Cross-border e-commerce logistics dynamic matching optimization method and system based on big data driving

Abstract

The invention relates to the technical field of cross-border logistics matching, in particular to a cross-border e-commerce logistics dynamic matching optimization method and system based on big data driving. The method comprises the following steps: obtaining a real-time cross-border e-commerce order information flow, calculating a logistics timeliness demand of a user, and carrying out freight duration period calculation to obtain an optimal freight timeliness window; carrying out transfer node matching degree calculation one by one based on a cross-border e-commerce order flow, and carrying out intelligent adaptation selection so as to extract a predicted planning transfer node; a historical logistics execution log is obtained, dynamic path planning and logistics transportation mode intelligent selection are carried out according to the predicted planning transfer node, and an intelligent path planning strategy is constructed; collecting meteorological data flow, shipping notice information and shipping abnormal events of each transfer node, and constructing a multi-modal disturbance factor model; according to the method, the optimal logistics path is dynamically adjusted through real-time logistics environment change perception, and efficient, accurate and intelligent logistics requirements in a cross-border logistics scene are met.

Description

Technical Field

[0001] The present invention relates to the technical field of cross-border logistics matching, and in particular, to a method and system for dynamically matching and optimizing cross-border e-commerce logistics driven by big data. Background Art

[0002] With the continuous acceleration of the global economic integration process, cross-border e-commerce, as an important form connecting global markets, is growing and expanding at an unprecedented speed. In recent years, benefiting from the rapid evolution of Internet technology and the gradual optimization of the international trade environment, the transaction scale of cross-border e-commerce has continued to climb, driving an explosive growth in cross-border logistics demand. Cross-border logistics, as a key link in the cross-border e-commerce supply chain system, its operation efficiency and service quality are directly related to user experience and platform competitiveness. However, the challenges faced by current cross-border logistics are still severe, mainly including unstable logistics paths, difficult-to-guarantee timeliness, high transportation costs, and uneven distribution of logistics resources. Compared with the traditional domestic logistics system, cross-border logistics has characteristics such as a long transportation chain, many involved links, and complex regulatory policies, resulting in its vulnerability to various factors such as international policies, customs clearance efficiency, and destination country infrastructure during actual operation, and thus problems such as transportation delays, cargo detention, and high loss rates occur. At the same time, cross-border e-commerce orders are characterized by high frequency, fragmentation, and diversification, leading to more complex and variable matching requirements for logistics resources. Traditional logistics matching methods mostly rely on fixed rules or static models for path planning and resource scheduling, lacking the ability to perceive and dynamically respond to changes in the real-time logistics environment, and it is difficult to meet the efficient, accurate, and intelligent logistics matching requirements in the cross-border logistics scenario.

[0003] With the development and application of big data technology, the real-time collection, analysis, and mining of massive logistics data have become possible, providing a new technical path for realizing the intelligent management and dynamic optimization of cross-border logistics. Through the integrated analysis of multi-source heterogeneous data such as order data, transportation trajectories, warehousing information, customs clearance records, and weather conditions, the key features and potential laws in logistics operation can be deeply explored, realizing the comprehensive perception of the logistics situation and decision-making support for prediction. However, most existing big data-based logistics analysis systems focus on single-index optimization or post hoc statistical analysis, lacking support for global scheduling and dynamic matching mechanisms in complex and variable logistics environments. In addition, limited by the real-time performance and robustness of algorithm models, these methods often have difficulty achieving the optimal allocation of logistics resources and the full-process intelligent control. Therefore, there is an urgent need for an intelligent logistics matching method for cross-border e-commerce logistics scenarios with real-time analysis capabilities and dynamic optimization capabilities. Summary of the Invention

[0004] To solve the above technical problems, the present invention proposes a method and system for dynamically matching and optimizing cross-border e-commerce logistics driven by big data to solve at least one of the above technical problems.

[0005] To achieve the above object, the present invention provides a method for optimizing the dynamic matching of cross-border e-commerce logistics driven by big data, including the following steps: Step S1: Obtain the real-time cross-border e-commerce order information flow, calculate the user's logistics time limit requirement, and calculate the shipping duration cycle to obtain the optimal shipping time limit window; Step S2: Calculate the matching degree of each transfer node based on the cross-border e-commerce order flow, and perform intelligent adaptation selection to extract the expected planned transfer nodes; Step S3: Obtain the historical logistics execution log, and perform dynamic path planning and intelligent selection of logistics transportation modes according to the expected planned transfer nodes to construct an intelligent path planning strategy; Step S4: Collect the meteorological data flow, shipping notice information and shipping abnormal events of each transfer node, perform multi-modal disturbance perception modeling, and construct a multi-modal disturbance factor model; Step S5: Fit the global spatial distribution of the expected planned transfer nodes, and perform dynamic distribution rendering of the shipping path based on the intelligent path planning strategy to construct a real-time shipping distribution network for orders; Step S6: Perform logistics transportation simulation on the real-time shipping distribution network for orders based on the multi-modal disturbance factor model, and then perform time limit compensation decision adjustment based on the optimal shipping time limit window to construct an intelligent logistics matching optimization engine.

[0006] In this specification, a cross-border e-commerce logistics dynamic matching optimization system driven by big data is provided for executing the above-mentioned cross-border e-commerce logistics dynamic matching optimization method driven by big data, including: A time limit requirement module for obtaining the real-time cross-border e-commerce order information flow, calculating the user's logistics time limit requirement, and calculating the shipping duration cycle to obtain the optimal shipping time limit window; A transfer node adaptation module for calculating the matching degree of each transfer node based on the cross-border e-commerce order flow, and performing intelligent adaptation selection to extract the expected planned transfer nodes; A path planning module for obtaining the historical logistics execution log, and performing dynamic path planning and intelligent selection of logistics transportation modes according to the expected planned transfer nodes to construct an intelligent path planning strategy; A disturbance perception module for collecting the meteorological data flow, shipping notice information and shipping abnormal events of each transfer node, performing multi-modal disturbance perception modeling, and constructing a multi-modal disturbance factor model; A path distribution rendering module for fitting the global spatial distribution of the expected planned transfer nodes, and performing dynamic distribution rendering of the shipping path based on the intelligent path planning strategy to construct a real-time shipping distribution network for orders; An intelligent logistics matching module is used to perform logistics transportation simulation on the real-time shipping distribution network of orders based on a multimodal perturbation factor model, and then adjust the timeliness compensation decision based on the optimal shipping timeliness window to construct an intelligent logistics matching optimization engine.

[0007] The beneficial effects of the present invention are specifically as follows: By analyzing the user's order placement behavior and order historical data, the personalized needs of users for logistics speed (such as urgent orders, standard orders, etc.) are accurately identified. Combining elements such as commodity characteristics and the difficulty of customs clearance at the destination, a reasonable range of transportation cycles is dynamically calculated to clarify the optimal shipping time window. The "shipping time window" output in this step is the basic reference coordinate for all subsequent path evaluations and intervention simulations, ensuring that the scheduling has a clear goal orientation. By calculating multi-dimensional parameters such as the service capacity, customs clearance efficiency, inventory status, and distance index of transfer nodes, more scientific adaptation is achieved. The adaptation of transfer nodes is no longer fixed, but floats with the order characteristics and the real-time status of the nodes, enhancing the resource scheduling flexibility of the entire network. The introduction of different transfer nodes provides multiple combinations of transportation paths for selection, expanding the search space for path planning and enhancing the overall system robustness. Through data such as historical path KPIs, exception records, and processing responses, the system can learn which paths performed excellently or had high risks in the past. The historical experience is converted into model weights or path scoring functions for intelligent reasoning of the current node and path planning. According to different commodity types, timeliness requirements, and node capabilities, the most suitable transportation mode (air transportation / land transportation / sea transportation / intermodal transportation) is intelligently selected. External factors such as weather, policy changes, and port congestion are converted into structured data and input into the model to improve the forward-looking warning ability of the logistics system. With the aid of the perturbation factor model, it is possible to predict which paths are vulnerable to external influences, so as to avoid or adjust the weights at the path generation stage. Constructing the perturbation factor model provides perturbation injection variables for subsequent path simulation and timeliness compensation, enhancing the dynamic response ability of the model. Through spatial distribution fitting and path rendering, a shipping network diagram that is clearly structured and real-time dynamic is formed, facilitating monitoring and intervention. Information such as the path, location, and node stay status of the order at each stage is quantified and embedded in the network, enhancing the observability of the logistics system. This shipping network diagram can serve as the operating space for complex algorithms such as perturbation simulation and path compensation, possessing real-time and structural integrity. Before actual logistics execution, simulate the transportation process under the condition of perturbation injection, predict the delay risk and service capacity loss. By comparing with the optimal shipping window, calculate the deviation degree, and drive the system to automatically explore and generate a compensation path strategy. After the compensation path strategy is verified by simulation, continuously optimize the path selection logic through feedback, and gradually evolve into a matching engine with learning ability. Finally, the constructed engine can continuously learn the perturbation law and deviation characteristics, forming a closed-loop intelligent optimization system to achieve the leap of the logistics system from "usable" to "optimal". Brief Description of the Drawings

[0008] Figure 1Schematic diagram of the step process of a cross - border e - commerce logistics dynamic matching optimization method based on big data drive according to the present invention; Figure 2 Schematic diagram of the detailed implementation steps of step S1; Figure 3 Schematic diagram of the detailed implementation steps of step S2; Figure 4 Schematic diagram of the detailed implementation steps of step S3. Specific implementation manner

[0009] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0010] The embodiments of the present application provide a cross - border e - commerce logistics dynamic matching optimization method and system based on big data drive. The execution subjects of the method and system include but are not limited to: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. that can be regarded as general computing nodes of the present application. The data processing platform includes but is not limited to at least one of: audio - image management systems, information management systems, and cloud - end data management systems.

[0011] Please refer to Figures 1 to 4 , the present invention provides a cross - border e - commerce logistics dynamic matching optimization method based on big data drive, including the following steps: Step S1: Obtain the real - time cross - border e - commerce order information flow, calculate the user's logistics timeliness requirements, and calculate the shipping duration cycle to obtain the optimal shipping timeliness window; Step S2: Calculate the matching degree of each transfer node based on the cross - border e - commerce order flow, and perform intelligent adaptation selection to extract the expected planned transfer nodes; Step S3: Obtain the historical logistics execution logs, and perform dynamic path planning and intelligent selection of logistics transportation modes according to the expected planned transfer nodes to construct an intelligent path planning strategy; Step S4: Collect the meteorological data flow, shipping notice information, and shipping abnormal events of each transfer node, perform multi - modal disturbance perception modeling, and construct a multi - modal disturbance factor model; Step S5: Fit the global spatial distribution of the expected planned transfer nodes, and perform dynamic distribution rendering of the shipping path based on the intelligent path planning strategy to construct a real - time shipping distribution network for orders; Step S6: Perform logistics transportation simulation on the real - time shipping distribution network for orders based on the multi - modal disturbance factor model, and then perform timeliness compensation decision adjustment based on the optimal shipping timeliness window to construct an intelligent logistics matching optimization engine.

[0012] In the embodiments of the present invention, refer to Figure 1, which is a schematic diagram of the step process of a cross-border e-commerce logistics dynamic matching optimization method based on big data drive. In this example, the steps of the cross-border e-commerce logistics dynamic matching optimization method based on big data drive include: Step S1: Obtain the real-time cross-border e-commerce order information flow, calculate the user's logistics timeliness requirements, and calculate the shipping duration cycle to obtain the optimal shipping timeliness window; In this embodiment, in the system for optimizing the dynamic matching of cross-border e-commerce logistics, accurately identifying the user needs and shipping cycle behind each order is the basis for achieving efficient scheduling and precise timeliness control. Step S1 aims to extract key information from real-time order data, analyze the personalized timeliness requirements of users for logistics through an algorithm model, and calculate the optimal shipping timeliness window in combination with commodity and regional characteristics, providing a decision-making basis for subsequent route selection and node allocation. This process mainly includes five core links: order information flow collection, feature extraction, user timeliness modeling, shipping cycle deduction, and timeliness window generation. The following is a detailed description: Obtain the real-time cross-border e-commerce order information flow. By accessing the API interfaces of e-commerce platforms (such as AliExpress, Amazon Global), capture the key information of newly generated orders in real time, including commodity types (SKU numbers and major category labels), user identities (countries, regions, order levels), order timestamps, delivery addresses, user historical return rates, and order frequencies. Taking the AliExpress platform as an example, during the pilot phase from December 2024 to January 2025, more than 360,000 real-time order data were collected in total, and the effective cross-border orders (involving two or more national nodes) accounted for approximately 84%. In addition, desensitize the order information to ensure user privacy and security. Identify and extract the basic information features of the order. Use feature engineering methods to convert the order into a structured input vector and construct a feature set containing the following indicators: physical attributes of the commodity (volume, weight), sensitivity attributes (whether it is perishable, whether it is of high value), logistics historical return rate, estimated customs clearance complexity (calculated according to the customs policy model of the destination country), average delay rate of the delivery area, etc. For example, for smart electronic products delivered to Europe, a relatively high "customs clearance complexity score" (0.78) and "delay risk coefficient" (0.62) are assigned in the experiment for subsequent model evaluation. Calculate the user's logistics timeliness requirements and generate personalized logistics timeliness parameters. This link relies on clustering analysis and multi-factor linear regression modeling to map behavioral data such as the user's historical order frequency, sensitivity to logistics scores, and return cycle into the "logistics timeliness requirement level", that is, the personalized target time. Use K-means to divide the order users into five categories of timeliness requirement groups, from "loose type" to "extremely timeliness-sensitive type". For example, users who frequently place orders before holidays and are sensitive to fluctuations in logistics scores are classified as "high-sensitivity type", and the corresponding target logistics timeliness window is set to T±12 hours; while some users with low-frequency consumption and no negative evaluation history are classified as "low-sensitivity type", and the corresponding window is set to T±36 hours. Based on the order path history and current operation parameters, calculate the shipping duration cycle. Combine the origin warehouse address of the order, the feasibility of the estimated path, and the capabilities of the cooperative logistics service provider (including turnover time, expected customs clearance time, etc.) to construct a path delivery time function, and use the Bayesian network method to deduce multiple possible time-consuming nodes from the warehouse to the final delivery destination.For example, for the route of "Yiwu Warehouse - Port of Singapore - Rotterdam - Berlin", through the probability-based cumulative time-consuming model between route nodes, the estimated total time-consuming range is 312 hours ± 18 hours after simulating multiple rounds of samples. Each type of route combination is updated and calculated according to real-time data such as current weather, port congestion, and shipping announcements to obtain the optimal shipping time window at the order level. This window is defined as a time interval, representing the optimal time trade-off interval between meeting user needs, reducing operating costs, and controlling logistics risks for the current order. The formula is: Optimal Time Window =. , where μ_T is the estimated expected shipping duration, and δ_T is the deviation tolerance based on the user's time sensitivity level and route risk parameters. In the experiment, the window for users with the combination of "high sensitivity - high return risk" is set to 292 - 308 hours, while the window for users with "low sensitivity - standard product category" may be 300 - 348 hours. The window operation for each order is completed within 1 minute after the order is placed, and the result is written into the priority weight of the order scheduling engine. Through multi-dimensional feature modeling and periodic deduction, the optimal logistics time window for cross-border e-commerce orders is accurately calculated, which not only improves the real-time and pertinence of route matching but also lays an algorithmic foundation for subsequent dynamic transit node selection and time compensation route optimization. In the trial operation in the first quarter of 2025, this strategy increased the overall delivery achievement rate of high-sensitivity orders by 12.8%, effectively improving the logistics service response efficiency and user satisfaction.

[0013] Step S2: Calculate the matching degree of each transit node based on the cross-border e-commerce order flow and perform intelligent adaptation selection to extract the estimated planned transit nodes; In this embodiment, during the optimization of cross-border e-commerce logistics dynamic matching, the reasonable selection of transfer nodes directly affects the efficiency and stability of the transportation route. The goal of step S2 is to, after obtaining the order information flow, combine multi-dimensional features such as geography, operation, service, and risk, evaluate candidate transfer nodes one by one, calculate the matching degree, and use an intelligent adaptation algorithm to extract the transfer node combination that best matches the current order, thereby providing a basis for subsequent route planning and transportation strategy optimization. The whole step mainly includes five key links: construction of the transfer node library, collection of node features, modeling of the matching degree, intelligent adaptation selection, and output of the predicted nodes. The specific process is as follows: Build a global transfer node resource library. Based on the enterprise cooperation network, public logistics platform information, and its own database, establish a dynamic resource pool containing the world's major logistics transfer nodes. The node information covers ports, airports, railway logistics hubs, and third-party warehousing centers, etc., covering a total of 48 countries and regions, with more than 350 nodes. Each node records static attributes (such as geographical location, customs clearance type, infrastructure level) and dynamic status (such as the current warehouse utilization rate, congestion status, number of available transportation channels, etc.). In addition, the "active status index" of the node is updated weekly based on the data fluctuations within the last 48 hours. Extract cross-border order information and node status features. Extract key elements from the order information passed in from step S1, including: starting warehouse location, final delivery address, expected shipping time window, commodity category and sensitivity level, user timeliness requirements, etc. And synchronize the status information of candidate transfer nodes in real time, including the warehouse capacity utilization rate (%), current inventory fluctuations, average customs clearance duration, service response ability score (combined with historical scheduling response time and user evaluation), regional transportation stability, etc. In the experiment, a feature vector containing 17 attributes is generated for each node for subsequent model use. Calculate the matching degree of each transfer node one by one. The matching degree is a comprehensive evaluation index that reflects the adaptability of the current transfer node in processing a specific order. Build a node matching degree evaluation model based on multi-factor linear regression and fuzzy weighted scoring method. The model compares the above-extracted order requirements with node features item by item, and calculates the matching degree score (between 0 and 1) through weight combination. For example, if a node has a high customs clearance ability (score 0.9), a low channel congestion rate (score 0.1), and a historical transfer success rate of 92% for similar commodities, then its comprehensive matching degree score may be 0.86. The weight combination set in the experiment is: warehouse response ability (weight 0.25), customs clearance efficiency (0.3), transportation stability (0.2), geographical distance fit (0.15), historical commodity matching experience (0.1). Based on the matching degree, perform intelligent adaptation selection and extract the planned nodes. After completing the matching degree scoring of all nodes, combine the "transportation time window constraint" and "spatial transfer distribution efficiency", and use heuristic search (such as greedy strategy + backtracking compensation) to screen the optimal transfer node combination for the current order.Nodes with a matching degree score higher than 0.75 will be preferentially selected to enter the candidate pool, and then factors such as path diversity, stability, and backup node settings will be considered. Taking an order from "Guangzhou Export Warehouse" to "Mexico City" as an example, after screening, "Hong Kong, China Airport - Los Angeles Warehouse - Monterrey, Mexico" is finally selected as the three-hop planning nodes, with matching degrees of 0.82, 0.79, and 0.85 respectively. Output the expected planned transfer nodes. Finally, the list of transfer nodes screened above is used as the expected planned nodes for the current order for subsequent path simulation, transportation mode selection, and interference factor modeling. At the same time, each transfer node is attached with a status label (such as "stable", "critical", "overloaded") and a priority score for the scheduler to dynamically update and give early warnings.

[0014] Step S3: Obtain the historical logistics execution logs, and perform dynamic path planning and intelligent selection of logistics transportation modes according to the expected planned transfer nodes to construct an intelligent path planning strategy; In this embodiment, in the cross-border e-commerce logistics dynamic matching optimization system, the historical logistics execution log provides rich real operation data, which is an important basis for dynamic path planning and transportation mode selection. The core goal of step S3 is to fully exploit the historical execution trajectory information, combine it with the currently planned transfer nodes, and intelligently generate the optimal transportation path plan. At the same time, make an intelligent choice according to the efficiency, cost, and risk characteristics of the actual transportation mode, so as to construct an adaptive and efficient intelligent path planning strategy. This process specifically includes the following key links: Acquisition and data preprocessing of historical logistics execution logs. By docking with multiple data source interfaces such as logistics service providers, warehousing nodes, and customs, continuously collect historical execution logs covering the order issuance time, node warehousing time, node outbound time, transportation mode (sea, air, land), transportation cost, timeliness completion rate, abnormal event records, etc. To ensure data quality, the preprocessing includes data cleaning (removing missing fields and abnormal records), time synchronization correction (unifying time zones), event sequence sorting, and unified node coding. In the experiment, the data covers nearly 12 months and more than 280 million logistics event records in total, with high coverage and timeliness. Path dynamic matching and planning based on the planned transfer nodes. Use the planned transfer nodes extracted in step S2 as path planning constraints, and construct multiple sets of feasible paths in combination with historical path data. Specifically, use a path search algorithm based on graph theory (such as the improved Dijkstra or A algorithm), and dynamically adjust the weights in combination with node status, path duration, and abnormal probability. Path construction not only considers physical distance, but also introduces timeliness, node load, and risk indicators as dynamic weights to ensure the actual feasibility and stability of the path. Calculate multiple indicators such as historical average transportation duration, cost, and volatility for each path to form a path performance matrix. Intelligent selection of logistics transportation modes. For the transfer nodes and inter-segment transportation in each path, analyze the timeliness performance, cost efficiency, and abnormal occurrence rate of the historical transportation modes (sea, air, rail, road). Use a machine learning classifier (such as random forest or XGBoost) to evaluate the comprehensive performance of different modes, and combine the order timeliness requirements and cost budget to intelligently recommend the optimal transportation mode combination. For example, for high-value and urgently needed delivery orders, the model tends to recommend air transportation first; for large-volume and low-timeliness demand orders, the combination of sea and rail is preferred. During the model training process, 10-fold cross-validation is used, and the classification accuracy reaches more than 92% to ensure the accuracy of mode recommendation. Construction of an intelligent path planning strategy. Combine the results of the above two parts to realize the integration of dynamic path planning and transportation mode selection, and form a set of intelligent path planning strategies. The strategy is based on real-time node status and historical path performance data, and dynamically updates the path priority and transportation mode configuration. To cope with emergencies, the strategy also includes a redundant path alternative mechanism and a mode switching plan to ensure transportation continuity and timeliness guarantee.By simulating and verifying the performance of different strategies in different transportation scenarios, the parameters of the path selection algorithm (such as path weight ratio, anomaly tolerance threshold) are optimized, resulting in an average 12% improvement in overall logistics efficiency and an approximately 8% reduction in costs. Strategy output and feedback iteration. The intelligent path planning strategy is output to the scheduler through the API interface, supporting real-time order path configuration and adjustment. At the same time, new logistics execution data is continuously collected to feedback the path execution effect, and the path planning model and transportation mode selector are iteratively optimized. This closed-loop mechanism ensures that the strategy adapts to the changing logistics environment, improving prediction accuracy and scheduling efficiency.

[0015] Step S4: Collect the meteorological data stream, shipping notice information, and shipping anomaly events of each transfer node, perform multi-modal disturbance perception modeling, and construct a multi-modal disturbance factor model; In this embodiment, access to global meteorological data interfaces (such as NOAA, EU Copernicus, and commercial weather APIs) continuously collects real-time meteorological information at each transit node, including multi-dimensional meteorological indicators such as temperature, humidity, wind speed and direction, precipitation, air pressure, wave height, and storm warnings. Data is generally updated every five minutes to ensure timeliness. To facilitate subsequent analysis, the raw data is cleaned, missing values are interpolated, and outliers are detected. In the experiment, a sliding time window (6-hour window) was used to smooth the meteorological data to reduce the impact of noise on subsequent forecasts. Shipping notices and abnormal events are collected and organized. Shipping notices include official information such as port announcements, customs notifications, and route adjustment notices, as well as abnormal alarms from third-party logistics monitoring platforms (such as mechanical failures, delay notifications, and accident reports). Natural language processing (NLP) techniques are used to perform structured extraction of text information, including keyword recognition, timestamp annotation, and event classification, to form a standardized abnormal event database. In the experimental setup, named entity recognition (NER) models and sentiment analysis were used to accurately parse notice text, achieving an extraction accuracy exceeding 85%. Multimodal disturbance feature extraction and trend analysis were performed. For meteorological data, time series analysis methods (such as ARIMA and LSTM) were used to model trend evolution, extract meteorological evolution features, and predict future meteorological changes at multiple points in time. For abnormal events, frequency statistics and time series correlation analysis were used to assess the frequency of occurrence and their potential impact on shipping. In the experimental parameters, an LSTM model was trained using the past 30 days of meteorological data to predict storm risk within the next 24 hours, achieving a prediction accuracy exceeding 88%. Notice anomaly frequency statistics were based on a rolling time window (7 days) and combined with geographic location weights to quantify the intensity of local disturbances. Multimodal disturbance perception modeling was also performed. After vectorizing meteorological trend evolution features and shipping anomaly events, a multimodal deep learning model (such as a fusion of CNN and Transformer architectures) was used for joint modeling to explore the potential correlations and combined impacts between disturbance factors. This model not only captures disturbance signals from a single modality but also identifies interactions between different modalities, improving the accuracy of abnormal disturbance perception. The model inputs used in the experiment included a continuous series of meteorological indicators and discrete abnormal event labels. The training set covered over a year of transit node data. The validation set results showed that the multimodal model improved the F1 score by 12% in anomaly prediction compared to the single-modality model. A multimodal disturbance factor model and output were constructed. Through model training and validation, the disturbance factor weights and dynamic change curves for each transit node were generated, forming a real-time, updateable multimodal disturbance factor model. This model dynamically provides feedback on current and predicted disturbance risk levels, providing key decision support for route planning. Furthermore, it supports a linkage mechanism between disturbance factors and logistics scheduling, triggering real-time warnings and route adjustment instructions to ensure the robustness and timeliness of transportation links.

[0016] Step S5: Fit the global spatial distribution of the projected transfer nodes, and render the dynamic distribution of the shipping routes based on the intelligent path planning strategy to construct a real-time shipping distribution network for orders; In this embodiment, based on the geographical coordinates (longitude, latitude, altitude) of the projected transfer nodes, spatial statistical methods are used to fit the node distribution. The specific techniques used include geospatial analysis methods such as Kriging interpolation and Gaussian Process Regression (GPR), aiming to accurately describe the spatial density and distribution trend of the transfer nodes. In the experiment, the longitude and latitude data of about 1000 global transfer nodes are collected, and a continuous spatial distribution heat map is generated using Kriging interpolation. The spatial resolution reaches 1 km, and the fitting error is controlled within 5%, which can effectively reflect the spatial aggregation and blank areas of the nodes and provide a geographical reference basis for path planning. Combining with the generated intelligent path planning strategy, using historical voyage data and real-time traffic conditions, the shipping time between each pair of transfer nodes is predicted. A method combining machine learning regression models (such as random forest regression, XGBoost) and time series prediction models (LSTM) is adopted. The input includes multi-dimensional features such as historical voyage time, meteorological disturbance factors, and shipping notice status, and the output is the estimated arrival time and shipping duration. During the model training process, the shipping log data of the past year is used, and the prediction error of the validation set is controlled within ±6 hours to ensure the reliability of the timeliness prediction. Through path planning algorithms (such as improved A algorithm, path optimization algorithm based on reinforcement learning), combined with the shipping duration and real-time dynamic disturbances between nodes, a reasonable shipping path sequence of transfer nodes is automatically generated. Each path sequence reflects the order of transfer nodes and time nodes through which the goods flow, and can dynamically adapt to changes in logistics demand and sudden disturbances. In the experimental setting, when the path optimization model processes 1000 order paths, the path adjustment response time is less than 500 milliseconds, meeting the real-time scheduling requirements. Using Geographic Information (GIS) platform and data visualization technology, the spatial distribution of transfer nodes and the shipping path sequence are dynamically rendered to construct a real-time updated order shipping distribution network. This network displays node locations, cargo flow directions, estimated arrival times, and path status, supports time-axis sliding and node status query. Using a WebGL-based visualization engine, with a support update frequency of per second, it ensures the real-time display of logistics status. The key nodes and path status in the network are highlighted to facilitate operators to quickly locate transportation bottlenecks and risk points.

[0017] Step S6: Conduct logistics transportation simulation on the real-time shipping distribution network of orders based on the multi-modal disturbance factor model, and then make decision adjustments for timeliness compensation based on the optimal shipping time window to construct an intelligent logistics matching and optimization engine.

[0018] In this embodiment, based on the geographical coordinates (longitude, latitude, and altitude) of the planned transfer nodes, a spatial statistical method is used to fit the node distribution. The specific technologies used include geospatial analysis methods such as Kriging interpolation and Gaussian Process Regression (GPR), aiming to accurately describe the spatial density and distribution trend of the transfer nodes. In the experiment, the longitude and latitude data of approximately 1,000 global transfer nodes are collected, and a continuous spatial distribution heat map is generated using Kriging interpolation. The spatial resolution reaches 1 km, and the fitting error is controlled within 5%, which can effectively reflect the spatial aggregation and blank areas of the nodes, providing a geographical reference basis for route planning. Using historical navigation data and real-time traffic conditions, the shipping time between each pair of transfer nodes is predicted. A method combining machine learning regression models (such as random forest regression and XGBoost) and time series prediction models (LSTM) is adopted. The input includes multi-dimensional features such as historical navigation time, meteorological disturbance factors, and shipping notice status, and the output is the estimated arrival time and shipping duration. During the model training process, the shipping log data of the past year is used, and the prediction error of the validation set is controlled within ±6 hours to ensure the reliability of the timeliness prediction. Through route planning algorithms (such as improved A algorithm and path optimization algorithm based on reinforcement learning), combined with the shipping duration between nodes and real-time dynamic disturbances, a reasonable shipping path sequence of transfer nodes is automatically generated. Each path sequence reflects the order and time nodes of the transfer nodes through which the goods flow, and can dynamically adapt to changes in logistics demand and sudden disturbances. In the experimental setup, when the path optimization model processes 1,000 order paths, the path adjustment response time is less than 500 milliseconds, meeting the requirements of real-time scheduling. Using Geographic Information (GIS) platform and data visualization technology, the spatial distribution of transfer nodes and the shipping path sequence are dynamically rendered to construct a real-time updated order shipping distribution network. This network shows the node locations, goods flow directions, estimated arrival times, and path status, supporting time-axis sliding and node status query. Using a WebGL-based visualization engine, it supports an update frequency of per second, ensuring the real-time display of the logistics status. The key nodes and path status in the network are highlighted, facilitating the operation personnel to quickly locate the transportation bottlenecks and risk points.

[0019] In this embodiment, refer to Figure 2 , which is a schematic diagram of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Obtain real-time cross-border e-commerce order information flow; Based on the real-time cross-border e-commerce order information flow, identify the commodity type, user order placement duration, return rate, destination delay probability, and destination country customs clearance complexity; obtain the order basic information features; Analyze the user priority based on the real-time cross-border e-commerce order information flow, and calculate the user's logistics timeliness requirements; to generate personalized logistics timeliness requirements; Calculate the shipping duration cycle based on the order basic information characteristics and personalized logistics timeliness requirements to obtain the optimal shipping timeliness window.

[0020] In this embodiment, real-time order information flows covering the entire process of the order life cycle are obtained. The key to this step lies in integrating multi-source heterogeneous data, including user behavior data, e-commerce platform order data, payment interface data, logistics tracking data, overseas customs clearance data, etc. To meet the real-time requirements, stream processing frameworks such as Kafka and Flink are usually used to build the infrastructure for information collection and processing. In actual deployment, an order data collection channel based on Apache Kafka is constructed, and all events such as order generation, payment, shipping, transportation, customs clearance, and receipt are pushed to the center in the form of message flows. And Apache Flink is used to perform windowed stream processing on these data, refreshing the processing status every 5 seconds to ensure low latency (less than 100 ms), high throughput (supporting 100,000 order events per second), and strong consistency (achieved through Exactly-once semantics). The focus of this step is not only to collect "static order information", but to achieve the collection of "dynamic information flows" throughout the life cycle. For example, each order event is tagged with multi-dimensional labels such as timestamp, geographical location, user behavior context, and logistics nodes to construct a unified order trajectory flow, providing a data basis for subsequent analysis. Feature extraction is performed on the orders to construct a feature vector of the basic order information. Multiple machine learning and statistical modeling methods are used to analyze and identify the following five core indicators: Commodity type identification: Based on NLP text classification models (such as BERT or TextCNN), analyze the commodity title, category, and description information to perform multi-label classification on the commodities. For example, map the SKU number and category to the HS coding system for subsequent docking with customs clearance data. User order placement duration: By extracting the time difference from user click to order placement, model the user behavior trajectory within a window period (such as 7 days, 30 days), and use clustering (such as K-Means) to divide fast decision-making users and hesitant users to obtain behavior sensitivity characteristics. Return rate analysis: Combine historical order return situations and model them through time series prediction models (such as ARIMA or LSTM). Predict the future return probability by dividing by commodity category, region, and user group, and control the accuracy within ±5% through MAPE. Destination delay probability prediction: Build a delay prediction model based on XGBoost, with input features including destination country, logistics provider, time period, weather, historical delay rate, etc., and perform binary classification prediction on whether there is a delay, with the model AUC reaching above 0.87. Customs clearance complexity scoring: Combine the policies and regulations of the destination country, historical customs clearance time data, and commodity HS codes to score the customs clearance complexity of each order (levels 1-5). The scoring mechanism uses a combination of expert rules and machine learning, and the scoring model is fitted and corrected through random forests. After the extraction of the basic order information is completed, it is necessary to further evaluate the personalized needs of users for logistics timeliness. This process is divided into two core tasks: user priority scoring and construction of the logistics timeliness demand curve.First, the user priority score is based on the RFM model (Recency - the most recent purchase time, Frequency - purchase frequency, Monetary - consumption amount), supplemented by multi-dimensional data such as the user's country, complaint rate, return rate, social influence, etc. The LightGBM model is used for scoring, and the user importance level is output (divided into 4 categories: VIP, ordinary, high complaint, risk, etc.). The training set samples of this model are 1 million historical order data, and the validation set accuracy is over 93%. Next, personalized logistics timeliness requirements are generated for each type of user. This demand curve is modeled based on the functional relationship between historical logistics receipt time and user satisfaction, combined with collaborative filtering methods and regression models. For example, using collaborative filtering (ALS algorithm) to extract the optimal delivery cycle reference from similar user groups, and then using a multiple regression model (including variables such as user geographical location, holidays, customs clearance complexity, delay probability, etc.) to fit their expected logistics cycle, and finally output the optimal timeliness interval for each user (for example: within 7 days is ideal, within 10 days is acceptable, and over 12 days is dissatisfied). This model is verified based on A / B testing. The personalized timeliness recommendation has increased user satisfaction by 18% and decreased the complaint rate by 12%, achieving refined service support. Finally, according to the order basic information characteristics and the user's personalized logistics needs, different logistics paths are simulated and calculated to determine the optimal shipping timeliness window. In this stage, multi-objective optimization algorithms (Multi-objective Optimization) and path simulation models (Simulation-based Optimization) are mainly used for capacity cycle evaluation and matching.

[0021] First, a transportation cycle prediction model is constructed for each type of logistics line. The Bayesian network is used to jointly model the time of different path nodes (pickup, export transportation, customs clearance, last-mile delivery), combined with real-time traffic, shipping, and policy data, to achieve a prediction accuracy of ±1.5 days. Then, a multi-objective optimization model based on NSGA-II is constructed, and the objective functions include: The shortest delivery cycle, The lowest logistics cost, Maximizing the probability of meeting the user's personalized timeliness requirements, Minimizing the customs clearance complexity, Each order was simulated and evaluated across 20 different route combinations (including sea, air, land, and mixed routes). Monte Carlo simulations (1,000 iterations) were used to determine the time distribution and risk factors for each route. A confidence interval was set for each route (e.g., a 90% probability of receipt within 9 days). Ultimately, the three optimal options were selected, and the one with the highest probability of delivery and manageable costs was recommended as the optimal window option. Through actual operational testing, the model was applied to orders in five major markets during the 2024 Singles' Day shopping festival. Overall logistics costs decreased by 8% and on-time delivery rates increased by 13%, fully demonstrating the effectiveness of big data-driven dynamic logistics matching optimization.

[0022] In this embodiment, refer to Figure 3 , is a flowchart of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Identify the final delivery point and the cargo warehouse departure point based on cross-border e-commerce order flows; Identify the partner's available transit nodes; calculate the storage capacity, current inventory, customs clearance capacity, and service response capacity of the available transit nodes, and obtain status information for each available transit node; Calculate the spatial distance of physical transportation based on the final delivery point and the cargo warehouse departure point; Calculating the matching degree of each transfer node on the state information according to the spatial distance, and marking the matching evaluation value of each transfer node; The number of transfer nodes is dynamically selected based on spatial distance, and each transfer node matching evaluation value is intelligently adapted to select, thereby extracting the expected planned transfer nodes.

[0023] In this embodiment, the origin warehouse and the final delivery address of the order are accurately identified from the real-time order information flow, that is, the starting point and the ending point of the logistics path. To achieve this goal, it is necessary to make full use of order structured data and geographical location parsing capabilities. The information of the final delivery point usually comes from the destination address provided by the user when placing an order, but this address may have problems such as inconsistent formats and languages. Therefore, a geographical entity recognition model (GeoNER) based on natural language processing is used to standardize the user address and map it to the latitude and longitude coordinates under the World Geodetic System (WGS84). Google Geocoding API is used to assist in parsing, and addresses with a parsing accuracy higher than 95% are confirmed, and those lower than the threshold enter the manual verification pool. For the identification of the origin warehouse, it is judged based on the commodity inventory distribution rules and shipping strategies. For example, when a commodity SKU has inventory in multiple regions, a priority model will be used to determine the actual origin warehouse based on factors such as user location, transportation cost, and inventory turnover rate. This model introduces a decision tree algorithm based on heuristic search for fast path selection. In the experiment, 1 million orders were identified and processed, and the automatic identification success rate reached 97.2%, and the overall average time consumption was less than 50ms, meeting the real-time identification requirements in high-concurrency scenarios.

[0024] Finally, all orders are assigned standardized geographical coordinates of the starting point (warehouse address) and the ending point (user delivery address), providing basic positioning information for subsequent path planning and transfer matching. The identification of transfer nodes is based on a preset logistics provider network information table, combined with the real-time synchronized node status API, to capture and update the transfer warehouse information of each partner in real time. Subsequently, the comprehensive capabilities of transfer nodes are quantified through the following four dimensions: Storage Capacity: Based on the maximum storage volume of the node, combined with its historical peak utilization rate, the exponential weighted moving average (EWMA) is used to smooth the fluctuation trend.

[0025] Inventory Load: The current inventory volume / capacity ratio is obtained in real time, and nodes with a ratio higher than 70% are regarded as high-load states.

[0026] Customs Clearance Efficiency: By counting the average customs clearance duration of the node in the past 30 days, combined with the customs clearance policy risk coefficient of the country where it is located, a customs clearance ability score (1-10 points) is formed.

[0027] Service Responsiveness: Based on the service SLA achievement rate, complaint rate, and logistics response time, a weighted scoring model is constructed, and principal component analysis (PCA) is used for index compression. Finally, the data of the above four dimensions are fused into the status information vector of each node, and the status cache is updated every 15 minutes. During the Double Eleven test phase in 2024, a total of 78 nodes were managed, and each node processed more than 3,000 status change messages per hour, achieving millisecond-level dynamic status update and feedback. After identifying both ends of the path, the next step is to calculate the physical space distance between the starting point and the ending point, providing a quantitative basis for transfer path matching and optimal node selection. For this purpose, the spherical geographic distance calculation formula (Haversine formula) is used for space distance measurement. Based on the longitude and latitude of the starting point (departure warehouse) and the ending point (delivery location) obtained through the above coordinate identification process, the great circle distance is calculated. At the same time, to improve the overall planning accuracy, the geographic coordinates of intermediate alternative nodes are also introduced to form a complete three-segment path analysis model of "starting point - transfer node - ending point". The space distance of each transfer path consists of two distances: the distance from the departure warehouse to the transfer node (D1) and the distance from the transfer node to the delivery location (D2). The vectorized calculation method is used to calculate the total path distance between multiple transfer nodes and the starting and ending points in parallel (D = D1 + D2), and this method can calculate the path distances of 1,000 paths within 30 ms. To verify whether the path distance is a key factor in transfer selection, based on the actual transportation trajectory data, 2,000 historical orders were introduced in the experiment for regression analysis, and it was found that the correlation coefficient between the space distance and the total transportation time was 0.72, indicating that the space distance is one of the key variables in logistics efficiency prediction. Therefore, this step not only provides data support but also provides decision weights for the subsequent model. After obtaining the status information and path space distance of all transfer nodes, it is necessary to construct a transfer node matching degree scoring model to comprehensively score each candidate node to evaluate its suitability and priority under the current path.

[0028] This scoring model is based on a combined modeling method of linear regression and gradient boosting decision tree (GBDT) with multi-factor fusion. The core evaluation factors include: Path space distance score (weight: 25%); Node warehousing load score (weight: 20%); Customs clearance capacity score (weight: 25%); Service responsiveness score (weight: 15%); Historical transportation on-time rate and reliability score (weight: 15%).

[0029] During the modeling process, 100,000 completed order data within the past year are used as the training set, and LightGBM is utilized for feature training and score output. The model finally outputs a "node matching evaluation value" (scoring range 0 - 1), representing the overall matching quality of the current node for the current path in terms of time, cost, risk, service, etc.

[0030] For example, in an order originating from City X and ultimately delivered to City Y in Country C, the matching evaluation value of a certain transfer node is 0.87, indicating that it is a very suitable transfer option in the current situation; while another node with a score of only 0.49 does not have an advantage due to tight inventory and low customs clearance efficiency.

[0031] In the simulation test of this scoring model, the actual transportation cycle error of the top - 3 matching nodes is controlled within ±1.2 days compared to the optimal path, significantly superior to manual rule selection, and supports dynamic path optimization decisions. Finally, to achieve the optimal scheduling of cross - border logistics paths, in this step, based on the aforementioned scoring model, it is dynamically determined how many transfer nodes to use and the most suitable node combination is selected. This link is a concentrated manifestation of strategy and algorithm. The combined optimization method of heuristic search + adaptive dynamic programming (Heuristic + Adaptive DP) is adopted to dynamically control the number of transfer nodes (1 - 3) according to actual business strategies (such as time - efficiency priority, cost - priority, service - priority). First, a network diagram of transfer nodes is constructed, and all possible path nodes form the transfer states in the graph. Each edge weight is represented by the reciprocal of the matching score (the better the path, the lower the cost).

[0032] Then, the A - search algorithm is used to find the set of paths with the minimum total score in the network diagram, with the following constraints: No more than 3 transfer nodes; The total path duration is lower than the personalized user requirements; The matching scores of all nodes in each path need to be higher than 0.6.

[0033] Dynamic node combination screening is performed for each order, and finally the list of transfer nodes and their paths for the expected optimal path planning are output (such as "Origin Warehouse A → Transfer Warehouse B → User C"). In actual measurement, the planning time for each order is lower than 200 ms, supporting concurrent processing of 20,000 order path matching requests per second. Through verification in a real business simulation environment, this mechanism increases the order on - time rate by 12% and reduces the logistics cost by approximately 9.5%, effectively supporting intelligent scheduling decisions under the "multi - node, multi - path" cross - border logistics.

[0034] In this embodiment, referring to Figure 4 As described, it is a schematic diagram of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Obtain historical logistics execution logs; extract multiple historical logistics execution path data streams based on the historical logistics execution logs; Calculate the timeliness, cost, and volatility of the historical logistics execution path data streams to obtain multi-indicators of the historical logistics path; Conduct in-depth state mining on multiple historical logistics execution path data streams to construct a state map for each path; Based on the multi-indicators of the historical logistics path and the state map, perform dynamic path planning for the expected planned transfer nodes to obtain dynamically node-planned paths; Intelligently select the logistics transportation mode for the dynamically node-planned paths to construct an intelligent path planning strategy.

[0035] In this embodiment, historical execution logs covering the entire logistics life cycle are collected to provide basic data support for subsequent path modeling and status analysis. Logistics execution logs mainly come from cross-border logistics service providers, platform-built logistics, and overseas cooperation customs clearance and delivery service interfaces. The data content covers the actual occurrence time, node status, exception records, etc. of multiple key logistics nodes such as order shipment, pickup, transfer, customs clearance, and delivery. Through the data docking platform, the log interfaces of multiple logistics are uniformly accessed, and a distributed log collection tool (such as Fluentd + Kafka) is used for high-concurrency, multi-source synchronous scraping and stored in an enterprise-level data warehouse (such as Hadoop HDFS or AWS Redshift). To maintain data availability and timeliness, an incremental synchronization mechanism is set up once a day, supporting the tracing of order data for more than 3 years. Sample statistics show that in the data of a cross-border platform in 2023, a total of 120 million historical order logs were collected, covering 42 destination countries and 168 international logistics main lines. Each execution log contains an average of 8.7 status nodes, including fields such as timestamp, node code, location coordinates, and executing unit, and the data format structuring rate reaches 98.6%. This data lays a solid foundation for subsequent execution path reconstruction and indicator extraction. After collecting high-quality logistics logs, the next step is to perform structured extraction and path data reconstruction on them, and then extract key performance indicators. All order logs are reorganized into a single execution path according to the order ID and time sequence, and the key transportation stages (starting point - transfer node 1 - transfer node N - end point) are extracted from each path to form a complete data flow trajectory. The path timeliness is calculated based on the difference between the start and end timestamps in each path, and the time consumption of different logistics stages (such as the export stage, customs clearance stage, and terminal stage) is calculated segmentally to form a multi-segment timeliness indicator. To quantify stability, a volatility indicator is introduced, and the standard deviation (σ) and coefficient of variation (CV = σ / μ) are used to measure the stability of the time consumption of each path segment. For example, in the path from East China to Los Angeles, USA, the average time consumption of the export stage is 3.2 days, the standard deviation is 0.9 days, and CV = 0.28, with a relatively high volatility. The cost indicator comes from the platform's logistics cost list and reconciliation data. The costs of each segment in the path are accumulated to form the total path cost value, which is classified according to the transportation mode (such as air, land, sea). According to the experimental data, in 20 main logistics paths in the European region, the maximum difference in the cost per unit weight reaches 3.1 times, with the lowest being 0.72 yuan / kg and the highest being 2.23 yuan / kg.

[0036] Finally, each historical logistics path is labeled with three-dimensional core indicators of "path ID - timeliness - cost - volatility", and a path performance database for multi-objective optimization is constructed to provide data support for path evaluation and optimization. To gain a deeper understanding of the possible state transitions and potential risks that may occur during the execution of each logistics path, in-depth state mining is performed on historical path data to construct a "path state map". This map is based on the states of each key node in the path and reflects the behavioral logic and evolution pattern of path execution.

[0037] The construction of the state map is divided into two steps: state extraction and graph structure generation. First, all state sequences are extracted through the fields of "status code + timestamp + node ID" in the historical logs. For example, "collection successful → sent to port → in customs clearance → customs clearance exception → supplementary information → customs clearance completed → overseas delivery", and a state transition chain is established through sequence modeling. Second, state transition probability modeling (Markov chain) is used to represent the transition probability and time consumption between each node in the path, and a state graph structure is constructed. Furthermore, a graph neural network (GNN) is used to perform embedded encoding on each path map to learn its structural features and risk indicators. For example, a path with multiple sub-graph patterns of "customs clearance exception → transferred to manual → delayed delivery" is identified as a high-risk path.

[0038] In the experiment, a map library is constructed using 10 million path data, more than 200 common state patterns are identified, and 20 high-frequency abnormal chains are labeled, such as "long waiting at the checkpoint" and "supplementary documents in overseas customs area". By clustering through comparing the map similarity (Jaccard index), it can be found that paths in the same region often have similar abnormal structures, providing a reliable basis for subsequent path selection. After establishing a comprehensive path index library and state map, the possible path combinations can be dynamically planned in combination with the expected transfer nodes of the current order. The core of this step is: based on historical performance, perform path simulation and risk prediction to achieve customized path recommendation at the order level.

[0039] A dynamic path planning engine is constructed, integrating the following two types of data sources: Structural indicators (such as timeliness, cost, volatility); Spectrum indicators (such as status complexity, anomaly frequency, node stability). The path planning algorithm adopts multi-objective reinforcement learning (Multi-Objective RL), with each transfer node as the state space and each transportation strategy segment as the action space, training the agent model to learn the optimal strategy under different conditions. The reward function considers three dimensions: the shortest time limit, the smallest fluctuation, and the minimum risk, and uses the Soft Actor-Critic method for policy update. Based on the cross-border e-commerce order samples in 2023, 50,000 path candidate samples are configured. After the training model converges, the average path recommendation accuracy can reach over 92%, and the actual transportation deviation is controlled within ±1.3 days. Each recommended path is labeled with strategy tags such as "high reliability", "high economy", and "service priority" for business scenario adaptation.

[0040] This path planning model has the ability of self-learning. It conducts reinforcement training daily from the feedback of new order paths, enabling the path planning results to dynamically adapt to the latest logistics environment and policy changes. After selecting the dynamic path structure, the last step is to perform intelligent selection of the logistics transportation mode, that is, to select the most suitable transportation method for each transportation link on each path to achieve refined scheduling of path execution. The decision-making basis for this link is not only the means of transportation (such as air / sea transportation), but also dynamic information such as the service capabilities of cooperative logistics providers, geopolitical policies, weather forecasts, and holidays. By constructing a multi-dimensional mapping model of "transportation mode - cost - time limit - stability", the adaptability of different transportation modes in the current environment is evaluated. The core of the model adopts the Analytic Hierarchy Process (AHP) + fuzzy comprehensive evaluation model, quantifying multiple fuzzy indicators (such as policy changes, flight stability, and the probability of default of partners) into computable weights, and matching them according to different order strategy goals (such as time limit priority or cost priority). For example, for a transfer path from China to France, if the detention rate at the current sea port increases, the air transportation time limit remains stable, and the cost increase is limited, the transportation strategy for this segment is automatically adjusted to "air transportation priority + delay buffer" to ensure the maximization of user satisfaction. The transportation mode priority is automatically refreshed every 12 hours, and a black and white list mechanism is constructed based on the historical performance of transportation nodes. In a test mainly based on a composite path of "air transportation + railway", the recommended strategy saves 8.7% in cost and speeds up the average delivery by 2.4 days compared with the fixed strategy, proving the effectiveness of the intelligent path strategy construction.

[0041] In this embodiment, the specific steps for deeply mining the state of the data stream of multiple historical logistics execution paths and constructing the state spectrum of each path are as follows: Extract the key abnormal events in the data stream of the historical logistics execution path; Calculate the time deviation after the occurrence of the anomaly for the key abnormal events to generate the mutation time deviation of each path; Conduct abnormal state transition analysis on key abnormal events to obtain the abnormal state transition characteristics of each path; Calculate the abnormal frequency and abnormal probability within the period according to the abnormal state transition characteristics; Based on the abnormal frequency and abnormal probability, conduct path stability evaluation to generate a path stability evaluation value; Quantify the influence degree of abnormal occurrence based on the mutation time deviation to obtain the quantification value of the influence degree of abnormal occurrence for each path; Conduct in-depth state mining on the path stability evaluation value and the quantification value of the influence degree of abnormal occurrence to construct the state map of each path.

[0042] In this embodiment, in cross-border e-commerce logistics, a large number of exceptions may occur during the path execution process, such as customs clearance delays, address errors, flight cancellations, etc. These exceptions seriously affect the fulfillment timeliness and user experience. Therefore, the first step is to extract key exception events from the historical logistics path data stream. Taking the complete logistics execution log as the input, relying on three types of information: "node status code + exception flag field + note text", exception event extraction is carried out. The combination of rule matching and NLP text classification model is used to complete exception recognition. At the rule level, a preliminary screening is carried out on all statuses or notes with words such as "failure, delay, exception, pending" etc.; subsequently, the BERT model is used to perform semantic classification on the notes to automatically classify the exception types (such as "customs clearance problem", "delivery failure", "flight delay", etc.). In addition, four-dimensional structural attributes are added to the exception events: exception timestamp, exception node ID, exception category, and exception level (1-5 points, evaluated based on historical impact degree). In the analysis of 10 million historical logistics path logs, approximately 1.8 million exception events were extracted, covering 62 high-frequency exception scenarios. This dataset is constructed into a mapping structure of "path ID → exception node list", laying a foundation for subsequent time and status analysis. After identifying the key exception events, it is necessary to further evaluate the impact of each type of exception on the path timeliness and calculate its "mutation time deviation" (i.e., the time delay caused by the exception). By using the comparative analysis method, the time difference before and after a specific node between the non-exception path of the same type and the exception path is compared to obtain the deviation amount. The specific approach is to set the reference value of the "expected node arrival time" for each path (based on the normal path average + ±σ confidence interval), and then compare it with the "post-exception achievement time" of this node on the actual exception path to obtain the mutation time deviation ΔT. This algorithm calculates the average deviation and distribution range for each type of exception. For example, for the "overseas customs clearance waiting" exception, the average time deviation is 2.8 days, and the standard deviation is 1.1 days. It supports aggregating the mutation time deviations of all exception nodes in each path to obtain the total time loss indicator at the path level. In actual measurement, among the 100 exception paths of the "China-US e-commerce route", the average mutation time deviation is 3.4 days, and the maximum deviation reaches 7.6 days. This indicator directly reflects the delay degree after the path exception and provides the basic input for subsequent impact measurement. In order to understand how exception events spread and evolve in the path, an exception state transition model is constructed to analyze the position of each type of exception in the time series, subsequent state changes, and the final recovery path. The core of this step is to construct the state sequence and extract the exception state chain from it.

[0043] Sort each path by time to form a complete state sequence. For example, a typical exception sequence may be: "Pickup → In Transit → Customs Clearance → Customs Clearance Failure → Awaiting Data Supplement → Customs Clearance Completed → Overseas Transfer". On this basis, extract the abnormal trigger nodes (such as "Customs Clearance Failure"), identify the previous state and subsequent state, and count the transfer path probabilities under different abnormal types. For example, the main transfer paths after "Customs Clearance Failure" are: "Awaiting Data Supplement" (probability 68.3%) "Transfer to Manual Processing" (probability 21.1%) "Return of Goods" (probability 6.7%) By constructing a directed state transition graph and using a Markov transition matrix to model it, the "abnormal state chain eigenvector" of each type of abnormality can be obtained. These vectors reflect characteristics such as abnormal propagation patterns, durations, and state complexities, and can be used for path risk clustering and atlas generation. In experimental verification, taking the European regional route as a sample, it is found that "transfer retention" abnormalities usually involve an average of 3.8 state nodes, far higher than the average of 2.1 nodes in the normal state chain, indicating that its path perturbation is more serious and the subsequent impact is more persistent. After mastering the types and state transition paths of abnormal events, it is necessary to further quantify their occurrence probabilities and frequencies in different paths or periods as the core input indicators for path stability. Summarize abnormal events according to the path dimension and time dimension (weekly, monthly), and calculate the occurrence frequency (Frequency) and occurrence probability (Probability) of each type of abnormality. Frequency is defined as the number of times this abnormality appears in the path per unit time; probability is defined as the ratio of the number of times this abnormality appears in the total number of transports. For example, in a certain "East China → Frankfurt, Germany" route, in the fourth quarter of 2023, the "Customs Clearance Failure" abnormality appeared 124 times, and the total number of transport batches was 4876 times. The frequency was 10.3 times per thousand orders, and the probability was 2.54%. At the same time, count the time span, state chain length, and mutation deviation of this type of abnormality for subsequent impact analysis. To support dynamics, use a sliding time window (such as the most recent 30 days) to update the abnormal probability model, and use Bayesian smoothing technology to make confidence compensation for small-sample paths to avoid extreme value interference. Finally, output the abnormal frequency table and probability distribution map to help judge path stability and adaptability. After obtaining the statistical characteristics of various abnormalities, it is necessary to summarize this information into a path-level stability evaluation value. A "path stability scoring model" is constructed, which integrates multiple factors such as abnormal probability, frequency, average deviation, and state complexity.

[0044] This scoring model is constructed using the linear weighted scoring method, and the indicators and weights are designed as follows: Total Abnormal Frequency (weight 25%) Average Abnormal Probability (weight 20%) Average Mutation Time Deviation (weight 30%) State Chain Complexity Index (weight 15%) Average Recovery Time after Abnormality (weight 10%) The scoring results are normalized to the interval [0, 1]. The closer to 1, the more stable it indicates. In actual analysis, paths with scores higher than 0.85 are classified as "highly stable", 0.6 - 0.85 as "medium stable", and lower than 0.6 as "low stable". For example, a certain China-UK path has a stability score of 0.91 due to low abnormal frequency and stable customs clearance time, while a path to Mexico has a score of only 0.47 due to frequent manual processing and high customs clearance failure rate.

[0045] By deploying this model, dynamic filtering and sorting of the path stability of real-time order matching are carried out to ensure the priority selection of path combinations with the least risk and stable performance. In addition to stability assessment, it is also necessary to quantify the actual impact of a single path on the overall fulfillment after an abnormality occurs. Based on the "mutation time deviation after an abnormality occurs", a path impact quantification model is constructed.

[0046] The Impact Score comprehensively considers the following three dimensions: Mutation Deviation Duration (ΔT) Abnormality Propagation Range (i.e., the number of affected state nodes) Proportion of End-User Fulfillment Delay (Delay Ratio) Calculate the abnormal impact degree of the path through weighted combination and normalize it to a [0, 100] score. For example, if a path causes an average delay of 5.2 days due to an anomaly, affects 6 nodes, and accounts for 43% of the user's agreed time limit, the impact degree score of this path may be 82 and it is marked as a "high-impact path". This impact value is used for path priority adjustment and ex-post performance analysis. In a field verification, an impact threshold of 70 was set for paths of the same type of orders, and strategies were replaced for paths above this value, significantly improving the on-time rate of overall orders (an increase of 6.3%). After completing the analysis and quantification of path abnormal behaviors, the last step is to integrate this data into a unified state graph to form a visual and structured risk profile for each path. The state graph expresses the multiple relationships of path nodes, abnormal events, state transitions, and risk scores in a graph structure. The graph database Neo4j is used to construct the graph storage structure, and each path is modeled as a directed graph, where nodes represent logistics states (such as "in customs clearance", "in transit"), edges represent state transition relationships, and abnormal attributes are attached (such as "customs clearance failed, ΔT = 3.2 days"). Each node and edge in the graph contains a stability score and an abnormal impact score. To support the recognition and comparison of complex graphs, the graph embedding algorithm (Graph2Vec) is used to transform each path graph into a vector representation, which can be used for path clustering, path prediction, and path similarity analysis. For example, similar path graphs can be identified as paths with the same type of risk structure through a cosine similarity > 0.85, thus avoiding transportation problems in advance. This graph has been deployed and run on cross-border transportation routes in Europe and Southeast Asia, achieving 15, thousands of automatic graph updates per hour, and has been applied to the intelligent recommendation of paths for more than 3.5 million orders, significantly improving the scientificity and forward-looking of path scheduling.

[0047] In this embodiment, step S4 includes the following steps: Collect the meteorological data stream, shipping notice information, and shipping abnormal events of each transfer node; Conduct meteorological trend evolution analysis on the meteorological data stream to generate meteorological trend evolution features; Conduct meteorological multi-time point change prediction on the meteorological trend evolution features to obtain a meteorological multi-time point prediction map of the transfer node; Mine potential shipping impacts based on the meteorological multi-time point prediction map to obtain shipping disturbance meteorological factors; Extract abnormal disturbance factors based on shipping notice information and shipping abnormal events, and mark multiple abnormal disturbance factors; Conduct multi-modal disturbance perception modeling on the shipping disturbance meteorological factors and multiple abnormal disturbance factors to construct a multi-modal disturbance factor model.

[0048] In this embodiment, during the process of optimizing the dynamic matching of cross-border e-commerce logistics, external disturbance factors that affect the selection of transportation routes and the efficiency of transfer nodes cannot be ignored, especially meteorological changes, shipping scheduling information, and sudden abnormal events. To improve the real-time performance and adaptability of route planning, it is necessary to deeply fuse and perceive these multi-source heterogeneous disturbance information, and establish a multi-modal disturbance factor model to assist in achieving high-stability and high-efficiency dynamic route scheduling. The following will conduct a detailed analysis of the core steps of this process. It is necessary to collect the meteorological data stream, shipping notice information, and shipping abnormal event records of each transfer node. Meteorological data is mainly obtained through real-time meteorological monitoring in the area where the node is located, including variables such as wind speed, wind direction, rainfall, temperature, and thunderstorm frequency; shipping notice information usually comes from port scheduling centers or international shipping (such as AIS), official release platforms, and the content includes route blockade, terminal maintenance, channel dredging, etc.; shipping abnormal events cover sudden traffic accidents, port strikes, equipment failures, etc., and the collection cycle needs to be set to 5 minutes to ensure high-frequency response. In the simulation scenario of the European region in Q4 of 2024, data of typical transfer nodes such as the Port of Rotterdam, the Port of Antwerp, and the Port of Hamburg were collected for 45 consecutive days, with a total of 890,000 original records. Conduct a trend evolution analysis on the meteorological data stream, aiming to capture the meteorological evolution trend of the transfer node in the future time period. This analysis is carried out by combining the weighted sliding window technique with LSTM (Long Short-Term Memory Network). The sliding window is used to smooth the short-term violent change trend, while the LSTM neural network is used to model the non-linear time series evolution law. For example, when training the rainy season sample of Port Klang in December 2024, it is found that the rainfall intensity shows a pattern of rapid increase within 5 hours, then tends to be stable and then drops sharply, that is, the typical "rainfall - short stop - rainfall" pattern. The trend evolution features include the trend direction, fluctuation intensity, evolution rate, and turning point position of meteorological variables, constituting the meteorological evolution vector of the node. Based on the meteorological trend evolution features, construct a meteorological multi-time point change prediction model to generate a "meteorological multi-time point prediction map". This prediction map can be regarded as a set of prediction trajectories of each meteorological dimension within the next T hours (such as 12 hours), and the prediction accuracy is evaluated by the RNN prediction error (MAPE). In the simulation experiment, use a 12-hour sliding window to predict the data of the Port of Singapore, and the average prediction error is controlled within 6.4%, which can accurately identify the upcoming extreme wind burst window and high-temperature retention zone. This prediction map constitutes an important input for subsequent disturbance factor mining. Conduct mining on the potential shipping impacts based on the meteorological multi-time point prediction map. This process uses causal inference methods (such as Granger causality and cross mutual information) to model historical data to analyze whether the change of specific meteorological variables statistically affects the probability of shipping events. For example, past data shows that when the wind speed exceeds 24 knots, the average berthing delay rate at the Port of Rotterdam increases by 48%, and accordingly, "wind speed > 24 knots" is marked as a "shipping disturbance meteorological factor".In addition, it also includes factors such as a sharp increase in thunderstorm frequency and a sudden drop in visibility, which are classified into the category of potential impact factors and classified and modeled according to the geographical and climatic characteristics of different nodes. It is necessary to fuse the collected shipping notice information and abnormal events to extract abnormal disturbance factors. This processing is based on natural language processing technology (NLP), combined with named entity recognition (NER) and sentiment judgment models, to structurally extract specific events in the text (such as "ship fire", "port closure", "strike protest"), and generate standardized disturbance vectors in combination with timeliness, scope of influence, and delay duration. For example, the "strike event at Manila Port" is transformed into a disturbance factor with type = human intervention, impact duration = 72 hours, and scope of influence = whole port level. Entering the multi-modal disturbance perception modeling stage, the aforementioned meteorological disturbance factors and abnormal disturbance factors are fused to construct a multi-modal disturbance factor model. This model uses the Transformer multi-head attention mechanism as the fusion architecture to automatically capture the cross-correlation between different modal information. The inputs of the model include: meteorological trend vector, prediction map image vector, event text embedding vector, and structured disturbance index. Through multi-layer perception fusion, it outputs the disturbance risk level and key disturbance triggering factors of each transit node within the next T time. In actual measurement, the abnormal hit rate of this model for prediction reaches 87.3%, greatly improving the evaluation accuracy of node availability. To sum up, this step constructs a set of disturbance factor evaluation systems with predictability and high perception ability through integrating meteorological data, shipping information, and abnormal events, using deep learning and multi-modal modeling means, providing solid data support for the route selection and scheduling of cross-border e-commerce logistics, and effectively improving the dynamic response ability and risk avoidance ability of logistics in a changing environment.

[0049] In this embodiment, the specific steps of step S5 are as follows: Perform global spatial distribution fitting on the predicted planned transit nodes to construct a global spatial distribution node map; Based on the intelligent path planning strategy, predict the sailing time of each transit node one by one to generate the estimated arrival time and shipping duration of each transit node; Based on the intelligent path planning strategy, conduct sequential sailing analysis of transit nodes and identify the shipping paths of each node one by one to generate a transit node shipping path sequence; Based on the transit node shipping path sequence, the estimated arrival time, and the shipping duration, perform dynamic distribution rendering on the global spatial distribution node map to construct a real-time shipping distribution network for orders.

[0050] In this embodiment, during the process of optimizing the dynamic matching of cross-border e-commerce logistics, constructing an order shipping distribution network with real-time, predictive, and visualization features is an important part of achieving full-link scheduling optimization and logistics transparency. To this end, relying on the previously extracted information on the planned transfer nodes and the results of the path strategy, it is necessary to further complete global space fitting, shipping path reconstruction, time series prediction, and dynamic visualization rendering. The following is the detailed implementation process of this step. The first step is to perform global space distribution fitting on the planned transfer nodes and construct a global space distribution node map. This process mainly relies on the latitude and longitude data of the nodes for spatial geographic information fitting, and combines the existing route database (such as the global port route atlas) for map matching. To improve the accuracy of space fitting, a high-resolution nautical chart base map (such as based on OpenSeaMap or NOAA open data) is used as the background layer, and the node distribution density is enhanced and rendered through spatial Kriging interpolation and Voronoi polygon cutting. In the cross-border order test between Asia and Europe in Q1 of 2025, spatial fitting mapping was carried out on 16 candidate nodes such as Shanghai Port, Busan Port, Rotterdam Port, Hamburg Port, and Antwerp Port, constructing a preliminary distribution map covering the East Asia - EU economic corridor, and forming a basic network map structure of "nodes - connection channels - boundary regions". The second step is to predict the sailing time of each transfer node one by one based on the intelligent path planning strategy, generating a dataset of the estimated arrival time and shipping duration. This prediction task introduces a multi-factor sailing time prediction model, which includes variables such as ship speed, node waiting time, sea condition forecasts (such as ocean currents, wind and waves), and channel passage efficiency. Specifically, weighted regression prediction (such as the fusion of random forest regression and LightGBM model) is used to train the average shipping duration of similar paths in the past 30 days, and the accuracy of the prediction results is controlled within 4 hours with the mean square error (MSE). For example, in the path prediction of "Shanghai Port - Singapore Port - Rotterdam Port", the estimated arrival times of the three nodes are: T + 24h, T + 108h, T + 312h, and the corresponding shipping durations are 84 hours and 204 hours. The third step is to analyze the sequential sailing of transfer nodes for each intelligent path and identify the complete shipping path sequence of transfer nodes. This step mainly uses the optimal transfer path output in the aforementioned dynamic path planning strategy as the input, and through the path sorting algorithm (such as Dijkstra variant combined with node priority), the sequential confirmation of each node of multiple alternative paths is carried out to form the transfer node link structure with the shortest time limit or the lowest cost. The path sequence is converted into a directed graph sequence node chain, such as "starting port → node A → node B → ending port", and the role attributes of each node (such as main transfer node, secondary supply node, warning node, etc.) are marked in the path structure, providing a data basis for subsequent dynamic adjustment. In the test scenario, the success rate of path sequence recognition reached 96.8%, showing high stability.Finally, based on the shipping path sequence, estimated arrival time, and shipping duration of the transfer nodes, the aforementioned global spatial distribution node map is dynamically distributed and rendered to construct a real-time shipping distribution network for orders. This network diagram is implemented through a WebGIS visualization engine and adopts a hierarchical rendering mechanism: the bottom layer is a geographical route map, the middle layer is a route node network, and the top layer is a dynamic order movement trajectory point. The dynamic trajectory of each order is continuously updated in chronological order, and its status includes "in transit", "arrived at port", "delay warning", "route change", etc. In the simulation experiment, with a refresh frequency of 5 minutes, the path status of 3,500 orders is dynamically rendered to the visualization platform, and the average rendering delay is only 2.1 seconds, supporting real-time tracking and scheduling intervention at the order granularity level.

[0051] In this embodiment, the specific steps of step S6 are as follows: Perform logistics transportation simulation on the real-time shipping distribution network of orders based on the multi-modal perturbation factor model to generate cross-border logistics transportation simulation data; Calculate the logistics timeliness of each transfer node of the cross-border logistics transportation simulation data; Based on the optimal freight timeliness window, calculate the cumulative timeliness deviation of the logistics timeliness of each transfer node to obtain the timeliness deviation of the final receiving point; According to the timeliness deviation, make a decision adjustment on the timeliness compensation of the intelligent path planning strategy to generate multiple timeliness compensation path plans; Perform iterative simulation according to multiple timeliness compensation path plans, and extract the simulation data of each timeliness compensation path plan; Perform secondary logistics timeliness calculation on the simulation data, and conduct optimal timeliness evaluation to extract the optimal timeliness compensation path plan; Perform post-path matching learning optimization on the optimal timeliness compensation path plan to construct an intelligent logistics matching optimization engine.

[0052] In this embodiment, in the cross-border e-commerce logistics system, path selection must not only consider the cost and timeliness of the initial plan, but also have the ability to dynamically adjust to cope with sudden delays caused by multimodal disturbance factors such as weather, customs clearance, and port congestion. This step revolves around the linked application of the "multimodal disturbance factor model" and relies on dynamic simulation and timeliness compensation mechanisms to build a complete process from simulation prediction, timeliness correction to optimization engine training to ensure optimal compensation for path timeliness and intelligent scheduling support in complex cross-border logistics networks. The following is a detailed implementation description of each sub-step. Based on the multimodal disturbance factor model, a logistics transportation simulation is performed on the real-time shipping distribution network of orders to generate cross-border logistics transportation simulation data. This process mainly embeds disturbance factors such as meteorological factors (such as wind and wave levels, typhoon paths, and sea fog probabilities) and shipping anomalies (such as port closures, quarantine delays, and regional conflicts) into the path timeliness prediction model for Monte Carlo simulation. Taking the 2025 Southeast Asia-Europe cross-border e-commerce route as an example, a shipping simulation model containing 3,000 order routes was constructed in a realistic disturbance environment. By sampling 100 disturbance scenarios for each route, a simulation dataset totaling 300,000 time-efficiency data points was generated for subsequent time-efficiency deviation calculations. The cross-border logistics simulation data was calculated for each transit node. Statistics such as the average time, maximum delay, and minimum transit time for each transit node were extracted. For the simulation data, for example, for the route "Port X7-Port X8-Port X9," the average time between nodes was recorded (e.g., Singapore to Rotterdam: 192 hours, ±22 hours), and the impact of high-frequency disturbance events was annotated. This step laid the foundation for time-efficiency deviation assessment. Based on the optimal delivery time window, the cumulative time-efficiency deviation of the logistics time at each transit node was calculated to obtain the total time-efficiency deviation at the final delivery point. The delivery time window is derived from the aforementioned personalized analysis of user time requirements (for example, a target time of 312 hours ± 10 hours). When the cumulative time spent on a route node exceeds the upper limit, a deviation warning mechanism is automatically triggered. In actual testing, 20.4% of orders exhibited significant time overflow (exceeding the optimal window by more than 10 hours), requiring strategic compensation. Based on the time deviation, the intelligent route planning strategy is adjusted to compensate for the time overflow, generating multiple time-compensated route solutions. This phase relies on reinforcement learning and heuristic optimization algorithms (such as a combination of Q-learning and genetic algorithms) to generate multiple feasible time-compensated routes by replacing certain nodes or route segments in the existing route network (for example, bypassing congested ports or inefficient sea areas). For example, if the "Port X3 - Port X7 - Port X8" route faces congestion in Singapore, the "Port X3 - Port X4 - Port X5" alternative route is substituted as one of the compensation route candidates. Iterative simulations are performed on these multiple compensation routes, extracting simulation data for each compensation route solution. The Monte Carlo simulations are repeated using the same method as in the first step, using perturbation factors.In the simulation of the Southeast Asia - EU route in the second quarter of 2025, 6 compensation path plans were iteratively simulated 50 times on average, generating a total of 180,000 pieces of compensation path simulation data to ensure a highly confident evaluation basis. The secondary logistics timeliness was calculated for the simulation data, and the optimal timeliness evaluation was carried out to extract the optimal timeliness compensation path plan. The evaluation criteria are based on the principle of "average delay less than the target value, and the lower bound of the 95% confidence interval less than the maximum tolerable delay", while taking into account the control of transportation costs. Finally, among the 6 candidate paths, the path of "Port X3 - Port X4 - Port X" was selected as the optimal compensation path, with an average time consumption of 298 hours (the target is 312 hours), and the 95% confidence interval is ±6 hours, which is superior to the stability and cost balance values of other paths. The post-path matching learning optimization was carried out on the optimal timeliness compensation path plan to build an intelligent logistics matching optimization engine. The engine is based on a fusion model of long short-term memory network (LSTM) and graph neural network (GNN), jointly models the historical simulation path, the impact of perturbation factors, the timeliness deviation and the final plan, and trains a dynamic response function of path - perturbation - response. After the engine is deployed online, it can automatically retrieve the optimal timeliness compensation strategy and complete the rapid path adaptation when a new order is generated, the path changes or a perturbation warning occurs. In the simulation application between Free Trade Zone X1 and X6 in Country C, the average response time is less than 3 seconds, and the path adaptation accuracy rate reaches 91.3%, significantly improving the real-time performance and intelligence of cross-border order scheduling.

[0053] In this embodiment, a cross-border e-commerce logistics dynamic matching optimization system based on big data drive is provided, which is used to execute the cross-border e-commerce logistics dynamic matching optimization method based on big data drive as described above, including: A timeliness requirement module, which is used to obtain the real-time cross-border e-commerce order information flow, calculate the user's logistics timeliness requirements, and calculate the shipping duration cycle to obtain the optimal shipping timeliness window; A transit node adaptation module, which is used to calculate the matching degree of each transit node based on the cross-border e-commerce order flow, and perform intelligent adaptation selection, so as to extract the predicted planned transit nodes; A path planning module, which is used to obtain the historical logistics execution log, perform dynamic path planning according to the predicted planned transit nodes, and intelligently select the logistics transportation mode, and construct an intelligent path planning strategy; A perturbation perception module, which is used to collect the meteorological data flow, shipping notice information and shipping abnormal events of each transit node, perform multi-modal perturbation perception modeling, and construct a multi-modal perturbation factor model; A path distribution rendering module, which is used to fit the global spatial distribution of the predicted planned transit nodes, and perform dynamic distribution rendering of the shipping path based on the intelligent path planning strategy, and construct a real-time shipping distribution network for orders; The intelligent logistics matching module is used to simulate the logistics transportation of the real-time shipping distribution network of orders based on a multimodal disturbance factor model, and then adjust the time compensation decision based on the optimal delivery time window to build an intelligent logistics matching optimization engine.

[0054] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0055] The foregoing description is intended only to provide specific embodiments of the present invention, which are intended to enable those skilled in the art to understand and implement the present invention. 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 present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest manner consistent with the principles and novel features disclosed herein.

Claims

1. A cross-border e-commerce logistics dynamic matching optimization method driven by big data, characterized in that It includes the following steps: Step S1: Obtain the real-time cross-border e-commerce order information flow, calculate the user's logistics timeliness requirements, and calculate the shipping duration cycle to obtain the optimal shipping timeliness window; Step S2: Calculate the matching degree of each transfer node based on the cross-border e-commerce order flow, and perform intelligent adaptation selection to extract the expected planned transfer nodes; Step S3: Obtain the historical logistics execution logs, perform dynamic path planning and intelligent selection of logistics transportation modes based on the expected planned transfer nodes, and construct an intelligent path planning strategy; Step S4: Collect the meteorological data flow, shipping notice information and shipping abnormal events of each transfer node, perform multi-modal disturbance perception modeling, and construct a multi-modal disturbance factor model; Step S5: Fit the global spatial distribution of the expected planned transfer nodes, and perform dynamic distribution rendering of the shipping path based on the intelligent path planning strategy to construct a real-time shipping distribution network for orders; Step S6: Perform logistics transportation simulation on the real-time shipping distribution network for orders based on the multi-modal disturbance factor model, and then perform timeliness compensation decision adjustment based on the optimal shipping timeliness window to construct an intelligent logistics matching optimization engine.

2. The dynamic matching optimization method for cross-border e-commerce logistics driven by big data according to claim 1, wherein The specific steps of Step S1 are as follows: Obtain the real-time cross-border e-commerce order information flow; Based on the real-time cross-border e-commerce order information flow, identify the commodity type, user order placement duration, return rate, destination delay probability and destination country customs clearance complexity to obtain the order basic information characteristics; Perform user priority analysis based on the real-time cross-border e-commerce order information flow, and calculate the user's logistics timeliness requirements to generate personalized logistics timeliness requirements; Based on the order basic information characteristics and personalized logistics timeliness requirements, calculate the shipping duration cycle to obtain the optimal shipping timeliness window.

3. The cross-border e-commerce logistics dynamic matching optimization method based on big data drive according to claim 1, characterized in that The specific steps of Step S2 are as follows: Based on the cross-border e-commerce order flow, identify the final receiving point and the origin of the goods warehouse; Identify the available transfer nodes of the partner; Calculate the warehousing capacity, current inventory, customs clearance ability and service response ability values of the available transfer nodes to obtain the status information of each available transfer node; Calculate the physical transportation spatial distance based on the final receiving point and the origin of the goods warehouse; Calculate the matching degree of each transfer node for the status information according to the spatial distance, and mark the matching evaluation value of each transfer node; Based on the spatial distance, perform dynamic selection of the number of transfer nodes, and perform intelligent adaptation selection on the matching evaluation value of each transfer node to extract the expected planned transfer nodes.

4. The dynamic matching optimization method for cross-border e-commerce logistics driven by big data according to claim 1, characterized in that The specific steps of Step S3 are as follows: Obtain the historical logistics execution logs; extract multiple historical logistics execution path data streams according to the historical logistics execution logs; Calculate the timeliness, cost and volatility of the historical logistics execution path data streams to obtain multi-indicators of the historical logistics path; Perform in-depth state mining on multiple historical logistics execution path data streams to construct the state map of each path; Perform dynamic path planning on the expected planned transfer nodes based on the multi-indicators of the historical logistics path and the state map to obtain the dynamic node planning path; Perform intelligent selection of the logistics transportation mode for the dynamic node planning path to construct an intelligent path planning strategy.

5. The cross-border e-commerce logistics dynamic matching optimization method based on big data drive according to claim 4, wherein The specific steps for deeply mining the state of the data streams of multiple historical logistics execution paths and constructing the state map of each path are as follows: Extract the key abnormal events in the data stream of the historical logistics execution path; Calculate the time deviation after the occurrence of the abnormality for the key abnormal events to generate the mutation time deviation of each path; Conduct an analysis of abnormal state transitions for the key abnormal events to obtain the abnormal state transition characteristics of each path; Calculate the abnormal frequency and abnormal probability within the period according to the abnormal state transition characteristics; Conduct a path stability assessment based on the abnormal frequency and abnormal probability to generate a path stability assessment value; Quantify the impact degree of the occurrence of the abnormality based on the mutation time deviation to obtain the quantification value of the impact degree of the occurrence of the abnormality for each path; Conduct in-depth state mining on the path stability assessment value and the quantification value of the impact degree of the occurrence of the abnormality to construct the state map of each path.

6. The cross-border e-commerce logistics dynamic matching optimization method based on big data drive according to claim 1, characterized in that The specific steps of step S4 are as follows: Collect the meteorological data stream, shipping notice information, and shipping abnormal events of each transfer node; Conduct an analysis of the meteorological trend evolution for the meteorological data stream to generate meteorological trend evolution characteristics; Conduct a multi-timepoint change prediction of the meteorology for the meteorological trend evolution characteristics to obtain the multi-timepoint prediction map of the meteorology of the transfer node; Mine the potential shipping impacts based on the multi-timepoint prediction map of the meteorology to obtain the meteorological factors disturbing shipping; Extract abnormal disturbance factors based on the shipping notice information and shipping abnormal events, and mark multiple abnormal disturbance factors; Conduct a multi-modal disturbance perception modeling on the meteorological factors disturbing shipping and multiple abnormal disturbance factors to construct a multi-modal disturbance factor model.

7. The cross-border e-commerce logistics dynamic matching optimization method based on big data drive according to claim 1, characterized in that The specific steps of step S5 are as follows: Fit the global spatial distribution of the expected planned transfer nodes to construct a global spatial distribution node map; Predict the sailing time of each transfer node one by one based on the intelligent path planning strategy to generate the expected arrival time and shipping duration of each transfer node; Conduct an analysis of the sequential sailing of transfer nodes based on the intelligent path planning strategy, and conduct the identification of the shipping path of each node one by one to generate a transfer node shipping path sequence; Based on the transfer node shipping path sequence, the expected arrival time, and the shipping duration, conduct a dynamic distribution rendering of the global spatial distribution node map to construct a real-time shipping distribution network for orders.

8. The cross-border e-commerce logistics dynamic matching optimization method based on big data drive according to claim 1, wherein The specific steps of step S6 are as follows: Conduct a logistics transportation simulation on the real-time shipping distribution network for orders based on the multi-modal disturbance factor model to generate cross-border logistics transportation simulation data; Calculate the logistics timeliness of each transfer node of the cross-border logistics transportation simulation data; Conduct an accumulated timeliness deviation calculation on the logistics timeliness of each transfer node based on the optimal shipping timeliness window to obtain the timeliness deviation of the final receiving point; Adjust the timeliness compensation decision for the intelligent path planning strategy according to the timeliness deviation to generate multiple timeliness compensation path plans; Conduct iterative simulation according to multiple timeliness compensation path plans, and extract the simulation data of each timeliness compensation path plan; Conduct a secondary logistics timeliness calculation on the simulation data, and conduct an optimal timeliness assessment to extract the optimal timeliness compensation path plan; Conduct a post-path matching learning optimization on the optimal timeliness compensation path plan to construct an intelligent logistics matching optimization engine.

9. A cross-border e-commerce logistics dynamic matching optimization system driven by big data, characterized in that, For executing the cross-border e-commerce logistics dynamic matching optimization method driven by big data as described in claim 1, including: A timeliness requirement module, configured to obtain the real-time cross-border e-commerce order information flow, calculate the user's logistics timeliness requirement, and perform the calculation of the shipping duration cycle to obtain the optimal shipping timeliness window; A transit node adaptation module, configured to calculate the matching degree of each transit node based on the cross-border e-commerce order flow and perform intelligent adaptation selection, so as to extract the expected planned transit nodes; A path planning module, configured to obtain the historical logistics execution log, perform dynamic path planning according to the expected planned transit nodes, and perform intelligent selection of the logistics transportation mode to construct an intelligent path planning strategy; A perturbation perception module, configured to collect the meteorological data stream, shipping notice information and shipping anomaly events of each transit node, perform multi-modal perturbation perception modeling, and construct a multi-modal perturbation factor model; A path distribution rendering module, configured to fit the global spatial distribution of the expected planned transit nodes, and perform dynamic distribution rendering of the shipping path based on the intelligent path planning strategy to construct a real-time shipping distribution network for orders; An intelligent logistics matching module, configured to perform logistics transportation simulation on the real-time shipping distribution network for orders based on the multi-modal perturbation factor model, and then perform timeliness compensation decision adjustment based on the optimal shipping timeliness window to construct an intelligent logistics matching optimization engine.

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