Supply chain logistics efficiency collaborative optimization method and system based on artificial intelligence

Abstract

The invention relates to the field of supply chain optimization, in particular to a supply chain logistics efficiency collaborative optimization method and system based on artificial intelligence. The method comprises the following steps: obtaining a real-time supply chain logistics data stream, carrying out node coupling density analysis and self-adaptive layered mapping, and constructing a multi-layer logistics cooperation network; performing path node load response backstepping on the multi-layer logistics cooperative network, and performing nonlinear distortion evaluation to obtain a nonlinear distortion degree of a response curve; and carrying out sensitivity quantitative evaluation according to the nonlinear distortion degree of the response curve, carrying out topological association reconstruction among nodes, and constructing a supply chain logistics topological optimization network. And performing future load demand rehearsal according to the real-time supply chain logistics data flow, performing call operation path evolution analysis, and generating call operation path evolution data. According to the invention, the configuration of supply chain logistics resources is optimized, and the overall operation efficiency and response capability of supply chain logistics are improved.

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] To address the aforementioned technical problems, this invention proposes an artificial intelligence-based collaborative optimization method and system for supply chain logistics efficiency, thereby resolving at least one of the aforementioned technical issues.

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

[0008] 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;

[0009] Step S2: Back-calculate the load response of the path nodes in the multi-layer logistics collaboration network and perform nonlinear distortion assessment to obtain the degree of nonlinear distortion of the response curve;

[0010] Step S3: Perform sensitivity quantification assessment based on the degree of nonlinear distortion of the response curve, and reconstruct the topological association between nodes to build a supply chain logistics topology optimization network.

[0011] Step S4: Based on the real-time supply chain logistics data flow, perform a pre-simulation of future load demand and conduct an evolution analysis of the call operation path to generate call operation path evolution data.

[0012] Step S5: Identify call path conflicts based on the supply chain logistics topology optimization network, then predict potential logistics risk links to obtain comprehensive risk prediction results;

[0013] Step S6: Based on the comprehensive risk prediction results, perform local logistics resource scheduling and allocation and global pre-processing to build an intelligent logistics collaborative optimization engine.

[0014] This specification provides an artificial intelligence-based supply chain logistics efficiency collaborative optimization system for executing the artificial intelligence-based supply chain logistics efficiency collaborative optimization method described above, including:

[0015] The multi-layer network module is used to acquire real-time supply chain logistics data streams, perform node coupling density analysis and adaptive hierarchical mapping, and construct a multi-layer logistics collaboration network.

[0016] The load response module is used to back-calculate the load response of path nodes in a multi-layer logistics collaboration network and to perform nonlinear distortion assessment, thereby obtaining the degree of nonlinear distortion of the response curve.

[0017] The topology reconstruction module is used to perform sensitivity quantification assessment based on the degree of nonlinear distortion of the response curve, and to reconstruct the topology associations between nodes to build a supply chain logistics topology 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 this invention are as follows: By acquiring real-time supply chain logistics data streams, this invention can monitor changes in each link of the supply chain in real time, dynamically adjust strategies, and reduce decision-making errors caused by information delays in traditional supply chain logistics. Adaptive hierarchical mapping and node coupling density analysis can optimize the coordination relationship between various links in the supply chain, improving the efficiency of resource allocation and information transmission. Different-level network structures help to flexibly adjust according to different needs or goals, maximizing collaborative benefits. Multi-layered collaborative networks ensure efficient interaction between nodes (such as production, warehousing, and transportation), avoiding information silos and insufficient coordination, making the entire supply chain logistics operation smoother. By back-calculating the load response of path nodes, the performance of each node in the supply chain logistics under different load conditions can be accurately understood. This helps to identify potential resource bottlenecks and inefficient links, and to 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 logistics is affected by external factors. Understanding the degree of nonlinear distortion helps to optimize decisions and avoid biases caused by over-reliance on linear assumptions. Through sensitivity quantification assessment, 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 prioritize critical components that have the greatest impact on system performance. Inter-node topology reconfiguration optimizes the overall network structure and improves resource flow efficiency while ensuring network connectivity. This optimization reduces redundant nodes and inefficient connections in supply chain logistics, improving overall collaboration efficiency. A topology-optimized network is better able to cope with changes in the external environment or fluctuations in internal resources, exhibiting greater resilience and adaptability. Real-time supply chain logistics data flow input enables advance prediction of load demand, helping companies plan resources and adjust strategies ahead of time. This predictive capability effectively avoids the problem of resource surplus or shortage in supply chain logistics. Evolutionary analysis of dispatch paths reveals potential trends in supply chain logistics operations, helping companies anticipate challenges and optimization opportunities that different paths may face. This facilitates proactive strategic adjustments, avoiding chaos and inefficiency in emergency responses. By identifying potential conflicts in dispatch paths, potential resource conflicts and capacity shortages in supply chain logistics can be detected in real time, thus avoiding the negative impact of these problems on the overall performance of supply chain logistics. By predicting potential risk links, companies can identify risk factors that may significantly impact supply chain logistics performance in advance. This provides businesses with an early warning mechanism, enabling them to take proactive measures and reduce the risk of supply chain disruptions or delays. Comprehensive risk forecasting results help decision-makers gain a holistic perspective, identifying risks at different levels and stages, and helping businesses better address complex supply chain challenges. Based on real-time risk forecasting results, precise allocation of local resources is possible.This maximizes the use of existing resources and avoids inefficiencies caused by resource waste or misallocation. Through proactive collaborative processing, coordination and synchronization can be carried out in advance across multiple stages of the supply chain logistics. This not only improves the efficiency of collaboration at each stage but also allows for rapid strategy adjustments in response to changes in demand or the occurrence of risk events, ensuring the 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 optimize without human intervention, improving the speed and accuracy of decision-making. This automated optimization capability can significantly improve the overall operational efficiency and responsiveness of the supply chain logistics. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the steps of a collaborative optimization method for supply chain logistics efficiency based on artificial intelligence according to the present invention.

[0023] Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1.

[0024] Figure 3 This is a detailed flowchart illustrating the implementation steps of step S2;

[0025] Figure 4 This is a flowchart illustrating the detailed implementation steps of step S3. Detailed Implementation

[0026] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0027] This application provides an AI-based collaborative optimization method and system for supply chain logistics efficiency. The executing entities of the AI-based collaborative optimization method and system include, but are not limited to, the following: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc., which can be considered general computing nodes of this application. The data processing platform includes, but is not limited to, at least one of the following: an audio-visual management system, an information management system, and a cloud-based data management system.

[0028] See also Figures 1 to 4 This invention provides an artificial intelligence-based collaborative optimization method for supply chain logistics efficiency, 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: Back-calculate the load response of the path nodes in the multi-layer logistics collaboration network and perform nonlinear distortion assessment to obtain the degree of nonlinear distortion of the response curve;

[0031] Step S3: Perform sensitivity quantification assessment based on the degree of nonlinear distortion of the response curve, and reconstruct the topological association between nodes to build a supply chain logistics topology optimization network.

[0032] Step S4: Based on the real-time supply chain logistics data flow, perform a pre-simulation of future load demand and conduct an evolution analysis of the call operation path to generate call operation path evolution data.

[0033] Step S5: Identify call path conflicts based on the supply chain logistics topology optimization network, then predict potential logistics risk links to obtain comprehensive risk prediction results;

[0034] Step S6: Based on the comprehensive risk prediction results, perform local logistics resource scheduling and allocation and global pre-processing to build an intelligent logistics collaborative optimization engine.

[0035] This invention acquires real-time supply chain logistics data streams, enabling enterprises to promptly grasp various dynamic information within the supply chain, such as demand fluctuations and supply risks, thereby allowing for more flexible and accurate decision-making. Through node coupling density analysis and adaptive hierarchical mapping, an efficient multi-layered collaborative network can be constructed. This network enables efficient scheduling and optimization of supply chain logistics resources, enhances synergy between various links, and reduces delays and distortions in information transmission. By back-calculating the load response of path nodes, the load status of each node can be accurately calculated. This helps identify potential bottlenecks and pressure points, providing a basis for supply chain logistics path optimization. Nonlinear distortion assessment can reveal complex nonlinear relationships within the supply chain logistics system, especially when the supply chain logistics network changes due to certain external or internal factors, these changes can have a profound impact on the system's response. Understanding these changes allows for proactive adjustments and optimizations. Assessing the sensitivity of each node to the supply chain logistics operation response helps identify which nodes are crucial to the overall supply chain logistics operation and which node changes have a smaller impact, thereby enabling priority allocation of resources. Reconstructing the topological relationships between nodes enables topology optimization of the supply chain logistics network. This not only enhances the resilience of the network structure and reduces potential risks, but also enables rapid adjustments in the event of node failures or changes in demand, ensuring the stability and efficient operation of the supply chain logistics. Real-time data streams and the prediction of future load demands can anticipate potential changes, including fluctuations in demand and changes in resource requirements. This helps companies prepare in advance and avoid resource shortages or surpluses. Evolutionary analysis of call paths allows for understanding how operational patterns in supply chain logistics change over time, due to demand, or external factors. This dynamic analysis helps companies optimize supply chain operations and avoid unnecessary resource waste. By identifying call path conflicts and potential risk links, bottlenecks or risk points in supply chain logistics, such as path congestion and resource conflicts, can be identified in advance. This facilitates proactive adjustments and reduces the occurrence of unforeseen risks. Comprehensive risk prediction helps companies identify potential supply chain problems from a global perspective, including external market fluctuations and internal production bottlenecks, allowing for the implementation of effective risk mitigation measures in advance to reduce losses. Based on the results of comprehensive risk prediction, precise allocation of local logistics resources can be achieved, ensuring the rational utilization of resources at each stage. Meanwhile, based on proactive collaborative processing, the work of each link can be coordinated in advance, ensuring that the supply chain logistics maintains high efficiency at the overall level. Through the construction of an intelligent logistics collaborative optimization engine, automated decision support can be achieved, improving the intelligence level of supply chain logistics operations. This engine can not only respond to various changes in supply chain logistics in real time, but also automatically optimize resource allocation, improving the overall operational efficiency and responsiveness of the supply chain logistics.

[0036] In the embodiments of the present invention, see Figure 1 The diagram below illustrates the steps of a collaborative optimization method for supply chain logistics efficiency based on artificial intelligence, according to the present invention. In this example, the steps of the collaborative optimization method for supply chain logistics efficiency 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 embodiment, real-time supply chain logistics data streams include order information, inventory status, transportation progress, and supplier feedback. This data typically comes from different information systems, such as ERP, WMS, and TMS. A standard and frequency for data acquisition are set, extracting the latest supply chain data from each system every 5 minutes. Data integration tools (such as Apache Kafka or Apache NiFi) are used to extract real-time data from multiple data sources. The order quantity, inventory level, and transportation status for the past 5 minutes are extracted. Assume that at a certain moment, the system's data shows 500 unprocessed orders, an inventory status of 3000 units, and 150 orders in transit. The collected data is stored in a real-time database (such as InfluxDB or MongoDB) for subsequent analysis and processing, ensuring data integrity and real-time performance. Node coupling density analysis is used to assess the interdependencies between nodes in the supply chain logistics network. High coupling usually indicates strong correlation between nodes, which may affect the stability of the overall supply chain logistics. The coupling degree is calculated using the formula: Coupling Degree = Number of Connection Edges / Total Number of Nodes. The adjacency matrix in graph theory can be used to represent the connections between nodes. A connection graph between logistics nodes is constructed by analyzing real-time data flow. Assume that in the current network, node A (supplier) has 10 direct connections to node B (warehouse), and node B has 8 connections to 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 interaction with other nodes. Record the coupling degrees of all nodes in the database for further analysis. Generate a coupling density analysis report to clarify 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 layers based on the coupling density and functional characteristics of nodes to improve overall efficiency and flexibility. Typically, it can be divided into a supply layer, a warehousing layer, and a distribution layer. Set mapping criteria to group nodes with high coupling degrees into the same layer to facilitate resource sharing and information flow. Based on the coupling density analysis results, the nodes are layered. If nodes A and B have high coupling degrees, they are grouped into the same layer (e.g., the supply layer). Node C (retailer) is classified as part of the distribution layer due to its low coupling. A hierarchical mapping diagram is generated to clearly show the hierarchical relationships between the nodes. The final mapping result is assumed to be: the first layer is suppliers (nodes A and B), the second layer is warehouses (node ​​D), and the third layer is retailers (node ​​C). The hierarchical mapping results are recorded in the system, and a hierarchical structure report is generated to support subsequent supply chain logistics network optimization and decision-making.

[0039] Step S2: Back-calculate the load response of the path nodes in the multi-layer logistics collaboration network and perform nonlinear distortion assessment to obtain the degree of nonlinear distortion of the response curve;

[0040] In this embodiment, load response backpropagation aims to analyze the load status of each path node in the supply chain logistics based on real-time data to determine its impact on the overall network performance. By analyzing the load changes of nodes, potential bottlenecks and high-risk areas can be identified. Backpropagation criteria are set, and the current load status of nodes is estimated using known load data (such as historical order volume, transportation capacity, etc.). Real-time supply chain logistics data is used to extract the current load status of each path node. Assume that node A's current load is 400 units, node B's load is 600 units, and node C's load is 300 units. The load backpropagation model is used to calculate the contribution of each node to the overall supply chain logistics. If node A's historical data shows that its effective support rate to the network is 80% at a load of 400 units, its response capability under the current load can be inferred. Load response data for each node is recorded, including load status, support rate, and its impact on the overall network, forming a load response report, providing basic data for subsequent nonlinear distortion assessment. Nonlinear distortion assessment is used to quantify the deviation of the response curve of path nodes when the load changes, identifying possible nonlinear characteristics. High twist typically indicates node instability under varying loads, potentially impacting overall performance. Evaluation criteria are established, using metrics such as mean squared error (MSE) and rate of change to quantify the degree of nonlinear twist. Load response data for each path node is analyzed to generate load response curves. Assume node A's response data under different loads are: 80 at 100 units, 150 at 200 units, and 300 at 400 units. These data points are used to construct the response curves. The degree of nonlinear twist 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's response at 400 units is 300, then its MSE is calculated: The nonlinear distortion degree of all path nodes is recorded in the database to form a nonlinear distortion evaluation report, which clarifies the response curve characteristics of each node and provides a basis for subsequent optimization decisions.

[0041] Step S3: Perform sensitivity quantification assessment based on the degree of nonlinear distortion of the response curve, and reconstruct the topological association between nodes to build a supply chain logistics topology optimization network.

[0042] In this embodiment, the sensitivity quantification assessment aims to evaluate the impact of each node on the overall supply chain logistics performance based on the degree of nonlinear distortion. Highly sensitive nodes may cause system instability during load changes and require priority attention. A sensitivity assessment standard is established, using a comprehensive score based on the degree of distortion and node importance indicators (such as historical load, connection strength, etc.). The degree of nonlinear distortion of each node is analyzed; assuming node A has a distortion degree of 0.3, node B 0.6, and node C 0.2. The node sensitivity is assessed by combining the distortion degree with historical load data. Node A exhibits greater response changes under high load, therefore its sensitivity score may be higher. The comprehensive sensitivity score is calculated using the formula: Sensitivity Score = Degree of Distortion × 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 assessment report to identify which nodes require priority optimization to reduce overall network risk. Node topology reconstruction aims to optimize node connections based on sensitivity assessment results, improving the flexibility and stability of the overall supply chain logistics. Reconstruction criteria are set, prioritizing connections between low-sensitivity and high-sensitivity nodes to mitigate risk and reduce load. Based on sensitivity scores, identify high-sensitivity nodes (e.g., node B) and low-sensitivity nodes (e.g., node C). If node B has a sensitivity score of 2.0 and node C has a score of 0.5, prioritize establishing stronger connections between them. Add new connections or adjust existing connections based on the existing topology. Node B and node C can be directly connected to form a new path, allowing load transfer via node C when node B is overloaded. Use network analysis tools to generate a new topology diagram, clearly showing the new connections and hierarchical structure between nodes. Record the reconstructed topology and generate a report to support subsequent resource allocation and optimization decisions.

[0043] Step S4: Based on the real-time supply chain logistics data flow, perform a pre-simulation of future load demand and conduct an evolution analysis of the call operation path to generate call operation path evolution data.

[0044] In this embodiment, the sensitivity score of a node reflects its impact on the entire supply chain logistics during load changes. Highly sensitive nodes may cause system instability during load changes; therefore, these nodes need to be prioritized. A sensitivity score threshold is set: nodes with a sensitivity score greater than 1.5 are considered highly sensitive, while nodes with a score less than 0.5 are considered low-sensitive. The sensitivity scores of all nodes are extracted from the sensitivity assessment results. For example, in the analysis, node B has a sensitivity score of 2.0, node C has 0.5, and other nodes such as D have 1.0. The sensitivity scores of all nodes are recorded to form a list, clearly identifying which nodes are highly sensitive and which are low-sensitive. This information is stored in a database for later use. A sensitivity node analysis report is generated, clearly identifying highly sensitive and low-sensitive nodes, providing basic information for subsequent topology reconfiguration. The connection strategy aims to distribute risk and reduce network load pressure by prioritizing connections between highly sensitive and low-sensitive nodes. Such connections can provide emergency paths when the load is too high, avoiding overload of a single node. Connection criteria are set, prioritizing nodes that already have connections but have low loads to reduce the overall burden on the network. Based on sensitivity scores, determine the connection priority between node B (high sensitivity) and node C (low sensitivity). Assume there was originally no direct connection between node B and node C. Assess the load situation between node B and node C. If node B's load is 700 units during peak load periods, while node C's load is 200 units, the risk can be mitigated by connecting them. Develop a connection scheme to ensure that under high load conditions, node B can transfer some load to node C, thereby relieving its own pressure. Use network analysis tools to assess the existing node connections and decide whether to add new connections or adjust existing connections. This process needs to consider the load capacity and response speed of each path. Set connection adjustment criteria; if the load of an existing connection exceeds 80% of its capacity, optimization needs to be considered. Use network analysis software (such as Gephi or Cytoscape), input the current node connection data, and generate a network structure diagram. Assume the network shows a lack of direct connection between node B and node C. Simulate adding 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 potential paths and the effects of load shifting. Update network data to create a new topology diagram, clearly defining the new connections between each node and visualizing them for subsequent analysis and decision-making. Record the reconstructed topology, including detailed information on all nodes and new connections, to facilitate subsequent resource allocation, monitoring, and optimization decisions. Establish a standard report format, including the topology diagram, node sensitivity analysis, and descriptions of connection changes. After topology reconstruction, generate a topology report containing all new connections. The report should detail the sensitivity score of each node, the nature of the new connections, and their expected effects.Suppose the report indicates that the connection between node B and node C is expected to reduce node B's load by 20% and improve the overall supply chain logistics responsiveness. Store the report in the supply chain logistics management system for later analysis. Ensure all relevant personnel have access to this crucial information and can allocate and optimize resources based on the new topology.

[0045] Step S5: Identify call path conflicts based on the supply chain logistics topology optimization network, then predict potential logistics risk links to obtain comprehensive risk prediction results;

[0046] In this embodiment, path conflict identification aims to detect whether different logistics requests in the supply chain logistics network will simultaneously attempt to use the same path, leading to resource contention or delays. Effective identification of conflicting paths helps optimize resource allocation and improve operational efficiency. A conflict identification criterion is set: when multiple orders need to use the same path within the same time period, it is marked as a conflict. All path information, including the current load and expected order flow of each path, is extracted from the optimized supply chain logistics topology network. Assume that the paths defined in the network include A to B, B to C, etc. Real-time order flow is monitored, and the usage of each path is recorded. Within a certain time period, the flow of path A to B is 300 units, and another order plans to use the same path to arrive at the same point. Network analysis tools (such as network visualization software) are used to analyze the data, marking conflicting paths within the time period. If path A to B is used by two orders at the same time, this path is recorded as a conflicting path, and its potential impact is analyzed. A conflicting path report is generated, clearly identifying all conflict situations and their corresponding time ranges for subsequent risk assessment and optimization decisions. Call path conflict identification aims to detect whether different logistics requests in a supply chain logistics network will simultaneously attempt to use the same path, leading to resource contention or delays. Effective identification of conflicting paths helps optimize resource allocation and improve operational efficiency. Conflict identification criteria are established: when multiple orders need to use the same path within the same time period, it is marked as a conflict. All path information, including the current load and expected order flow of each path, is extracted from the optimized supply chain logistics topology network. Assume that the paths defined in the network include A to B, B to C, etc. Real-time order flow is monitored, recording the usage of each path. Within a certain time period, the flow of path A to B is 300 units, and another order plans to use the same path to arrive at the same point. Network analysis tools (such as network visualization software) are used to analyze the data, marking conflicting paths within the time period. If path A to B is used by two orders at the same time, this path is recorded as a conflicting path, and its potential impact is analyzed. A conflicting path report is generated, clearly identifying all conflict situations and their corresponding time ranges for subsequent risk assessment and optimization decisions. Potential risk link prediction identifies links that could potentially cause bottlenecks in supply chain logistics by analyzing conflict paths. This step aims to identify risks in advance so that preventative measures can be taken. Risk prediction criteria are set; if the conflict frequency of a path exceeds a set threshold (e.g., 3 times / hour), the path is considered high-risk. For identified conflict paths, their performance and conflict frequency in historical data are analyzed. If path A to B has experienced 5 conflicts in the past week, this path is marked as a potentially high-risk link. The comprehensive risk prediction results are combined with the path conflict identification and risk link prediction results to provide a holistic risk assessment.This result will provide a basis for subsequent decision-making, helping to optimize resource allocation and reduce risks. A comprehensive evaluation standard is established, and a final comprehensive risk score is formed through a weighted assessment of conflict frequency and risk scores. 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%), and path C to D (risk score 15%), a comprehensive evaluation can be performed using a weighted average or sum: Comprehensive Risk Score = (10 + 5 + 15) / 3 = 10%. A comprehensive risk prediction report is generated, 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. The comprehensive risk prediction results are input into the supply chain logistics management system to support resource allocation, emergency response, and the formulation of subsequent optimization strategies.

[0047] Step S6: Based on the comprehensive risk prediction results, perform local logistics resource scheduling and allocation and global pre-processing 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 regions or nodes based on comprehensive risk prediction results, thereby improving the flexibility and responsiveness of the overall supply chain logistics. This step focuses on high-risk areas to ensure that resources are rationally allocated to where they are most needed. Scheduling criteria are set, and resource allocation priorities are determined through comprehensive risk scoring; nodes with higher scores will receive more resources. High-risk nodes and paths are identified based on the comprehensive risk prediction results. If the risk score for path A to B is 15%, while the score for path B to C is 5%, resource scheduling is prioritized for path A to B. A local resource scheduling strategy is formulated using a scheduling optimization model (such as linear programming or heuristic algorithms). Assuming the current load on path A to B is 600 units, exceeding its carrying capacity (500 units), an additional 100 units of transportation capacity needs to be scheduled. Resources are temporarily allocated from other low-risk paths (such as path B to C) through the scheduling system. Assuming the load on 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 to generate a local logistics resource scheduling and allocation report, ensuring that subsequent decisions are based on the latest resource allocation status. Global pre-collaboration aims to coordinate local resource scheduling with the overall supply chain logistics needs, ensuring efficient collaboration among nodes and routes. This step requires comprehensive consideration of the resource needs and risks of all nodes. Establish collaboration standards to ensure that resource allocation across all stages is balanced overall, avoiding local oversupply or shortages. Analyze the global resource demand after local scheduling to assess whether it meets the overall supply chain logistics requirements. If the global demand is 1000 units, and the available resources after local scheduling are 900 units, further resource adjustments are needed. Perform global resource allocation to ensure that high-risk nodes do not experience a decline in overall supply chain logistics performance due to resource shortages. Use data analysis tools to simulate different resource allocation schemes and assess their impact on the overall supply chain logistics response speed. Generate a global pre-collaboration strategy document, clarifying the resource allocation status and collaboration requirements of each node, ensuring that all relevant personnel understand the current resource status and future scheduling plans. The intelligent logistics collaborative optimization engine aims to dynamically adjust the allocation of supply chain logistics resources through real-time data analysis and machine learning models, thereby improving overall efficiency and responsiveness. This engine can automatically optimize resource allocation strategies based on historical data and real-time feedback. Engine construction standards are set, and historical data is used to train machine learning models to improve prediction accuracy and the intelligence of resource allocation. Historical supply chain data is collected to build machine learning models (such as random forests and neural networks) to predict future resource needs and risks. It is assumed that order data and load change data from the past year are used for model training. The model parameters are dynamically updated by incorporating real-time data streams.If actual load exceeds forecasts during a peak period, the engine will automatically adjust its model to improve future forecast accuracy. An interface for the intelligent logistics collaborative optimization engine is generated, allowing decision-makers to monitor supply chain status, resource allocation, and optimization suggestions in real time. The engine is ensured to provide real-time feedback, helping management respond quickly to market changes. The engine's performance and feedback are recorded for continuous optimization and adjustment to ensure its adaptability and effectiveness.

[0049] In this embodiment, see Figure 2 The diagram below illustrates the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0050] Acquire real-time supply chain logistics data streams; identify multiple logistics nodes based on real-time supply chain logistics data streams;

[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 timestamp, and stacking density for each of the aforementioned type label mapping nodes to obtain a multi-dimensional node feature tensor;

[0053] Calculate node turnover frequency and transportation unit thermal trajectory based on real-time supply chain logistics data flow;

[0054] Based on the node turnover frequency and the thermal trajectory of the transportation unit, the multi-path logistics interaction frequency distribution is mined to generate the logistics interaction frequency distribution characteristics between paths.

[0055] Node coupling density analysis is performed based on the frequency distribution characteristics of logistics interactions between paths to obtain the node coupling relationship density.

[0056] An adaptive hierarchical mapping of multidimensional node feature tensors based on node coupling relationship density is used to construct a multi-layer logistics collaboration network.

[0057] In this embodiment, sensors or data acquisition devices are deployed at each node of the supply chain logistics (such as suppliers, warehouses, transportation companies, retailers, etc.) to send data to the central processing system in real time. Inventory data for a warehouse is updated every 5 minutes and sent to the data center via an API interface. The stability and accuracy of the data stream are monitored to ensure real-time data updates and reduce decision-making errors caused by data delays. If a delay is detected at a transportation node during monitoring, the system immediately records and marks the event for subsequent analysis. Based on the collected real-time data stream, data mining and machine learning algorithms are applied to identify key nodes in the supply chain logistics. These nodes include, but are not limited to, suppliers, warehouses, transportation hubs, and retail terminals. Criteria for node identification are set, such as node function, location, and transaction frequency. Clustering algorithms (such as K-means or hierarchical clustering) are used to analyze the data and automatically identify different logistics nodes. By analyzing transportation route data, the system can discover that a specific warehouse frequently serves as a major transit point, thus marking it as a key node. Each identified node and its characteristics, such as location, type, and transaction frequency, are recorded and stored in a database for subsequent analysis and decision-making. The identified logistics nodes are categorized into different types, such as production nodes, warehousing nodes, distribution nodes, and retail nodes. Standards and rules for node types are established; if a node is primarily used for storing and managing inventory, it is labeled a "warehousing node." Each node is classified by analyzing its function and data flow. A node primarily handling raw material input and output is labeled a "production node," while a node primarily responsible for finished product storage is labeled a "warehousing node." A node label mapping is generated, combining each node's function with its type label to form a structured node label database, facilitating subsequent data querying and analysis. Logistics characteristics are calculated for each node, obtaining multi-dimensional features such as inventory level, arrival timestamp, and stacking density. These features will be used to evaluate the node's operational efficiency and inventory management level. The calculation methods for each feature are defined: inventory level is the quantity of items currently stored at the node, and the arrival timestamp is the time of the most recent entry into the warehouse. Logistics data for each node is extracted from the database in real time to calculate various features. For a specific warehouse node, its real-time inventory is 500 units, the most recent arrival date is April 25, 2023, and the stacking density is 500 units per cubic meter. These characteristic data are aggregated to generate a multi-dimensional node feature tensor for subsequent analysis and decision support. The turnover frequency of each node is calculated, i.e., the quantity of goods entering and leaving the node per unit time, reflecting the node's operational efficiency. Simultaneously, the thermal trajectory of transportation units is tracked to analyze their paths and dwell times during transportation. The calculation formulas for turnover frequency and thermal trajectory are defined as follows: Turnover frequency = (Inbound quantity + Outbound quantity) / Time period. Data is collected periodically for each node, and the turnover frequency is calculated in real time.If a node receives 200 units and sends 150 units in the past week, its turnover rate is 350 units / week. Using Geographic Information System (GIS) tools, the trajectories of transport units are tracked, their routes and dwell times are recorded, and heatmaps are generated to show the activity density of transport units. If a transport unit frequently passes through a route within a specific time period, that route has a higher heatmap value. The frequency of logistics interactions between nodes, i.e., the frequency of cargo flow on different paths, is analyzed to identify major logistics channels and interaction patterns. Appropriate data mining algorithms are set, such as using frequent pattern mining algorithms (e.g., FP-Growth) to identify high-frequency interaction paths. Historical and real-time data are used to statistically analyze the interaction frequency between different nodes. If the cargo flow frequency between node A and node B is 20 times per week, and between node A and node C is 5 times per week, these interaction frequencies are recorded. A feature matrix is ​​generated to show the distribution characteristics of logistics interaction frequencies between paths, reflecting the interaction intensity of different paths. Based on the distribution characteristics of interaction frequencies between paths, the coupling relationships between different nodes are analyzed to identify the degree of dependence and interaction intensity between nodes. A coupling density calculation formula is established: Coupling density = Average interaction frequency / Total number of nodes. The interaction frequency of each node is statistically analyzed to calculate the coupling density. If node A interacts with other nodes 20 times, node B 15 times, and node C 5 times, the coupling density between these nodes is calculated. A coupling density map is generated to visually display the strength of relationships between nodes, helping decision-makers identify key nodes and potential bottlenecks. Based on the node coupling density and feature tensors, an adaptive hierarchical mapping is performed to establish a multi-layer collaborative network. This network structure can optimize supply chain logistics resource allocation and efficiency. Graph theory and network analysis methods are used to construct the connections between nodes. Node feature tensors are combined with coupling relationships to construct a multi-layer network using a graph model. The connections between core nodes and surrounding nodes form a complex network, showing the interaction relationships between nodes. Analyzing the network structure identifies key nodes and potential collaboration opportunities, optimizes logistics paths and resource allocation, and improves the overall responsiveness and efficiency of supply chain logistics.

[0058] In this embodiment, see Figure 3 The diagram below illustrates the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0059] Real-time logistics path congestion calculation is performed on a multi-layered logistics collaboration network to extract logistics path congestion characteristics.

[0060] Based on the statistical distribution of path flow overlap in the multi-layered logistics collaboration network, the logistics interaction flow overlap is obtained.

[0061] Based on the overlap of logistics interaction and the characteristics of logistics path congestion, peak load of transportation is analyzed to generate peak load value of logistics transportation.

[0062] Based on the peak load value of logistics transportation, the load response of path nodes in the multi-layer logistics cooperation network is back-inferred, and time-series load response curves of multiple nodes are constructed.

[0063] The nonlinear distortion of the time-series load response curves of multiple nodes is evaluated to obtain the degree of nonlinear distortion of the response curves.

[0064] In this embodiment, the congestion status of logistics routes is monitored in real time within a multi-layered logistics collaboration network. Congestion calculation is primarily achieved by analyzing traffic flow data and transportation time at transport nodes, using a traffic flow model to calculate the congestion level of each route. A congestion calculation standard is set: when the transportation time of a route exceeds 120% of a predetermined time, the route is considered congested. A real-time data collection system monitors the traffic flow and transportation status of each logistics route. For example, a certain transportation route has a real-time flow of 300 units per hour, a standard flow of 250 units per hour, a transportation time of 2 hours, and a predetermined time of 1.5 hours. The congestion value is calculated; if the actual transportation time of a route is 2 hours, exceeding 120% of the predetermined time, it is marked as "congested" and recorded for subsequent analysis. The system updates the congestion data in real time, forming a congestion feature database. The overlap of flow between logistics routes is statistically analyzed to assess the frequency of interaction between different routes. The overlap of flow reflects the commonalities and interactions of cargo flows on different routes. The formula for calculating overlap is defined as: Overlap Rate = (Number of Overlapping Transfers / Total Number of Transfers) × 100%. Transfer data for each route is collected to analyze which routes have the same cargo transfers within a certain time period. If routes A and B each have 100 transfers within a week, and 30 transfers overlap, the overlap rate is 30%. A statistical distribution map of overlap rate is generated using data analysis tools to help decision-makers identify the degree of interaction between routes and optimize logistics route configuration. Peak load is analyzed by combining overlap rate and route congestion characteristics. Peak load reflects the maximum carrying capacity and utilization efficiency of a logistics route within a specific time period. A method for calculating peak load is defined, and load prediction is performed based on historical and real-time data. Overlap rate and congestion characteristic data are extracted from the real-time monitoring system to analyze the transportation situation during peak periods. If a logistics route has a 30% overlap rate, indicating severe congestion, and an actual carrying capacity of 500 units, then the peak load value for that route is calculated to be 500 units. A peak load report is generated, displaying the maximum carrying capacity and actual usage of each route to facilitate adjustments and optimizations. Based on the peak load value, the load response of each node within a specific time period is inferred. The load response curve displays the load change trend of nodes at different time points. A time window for response inference is set, for example, collecting and analyzing load data hourly. Using historical and real-time data, the load changes of each node within a specific time period are calculated. If a node's load is 700 units during peak hours and 400 units during normal hours, these data are recorded. A time-series load response curve is constructed to display the load status of nodes at different time points, enabling analysis of node performance and responsiveness. Chart tools are used to visually display the load response changes of each node.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, a transient transition refers to a significant change in the load response curve within a short period of time, typically associated with sudden events (such as order surges, equipment failures, etc.). These abrupt changes are identified by setting a threshold. A standard for transient load abrupt changes is set; a change rate in the load response curve exceeding a certain set value (e.g., 20%) is considered a transient transition. A real-time monitoring system continuously acquires the time-series load response data of nodes. Assuming the load data of a node is [400, 410, 500, 800, 430] units, the formula for calculating the load change rate is: Change Rate = (Current Load - Previous Load) / Previous Load × 100%. For each time point, if the change rate at t=3 is: Change Rate = (800 - 500) / 500 = 60%, then t=3 is marked as a transient load abrupt change point. The timestamps and corresponding load values ​​of all abrupt change points are recorded to form a abrupt change point database for subsequent analysis. For identified transient load abrupt change points, their specific timestamps are extracted for further analysis and statistics. A timestamp refers to the specific time when a mutation point occurs, usually measured in seconds or minutes. Time information is extracted from mutation point data. If a mutation point is marked at t=3, the timestamp is recorded as "2023-04-01 10:00:03". All mutation point timestamps are collected to form a timestamp list, facilitating subsequent frequency calculations and spatiotemporal analysis. The frequency of mutation points reflects the number of mutation events occurring per unit time, helping to analyze the regularity of load fluctuations. A time window for frequency calculation is set, performing frequency statistics hourly or daily. The number of transient load mutation points within a certain time period is counted. If 10 mutation points are identified in the past 24 hours, the frequency is calculated as: Frequency = Number of mutation points / Time period (hours) = 10 / 24 ≈ 0.42 points / hour. By analyzing the timestamps of mutation points, the spatiotemporal distribution characteristics of load mutations are generated. These characteristics reveal the temporal patterns and spatial distribution of mutation events. Spatiotemporal distribution analysis indicators are set, including the frequency and location distribution of mutation points within a specific time period. Timestamp data was categorized, and the number of abrupt change points within each time period was counted. There were 5 abrupt change points between 10:00 AM and 11:00 AM, and 3 between 11:00 AM and 12:00 PM. A spatiotemporal distribution map of load abrupt changes was plotted, incorporating spatial dimensions (such as node location), to show the distribution of abrupt change points across different time periods, helping to identify potentially high-risk periods and locations. Load fluctuation amplitude reflects the degree of load change before and after abrupt change points and is an important indicator for assessing supply and demand fluctuations. A calculation formula was established: Fluctuation Amplitude = Maximum Load Value - Minimum Load Value. For each abrupt change point, the load values ​​before and after the change were recorded. If the load was 500 units before the change point and 800 units after the change point, the fluctuation amplitude was calculated as: Fluctuation Amplitude = 800 - 500 = 300 units. The fluctuation amplitudes of all abrupt change points were summarized to generate a list of load fluctuation amplitude values ​​for subsequent analysis.Evaluating the degree of nonlinear distortion in the response curve helps identify the stability and reliability of the load response. Nonlinear distortion can be assessed by comparing the actual response curve with the ideal linear response curve. A calculation metric for nonlinear distortion is defined, using mean squared error (MSE) to quantify the degree of nonlinearity. Using collected fluctuation amplitude data, an actual response curve is constructed and compared with the ideal linear response curve. If the load values ​​of the actual curve differ significantly from the ideal values ​​at certain points, the MSE value is calculated. A calculated MSE value of 25 indicates that the response curve exhibits some degree of nonlinear distortion. This metric is recorded for subsequent optimization and improvement.

[0073] In this embodiment, see Figure 4 The diagram below illustrates the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0074] Identify and mark the nonlinear distortion peak points based on the degree of nonlinear distortion of the response curve.

[0075] Based on the nonlinear twisting peak point, potential logistics bottlenecks in a multi-layer logistics collaboration network are located, and the logistics bottleneck points of the logistics network are obtained.

[0076] A sensitivity quantitative assessment of logistics bottlenecks in the logistics network is conducted to obtain a bottleneck node stability index.

[0077] Based on the bottleneck node stability index, the spatial distribution of bottleneck nodes is fitted to construct a sensitivity heatmap of logistics bottleneck points.

[0078] Based on the sensitivity heatmap of logistics bottlenecks, the topological associations between nodes are reconstructed to build a supply chain logistics topology optimization network.

[0079] In this embodiment, the nonlinear distortion peak point refers to the point in the load response curve where the distortion reaches its maximum value. These points typically correspond to the system's limit state and may affect overall logistics efficiency. An identification criterion is set: when the rate of change of the nonlinear distortion exceeds a certain threshold (e.g., 5%), it is marked as a peak point. The curve's changes are analyzed using the previous nonlinear distortion assessment results. If, at a certain time point, the nonlinear distortion is 30%, and at an adjacent time point it is 35%, with a change rate of: change rate = (35−30) / 30×100% = 16.67%, then that time point is marked as a nonlinear distortion peak point. All identified peak points and their corresponding timestamps and distortion levels are recorded for subsequent analysis and decision support. By analyzing the nonlinear distortion peak points, potential logistics bottlenecks are identified. These bottlenecks typically refer to nodes that obstruct logistics flow under high load or high distortion conditions. A bottleneck identification criterion is set: if the load of a node near the distortion peak point exceeds a set threshold (e.g., 80%), then that node can be considered a potential bottleneck. The load status of relevant nodes is monitored in real time by combining the timestamps of the nonlinear distortion peak points. Suppose that at a certain peak point, node A's load is 750 units, and its standard load is 900 units. This node is then recorded as a potential bottleneck. Identified bottlenecks are recorded in a database to form a bottleneck node list, facilitating subsequent sensitivity assessment and optimization analysis. Sensitivity quantification assessment analyzes the impact of bottleneck nodes on the overall system performance. A higher stability index indicates a node's stronger resistance to load changes. A formula for calculating the stability index is defined, for example: Stability Index = Maximum Load / Current Load × 100%. Load data analysis is performed on each identified bottleneck node to calculate its stability index. If node A's maximum load is 900 units and its current load is 750 units, then the stability index is: Stability Index = 900 / 750 × 100% = 120%. The stability indices of all bottleneck nodes are recorded in the database for subsequent spatial distribution fitting and heatmap construction. A sensitivity heatmap is constructed by performing spatial distribution analysis on the bottleneck node stability indices. The heatmap visually reflects the sensitivity of each node and helps identify high-risk areas. Spatial distribution fitting is performed using interpolation methods (such as Kriging interpolation or inverse distance weighting). Stability indices and geographical location information of all bottleneck nodes are collected, and spatial analysis is performed using Geographic Information System (GIS) tools. If the stability index of node A is 120 and that of node B is 90, the area corresponding to node A is displayed as a high-risk area in the heatmap. A heatmap is generated to show the sensitivity levels of different areas, helping decision-makers identify potential logistics bottlenecks and high-risk areas. Based on the sensitivity heatmap of bottleneck points, the topological relationships between nodes are analyzed, and optimization and reconstruction are performed. The goal is to reduce the impact of bottleneck nodes on overall logistics flow and improve the network's flexibility and efficiency.Establish reconfiguration criteria, prioritizing connections to nodes with low sensitivity to distribute load and reduce risk. Identify nodes with lower sensitivity based on heatmaps and develop new connection strategies. Directly connect high-sensitivity node A with low-sensitivity node B to optimize logistics paths. Perform network reconfiguration, and evaluate the reconfigured network performance through simulation analysis to ensure efficient and stable logistics flow. Record the optimized topology in the system for continuous updating and adjustment.

[0080] In this embodiment, the specific steps for reconstructing the topological associations between nodes based on the sensitivity heatmap of logistics bottlenecks and constructing the supply chain logistics topology optimization network are as follows:

[0081] Based on a multi-layered logistics collaboration network, upstream and downstream related nodes are identified and marked as logistics bottlenecks in the logistics network.

[0082] Based on upstream and downstream related nodes, potential related paths are mined to generate potential logistics paths;

[0083] Based on the heat map of the sensitivity to sudden changes in logistics bottlenecks, real-time logistics flow migration decisions are made for potential logistics routes to obtain logistics load flow migration data.

[0084] Based on logistics load flow migration data, bottleneck point topology association hierarchy analysis is performed to extract and identify implicit topology association hierarchy.

[0085] Based on the implicit topological association hierarchy, the topological associations between nodes in the multi-layer logistics collaboration network are reconstructed to build a 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 nodes that provide raw materials or information, while downstream nodes are nodes that receive products or services. Identification criteria are set: if a node's transport path directly connects to the bottleneck node, it is marked as an upstream-downstream related node. Using a supply chain logistics management system, the supply chain logistics network structure is analyzed to extract all nodes directly connected to the bottleneck node. If bottleneck node A's upstream nodes are B and C, and its downstream nodes are D and E, these nodes are recorded. The identified upstream and downstream related nodes are marked and classified to form a node relationship graph for subsequent analysis. Graph theory algorithms (such as depth-first search) can be used in this process to ensure that all relevant nodes are accurately identified. Based on upstream and downstream related nodes, potential logistics paths are mined to identify possible logistics flow channels. These paths can be determined by analyzing transport frequency, historical data, and node connection relationships. Path mining criteria are set: if a path appears more than a certain threshold (e.g., 10 times) in historical data, it is marked 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. Data mining techniques (such as association rule mining) are used to identify these potential paths and record them in a database, forming a list of potential logistics paths. Using a logistics bottleneck mutation sensitivity heatmap, the carrying capacity and risk of each potential path are analyzed to formulate real-time traffic migration decisions. This decision aims to optimize logistics flow and avoid excessive congestion near bottleneck nodes. Decision criteria are set: when a path has a low sensitivity heatmap value, traffic is prioritized for migration to that path. Combining heatmap data, the risk and carrying capacity of each potential logistics path are assessed in real time. If the sensitivity heatmap value of path B is 30 and that of path C is 70, path B is prioritized for traffic migration. During the decision-making process, traffic migration data for each path is recorded, including the load before and after migration, forming a logistics load traffic migration dataset for subsequent analysis and optimization. By analyzing the logistics load traffic migration data, the implicit topological relationship hierarchy between bottleneck points and other nodes is identified. This analysis helps to understand the mutual influence and dependencies between nodes. A hierarchical analysis standard is established: if a node appears frequently during traffic migration, its correlation with the bottleneck point is high. The frequency and correlation of each bottleneck point in the traffic migration data are statistically analyzed to form a correlation matrix between nodes. If node A's traffic migration frequency with the bottleneck point is 50%, while node B's is 30%, then node A's correlation is higher. A topology hierarchy diagram is generated using network analysis tools to show the hierarchical relationship between each node and the bottleneck point, facilitating subsequent network optimization. Based on the implicit topology hierarchy, the connections between nodes are reconstructed to optimize the efficiency of the supply chain logistics network. The goal is to improve the overall network's flexibility and responsiveness, and reduce the impact of bottlenecks on logistics flow.Establish reconstruction criteria, prioritizing connections between highly correlated nodes and less sensitive nodes to distribute load and reduce risk. Analyze the implicit topology hierarchy to identify nodes requiring reconstruction. If node A has a high correlation with the bottleneck point while node B has low sensitivity, connect them to optimize the logistics path. Perform network reconstruction and use simulation tools to evaluate the reconstructed network performance, ensuring efficient and stable logistics flow. Record the optimized topology in the system for continuous updating and adjustment.

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

[0088] Unprocessed orders are extracted based on real-time supply chain logistics data flow;

[0089] Analyze order cycle patterns in real-time supply chain logistics data flow to generate periodic order characteristics;

[0090] Based on the characteristics of periodic orders, multi-time point order trend prediction is performed to generate multi-time point order prediction trends;

[0091] The future load demand is simulated based on the forecast trends of the unprocessed orders and multi-time point orders to generate the future supply chain logistics load demand.

[0092] Based on future supply chain logistics load demand, we conduct future dispatch and operation path evolution analysis to generate dispatch and operation path evolution data.

[0093] In this embodiment, an identification criterion for unprocessed orders is set by querying the database for orders with a status of "pending" or "not shipped". At regular intervals (e.g., every 5 minutes), the system automatically extracts unprocessed orders from the real-time supply chain logistics data stream. Assume that at a certain moment, the system finds 150 unprocessed orders, including orders for different products such as electronic devices and furniture. This data is stored in a temporary database, recording detailed information for each order, including order number, customer information, product type, and order time, for subsequent analysis and processing. Periodic pattern analysis is performed on the extracted unprocessed orders to identify cyclical characteristics, such as daily, weekly, or seasonal variations. Analysis methods are defined, such as time series analysis, to identify fluctuations in order numbers. Historical order data is combined with real-time data on unprocessed orders to analyze changes in order volume over different time periods. For example, historical data from the past month shows a significant increase in order volume on weekends (Friday to Sunday), averaging 200 orders, compared to an average of 100 orders on weekdays. Record these periodic characteristics to generate periodic order characteristic reports, clearly defining features such as "weekend peak" or "order surge before and after holidays" for subsequent trend prediction. Based on periodic order characteristics, use statistical methods or machine learning algorithms (such as ARIMA models or LSTM networks) to perform multi-time-point order trend prediction, aiming to predict order volume within a specific future time period. Set the prediction time range, such as the next week or month. Build a prediction model using periodic characteristics and historical data. Use the ARIMA model for modeling, input historical order data, and generate a predicted order trend for the next 7 days. If the prediction results show that the order volume will reach 250 orders on the weekend and remain at 120 orders on weekdays, visualize the predicted trend data to generate a trend chart, intuitively showing the changes in order volume at various future time points, providing a basis for subsequent load demand simulation. Combine unprocessed orders and multi-time-point order prediction trends to perform future load demand simulation. This process aims to assess the overall load demand over a future period, including manpower, materials, and transportation capacity. Set the load demand calculation formula: Load Demand = Current Unprocessed Orders + Future Predicted Orders. By combining the number of outstanding orders (150) with projected future order trends, the future supply chain logistics load demand is calculated. If the projected order volume for the next 7 days is 400 orders, then the future load demand is: 150 + 400 = 550 orders. This load demand data is recorded, and a load demand report is generated to clarify the overall demand situation over a future period, facilitating resource allocation and planning. Based on the future supply chain logistics load demand, future logistics dispatch routes are analyzed and predicted. This analysis aims to optimize operational routes and ensure efficient resource utilization. Evolutionary analysis criteria are established, and efficient transportation routes and solutions are identified using historical logistics route data.By analyzing historical logistics routes using load demand data, the optimal operating route can be determined for future load demands. If the current load demand is 550 orders, historical data can reveal that route X offers the lowest transportation time and cost when meeting this demand. The evolution data of the operating routes is recorded and generated to form a route optimization report, which is provided to relevant decision-makers. This report will show the best future route selection and its expected effects.

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

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

[0096] Based on the call to operational path evolution data, path conflicts are identified in occupied logistics paths, and conflicting paths are marked.

[0097] Based on the conflict paths, the compatibility matching calculation of logistics operations is performed to generate a supply chain logistics network compatibility matching value.

[0098] Predict potential logistics risk links based on supply chain logistics network compatibility matching values, and identify delayed transmission paths and resource shortage nodes.

[0099] A comprehensive risk assessment was conducted on the delayed transmission path and resource shortage nodes to obtain comprehensive risk prediction results.

[0100] In this embodiment, in the supply chain logistics network, occupied logistics paths refer to transportation routes that are currently in use or locked. Identifying these paths is crucial for subsequent conflict detection and resource optimization. Identification criteria are established, and transportation status is extracted from real-time data streams. Paths with a status of "occupied" or "in transit" are marked as occupied paths. The status of all transportation paths is monitored using a supply chain logistics management system. Assume that at a certain moment, the system detects that paths A, B, and C are "occupied," while paths D and E are "idle." The data for these occupied paths is recorded in a database, generating a list of occupied paths, including the start and end points, current status, and estimated completion time for each path, to facilitate further analysis and decision-making. Path conflicts occur when multiple logistics requests simultaneously attempt to use the same transportation path. This situation can lead to delays and resource waste, thus requiring identification. Conflict identification criteria are established; when the estimated completion times of two or more logistics requests overlap and their paths are the same, they are marked as conflicting paths. The estimated completion time and route information for all logistics requests are extracted from the call operation path evolution data. Suppose path A has two simultaneous requests, completed at times t1 and t2 respectively, and t1 and t2 overlap; this path is then marked as a conflicting path. Information on all conflicting paths is recorded, including the number of conflicting requests, estimated completion time, and impact level, forming a conflicting path report for subsequent compatibility matching calculations. The compatibility matching value is used to assess whether different logistics requests can be processed simultaneously on the same path. A high compatibility value indicates that the path can handle multiple requests simultaneously, while a low compatibility value indicates a risk of conflict or insufficient resources. A compatibility matching calculation formula is defined, for example: Compatibility Matching Value = Available Resources / Required Resources. For each conflicting path, its compatibility matching value is calculated. For path A, if the available resources are 100 units and the required resources are 150 units, then the compatibility matching value is: Compatibility Matching Value = 100 / 150 = 0.67. The compatibility matching values ​​of all conflicting paths are recorded in the database, and a compatibility matching report is generated to identify high-risk paths. The compatibility matching values ​​are then used to predict potential risky links. Compatibility matching values ​​below a certain threshold (e.g., 0.5) are considered potential risk links. Prediction criteria are established to identify paths with potential delays and resource shortage nodes, which may impact the overall efficiency of supply chain logistics. The compatibility matching values ​​of all conflicting paths are analyzed, and paths below 0.5 are marked. For example, if path A has a compatibility matching value of 0.67, but increased demand during peak periods leads to a decrease in available resources, recalculation may reduce the value to 0.4. Delayed paths and resource shortage nodes are recorded, and a risk link prediction report is generated for subsequent risk assessment and decision-making. A comprehensive risk assessment is conducted on the identified delayed paths and resource shortage nodes to quantify their impact on the overall operation of supply chain logistics.This assessment will consider multiple factors, such as historical performance, current load, and potential conflicts. An assessment metric is set: Risk Score = (Lapsion Risk Coefficient + Resource Shortage Coefficient) / 2. A detailed analysis is conducted for each lag path and shortage node, calculating its risk score. If the lag risk coefficient for a lag path is 0.3 and the resource shortage coefficient is 0.4, then the overall risk score is: Risk Score = (0.3 + 0.4) / 2 = 0.35. The risk scores for all paths and nodes are recorded, and a comprehensive risk prediction report is generated and provided to management to support decision-making. This report will help identify key risk points and develop corresponding mitigation measures.

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

[0102] Based on the comprehensive risk prediction results, a local logistics resource scheduling and allocation strategy is obtained.

[0103] The local logistics resource scheduling and allocation strategy is processed globally in advance to construct a global in advance coordination strategy;

[0104] Perform end-to-end compression encoding on the global pre-coordination strategy and extract global optimized end-to-end process samples;

[0105] Dynamic node update and migration learning are performed on the global optimization end-to-end process samples to build an intelligent logistics collaborative optimization engine.

[0106] In this embodiment, local logistics resource scheduling and allocation aims to optimize resource allocation in specific regions or links based on comprehensive risk prediction results, thereby reducing risks and improving efficiency. This step focuses on the weakest links to ensure that resources are rationally allocated to where they are most needed. Scheduling standards are set, and resource allocation priorities are determined through comprehensive risk scoring; nodes with higher scores will receive more resources. High-risk nodes and routes are identified based on the comprehensive risk prediction results. If a route has a risk score of 0.35 and experiences resource shortages, then resources for that route should be prioritized. A local logistics resource scheduling and allocation strategy is developed using resource scheduling tools, allocating additional transport vehicles and manpower to high-risk nodes. For example, if the original transport capacity in a certain region is 100 units, it is increased to 150 units after scheduling. The adjusted resource allocation is recorded, forming a local logistics resource scheduling and allocation report for subsequent global pre-collaboration processing. The global pre-collaboration strategy coordinates local resource scheduling with the overall supply chain logistics needs, ensuring efficient collaborative operation across all links. This strategy should consider the resource needs and risk situations of different links. Establish collaborative processing standards to ensure that resource allocation across all stages is balanced overall, avoiding local oversupply or shortage. Analyze global resource requirements and allocation based on local resource scheduling strategies. For example, if local scheduling increases the resources of a node, but global demand does not increase accordingly, adjustments are necessary. Optimize resource allocation through data analysis. If, after local scheduling, a node's resources increase to 150 units, while the global demand is 200 units, resources need to be allocated from other low-risk nodes to achieve better balance. Create a global pre-collaboration strategy document, clearly defining the resource configuration and collaboration requirements of each node to ensure subsequent process integration and optimization. End-to-end compression coding of the entire process aims to transform the global pre-collaboration strategy into a more easily processed and analyzed form. Through compression coding, key process samples can be extracted, facilitating subsequent learning and optimization. Set coding standards and use data compression algorithms to compress process samples to 20%-30% of the original data. Employ appropriate compression coding methods (such as principal component analysis or clustering algorithms) to process the global pre-collaboration strategy. Assuming that after encoding, the original 5000 data points are compressed to 1000, key process samples are extracted, and the main characteristics of each sample are recorded, such as resource allocation, time nodes, and risk assessment results. This forms a compressed sample library to support subsequent dynamic node updates and transfer learning. An end-to-end process sample report is generated, clarifying the characteristics and changes of the samples for subsequent analysis and decision-making. Dynamic node update transfer learning aims to continuously optimize the intelligent collaborative capabilities of supply chain logistics based on real-time data and historical samples. Through the transfer learning model, it can quickly adapt to new environments and changing demands. Learning standards are set, and model parameters are updated based on feedback from historical samples to improve prediction accuracy and the intelligence of resource allocation.Using a machine learning framework, compressed end-to-end process samples are dynamically updated. Assuming the use of LSTM or transfer learning algorithms, model parameters are updated in real time to adapt to new supply chain environments. The learning strategy is continuously adjusted based on real-time data streams. If a significant increase in load demand at a node is detected within a certain time period, the system will automatically adjust resource allocation strategies. This forms an intelligent logistics collaborative optimization engine capable of processing and responding to changes in supply chain logistics in real time, improving overall efficiency and response speed. The model's performance and feedback are recorded for continuous optimization and adjustment.

[0107] In this embodiment, an artificial intelligence-based supply chain logistics efficiency collaborative optimization system is provided, used to execute the artificial intelligence-based supply chain logistics efficiency collaborative optimization method described above, including:

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

[0109] The load response module is used to back-calculate the load response of path nodes in a multi-layer logistics collaboration network and to perform nonlinear distortion assessment, thereby obtaining the degree of nonlinear distortion of the response curve.

[0110] The topology reconstruction module is used to perform sensitivity quantification assessment based on the degree of nonlinear distortion of the response curve, and to reconstruct the topology associations between nodes to build a supply chain logistics topology optimization network.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] This invention, by acquiring real-time supply chain logistics data streams, enables real-time monitoring of changes in each link of the supply chain logistics chain, dynamically adjusting strategies, and reducing decision-making errors caused by information delays in traditional supply chain logistics. Adaptive hierarchical mapping and node coupling density analysis can optimize the coordination relationship between various links in the supply chain logistics chain, improving the efficiency of resource allocation and information transmission. Different-level network structures facilitate flexible adjustments according to different needs or objectives, maximizing collaborative benefits. Multi-layered collaborative networks ensure efficient interaction between nodes (such as production, warehousing, and transportation), avoiding information silos and insufficient coordination, making the entire supply chain logistics operation smoother. By back-calculating the load response of path nodes, the performance of each node in the supply chain logistics chain under different load conditions can be accurately understood. This helps to identify potential resource bottlenecks and inefficient links, and to effectively optimize them. By evaluating the nonlinear distortion of the response curve, complex dynamic relationships in the supply chain logistics chain can be identified, especially the response changes when the supply chain is affected by external factors. Understanding the degree of nonlinear distortion helps to optimize decisions and avoid biases caused by over-reliance on linear assumptions. Through sensitivity quantification assessment, the nodes or links with the greatest impact on the overall performance of the supply chain logistics chain can be identified. This allows resource allocation and optimization to prioritize critical components that have the greatest impact on system performance. Inter-node topology reconfiguration optimizes the overall network structure and improves resource flow efficiency while ensuring network connectivity. This optimization reduces redundant nodes and inefficient connections in supply chain logistics, improving overall collaboration efficiency. A topology-optimized network is better able to cope with changes in the external environment or fluctuations in internal resources, exhibiting greater resilience and adaptability. Real-time supply chain logistics data flow input enables advance prediction of load demand, helping companies plan resources and adjust strategies ahead of time. This predictive capability effectively avoids the problem of resource surplus or shortage in supply chain logistics. Evolutionary analysis of dispatch paths reveals potential trends in supply chain logistics operations, helping companies anticipate challenges and optimization opportunities that different paths may face. This facilitates proactive strategic adjustments, avoiding chaos and inefficiency in emergency responses. By identifying potential conflicts in dispatch paths, potential resource conflicts and capacity shortages in supply chain logistics can be detected in real time, thus avoiding the negative impact of these problems on the overall performance of supply chain logistics. By predicting potential risk links, companies can identify risk factors that may have a significant impact on supply chain logistics operations in advance. This provides businesses with an early warning mechanism, enabling them to take proactive measures and reduce the risk of supply chain disruptions or delays. Comprehensive risk forecasting results help decision-makers gain a holistic perspective, identifying risks at different levels and stages, and helping businesses better address the complex challenges of supply chain logistics operations. Based on real-time risk forecasting results, precise allocation of local resources can be achieved. This maximizes the utilization of existing resources and avoids inefficiencies caused by resource waste or improper allocation.By enabling proactive collaborative processing, coordination and synchronization can be achieved across multiple stages of the supply chain logistics. This not only improves the efficiency of collaboration at each stage but also allows for rapid strategy adjustments in response to changes in demand or the occurrence of risk events, ensuring the smooth operation of the entire supply chain logistics. Intelligent logistics collaborative optimization engines based on big data and AI algorithms can automatically make decisions and optimize without human intervention, improving the speed and accuracy of decision-making. This automated optimization capability can significantly enhance the overall operational efficiency and responsiveness of the supply chain logistics.

[0115] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

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

Claims

1. A collaborative optimization method for supply chain logistics efficiency based on artificial intelligence, characterized in that, Includes the following steps: 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; Step S2: Back-calculate the load response of the path nodes in the multi-layer logistics collaboration network and perform nonlinear distortion assessment to obtain the degree of nonlinear distortion of the response curve; Step S3: Perform sensitivity quantification assessment based on the degree of nonlinear distortion of the response curve, reconstruct the topological association between nodes, and construct a supply chain logistics topology optimization network; Step S4: Based on the real-time supply chain logistics data flow, perform a pre-simulation of future load demand and conduct an evolution analysis of the call operation path to generate call operation path evolution data. Step S5: Identify call path conflicts based on the supply chain logistics topology optimization network, then predict potential logistics risk links to obtain comprehensive risk prediction results; Step S6: Based on the comprehensive risk prediction results, perform local logistics resource scheduling and allocation and global pre-processing to build an intelligent logistics collaborative optimization engine.

2. The supply chain logistics efficiency collaborative optimization method based on artificial intelligence according to claim 1, characterized in that, The specific steps of step S1 are as follows: Acquire real-time supply chain logistics data streams; identify multiple logistics nodes based on real-time supply chain logistics data streams; 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 timestamp, and stacking density for each of the aforementioned type label mapping nodes to obtain a multi-dimensional node feature tensor; Calculate node turnover frequency and transportation unit thermal trajectory based on real-time supply chain logistics data flow; Based on the node turnover frequency and the thermal trajectory of the transportation unit, the multi-path logistics interaction frequency distribution is mined to generate the logistics interaction frequency distribution characteristics between paths. Node coupling density analysis is performed based on the frequency distribution characteristics of logistics interactions between paths to obtain the node coupling relationship density. An adaptive hierarchical mapping of multidimensional node feature tensors based on node coupling relationship density is used to construct a multi-layer logistics collaboration network.

3. The supply chain logistics efficiency collaborative optimization method based on artificial intelligence according to claim 1, characterized in that, The specific steps of step S2 are as follows: Real-time logistics path congestion calculation is performed on a multi-layered logistics collaboration network to extract logistics path congestion characteristics. Based on the statistical distribution of path flow overlap in the multi-layered logistics collaboration network, the logistics interaction flow overlap is obtained. Based on the overlap of logistics interaction and the characteristics of logistics path congestion, peak load of transportation is analyzed to generate peak load value of logistics transportation. Based on the peak load value of logistics transportation, the load response of path nodes in the multi-layer logistics cooperation network is back-inferred, and time-series load response curves of multiple nodes are constructed. The nonlinear distortion of the time-series load response curves of multiple nodes is evaluated to obtain the degree of nonlinear distortion of the response curves.

4. The supply chain logistics efficiency collaborative optimization method based on artificial intelligence according to claim 3, characterized in that, 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: Identify the instantaneous transitions in the time-series load response curves of the multiple nodes and mark the transient load abrupt change points; Extract the timestamps of the transient load abrupt change points; Calculate the frequency of the transient load abrupt change points; Based on the occurrence timestamp, the frequency is analyzed for spatiotemporal distribution to generate load mutation spatiotemporal distribution characteristics; The load fluctuation amplitude is calculated based on the spatiotemporal distribution characteristics of load mutations, and the load fluctuation amplitude value is generated. The degree of nonlinear distortion of the response curve is obtained by evaluating the nonlinear distortion based on the load fluctuation amplitude value.

5. The supply chain logistics efficiency collaborative optimization method based on artificial intelligence according to claim 1, characterized in that, Step S3 is as follows: Identify and mark the nonlinear distortion peak points based on the degree of nonlinear distortion of the response curve. Based on the nonlinear twisting peak point, potential logistics bottlenecks in a multi-layer logistics collaboration network are located, and the logistics bottleneck points of the logistics network are obtained. A sensitivity quantitative assessment of logistics bottlenecks in the logistics network is conducted to obtain a bottleneck node stability index. Based on the bottleneck node stability index, the spatial distribution of bottleneck nodes is fitted to construct a sensitivity heatmap of logistics bottleneck points. Based on the sensitivity heatmap of logistics bottlenecks, the topological associations between nodes are reconstructed to build a supply chain logistics topology optimization network.

6. The supply chain logistics efficiency collaborative optimization method based on artificial intelligence according to claim 5, characterized in that, The specific steps for reconstructing the topological relationships between nodes based on the sensitivity heatmap of logistics bottlenecks and constructing a supply chain logistics topology optimization network are as follows: Based on a multi-layered logistics collaboration network, upstream and downstream related nodes are identified and marked as logistics bottlenecks in the logistics network. Based on upstream and downstream related nodes, potential related paths are mined to generate potential logistics paths; Based on the heat map of the sensitivity to sudden changes in logistics bottlenecks, real-time logistics flow migration decisions are made for potential logistics routes to obtain logistics load flow migration data. Based on logistics load flow migration data, bottleneck point topology association hierarchy analysis is performed to extract and identify implicit topology association hierarchy. Based on the implicit topological association hierarchy, the topological associations between nodes in the multi-layer logistics collaboration network are reconstructed to build a supply chain logistics topology optimization network.

7. The supply chain logistics efficiency collaborative optimization method based on artificial intelligence according to claim 1, characterized in that, The specific steps of step S4 are as follows: Unprocessed orders are extracted based on real-time supply chain logistics data flow; Analyze order cycle patterns in real-time supply chain logistics data flow to generate periodic order characteristics; Based on the characteristics of periodic orders, multi-time point order trend prediction is performed to generate multi-time point order prediction trends; The future load demand is simulated based on the forecast trends of the unprocessed orders and multi-time point orders to generate the future supply chain logistics load demand. Based on future supply chain logistics load demand, we conduct future dispatch and operation path evolution analysis to generate dispatch and operation path evolution data.

8. The supply chain logistics efficiency collaborative optimization method based on artificial intelligence according to claim 1, characterized in that, The specific steps of step S5 are as follows: Identify occupied logistics paths in the supply chain logistics topology optimization network; Based on the call to operational path evolution data, path conflicts are identified in occupied logistics paths, and conflicting paths are marked. Based on the conflict paths, the compatibility matching calculation of logistics operations is performed to generate a supply chain logistics network compatibility matching value. Predict potential logistics risk links based on supply chain logistics network compatibility matching values, and identify delayed transmission paths and resource shortage nodes. A comprehensive risk assessment was conducted on the delayed transmission path and resource shortage nodes to obtain comprehensive risk prediction results.

9. The method according to claim 1, characterized in that, The specific steps of step S6 are as follows: Based on the comprehensive risk prediction results, a local logistics resource scheduling and allocation strategy is obtained. The local logistics resource scheduling and allocation strategy is processed globally in advance to construct a global in advance coordination strategy; Perform end-to-end compression encoding on the global pre-coordination strategy and extract global optimized end-to-end process samples; Dynamic node update and migration learning are performed on the global optimization end-to-end process samples to build an intelligent logistics collaborative optimization engine.

10. A supply chain logistics efficiency collaborative optimization system based on artificial intelligence, characterized in that, The method for performing the AI-based collaborative optimization of supply chain logistics efficiency as described in claim 1 includes: The multi-layer network module is used to acquire real-time supply chain logistics data streams, perform node coupling density analysis and adaptive hierarchical mapping, and construct a multi-layer logistics collaboration network. The load response module is used to back-calculate the load response of path nodes in a multi-layer logistics collaboration network and to perform nonlinear distortion assessment, thereby obtaining the degree of nonlinear distortion of the response curve. The topology reconstruction module is used to perform sensitivity quantification assessment based on the degree of nonlinear distortion of the response curve, and to reconstruct the topology associations between nodes to build a supply chain logistics topology optimization network. 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. 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. 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.

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