal allocation of resources.

[0004] Meanwhile, the continuous maturation of next-generation information technologies such as artificial intelligence, big data, and cloud computing has provided new solutions for improving supply chain logistics efficiency. In particular, artificial intelligence has demonstrated strong application potential in predictive analytics, route optimization, anomaly detection, and decision support, becoming a crucial force driving the evolution of supply chain logistics from passive response to proactive collaboration. By constructing intelligent analysis and optimization models that integrate real-time sensing data with historical business data, it is possible not only to dynamically adjust logistics routes and predict supply risks but also to achieve information sharing and intelligent collaboration among multiple links, significantly improving the response speed and operational efficiency of the entire supply chain system.

[0005] However, current AI-based supply chain logistics optimization systems still face numerous challenges, such as complex data integration, insufficient model generalization ability, and difficulty in balancing conflicting optimization objectives with real-time decision-making, making them difficult to fully implement in practice. Especially in scenarios involving multiple nodes and concurrent tasks, the lack of a real-time, intelligent, and collaborative optimization method and system framework has become a key bottleneck restricting the development of intelligent supply chain logistics. Summary of the Invention

[0006] The application provides a supply chain logistics efficiency collaborative optimization method and system based on artificial intelligence to solve at least one of the above technical problems.

[0007] To achieve the above object, the application provides a supply chain logistics efficiency collaborative optimization method based on artificial intelligence, comprising the following steps:

[0008] Step S1: acquiring real-time supply chain logistics data flow, performing node coupling density analysis and adaptive hierarchical mapping, and constructing a multi-layer logistics collaboration network;

[0009] Step S2: performing path node load response backstepping on the multi-layer logistics collaboration network, and performing nonlinear distortion evaluation, so as to obtain the nonlinear distortion degree of the response curve;

[0010] Step S3: performing sensitivity quantitative evaluation according to the nonlinear distortion degree of the response curve, and performing inter-node topological association reconstruction, and constructing a supply chain logistics topological optimization network.

[0011] Step S4: performing future load demand pre-play according to the real-time supply chain logistics data flow, and performing calling operation path evolution analysis, and generating calling operation path evolution data;

[0012] Step S5: performing calling path conflict identification based on the supply chain logistics topological optimization network, and then performing potential logistics risk link prediction, and obtaining a comprehensive risk prediction result;

[0013] Step S6: performing local logistics resource scheduling and distribution and global pre-position collaborative processing based on the comprehensive risk prediction result, and constructing an intelligent logistics collaborative optimization engine.

[0014] In the present specification, a supply chain logistics efficiency collaborative optimization system based on artificial intelligence is provided for executing the supply chain logistics efficiency collaborative optimization method based on artificial intelligence as described above, comprising:

[0015] A multi-layer network module is configured to acquire real-time supply chain logistics data flow, perform node coupling density analysis and adaptive hierarchical mapping, and construct a multi-layer logistics collaboration network;

[0016] A load response module is configured to perform path node load response backstepping on the multi-layer logistics collaboration network, and perform nonlinear distortion evaluation, so as to obtain the nonlinear distortion degree of the response curve;

[0017] A topological reconstruction module is configured to perform sensitivity quantitative evaluation according to the nonlinear distortion degree of the response curve, and perform inter-node topological association reconstruction, and construct a supply chain logistics topological optimization network;

[0018] The path evolution module is used to predict future load demand based on real-time supply chain logistics data flow, and to perform call operation path evolution analysis to generate call operation path evolution data.

[0019] The risk prediction module is used to identify call path conflicts based on the supply chain logistics topology optimization network, and then predict potential logistics risk links to obtain comprehensive risk prediction results.

[0020] The collaborative optimization module is used to perform local logistics resource scheduling and allocation and global pre-processing based on comprehensive risk prediction results, thus building an intelligent logistics collaborative optimization engine.

[0021] The beneficial effects of the present application are as follows: The present application can monitor the changes of each supply chain link in real time by obtaining real-time supply chain logistics data flow, dynamically adjust the strategy, and reduce the decision-making errors caused by information delay in traditional supply chain logistics. Adaptive hierarchical mapping and node coupling density analysis can optimize the coordination relationship between each link of the supply chain, and improve the efficiency of resource allocation and information transmission. Different levels of network structure help to flexibly adjust according to different needs or goals, maximize the cooperation benefit. The multi-layer cooperative network ensures the efficient interaction between nodes (such as production, storage, transportation, etc.), avoids the problem of information island and insufficient coordination, and makes the operation of the whole supply chain logistics more smooth. Through path node load response backstepping, the performance of each node in the supply chain logistics under different load conditions can be accurately understood. This helps to find possible resource bottlenecks, inefficient links, and effectively optimize. By evaluating the nonlinear distortion of the response curve, the complex dynamic relationship in the supply chain logistics can be identified, especially the response changes when the supply chain logistics is affected by external factors. Understanding the degree of nonlinear distortion helps to optimize decision-making and avoid bias caused by excessive reliance on linear assumptions. Through sensitivity quantitative evaluation, the nodes or links that have the greatest impact on the overall performance of the supply chain logistics can be identified. This makes the allocation and optimization of resources focus on the key parts that have the greatest impact on system performance. Topology reconstruction between nodes can optimize the overall structure of the network while ensuring network connectivity, improving the efficiency of resource flow. Through this optimization, redundant nodes and inefficient connections in the supply chain logistics can be reduced, and the overall cooperation efficiency can be improved. The network after topology optimization can better respond to external environmental changes or internal resource fluctuations, and has stronger flexibility and adaptability. The input of real-time supply chain logistics data flow makes it possible to predict load demand in advance, helping enterprises to plan resources and adjust strategies early. This prediction ability effectively avoids the problem of excess or shortage of supply chain logistics resources. Evolution analysis of the calling operation path can reveal the potential trend of changes in the operation process of the supply chain logistics, helping enterprises to foresee the challenges and optimization opportunities that different paths may face. This helps to make strategic adjustments in advance and avoid chaos and inefficiency in emergency response. By identifying potential conflicts in the calling path, it can be found in real time that there may be resource conflicts, insufficient transportation capacity, etc. in the supply chain logistics, so as to avoid the negative impact of these problems on the overall performance of the supply chain logistics. By predicting potential risk links, enterprises can identify risk factors that may have a significant impact on the performance of the supply chain logistics in advance. This provides an early warning mechanism for enterprises to take action in advance and reduce the risk of supply chain logistics disruption or delay. The comprehensive risk prediction results help decision-makers to have a global perspective and identify risks at different levels and nodes, helping enterprises to better cope with complex supply chain challenges. On the basis of real-time risk prediction results, local resources can be accurately scheduled.This can maximize the use of existing resources and avoid waste or misallocation of resources. Through pre-collaborative processing, coordination and synchronization can be performed in advance between multiple links of the supply chain logistics. This not only improves the collaboration efficiency of each link, but also quickly adjusts the strategy when demand changes or risk events occur, ensuring smooth operation of the entire supply chain logistics. The intelligent logistics collaborative optimization engine based on big data and AI algorithms can automatically make decisions and optimizations without human intervention, improving decision speed and accuracy. This automated optimization capability can significantly improve the overall operation efficiency and response speed of the supply chain logistics. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 A flowchart of the steps of the supply chain logistics efficiency collaborative optimization method based on artificial intelligence;

[0023] Figure 2 A detailed implementation step flowchart for step S1;

[0024] Figure 3 A detailed implementation step flowchart for step S2;

[0025] Figure 4 A detailed implementation step flowchart for step S3. DETAILED DESCRIPTION

[0026] It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0027] The present application provides a supply chain logistics efficiency collaborative optimization method and system based on artificial intelligence. The execution subject of the supply chain logistics efficiency collaborative optimization method based on artificial intelligence includes but is not limited to the following: mechanical equipment, data processing platform, cloud server node, network upload device, etc. which can be regarded as general computing nodes of the present application, and the data processing platform includes but is not limited to: audio image management system, information management system, cloud data management system at least one.

[0028] Please refer to Figures 1 to 4 The present application provides a supply chain logistics efficiency collaborative optimization method based on artificial intelligence, which includes the following steps:

[0029] Step S1: Obtain real-time supply chain logistics data flow, perform node coupling density analysis and adaptive hierarchical mapping, and construct a multi-layer logistics collaboration network;

[0030] Step S2: Perform path node load response backstepping on the multi-layer logistics collaboration network, and perform nonlinear distortion evaluation to obtain the nonlinear distortion degree of the response curve;

[0031] Step S3: According to the degree of nonlinear distortion of the response curve, the sensitivity quantitative evaluation is carried out, and the inter-node topology correlation reconstruction is carried out, and the supply chain logistics topology optimization network is constructed.

[0032] Step S4: According to the real-time supply chain logistics data flow, the future load demand is preformed, and the calling operation path evolution analysis is carried out, and the calling operation path evolution data is generated;

[0033] Step S5: Based on the supply chain logistics topology optimization network, the calling path conflict is identified, and the potential logistics risk link is predicted, and the comprehensive risk prediction result is obtained;

[0034] Step S6: Based on the comprehensive risk prediction result, the local logistics resource scheduling and distribution and the global pre-position collaborative processing are carried out, and the intelligent logistics collaborative optimization engine is constructed.

[0035] The application obtains real-time supply chain logistics data flow, which can help enterprises timely grasp various dynamic information in supply chain logistics, such as demand fluctuations and supply risks, so as to make more flexible and accurate decisions. Through node coupling density analysis and adaptive hierarchical mapping, an efficient multi-layer collaborative network can be constructed. This network can realize efficient scheduling and optimization of supply chain logistics resources, improve the synergy between each link, and reduce the delay and distortion in information transmission. Through path node load response backstepping, the load of each node can be accurately calculated. This can help identify possible bottlenecks and pressure points, providing a basis for supply chain logistics path optimization. Nonlinear distortion evaluation can reveal the complex nonlinear relationships in the supply chain logistics system, especially when the supply chain logistics network changes due to some external or internal factors, which may have a profound impact on the system's response. By understanding these changes, forward-looking adjustments and optimizations can be made. Evaluating the sensitivity of each node to the operation of the supply chain logistics system can help identify which nodes are critical to the overall operation of the supply chain logistics system and which nodes have less impact on the overall operation of the supply chain logistics system, thereby achieving priority allocation of resources. Through the reconstruction of the topological association between nodes, the topological optimization of the supply chain logistics network can be achieved. This not only improves the resilience of the network structure and reduces potential risks, but also quickly adjusts when nodes fail or demand changes, ensuring the stability and efficient operation of the supply chain logistics. Real-time data flow and future load demand prediction can predict possible future changes, including demand fluctuations and changes in resource demand. This helps enterprises prepare in advance to avoid resource shortages or surpluses. Through evolution analysis of the calling path, it can be understood how the operation mode in the supply chain logistics changes over time, demand or external factors. This dynamic analysis can help enterprises optimize supply chain operations and avoid unnecessary resource waste. By identifying calling path conflicts and potential risk links, potential bottlenecks or risk points in the supply chain logistics can be identified, such as path congestion and resource conflicts. This helps to make adjustments in advance to reduce the occurrence of sudden risks. Comprehensive risk prediction can help enterprises identify potential supply chain problems from a global perspective, including external market fluctuations and internal production bottlenecks, so that effective risk avoidance measures can be taken in advance to reduce losses. Based on the results of comprehensive risk prediction, local logistics resource scheduling and allocation can be accurately performed to ensure that resources are reasonably utilized in each link. At the same time, based on the pre-cooperative processing, the work of each link can be coordinated in advance to ensure that the supply chain logistics always operates efficiently at the overall level. Through the construction of an intelligent logistics collaborative optimization engine, automated decision support can be realized to improve the intelligent level of supply chain logistics operation. The engine not only can respond to various changes in supply chain logistics in real time, but also can automatically optimize resource allocation to improve the overall operation efficiency and response capability of the supply chain logistics.

[0036] In the embodiment of the present application, referring to Figure 1 A step flow diagram of a supply chain logistics efficiency collaborative optimization method based on artificial intelligence according to the present application, in the present example, the steps of the supply chain logistics efficiency collaborative optimization method based on artificial intelligence include:

[0037] Step S1: Obtain real-time supply chain logistics data flow, perform node coupling density analysis and adaptive hierarchical mapping, and construct a multi-layer logistics collaboration network;

[0038] In this example, the real-time supply chain logistics data stream includes order information, inventory status, transportation progress, and supplier feedback. These data usually come from different information systems, such as ERP, WMS, and TMS. Set the standard and frequency of data acquisition, extract the latest supply chain data from each system every 5 minutes. Use data integration tools (such as Apache Kafka or Apache NiFi) to extract real-time data from multiple data sources. Extract the number of orders, inventory levels, and transportation status in the past 5 minutes. Assume that at a certain moment, the data obtained by the system shows that there are currently 500 unprocessed orders, the inventory status is 3000 units, and the transportation progress shows that there are 150 orders in transit. Store the collected data in a real-time database (such as InfluxDB or MongoDB) for subsequent analysis and processing, and ensure the integrity and real-time nature of the data. Node coupling density analysis is used to evaluate the interdependence between nodes in the supply chain logistics network. High coupling degree usually represents strong correlation between nodes, which may affect the stability of the overall supply chain logistics. Set the formula for calculating the coupling degree, coupling degree = number of connection edges / total number of nodes. The adjacency matrix in graph theory can be used to represent the connection relationship between nodes. By analyzing the real-time data stream, a connection graph between logistics nodes is constructed. Assume that in the current network, node A (supplier) has 10 direct connections with node B (warehouse), and node B has 8 connections with node C (retailer). Calculate the coupling degree of each node. If the coupling degree of node B is (10 + 8) / 3 = 6, it indicates that node B has strong linkage with other nodes. Record the coupling degrees of all nodes in the database for further analysis. Generate a coupling density analysis report to clearly define the coupling degree of each node, providing a basis for subsequent hierarchical mapping. Adaptive hierarchical mapping aims to divide the supply chain logistics network into different levels according to the coupling density and functional characteristics of the nodes, in order to improve overall efficiency and flexibility. It can usually be divided into supply layer, warehouse layer, and distribution layer. Set the mapping criteria to divide nodes with high coupling degrees into the same layer to facilitate resource sharing and information flow. According to the coupling density analysis results, the nodes are divided into layers. If nodes A and B have high coupling degrees, they are classified into the same layer (such as the supply layer). Node C (retailer) is classified into the distribution layer due to its low coupling degree. Generate a hierarchical mapping diagram to clearly show the hierarchical relationship of each node. Assume that the final mapping result is: the first layer is the supplier (nodes A and B), the second layer is the warehouse (node D), and the third layer is the retailer (node C). Record the hierarchical mapping results in the system and generate a hierarchical structure report to support subsequent optimization and decision-making of the supply chain logistics network.

[0039] Step S2: Perform path node load response backstepping on the multi-layer logistics collaboration network, and perform nonlinear distortion evaluation to obtain the nonlinear distortion degree of the response curve;

[0040] In this embodiment, the load response backstepping aims to analyze the load status of each path node in the supply chain logistics according to real-time data, and determine its impact on the overall network performance. By analyzing the load changes of the nodes, potential bottlenecks and high-risk areas can be identified. Set the backstepping standard, and calculate the current load status of the node through known load data (such as historical order volume, transportation capacity, etc.). Use real-time supply chain logistics data to extract the current load status of each path node. Assume that the current load of node A is 400 units, the load of node B is 600 units, and the load of node C is 300 units. Through the load backstepping model, the contribution of each node to the overall supply chain logistics is calculated. If the historical data of node A shows that its effective support rate for the network is 80% when the load is 400 units, then its response capability under the current load can be inferred. Record the load response data of each node, including load status, support rate and its impact on the overall network, to form a load response report, which provides basic data for subsequent nonlinear distortion evaluation. Nonlinear distortion evaluation is used to quantify the deviation of the response curve of the path node when the load changes, and to identify possible nonlinear characteristics. High distortion degree usually means that the node is unstable when the load changes, which may affect the overall performance. Set the evaluation standard, use mean square error (MSE) and change rate to quantify the degree of nonlinear distortion. Analyze the load response data of each path node to generate a load response curve. Assume that the response data of node A under different loads is: 100 units, response is 80, 200 units, response is 150, and 400 units, response is 300. These data points are used to construct the response curve. The degree of nonlinear distortion is evaluated by calculating the deviation between the actual response curve and the ideal linear response curve. If the ideal linear response curve is y=1.5x and the actual curve responds to 300 units at 400 units, then the MSE is calculated: Record the nonlinear distortion degree of all path nodes in the database to form a nonlinear distortion evaluation report, and clearly define the response curve characteristics of each node to provide a basis for subsequent optimization decisions.

[0041] Step S3: Sensitivity quantification evaluation according to the nonlinear distortion degree of the response curve, and reconstruction of the topology correlation between nodes to construct a supply chain logistics topology optimization network.

[0042] In this embodiment, the sensitivity quantification evaluation aims to evaluate the impact of each node on the overall supply chain logistics performance according to the degree of non-linear distortion of the node. High sensitivity nodes may cause instability of the system when the load changes, and need to be prioritized. Set the sensitivity evaluation standard, use the distortion degree and node importance indicators (such as historical load, connection strength, etc.) to make a comprehensive score. Analyze the non-linear distortion degree of each node, assuming that the distortion degree of node A is 0.3, node B is 0.6, and node C is 0.2. By combining the distortion degree with historical load data, the sensitivity of the node is evaluated. Node A has a larger response change under high load, so its sensitivity score may be higher. Calculate the comprehensive sensitivity score, set the formula as: sensitivity score = distortion degree x connection strength, if the connection strength of node A is 5, then its sensitivity score is . Record the sensitivity scores of all nodes and generate a sensitivity evaluation report to clearly identify which nodes need to be prioritized for optimization to reduce the overall network risk. Topology association reconstruction between nodes aims to optimize the connection relationship of nodes in the network according to the sensitivity evaluation results, to improve the flexibility and stability of the overall supply chain logistics. Set the reconstruction standard, prioritize connecting low sensitivity nodes with high sensitivity nodes to disperse risks and reduce load. According to the sensitivity score, identify high sensitivity nodes (such as node B) and low sensitivity nodes (such as node C). If the sensitivity score of node B is 2.0 and the score of node C is 0.5, then they are prioritized to establish stronger connections between them. On the basis of the existing topology, new connections are added or existing connections are adjusted. Node B and node C can be directly connected to form a new path, so that when node B has too high load, load transfer can be performed through node C. Use network analysis tools to generate a new topology structure diagram to clearly show the new connection relationship and hierarchy between nodes. Record the topology after reconstruction and generate a report to support subsequent resource allocation and optimization decisions.

[0043] Step S4: Perform future load demand rehearsal according to real-time supply chain logistics data flow, and perform call operation path evolution analysis to generate call operation path evolution data;

[0044] In this example, the sensitivity score of a node reflects its impact on the overall supply chain logistics when load changes. High sensitivity nodes can cause instability in the system when load changes, so they need to be prioritized. Set a sensitivity score threshold, nodes with a sensitivity score greater than 1.5 are considered high sensitivity nodes, and nodes with a score less than 0.5 are considered low sensitivity nodes. Extract the sensitivity scores of all nodes from the sensitivity evaluation results. Assume that in the analysis, the sensitivity score of node B is 2.0, node C is 0.5, and other nodes such as D are 1.0. Record the sensitivity scores of all nodes to form a list, and clearly identify which nodes belong to high sensitivity and which nodes belong to low sensitivity. Store this information in the database for subsequent use. Generate a sensitivity node analysis report to clearly identify high sensitivity and low sensitivity nodes, providing basic information for subsequent topology reconstruction. Connection strategy aims to disperse risk by prioritizing the connection of high sensitivity nodes with low sensitivity nodes to reduce network load pressure. Such connections can provide emergency paths when load is too high, avoiding the overload of a single node. Set connection standards, prioritize nodes that already have connections but have low load to reduce the overall burden of the network. According to the sensitivity score, determine the connection priority between node B (high sensitivity) and node C (low sensitivity). Assume that there is no direct connection between node B and node C. Evaluate the load between node B and node C. If node B's load is 700 units during the peak load period, and node C's load is 200 units, then connecting them can disperse risk. Develop a connection plan to ensure that node B can transfer part of the load to node C under high load conditions, thereby relieving its own pressure. Use network analysis tools to evaluate the existing node connection situation and decide whether to add new connections or adjust existing connections. This process needs to consider the load carrying capacity and response speed of each path. Set connection adjustment standards, if the load of an existing connection exceeds 80% of its carrying capacity, optimization needs to be considered. Use network analysis software (such as Gephi or Cytoscape) to input the current node connection data and generate a network structure diagram. Assume that the network shows a lack of direct connection between node B and node C. Simulate the addition of a new connection in the network diagram, connecting node B and node C, and analyze the impact of the new connection on the overall network. Mark out potential paths and the effect of load transfer. Update the network data to form a new topology structure diagram, clearly showing the new connection relationship between each node, and visualize it for subsequent analysis and decision-making. Record the reconstructed topology structure, including detailed information of all nodes and new connections, for subsequent resource allocation, monitoring and optimization decision-making. Set the standard format of the report, including topology structure diagram, node sensitivity analysis and connection change description. After the topology reconstruction is completed, generate a topology structure report containing all new connections. The report should detail the sensitivity score of each node, the nature of the new connection and its expected effect.Assuming the report indicates that the connection between Node B and Node C is expected to reduce the load of Node B by 20% and improve the overall supply chain logistics response capability. Store the report in the supply chain logistics management system for subsequent analysis. Ensure that all relevant personnel have access to this important information and allocate resources and optimize according to the new topology.

[0045] Step S5: Based on the supply chain logistics topology optimization network, call path conflict identification and potential logistics risk link prediction are performed to obtain comprehensive risk prediction results;

[0046] In this embodiment, the path conflict identification aims to detect whether different logistics requests in the supply chain logistics network will attempt to use the same path simultaneously, causing resource contention or delays. Effective identification of conflicting paths helps optimize resource allocation and improve operational efficiency. The criteria for conflict identification are set when multiple orders need to pass through the same path within the same time period, marking it as a conflict. All path information is extracted from the optimized supply chain logistics topology network, including the current load of each path and the expected order flow. Suppose the defined paths in the network include A to B, B to C, etc. Monitor real-time order flow and record the usage of each path. Within a certain time period, the flow of path A to B is 300 units, while another order also plans to use the same path when it arrives at the same point. Use network analysis tools (such as network visualization software) to analyze the data and mark the paths that have conflicts within the time period. If path A to B is used by two orders at the same time, record this path as a conflict path and analyze its potential impact. Generate a conflict path report to clearly identify all conflict situations and their corresponding time ranges for subsequent risk assessment and optimization decisions. Potential risk link prediction is achieved by analyzing the conflict path situation to identify links that may cause bottlenecks in supply chain logistics operations. This step aims to identify risks in advance to take preventive measures. The risk prediction criteria are set, and if the conflict frequency of a path exceeds the set threshold (such as 3 times / hour), it is considered a high-risk path. For the identified conflict paths, analyze their performance in historical data and conflict frequency. If path A to B has appeared 5 times in the past week, this path is marked as a potential high-risk link. The comprehensive risk assessment will combine the results of path conflict identification and risk link prediction to provide a comprehensive risk assessment.This result will provide a basis for subsequent decision-making, helping to optimize resource allocation and reduce risks. Set comprehensive evaluation criteria, through the weighted evaluation of conflict frequency and risk score, form the final comprehensive risk score. All identified risk links and conflict paths are summarized to calculate the comprehensive risk score. Assuming there are three paths: path A to B (risk score 10%), path B to C (risk score 5%), path C to D (risk score 15%), the comprehensive evaluation can be done by weighted average or sum: comprehensive risk score = (10+5+15) / 3 = 10%, generate a comprehensive risk prediction report, detailing the risk score of each path, and the overall network risk status. The report should include a visual risk map to help decision-makers intuitively understand the current network risk distribution. Input the comprehensive risk prediction results into the supply chain logistics management system to support resource allocation, emergency response, and subsequent optimization strategy formulation.

[0047] Step S6: Based on the comprehensive risk prediction results, local logistics resource scheduling and allocation and global pre-collaborative processing are performed to build an intelligent logistics collaborative optimization engine.

[0048] In this embodiment, local logistics resource scheduling and allocation aims to optimize resource allocation in specific areas or nodes based on comprehensive risk prediction results, to improve the flexibility and response speed of the overall supply chain logistics. This step focuses on high-risk areas to ensure that resources can be reasonably allocated to where they are most needed. Set scheduling standards to determine the priority of resource allocation based on comprehensive risk scores, and nodes with higher scores will receive more resources. According to the results of comprehensive risk prediction, identify high-risk nodes and paths. If the risk score of path A to B is 15%, while the score of path B to C is 5%, prioritize resource scheduling at path A to B. Use scheduling optimization models (such as linear programming or heuristic algorithms) to develop local resource scheduling strategies. Assuming that the current load of path A to B is 600 units, exceeding its carrying capacity (500 units), 100 units of transportation capacity need to be scheduled. Through the scheduling system, temporarily allocate resources from other low-risk paths (such as path B to C). Assuming that the load of path B to C is 200 units, 50 units of transportation capacity can be allocated to path A to B. Record the adjusted resource allocation, form a local logistics resource scheduling and allocation report, and ensure that subsequent decisions can be based on the latest resource allocation status. Global pre-collaborative processing aims to coordinate local resource scheduling with the overall demand of supply chain logistics, ensuring efficient collaboration of all nodes and paths. This step needs to consider the resource demand and risk of all nodes. Set collaborative processing standards to ensure that resource allocation in each link is balanced overall, avoiding local excess or shortage. Analyze the global resource demand after local scheduling, and evaluate whether it meets the overall demand of supply chain logistics. If the global demand is 1000 units, and the available resources after local scheduling are 900 units, further resource adjustment is needed. Perform global resource allocation to ensure that high-risk nodes do not cause the overall performance of supply chain logistics to decline due to insufficient resources. Use data analysis tools to simulate different resource allocation schemes and evaluate their impact on the response speed of the overall supply chain logistics. Generate a global pre-collaborative strategy document to clearly define the resource allocation of each node and collaboration requirements, ensuring that all relevant personnel can understand the current resource status and future scheduling plan. The construction of the intelligent logistics collaborative optimization engine aims to dynamically adjust the configuration of supply chain logistics resources through real-time data analysis and machine learning models, to improve overall efficiency and response capability. The engine can automatically optimize resource allocation strategies based on historical data and real-time feedback. Set engine construction standards, use historical data to train machine learning models to improve the accuracy of predictions and the intelligence of resource allocation. Collect historical supply chain data and build machine learning models (such as random forests, neural networks) to predict future resource demand and risk. Assuming that the order data and load change data in the past year are used to train the model. Combine real-time data streams to dynamically update model parameters.In certain peak periods, the actual load is observed to exceed the predicted value, and the engine will automatically adjust the model to improve future prediction accuracy. An operation interface of the intelligent logistics collaborative optimization engine is generated for decision makers to monitor the supply chain state, resource allocation and optimization suggestions in real time. Ensure that the engine can provide real-time feedback to help management quickly respond to market changes. Record the running effect and feedback of the engine, and continuously optimize and adjust to ensure its adaptability and effectiveness.

[0049] In this embodiment, refer to Figure 2 For the detailed implementation step flowchart of step S1, in this embodiment, the detailed implementation steps of step S1 include:

[0050] Obtain real-time supply chain logistics data stream; identify multiple logistics nodes according to real-time supply chain logistics data stream;

[0051] Perform node type analysis on the multiple logistics nodes, and generate corresponding node labels to obtain type label mapping nodes;

[0052] Calculate the logistics inventory, arrival time stamp and stacking density of each type label mapping node to obtain a multi-dimensional node feature tensor;

[0053] Calculate node turnover frequency and transportation unit thermal trajectory according to real-time supply chain logistics data stream;

[0054] According to the node turnover frequency and transportation unit thermal trajectory, perform multi-path logistics interaction frequency distribution mining to generate path-to-path logistics interaction frequency distribution features;

[0055] Based on the path-to-path logistics interaction frequency distribution features, perform node coupling density analysis to obtain node coupling relationship density;

[0056] Based on the node coupling relationship density, perform self-adaptive hierarchical mapping on the multi-dimensional node feature tensor to construct a multi-layer logistics collaboration network.

[0057] In this embodiment, sensors or data collection devices are deployed at each node of the supply chain logistics (such as suppliers, warehouses, transportation companies, retailers, etc.), and real-time data is sent to the central processing system. The inventory data of a certain warehouse is updated every 5 minutes, and the data is sent to the data center through the API interface. Monitor the stability and accuracy of the data flow to ensure real-time data updates and reduce decision-making errors caused by data delays. Suppose a certain transportation node is found to be delayed during monitoring, the system will immediately record and mark the event for subsequent analysis. Based on the collected real-time data flow, apply data mining and machine learning algorithms to identify key nodes in the supply chain logistics. These nodes include but are not limited to suppliers, warehouses, transportation hubs and retail terminals. Set standards for node identification, such as node function, location, transaction frequency, etc. Use clustering algorithms (such as K-means or hierarchical clustering) to analyze the data and automatically identify different logistics nodes. By analyzing transportation path data, the system can find that a certain warehouse often serves as a major transit station, and thus marks it as a key node. Record each identified node and its characteristics, such as location, type and transaction frequency, and store this information in a database for subsequent analysis and decision-making. Analyze the types of identified logistics nodes and classify them into different types, such as production nodes, storage nodes, distribution nodes and retail nodes. Set standards and rules for node type, and if a node is mainly used for storing and managing inventory, it is marked as a "storage node". By analyzing the function and data flow of each node, it is classified. A node that mainly handles the input and output of raw materials is marked as a "production node", while a node that mainly stores finished products is marked as a "storage node". Generate node label mapping, combine the function of each node with the type label to form a structured node label database for subsequent data query and analysis. Calculate the logistics characteristics of each node to obtain its inventory, arrival timestamp and stacking density, etc. These features will be used to evaluate the operating efficiency and inventory management level of the node. Set the calculation method of each feature, the inventory is the number of items stored in the current node, and the arrival timestamp is the time of the last arrival. Real-time extraction of node logistics data from the database to calculate each feature. For a warehouse node, the real-time inventory is 500 units, the last arrival time is April 25, 2023, and the stacking density is 500 units per cubic meter. Aggregate these feature data to generate a multi-dimensional node feature tensor for subsequent analysis and decision support. Calculate the turnover frequency of each node, which is the number of goods in and out of the node per unit time, reflecting the operating efficiency of the node. At the same time, track the thermal trajectory of the transportation unit and analyze its path and dwell time during transportation. Set the calculation formula of turnover frequency and thermal trajectory, turnover frequency = (inbound quantity + outbound quantity) / time period. Periodically collect data from each node and calculate the turnover frequency in real time.If a node has 200 units of goods in and 150 units of goods out in the past week, the turnover frequency is 350 units / week. Using GIS tools, track the trajectory of the transportation unit, record its running path and stay time, and generate a heat map to show the activity density of the transportation unit. If a transportation unit frequently passes through a certain route within a certain time period, the heat value of that route is higher. Analyze the frequency of logistics interaction between nodes, that is, the frequency of goods flow on different paths, and identify the main logistics channels and interaction modes. Set the corresponding mining algorithm, such as using the frequent pattern mining algorithm (such as FP-Growth) to identify high-frequency interaction paths. Use historical data and real-time data to count the interaction frequency between different nodes. If the frequency of goods flow between node A and node B is 20 times per week, and the frequency between node A and node C is 5 times per week, record these interaction frequencies. Generate the distribution characteristics of inter-path logistics interaction frequency to form a feature matrix reflecting the interaction intensity of different paths. Based on the inter-path interaction frequency distribution characteristics, analyze the coupling relationship between different nodes to identify the dependence and interaction intensity between nodes. Set the coupling density calculation formula, coupling density = average value of interaction frequency / total number of nodes. Count the interaction frequency of each node and calculate the coupling density. If the interaction frequency of node A with other nodes is 20 times, the interaction frequency of node B is 15 times, and the interaction frequency of node C is 5 times, the coupling density between these nodes is calculated. Generate a coupling relationship density map to visually display the relationship strength between nodes, helping decision-makers identify key nodes and potential bottlenecks. According to the node coupling relationship density and feature tensor, perform adaptive hierarchical mapping to establish a multi-layer collaborative network. This network structure can optimize the allocation of supply chain logistics resources and logistics efficiency. Use graph theory and network analysis methods to construct the connection relationship between nodes. Combine the node feature tensor with the coupling relationship to construct a multi-layer network using a graph model. The connection between core nodes and peripheral nodes forms a complex network, showing the interaction relationship between nodes. Analyze the network structure, identify key nodes and potential collaboration opportunities, optimize logistics paths and resource allocation, and improve the overall response capability and efficiency of the supply chain logistics.

[0058] In this embodiment, referring to Figure 3 , the detailed implementation steps of step S2 include:

[0059] Real-time logistics path congestion calculation is performed on the multi-layer logistics collaborative network to extract logistics path congestion features;

[0060] According to the multi-layer logistics collaborative network, the path flow coincidence degree is counted and distributed to obtain the logistics interaction flow coincidence degree;

[0061] Based on the logistics interaction flow coincidence degree and the logistics path congestion features, the peak load of the transportation is analyzed to generate the peak load value of the logistics transportation.

[0062] Based on the logistics carrying peak load value, the path node load response of the multi-layer logistics cooperation network is reversed, and a plurality of node time sequence load response curves are constructed;

[0063] The non-linear distortion of the plurality of node time sequence load response curves is evaluated, so as to obtain the non-linear distortion degree of the response curve.

[0064] In this embodiment, the congestion of the logistics path in the multi-layer logistics cooperation network is monitored in real time. The congestion calculation mainly analyzes the traffic data and transportation time of the transportation nodes, and calculates the congestion degree of each path using the traffic model. The calculation standard of congestion is set, when the transportation time of a path exceeds 120% of the predetermined time, the path is considered to be congested. Using the real-time data collection system, the traffic flow and transportation status of each logistics path are monitored. The real-time traffic of a certain transportation route is 300 units per hour, the standard traffic is 250 units per hour, the transportation time is 2 hours, and the set predetermined time is 1.5 hours. The congestion value is calculated, if the actual transportation time of the path is 2 hours, which exceeds 120% of the predetermined time, it is marked as “congestion” state, and this state is recorded for subsequent analysis. The system will update the congestion data in real time, forming a congestion feature database. The flow coincidence degree between each logistics path is calculated to analyze the interaction frequency between different paths. The flow coincidence degree reflects the commonality and interaction of goods flow on different paths. The calculation formula of coincidence degree is set, flow coincidence degree = (coincidence flow times / total flow times) × 100%. Collect the flow data of each path, analyze which paths have the same goods flow within a certain period of time. If path A and path B have 100 flow times within a week, and 30 flow coincidences, the coincidence degree is 30%. Through the data analysis tool, the flow coincidence degree statistical distribution chart is generated, which helps decision makers to identify the interaction degree between paths, and then optimizes the logistics path configuration. Combined with the flow coincidence degree and path congestion feature, the peak load of the carrier is analyzed. The peak load of the carrier reflects the maximum carrying capacity and its use efficiency of the logistics path within a certain period of time. The calculation method of peak load of the carrier is set, and the load is predicted based on historical data and real-time data. The flow coincidence degree and congestion feature data are extracted from the real-time monitoring system to analyze the carrying capacity during peak hours. If the flow coincidence degree of a certain logistics path is 30%, the congestion is serious, and the actual carrying capacity is 500 units, the peak load of the carrier is calculated as 500 units. The peak load report of the carrier is generated to show the maximum carrying capacity and actual use of each path, so as to make corresponding adjustment and optimization. According to the peak load of the carrier, the load response of each node within a certain period of time is deduced. The load response curve can show the load change trend of the node at different time points. The time window of response deduction is set, for example, the load data is collected and analyzed every hour. Using historical and real-time data, the load change of each node within a certain period of time is calculated. If the load of a certain node is 700 units during peak hours, and 400 units during normal hours, these data are recorded. The time sequence load response curve is constructed to show the load of the node at different time points, so as to analyze the performance and response ability of the node. Through the chart tool, the load response change of each node is intuitively displayed.To assess the degree of nonlinear distortion in the time-series load response curves of nodes, nonlinear regression analysis is employed to evaluate the shape and changes of the curves. A calculation metric for nonlinear distortion is defined, and the mean squared error (MSE) is used to assess the difference between the actual response curve and the ideal linear response curve. A nonlinear regression model is applied to fit the time-series load response curve for each node, and the difference from the ideal curve is calculated. If the MSE between the actual load response curve and the ideal linear response curve is 20, it indicates the presence of some degree of nonlinear distortion. By comparing the degree of nonlinear distortion across different nodes, poorly performing nodes are identified, and targeted optimization suggestions are provided to help improve the overall load management capabilities of the supply chain logistics.

[0065] In this embodiment, the specific steps for evaluating the nonlinear distortion of the time-series load response curves of multiple nodes to obtain the degree of nonlinear distortion of the response curves are as follows:

[0066] Identify the instantaneous transitions in the time-series load response curves of the multiple nodes and mark the transient load abrupt change points;

[0067] Extract the timestamps of the transient load abrupt change points;

[0068] Calculate the frequency of the transient load abrupt change points;

[0069] Based on the occurrence timestamp, the frequency is analyzed for spatiotemporal distribution to generate load mutation spatiotemporal distribution characteristics;

[0070] The load fluctuation amplitude is calculated based on the spatiotemporal distribution characteristics of load mutations, and the load fluctuation amplitude value is generated.

[0071] The degree of nonlinear distortion of the response curve is obtained by evaluating the nonlinear distortion based on the load fluctuation amplitude value.

[0072] In this embodiment, transient transition refers to significant changes in the load response curve within a short period of time, usually related to sudden events such as order surges, equipment failures, etc. These mutation points are identified by setting thresholds. Set the standard for transient load mutation, the rate of change of the load response curve is considered a transient transition when it exceeds a certain set value (such as 20%). Through real-time monitoring of the system, the time series load response data of the node is continuously obtained. Assuming that the load data of a certain node is [400, 410, 500, 800, 430] units, use the formula to calculate the load change rate: change rate = (current load - previous load) / previous load × 100%, calculate for each time point, if at t=3, the change rate is: change rate = (800-500) / 500=60%, mark t=3 as a transient load mutation point. Record the timestamp and corresponding load value of all mutation points to form a mutation point database for subsequent analysis. For the identified transient load mutation points, extract their specific timestamps for subsequent analysis and statistics. The timestamp refers to the specific time when the mutation point occurs, usually in seconds or minutes. Extract the time information from the mutation point data. If marked as a mutation point at t=3, record the timestamp as "2023-04-01 10:00:03". Collect the timestamps of all mutation points to form a timestamp list for subsequent frequency calculation and spatio-temporal analysis. The frequency of mutation points reflects the number of mutation events per unit time, which can help analyze the regularity of load fluctuations. Set the time window for frequency calculation, perform frequency statistics every hour or every day. Count the number of transient load mutation points in a certain time period. In the past 24 hours, if 10 mutation points are identified, the frequency is calculated as: frequency = number of mutation points / time period (hours) = 10 / 24 ≈ 0.42 per hour. By analyzing the timestamps of the mutation points, generate the spatio-temporal distribution characteristics of the load mutation. These characteristics can reveal the time regularity and spatial distribution of mutation events. Set the analysis indicators of spatio-temporal distribution, the frequency and location distribution of mutation points in a certain time period. Classify the timestamp data and count the number of mutation points in each time period. There are 5 mutation points between 10am and 11am, and 3 mutation points between 11am and 12pm. Combine the spatial dimension (such as node location) to draw the spatio-temporal distribution map of load mutation, showing the distribution of mutation points in different time periods, helping to identify potential high-risk periods and locations. Load fluctuation amplitude reflects the degree of change in load before and after the mutation point, which is an important indicator for evaluating supply and demand fluctuations. Set the calculation formula, fluctuation amplitude = maximum load value - minimum load value. For each mutation point, record the load values before and after the mutation. If the load before the mutation point is 500 units and the load after the mutation is 800 units, the fluctuation amplitude is calculated as: fluctuation amplitude = 800-500 = 300 units. Summarize the fluctuation amplitudes of all mutation points to generate a list of load fluctuation amplitude values for subsequent analysis.The degree of non-linear distortion of the response curve can help identify the stability and reliability of the load response. Non-linear distortion can be evaluated by comparing the difference between the actual response curve and the ideal linear response curve. Set the calculation index of non-linear distortion, use mean square error (MSE) to quantify the degree of non-linearity. Using the collected fluctuation amplitude data, construct the actual response curve, and compare it with the ideal linear response curve. If the actual curve has a large difference in load value from the ideal value at some points, the MSE value is calculated. Through calculation, if the MSE value is 25, it indicates that the response curve has a certain degree of non-linear distortion. Record this index for subsequent optimization and improvement.

[0073] In this embodiment, referring to Figure 4 For the detailed implementation step flow diagram of step S3, in this embodiment, the detailed implementation steps of step S3 include:

[0074] According to the degree of non-linear distortion of the response curve, identify the non-linear distortion peak point, and mark the non-linear distortion peak point;

[0075] Based on the non-linear distortion peak point, locate the potential logistics bottleneck of the multi-layer logistics collaboration network, and obtain the logistics bottleneck point of the logistics network;

[0076] Quantitatively evaluate the sensitivity of the logistics network bottleneck point, and obtain the stability index of the bottleneck node;

[0077] Based on the stability index of the bottleneck node, fit the spatial distribution of the bottleneck node, and construct the sensitivity heat map of the logistics bottleneck point;

[0078] Based on the sensitivity heat map of the logistics bottleneck point, reconstruct the topological association between nodes, and construct the topological optimization network of the supply chain logistics.

[0079] In this embodiment, the nonlinear distortion peak point refers to the point in the load response curve where the distortion degree reaches its maximum. These points usually correspond to the system's limit state, which may affect the overall logistics efficiency. Set the identification criteria, when the rate of change of nonlinear distortion degree exceeds a certain threshold (such as 5%), it is marked as a peak point. Use the previous nonlinear distortion evaluation results to analyze the curve changes. If at a certain time point, the nonlinear distortion degree is 30%, and at the adjacent time point it is 35%, the change rate is: change rate = (35-30) / 30×100% = 16.67%, then this time point is marked as a nonlinear distortion peak point. Record all identified peak points and their corresponding time stamps and distortion degrees for subsequent analysis and decision support. By analyzing the nonlinear distortion peak points, potential logistics bottlenecks are identified. These bottlenecks usually refer to nodes that hinder the flow of logistics under high load or high distortion conditions. Set the identification criteria for bottlenecks, if the load of a node near a distortion peak point exceeds a certain threshold (such as 80%), it is considered a potential bottleneck. Combine the time stamps of the nonlinear distortion peak points to monitor the load of the relevant nodes in real time. Assuming that at a certain peak point, the load of node A is 750 units, and the standard load is 900 units, then record this node as a potential bottleneck. Record the identified bottleneck points in the database to form a bottleneck node list, which facilitates subsequent sensitivity evaluation and optimization analysis. Sensitivity quantitative evaluation is used to analyze the impact of bottleneck nodes on the overall performance of the system. The higher the stability index, the stronger the node's resistance to load changes. Set the calculation formula of the stability index, for example: stability index = maximum load / current load × 100%, analyze the load data of each identified bottleneck node to calculate its stability index. If the maximum load of node A is 900 units and the current load is 750 units, the stability index is: stability index = 900 / 750 × 100% = 120%, record the stability indices of all bottleneck nodes in the database for subsequent spatial distribution fitting and heat map construction. Through spatial distribution analysis of the stability indices of bottleneck nodes, a sensitivity heat map is constructed. The heat map can intuitively reflect the sensitivity of each node and help identify high-risk areas. Use interpolation methods (such as Kriging interpolation or inverse distance weighting) for spatial distribution fitting. Collect the stability indices and geographic location information of all bottleneck nodes and use geographic information system (GIS) tools for spatial analysis. If the stability index of node A is 120 and that of node B is 90, the area corresponding to node A in the heat map is displayed as a high-risk area. Generate a heat map to show the sensitivity of different areas and help decision-makers identify potential logistics bottlenecks and high-risk areas. According to the sensitivity heat map of the bottleneck points, analyze the topological relationship between nodes and perform optimization reconstruction. The goal is to reduce the impact of bottleneck nodes on overall logistics flow and improve network flexibility and efficiency.Set reconstruction criteria, prioritize connecting low sensitivity nodes to disperse load and reduce risk. According to the heat map, identify low sensitivity nodes and develop new connection strategies. Connect high sensitivity node A with low sensitivity node B directly to optimize logistics path. Network reconstruction, through simulation analysis, evaluate the performance of the reconstructed network, ensure the efficiency and stability of logistics flow. Record the optimized topology in the system, update and adjust in time.

[0080] In this embodiment, the specific steps of constructing the supply chain logistics topology optimization network based on the logistics bottleneck point sensitivity heat map for inter-node topology association reconstruction are as follows:

[0081] Based on the multi-layer logistics cooperation network, identify the upstream and downstream associated nodes of the logistics network logistics bottleneck point, and mark the upstream and downstream associated nodes;

[0082] According to the upstream and downstream associated nodes, mine potential associated paths to generate potential logistics paths;

[0083] Based on the logistics bottleneck mutation sensitivity heat map, make real-time logistics flow migration decision for potential logistics paths to obtain logistics load flow migration data;

[0084] Based on the logistics load flow migration data, analyze the bottleneck point topology association level, and extract and identify the implicit topology association level;

[0085] Based on the implicit topology association level, reconstruct the topology association between nodes in the multi-layer logistics cooperation network, and construct the supply chain logistics topology optimization network.

[0086] In this embodiment, upstream and downstream nodes refer to nodes directly related to a specific bottleneck node in the supply chain logistics. Upstream nodes are typically those that provide raw materials or information, while downstream nodes are those that receive products or services. Set the identification criteria, if the delivery path of a node is directly connected to the bottleneck node, mark it as an upstream or downstream associated node. Use the supply chain logistics management system to analyze the supply chain logistics network structure and extract all nodes directly connected to the bottleneck node. If the upstream nodes of bottleneck node A are B and C, and the downstream nodes are D and E, record these nodes. Mark and classify the identified upstream and downstream associated nodes to form a node relationship diagram for subsequent analysis. During this process, graph theory algorithms (such as depth-first search) can be used to ensure that all relevant nodes are accurately identified. Based on the upstream and downstream associated nodes, potential logistics paths are mined to identify possible logistics flow channels. These paths can be determined by analyzing transportation frequency, historical data, and node connection relationships. Set the path mining criteria, if a path appears more than a certain threshold (such as 10 times) in historical data, mark it as a potential path. By analyzing the connection relationships between upstream and downstream nodes, all possible logistics paths are extracted. If there are multiple flow records between nodes B and D, they can be considered potential logistics paths. Use data mining techniques (such as association rule mining) to identify these potential paths and record them in the database to form a list of potential logistics paths. Use the logistics bottleneck mutation sensitivity heat map to analyze the carrying capacity and risk of each potential path and make real-time flow migration decisions. This decision aims to optimize logistics flow and avoid excessive congestion near bottleneck nodes. Set the decision criteria, when the sensitivity heat value of a path is low, preferentially consider migrating traffic to that path. Combine heat map data to evaluate the risk and carrying capacity of each potential logistics path in real time. If the sensitivity heat value of path B is 30 and the heat value of path C is 70, preferentially select path B for traffic migration. During the decision-making process, record the traffic migration data for each path, including the load before and after migration, to form a logistics load traffic migration data set for subsequent analysis and optimization. By analyzing the logistics load traffic migration data, identify the implicit topological association hierarchy between bottleneck nodes and other nodes. This analysis helps understand the mutual influence and dependence between nodes. Set the level analysis criteria, if a node frequently appears in the traffic migration process, its association with the bottleneck node is higher. Calculate the frequency and association degree of each bottleneck node in the traffic migration data to form a node association matrix. If node A has a traffic migration frequency of 50% with the bottleneck node, while node B has a frequency of 30%, node A has a higher association degree. Generate a topological association hierarchy diagram using network analysis tools to show the hierarchical relationship between each node and the bottleneck node for subsequent network optimization. According to the implicit topological association hierarchy, reconstruct the connection relationship between nodes to optimize the efficiency of the supply chain logistics network. The goal is to improve the flexibility and responsiveness of the overall network and reduce the impact of bottlenecks on logistics flow.Set reconstruction criteria, prioritize connecting high correlation nodes and low sensitivity nodes to disperse load and reduce risk. Analyze the implicit topology correlation hierarchy to determine which nodes need to be reconstructed. If node A has a high correlation with the bottleneck point, and node B has low sensitivity, connect them to optimize the logistics path. Network reconstruction, use simulation tools to evaluate the performance of the reconstructed network, ensure the efficiency and stability of logistics flow. Record the optimized topology structure in the system, update and adjust in time.

[0087] In this embodiment, step S4 includes the following steps:

[0088] Extracting unprocessed orders from real-time supply chain logistics data streams;

[0089] Analyzing the order cycle regularity of real-time supply chain logistics data streams to generate periodic order characteristics;

[0090] According to the periodic order characteristics, multi-time point order trend prediction is carried out to generate multi-time point order prediction trend;

[0091] Future load demand pre-play is performed on the unprocessed orders and multi-time point order prediction trend to generate future supply chain logistics load demand;

[0092] Based on the future supply chain logistics load demand, the future calling operation path evolution analysis is carried out to generate the calling operation path evolution data.

[0093] In this example, the identification criteria for unprocessed orders are set by querying the database for orders with a status of "pending" or "unshipped". Every certain time interval (e.g., 5 minutes), the system automatically extracts unprocessed orders from the real-time supply chain logistics data stream. Assuming at a certain moment, the system queries and finds 150 unprocessed orders, including orders for different products such as electronics, furniture, etc. The data of these unprocessed orders is stored in a temporary database, recording detailed information for each order, including order number, customer information, product type, order time, etc., for subsequent analysis and processing. Periodic regular analysis is performed on the extracted unprocessed orders to identify the periodic characteristics of the orders, such as daily, weekly, or seasonal changes. The analysis method is set, for example, using time series analysis techniques to identify the fluctuation rules of order quantity. Using historical order data combined with real-time data of unprocessed orders, the change of order quantity in different time periods is analyzed. Assuming that in the past month of historical data, it is found that the order quantity significantly increases on weekends (Friday to Sunday), with an average order quantity of 200 orders, while the average order quantity on weekdays is 100 orders. Record these periodic characteristics and generate a periodic order characteristic report, clearly identifying characteristics such as "weekend peak" or "order surge before and after holidays", etc., for subsequent trend prediction. Based on the periodic order characteristics, use statistical methods or machine learning algorithms (such as ARIMA model or LSTM network) for multi-time point order trend prediction, aiming to predict the order quantity in a certain time period in the future. Set the prediction time range, such as the next week, a month, etc. Use periodic characteristics and historical data to build a prediction model. Use the ARIMA model for modeling, input historical order data, and generate a 7-day order prediction trend. If the prediction result shows that the order quantity will reach 250 orders on the weekend and the order quantity on weekdays will remain at 120 orders, visualize the prediction trend data and generate a trend chart to visually display the order quantity changes at each time point in the future, providing a basis for subsequent load demand rehearsal. Combine unprocessed orders and multi-time point order prediction trends to perform future load demand rehearsal. This process aims to evaluate the overall load demand in a certain period of time, including manpower, materials, and transportation capacity, etc. Set the load demand calculation formula, load demand = current unprocessed order quantity + future predicted order quantity. Combine the unprocessed order quantity (150 orders) with the future predicted order trend to calculate the future supply chain logistics load demand. If the predicted order quantity for the next 7 days is 400 orders, then the future load demand is: 150 + 400 = 550 orders, record this load demand data and generate a load demand report to clearly indicate the overall demand in a certain period of time in the future, in order to allocate resources and plan. Based on the future supply chain logistics load demand, analyze and predict the future logistics operation path. This analysis aims to optimize the operation path to ensure efficient use of resources. Set the evolution analysis criteria to identify efficient transportation routes and solutions through historical logistics path data.Using the load demand data, analyze the historical logistics path to determine the optimal operating path under future load demand. If the current load demand is 550 units, it can be found from the historical data that path X has the lowest transportation time and cost when meeting the demand. Record and generate the call operating path evolution data to form a path optimization report and provide it to the relevant decision makers. This report will show the best path selection in the future and its corresponding expected effect.

[0094] In this embodiment, the specific steps of step S5 are:

[0095] Identify the occupied logistics path in the supply chain logistics topology optimization network;

[0096] Based on the call operating path evolution data, identify the path conflict of the occupied logistics path, and mark the conflict path;

[0097] According to the conflict path, calculate the logistics operation compatibility matching, and generate the supply chain logistics network compatibility matching value;

[0098] Based on the supply chain logistics network compatibility matching value, predict the potential logistics risk link, identify the lag transmission path and resource shortage node;

[0099] Comprehensive risk assessment is performed on the lag transmission path and resource shortage node to obtain a comprehensive risk prediction result.

[0100] In this embodiment, in the supply chain logistics network, the occupied logistics path refers to the transportation route that is currently in use or locked. The identification of these paths is crucial for subsequent conflict detection and resource optimization. Set the identification criteria, extract the transportation status from the real-time data stream, and the paths with status "occupied" or "in transit" will be marked as occupied paths. Use the supply chain logistics management system to monitor the status of all transportation paths. Assume that at a certain time, the system detects that paths A, B, and C are in the "occupied" state, while paths D and E are in the "idle" state. Record the data of these occupied paths in the database and generate an occupied path list, including the starting point, ending point, current state, and estimated completion time of each path, to facilitate further analysis and decision-making. Path conflict refers to multiple logistics requests attempting to use the same transportation path at the same time. This situation may cause delays and waste of resources, so it needs to be identified. Set the conflict identification criteria, when the estimated completion time of two or more logistics requests overlaps and their paths are the same, mark it as a conflict path. Extract the estimated completion time and route information of all logistics requests from the call operation path evolution data. Assume that path A has two requests at the same time, which will be completed at time t1 and t2, and t1 and t2 overlap, then the path is marked as a conflict path. Record the information of all conflict paths, including the number of conflicting requests, estimated completion time and impact degree, form a conflict path report for subsequent compatibility matching calculation. Compatibility matching value is used to evaluate whether different logistics requests can be carried out at the same time on the same path. High compatibility value indicates that the path can carry multiple requests at the same time, while low compatibility value indicates the risk of conflict or resource shortage. Set the calculation formula of compatibility matching, for example: compatibility matching value = available resources / demand resources, calculate the compatibility matching value of each conflict path. For path A, if the available resources are 100 units and the demand resources are 150 units, the compatibility matching value is: compatibility matching value = 100 / 150 = 0.67, record the compatibility matching value of all conflict paths in the database and generate a compatibility matching report to identify high-risk paths. Use the compatibility matching value to predict potential risk links. Compatibility matching value below a certain threshold (such as 0.5) will be considered as a potential risk link. Set the prediction criteria to identify paths that may have delayed delivery and resource shortage nodes, which may affect the overall efficiency of the supply chain logistics. Analyze the compatibility matching values of all conflict paths and mark the paths below 0.5. If the compatibility matching value of path A is 0.67, but the demand resources increase during peak hours, resulting in a decrease in actual available resources, the matching value may drop to 0.4 after re-calculation. Record the delayed delivery paths and resource shortage nodes and generate a risk link prediction report for subsequent risk assessment and decision-making. Conduct a comprehensive risk assessment on the identified delayed delivery paths and resource shortage nodes to quantify their impact on the overall operation of the supply chain logistics.This evaluation will consider multiple factors such as historical performance, current load, and potential conflicts. Set the evaluation index, risk score = (lagging risk coefficient + resource shortage coefficient) / 2. Detailed analysis of each lagging path and shortage node, calculate its risk score. If the lagging risk coefficient of the lagging path is 0.3 and the resource shortage coefficient is 0.4, the comprehensive risk score is: risk score = (0.3 + 0.4) / 2 = 0.35. Record the risk score of all paths and nodes, and generate a comprehensive risk prediction report to provide to management to support decision-making. This report will help identify key risk points and develop appropriate mitigation measures.

[0101] In this embodiment, step S6 includes the following steps:

[0102] Based on the comprehensive risk prediction results, local logistics resource scheduling and allocation is carried out to obtain a local logistics resource scheduling and allocation strategy;

[0103] The local logistics resource scheduling and allocation strategy is subjected to global pre-collaboration processing to construct a global pre-collaboration strategy;

[0104] The global pre-collaboration strategy is subjected to full-process end-to-end compression encoding to extract a global optimized end-to-end process sample;

[0105] The global optimized end-to-end process sample is subjected to dynamic node update migration learning to construct an intelligent logistics collaboration optimization engine.

[0106] In this embodiment, local logistics resource scheduling and allocation aims to optimize resource allocation in specific areas or links based on comprehensive risk prediction results, in order to reduce risks and improve efficiency. This step will focus on the weakest link to ensure that resources can be reasonably allocated to where they are most needed. Set scheduling standards, determine the priority of resource allocation through comprehensive risk scoring, and nodes with higher scores will get more resources. According to the results of comprehensive risk prediction, identify high-risk nodes and paths. If the risk score of a path is 0.35 and there is a resource shortage, the path needs to be prioritized for resource scheduling. Through the resource scheduling tool, develop a local logistics resource scheduling and allocation strategy to allocate additional transportation vehicles and manpower to high-risk nodes. Assume that the original transportation capacity in a certain area is 100 units, which is increased to 150 units after scheduling. Record the adjusted resource allocation, and form a local logistics resource scheduling and allocation report to facilitate subsequent global pre-collaboration processing. Global pre-collaboration strategy coordinates local resource scheduling with overall supply chain logistics demand to ensure efficient operation of all links. This strategy should consider resource demand and risk in different links. Set coordination processing standards to ensure that resource allocation in each link is balanced overall, avoiding local overcapacity or shortage. According to the local resource scheduling strategy, analyze global resource demand and allocation. Assume that local scheduling has increased the resources of a node, but global demand has not increased accordingly, so adjustments are needed. Through data analysis, optimize resource allocation. If a node's resources are increased to 150 units after local scheduling, and the global demand is 200 units, resources need to be allocated from other low-risk nodes to achieve better balance. Form a global pre-collaboration strategy document to clearly define resource allocation and collaboration requirements for each node to ensure subsequent process integration and optimization. End-to-end compression encoding aims to convert the global pre-collaboration strategy into a more easily processed and analyzed form. Through compression encoding, key process samples can be extracted for subsequent learning and optimization. Set encoding standards and use data compression algorithms to compress process samples to 20-30% of the original data. Use appropriate compression encoding methods (such as principal component analysis or clustering algorithm) to process the global pre-collaboration strategy. Assume that after encoding, the original 5000 data points are compressed to 1000. Extract key process samples and record the main characteristics of each sample, such as resource allocation, time nodes and risk assessment results. Form a compressed sample library to support subsequent dynamic node updates and transfer learning. Generate an end-to-end process sample report to clearly define the characteristics and changes of the samples for subsequent analysis and decision-making. Dynamic node update transfer learning aims to continuously optimize supply chain logistics intelligent collaboration capabilities based on real-time data and historical samples. Through the transfer learning model, it can quickly adapt to new environments and changes in demand. Set learning standards to update model parameters through feedback from historical samples to improve the accuracy of predictions and the intelligence of resource allocation.Using a machine learning framework, the compressed end-to-end process samples are dynamically updated. Assuming the use of LSTM or transfer learning algorithms, real-time updating of model parameters is performed to adapt to new supply chain environments. Combined with real-time data flow, the learning strategy of the model is continuously adjusted. If the load demand of a certain node is found to increase significantly within a certain period of time, the system will automatically adjust the resource allocation strategy. An intelligent logistics collaborative optimization engine is formed, which can process and respond to changes in supply chain logistics in real time, improving overall efficiency and response speed. Record the running effect and feedback of the model to continuously optimize and adjust.

[0107] In this embodiment, an artificial intelligence-based supply chain logistics efficiency collaborative optimization system is provided for performing the artificial intelligence-based supply chain logistics efficiency collaborative optimization method as described above, comprising:

[0108] A multi-layer network module is used to obtain real-time supply chain logistics data flow, perform node coupling density analysis and adaptive hierarchical mapping, and construct a multi-layer logistics collaboration network.

[0109] A load response module is used to perform path node load response backpropagation on the multi-layer logistics collaboration network and perform nonlinear distortion evaluation to obtain the nonlinear distortion degree of the response curve.

[0110] A topology reconstruction module is used to perform sensitivity quantification evaluation based on the nonlinear distortion degree of the response curve and perform inter-node topology association reconstruction to construct a supply chain logistics topology optimization network.

[0111] A path evolution module is used to perform future load demand pre-play based on real-time supply chain logistics data flow and perform call operation path evolution analysis to generate call operation path evolution data.

[0112] A risk prediction module is used to perform call path conflict identification based on the supply chain logistics topology optimization network and perform potential logistics risk link prediction to obtain comprehensive risk prediction results.

[0113] A collaborative optimization module is used to perform local logistics resource scheduling and distribution and global pre-position collaborative processing based on the comprehensive risk prediction results to construct an intelligent logistics collaborative optimization engine.

[0114] The present application can monitor the changes of each supply chain logistics link in real time, dynamically adjust the strategy, and reduce the decision-making errors caused by information delay in traditional supply chain logistics by obtaining real-time supply chain logistics data flow. Adaptive hierarchical mapping and node coupling density analysis can optimize the coordination between each link of supply chain logistics, and improve the efficiency of resource allocation and information transmission. Different levels of network structure help to flexibly adjust according to different needs or goals, maximizing the collaborative benefits. The multi-layer collaborative network ensures efficient interaction between nodes (such as production, warehousing, transportation, etc.), avoids the problem of information island and insufficient coordination, and makes the whole supply chain logistics operation more smooth. Through path node load response backstepping, the performance of each node in the supply chain logistics under different load conditions can be accurately understood. This helps to find possible resource bottlenecks, inefficient links, and effectively optimize them. By evaluating the nonlinear distortion of the response curve, complex dynamic relationships in the supply chain logistics can be identified, especially the response changes when the supply chain is affected by external factors. Understanding the degree of nonlinear distortion helps optimize decision-making and avoid bias caused by excessive reliance on linear assumptions. Through sensitivity quantitative evaluation, the nodes or links that have the greatest impact on the overall performance of the supply chain logistics can be identified. This allows resource allocation and optimization to focus on the key parts that have the greatest impact on system performance. Topology reconstruction between nodes can optimize the overall structure of the network while ensuring network connectivity, improving the efficiency of resource flow. Through this optimization, redundant nodes and inefficient connections in the supply chain logistics can be reduced, and the overall collaboration efficiency can be improved. The network after topology optimization can better respond to changes in the external environment or fluctuations in internal resources, and has stronger flexibility and adaptability. The input of real-time supply chain logistics data flow allows load demand to be predicted in advance, helping enterprises plan resources and adjust strategies early. This prediction capability effectively avoids the problem of excess or shortage of supply chain logistics resources. Evolution analysis of the calling operation path can reveal potential trends in the operation process of the supply chain logistics, helping enterprises anticipate challenges and optimization opportunities that different paths may face. This helps to make strategic adjustments in advance and avoid chaos and inefficiency in emergency response. By identifying potential conflicts in the calling path, resource conflicts, insufficient transportation capacity, and other problems that may occur in the supply chain logistics can be discovered in real time, thereby avoiding the negative impact of these problems on the overall performance of the supply chain logistics. By predicting potential risk links, enterprises can identify risk factors that may have a significant impact on the operation of the supply chain logistics in advance. This provides an early warning mechanism for enterprises to take action in advance and reduce the risk of supply chain logistics disruption or delay. Comprehensive risk prediction results help decision-makers have a global perspective and identify risks at different levels and nodes, helping enterprises better cope with complex supply chain logistics operation challenges. Based on real-time risk prediction results, local resources can be accurately scheduled. This can maximize the use of existing resources and avoid the inefficiency caused by resource waste or improper allocation.Through pre-coordinated processing, coordination and synchronization can be performed in advance among multiple links of the supply chain logistics. This not only improves the collaboration efficiency of each link, but also quickly adjusts the strategy when demand changes or risk events occur, ensuring the smooth operation of the entire supply chain logistics. The intelligent logistics coordination optimization engine based on big data and AI algorithms can automatically make decisions and optimizations without human intervention, improving decision speed and accuracy. This automated optimization capability can significantly improve the overall operation efficiency and response speed of the supply chain logistics.

[0115] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application being defined by the appended claims and not by the above description, therefore all variations falling within the meaning and scope of the equivalent requirements of the application file are intended to be included in the present application.

[0116] The above description is merely that of specific embodiments of the present application, enabling a person skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An artificial intelligence-based supply chain logistics efficiency collaborative optimization method, characterized in that, The method comprises the following steps: Step S1: obtaining real-time supply chain logistics data stream, performing node coupling density analysis and adaptive hierarchical mapping, and constructing a multi-layer logistics collaboration network; Step S2: performing path node load response backstepping on the multi-layer logistics collaboration network, and performing nonlinear distortion evaluation to obtain the nonlinear distortion degree of the response curve; Step S3: performing sensitivity quantification evaluation according to the nonlinear distortion degree of the response curve, and performing inter-node topology correlation reconstruction to construct a supply chain logistics topology optimization network; Step S4: performing future load demand pre-play according to the real-time supply chain logistics data stream, and performing calling operation path evolution analysis to generate calling operation path evolution data; Step S5: identifying calling path conflicts based on the supply chain logistics topology optimization network, and then predicting potential logistics risk links to obtain comprehensive risk prediction results; Step S6: performing local logistics resource scheduling and distribution and global pre-positioned collaborative processing based on the comprehensive risk prediction results to construct an intelligent logistics collaborative optimization engine; In step S1, the specific steps are as follows: Obtain real-time supply chain logistics data stream; identify multiple logistics nodes according to the real-time supply chain logistics data stream; Perform node type analysis on the multiple logistics nodes, and generate corresponding node labels to obtain type label mapping nodes; Calculate the logistics inventory, arrival time stamp, and stacking density of each type label mapping node to obtain a multi-dimensional node feature tensor; Calculate the node turnover frequency and transportation unit thermal trajectory according to the real-time supply chain logistics data stream; Perform multi-path logistics interaction frequency distribution mining according to the node turnover frequency and transportation unit thermal trajectory to generate inter-path logistics interaction frequency distribution features; Perform node coupling density analysis based on the inter-path logistics interaction frequency distribution features to obtain node coupling relationship density; Perform adaptive hierarchical mapping on the multi-dimensional node feature tensor based on the node coupling relationship density to construct a multi-layer logistics collaboration network.

2. The artificial intelligence-based supply chain logistics efficiency collaborative optimization method according to claim 1, characterized in that, The specific steps of step S2 are as follows: Perform real-time logistics path congestion calculation on the multi-layer logistics collaboration network, and extract logistics path congestion features; Perform path flow conversion coincidence degree statistical distribution on the multi-layer logistics collaboration network to obtain logistics interaction flow conversion coincidence degree; Perform load peak value analysis based on the logistics interaction flow conversion coincidence degree and the logistics path congestion features to generate logistics load peak value; Perform path node load response backstepping on the multi-layer logistics collaboration network based on the logistics load peak value to construct multiple node time sequence load response curves; Perform nonlinear distortion evaluation on the multiple node time sequence load response curves to obtain the nonlinear distortion degree of the response curve. 3.The artificial intelligence-based supply chain logistics efficiency collaborative optimization method according to claim 2, characterized in that, The specific steps of performing nonlinear distortion evaluation on the multiple node time sequence load response curves to obtain the nonlinear distortion degree of the response curve are as follows: Identify the instantaneous transition of the multiple node time sequence load response curves, and mark the transient load mutation points; Extract the occurrence time stamp of the transient load mutation points; Calculate the frequency of the transient load mutation points; Perform spatiotemporal distribution analysis on the frequency according to the occurrence time stamp to generate load mutation spatiotemporal distribution features; According to the space-time distribution characteristics of load mutation, the load fluctuation amplitude is calculated, and the load fluctuation amplitude value is generated; Based on the load fluctuation amplitude value, the non-linear distortion evaluation is carried out, and the non-linear distortion degree of the response curve is obtained. 4.The artificial intelligence-based supply chain logistics efficiency collaborative optimization method according to claim 1, characterized in that, The specific steps of step S3 are: According to the non-linear distortion degree of the response curve, the non-linear distortion peak point is identified, and the non-linear distortion peak point is marked; Based on the non-linear distortion peak point, the potential logistics bottleneck of the multi-layer logistics cooperation network is located, and the logistics network logistics bottleneck point is obtained; The sensitivity of the logistics network logistics bottleneck point is quantitatively evaluated, and the bottleneck node stability index is obtained; Based on the bottleneck node stability index, the bottleneck node space distribution fitting is carried out, and the logistics bottleneck point sensitivity heat map is constructed; Based on the logistics bottleneck point sensitivity heat map, the topological correlation reconstruction between nodes is carried out, and the supply chain logistics topological optimization network is constructed. 5.The artificial intelligence based supply chain logistics efficiency synergy optimization method according to claim 4, wherein, The specific steps of constructing the supply chain logistics topological optimization network based on the logistics bottleneck point sensitivity heat map and the topological correlation reconstruction between nodes are: Based on the multi-layer logistics cooperation network, the upstream and downstream associated nodes of the logistics network logistics bottleneck point are identified, and the upstream and downstream associated nodes are marked; According to the upstream and downstream associated nodes, the potential associated path is mined, and the potential logistics path is generated; Based on the logistics bottleneck mutation sensitivity heat map, the real-time logistics flow migration decision of the potential logistics path is carried out, and the logistics load flow migration data is obtained; Based on the logistics load flow migration data, the bottleneck point topological correlation level analysis is carried out, and the implicit topological correlation level is extracted and identified; Based on the implicit topological correlation level, the topological correlation reconstruction between nodes of the multi-layer logistics cooperation network is carried out, and the supply chain logistics topological optimization network is constructed. 6.The artificial intelligence based supply chain logistics efficiency co-optimization method of claim 1, wherein, The specific steps of step S4 are: According to the real-time supply chain logistics data stream, the unprocessed order is extracted; The order cycle regularity analysis is carried out on the real-time supply chain logistics data stream, and the periodic order characteristics are generated; According to the periodic order characteristics, the multi-time point order trend prediction is carried out, and the multi-time point order prediction trend is generated; The future load demand is preformed on the unprocessed order and the multi-time point order prediction trend, and the future supply chain logistics load demand is generated; Based on the future supply chain logistics load demand, the future calling operation path evolution analysis is carried out, and the calling operation path evolution data is generated.

7. The artificial intelligence-based supply chain logistics efficiency collaborative optimization method according to claim 1, characterized in that, The specific steps of step S5 are: Identify the occupied logistics path in the supply chain logistics topological optimization network; Based on the calling operation path evolution data, the path conflict identification is carried out on the occupied logistics path, and the conflict path is marked; According to the conflict path, the logistics operation compatibility matching calculation is carried out, and the supply chain logistics network compatibility matching value is generated; Based on the supply chain logistics network compatibility matching value, the potential logistics risk link prediction is carried out, and the lag transfer path and resource shortage node are identified; The comprehensive risk assessment is carried out on the lag transfer path and resource shortage node, and the comprehensive risk prediction result is obtained.

8. The method of claim 1, wherein, The specific steps of step S6 are: Based on the comprehensive risk prediction result, the local logistics resource scheduling and allocation strategy is obtained; The global pre-positioning collaborative strategy is constructed by carrying out global pre-positioning collaborative processing on the local logistics resource scheduling and allocation strategy. The global pre-collaboration strategy is compressed and encoded in the whole process, and the global optimization end-to-end process sample is extracted; The global optimization end-to-end process sample is dynamically updated and migrated, and the intelligent logistics collaborative optimization engine is constructed.

9. An artificial intelligence-based supply chain logistics efficiency collaborative optimization system, characterized in that, The method for performing the artificial intelligence-based supply chain logistics efficiency collaborative optimization method according to claim 1 comprises: A multi-layer network module is used to obtain real-time supply chain logistics data flow, perform node coupling density analysis and adaptive hierarchical mapping, and construct a multi-layer logistics collaboration network; A load response module is used to perform path node load response backstepping on the multi-layer logistics collaboration network, and perform nonlinear distortion evaluation to obtain the nonlinear distortion degree of the response curve; A topology reconstruction module is used to perform sensitivity quantification evaluation according to the nonlinear distortion degree of the response curve, and perform inter-node topology association reconstruction to construct a supply chain logistics topology optimization network; A path evolution module is used to perform future load demand pre-play according to real-time supply chain logistics data flow, and perform call operation path evolution analysis to generate call operation path evolution data; A risk prediction module is used to perform call path conflict identification based on the supply chain logistics topology optimization network, and then perform potential logistics risk link prediction to obtain comprehensive risk prediction results; A collaborative optimization module is used to perform local logistics resource scheduling and allocation and global pre-collaboration processing based on the comprehensive risk prediction results to construct an intelligent logistics collaborative optimization engine.

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