Cross-border logistics path optimization system based on customs clearance timeliness prediction

Abstract

The invention discloses a cross-border logistics path optimization system based on customs clearance timeliness prediction. Quantitative modeling of customs clearance timeliness is realized through customs clearance data acquisition and feature construction, and customs clearance time under different customs clearance ports and conditions is evaluated in advance through a customs clearance timeliness prediction module. According to the cross-border logistics path optimization system based on customs clearance timeliness prediction, customs clearance timeliness is introduced into a path optimization decision through a cross-border logistics path generation and comprehensive evaluation module, and a subsequent path is reconstructed in real time during customs clearance deviation through a path dynamic adjustment module, so that intelligent optimization of a cross-border logistics path is realized, and timeliness is controllable and risks are reduced. The system is characterized by comprising a customs clearance timeliness prediction module, a cross-border logistics path generation module, a path comprehensive evaluation and optimization module and a path dynamic adjustment module, and the customs clearance timeliness prediction module comprises a customs clearance data acquisition sub-module, a customs clearance feature construction sub-module, a customs clearance timeliness prediction sub-module and a prediction result correction sub-module.

Description

Technical Field

[0001] This invention relates to a cross-border logistics route optimization system based on customs clearance timeliness prediction. It is a data processing system that integrates customs clearance timeliness prediction and route planning, belonging to the field of intelligent decision-making technology for cross-border logistics. Specifically, it relates to a system that achieves quantitative modeling of customs clearance timeliness through customs clearance data collection and feature construction. The system uses a customs clearance timeliness prediction module to pre-assess clearance times under different ports and conditions. A cross-border logistics route generation and comprehensive evaluation module incorporates customs clearance timeliness into route optimization decisions. A route dynamic adjustment module reconstructs subsequent routes in real time when customs clearance deviations occur. This system achieves intelligent optimization of cross-border logistics routes, controllable timeliness, and reduced risk. Background Technology

[0002] With the continuous expansion of cross-border trade and logistics, cross-border logistics route planning has gradually become a crucial factor affecting logistics timeliness and operating costs. Current mainstream cross-border logistics route planning technologies typically make decisions based on transportation distance, mode of transport, freight rates, and established logistics network structures. Some systems estimate the overall transportation cycle by incorporating historical transportation times. In practical applications, customs clearance is usually treated as a fixed node in the route, and its clearance time is often estimated using empirical values ​​or historical averages. There is a lack of detailed modeling to account for the differences in clearance timeliness under different customs ports, cargo categories, and regulatory conditions. Due to the lack of prediction and quantification of clearance timeliness, existing technologies struggle to reflect the timeliness in a timely manner during the route planning stage. The uncertainty of customs clearance means that when the actual clearance time deviates significantly from the expected time, existing systems can usually only adjust subsequent transportation arrangements after clearance is completed. This results in the overall timeliness of cross-border logistics being greatly affected by customs clearance fluctuations, and insufficient stability and controllability of route execution. In addition, during the execution of cross-border logistics, existing technologies mostly adopt a one-time determination of the route plan, lacking continuous monitoring of clearance progress and a dynamic route adjustment mechanism based on changes in clearance status. When there are delays or abnormalities in the clearance progress, manual intervention is often required to re-coordinate subsequent transportation nodes. Moreover, it is difficult to balance the connection between completed transportation links and subsequent routes during the adjustment process, increasing operational complexity and reducing the continuity and overall efficiency of cross-border logistics operations.

[0003] Publication No. CN117252324B discloses a logistics route optimization method and system. First, a semantic logistics network graph is constructed, including detailed information on each logistics node and edge. Then, an attention mechanism is used to calculate dynamic logistics weights to optimize route selection in a targeted manner. Next, a multilayer perceptron is used to predict the cost of different routes, and finally, a search algorithm is used to find the logistics route with the lowest cost based on the prediction results. This method effectively improves the accuracy of route planning and its responsiveness to real-time changes, optimizes resource allocation, and reduces logistics costs. However, although the above logistics optimization system introduces a predictive model to evaluate route costs or timeliness, its technical solution still focuses on one-time route optimization during the planning stage, lacking a continuous monitoring and dynamic response mechanism for changes in customs clearance progress during cross-border logistics execution. Summary of the Invention

[0004] To improve the above situation, the present invention provides a cross-border logistics route optimization system based on customs clearance timeliness prediction. This system achieves quantitative modeling of customs clearance timeliness through customs clearance data collection and feature construction, pre-assesses customs clearance time under different customs clearance ports and conditions through a customs clearance timeliness prediction module, incorporates customs clearance timeliness into route optimization decisions through a cross-border logistics route generation and comprehensive evaluation module, and reconstructs subsequent routes in real time when customs clearance deviations occur through a route dynamic adjustment module. This system enables intelligent optimization of cross-border logistics routes, controllable timeliness, and reduced risk.

[0005] The cross-border logistics route optimization system based on customs clearance timeliness prediction of this invention is implemented as follows: The cross-border logistics route optimization system based on customs clearance timeliness prediction of this invention includes a customs clearance timeliness prediction module, a cross-border logistics route generation module, a route comprehensive evaluation and optimization module, and a route dynamic adjustment module. The feature is that the customs clearance timeliness prediction module establishes signal interaction with the cross-border logistics route generation module, the route comprehensive evaluation and optimization module, and the route dynamic adjustment module; the cross-border logistics route generation module establishes signal interaction with the route comprehensive evaluation and optimization module and the route dynamic adjustment module; and the route comprehensive evaluation and optimization module establishes signal interaction with the route dynamic adjustment module. The customs clearance timeliness prediction module includes a customs clearance data acquisition submodule, a customs clearance feature construction submodule, a customs clearance timeliness prediction submodule, and a prediction result correction submodule. The customs clearance data acquisition submodule is used to collect historical and operational data related to the customs clearance process. Preferably, the historical data and operational data include at least the exporting country's customs clearance port identifier and the importing country's customs clearance port identifier, the goods category or commodity code information for identifying the goods' attributes, the transportation mode information for identifying the transportation mode, and the customs clearance declaration start time, customs clearance release completion time, and customs clearance status change records for calculating customs clearance timeliness. The customs clearance data collection submodule calculates the time difference between the start time of the customs clearance declaration and the completion time of customs clearance release to obtain historical customs clearance time sample data under different customs clearance ports and different cargo conditions. Preferably, the customs clearance data collection submodule marks atypical customs clearance data caused by declaration errors, document corrections, or abnormal inspections, and reduces the weight of such data or removes it during subsequent prediction processes to avoid extreme cases interfering with the prediction results. The customs clearance feature construction submodule is used to perform structured processing and feature extraction on the collected customs clearance data, converting factors affecting customs clearance timeliness into feature parameters that can be used for calculation. Preferably, the structured processing includes encoding discrete data such as customs clearance port, cargo category, and mode of transport, and standardizing time-based data such as customs declaration start time and customs clearance release completion time. Preferably, the feature extraction includes statistical analysis of clearance times for different ports of entry, different cargo categories, and different modes of transport based on historical clearance data, to extract statistical features characterizing clearance efficiency and stability. Preferably, the characteristic parameters include at least the following: customs clearance port characteristic parameters reflecting the regulatory intensity and historical processing efficiency of different customs clearance ports; cargo category characteristic parameters reflecting the differences in regulatory requirements for different goods; transportation mode characteristic parameters reflecting the differences in customs clearance procedures under different modes of transportation; and statistical characteristic parameters reflecting historical customs clearance time. Preferably, the customs clearance feature construction submodule encodes and normalizes various feature parameters to construct feature vectors in a unified format, thereby improving the stability and comparability of subsequent prediction calculations. The customs clearance time prediction submodule, based on the feature vector, uses time series analysis to model the correspondence between historical customs clearance time data and the feature vector. Based on the modeling results, it predicts the time required for goods to complete customs clearance under designated customs ports and given declaration conditions, and outputs the predicted customs clearance time result. Preferably, the customs clearance time prediction submodule can also employ a statistical regression model or a machine learning model to model the relationship between the feature vector and historical customs clearance time, thereby improving the prediction accuracy under complex customs clearance conditions. Preferably, the predicted customs clearance timeliness results include estimated customs clearance time values, customs clearance time intervals, or customs clearance timeliness stability indicators, used to reflect the time cost and uncertainty level of the customs clearance process. The prediction result correction submodule is used to compare and analyze the actual customs clearance time with the predicted customs clearance time. When there is a deviation between the predicted and actual results, the deviation distribution is statistically analyzed based on the customs clearance time samples over a recent period, and the statistical parameters or weight parameters of the prediction model are updated accordingly. This allows the prediction results to be dynamically corrected as port clearance efficiency changes, thereby improving the accuracy and adaptability of subsequent customs clearance time predictions. The cross-border logistics route generation module includes a route element definition submodule, a route combination rule submodule, and a candidate route construction submodule. The route element definition submodule is used to determine the optional basic route elements in cross-border logistics routes. Preferably, the route elements include at least the export customs clearance port of the country of origin, the mode of transport, the transit country or transit node, and the import customs clearance port of the destination country. Each route element corresponds to a key node at a different stage in the cross-border logistics process. The route combination rules submodule is used to define the combination constraints between different route elements to ensure that the generated cross-border logistics routes meet the actual transportation and customs clearance conditions. Preferably, the combined constraint relationship includes at least transportation mode compatibility constraints, customs clearance port adaptation constraints, and transit node reachability constraints. The transportation mode compatibility constraints limit the executable relationship between transportation modes and customs clearance ports; the customs clearance port adaptation constraints limit the adaptation relationship between different cargo categories and customs clearance ports; and the transit node reachability constraints limit whether there are executable transportation and customs clearance connection conditions between transit nodes, thereby filtering out path combinations that do not meet actual execution conditions during the path generation stage. Preferably, the path combination rule submodule can dynamically adjust the combination constraint relationship based on historical path execution results. These historical execution results include records of different path element combinations being determined as unexecutable or failing during actual execution. When the proportion of an unexecutable path element combination in historical execution exceeds a preset threshold, the system marks the corresponding combination constraint as a high-risk constraint and increases its removal priority in subsequent path generation. When an unexecutable path element combination remains executable in multiple consecutive historical executions, the system reduces the constraint strength of the corresponding constraint, thereby reducing the proportion of unfeasible paths generated. The candidate route construction submodule constructs multiple cross-border logistics candidate routes based on the route elements and route combination rules, according to a preset route generation logic. Each candidate route corresponds to a unique sequence of transportation nodes and a combination of clearance nodes, which is used to provide a set of routes for subsequent route evaluation and optimization. The route comprehensive evaluation and optimization module includes a route mapping submodule, a route timeliness calculation submodule, and a route optimization decision submodule. The path mapping submodule is used to automatically associate the predicted clearance time output by the clearance time prediction module with the clearance points in the corresponding candidate paths based on the identification information of the clearance points in the candidate paths, thereby realizing the node-level mapping of the predicted clearance time in the path structure. The route timeliness calculation submodule is used to perform segmented cumulative calculations of the predicted customs clearance timeliness of each clearance point and the transportation time of each transportation node according to the sequential structure of each node in the candidate route, and generate a comprehensive route timeliness parameter to characterize the overall time cost of the candidate route. Preferably, during the calculation process, the route timeliness calculation submodule assigns a higher weight to clearance points than to transportation nodes based on historical timeliness fluctuation characteristics or predicted stability parameters, in order to reduce the impact of customs clearance uncertainty on the overall route timeliness assessment results. The route optimization decision-making submodule compares, ranks, or filters multiple candidate routes based on the comprehensive timeliness parameters of the routes, and outputs the cross-border logistics route with the best comprehensive timeliness or the lowest timeliness risk under preset technical constraints, thereby achieving the optimal selection of cross-border logistics routes. The path dynamic adjustment module includes a path status monitoring submodule and a path reconstruction submodule. The path status monitoring submodule is used to acquire customs clearance status information at each clearance point in real time or periodically during the cross-border logistics process, and compare the actual customs clearance progress with the corresponding predicted customs clearance time to determine whether there are any deviations. The route reconstruction submodule is used to reassess and adjust the cross-border logistics route when the customs clearance progress deviates from the predicted customs clearance time. When the deviation between the actual customs clearance progress and the predicted result exceeds a preset threshold, the path reconstruction submodule triggers a path reassessment process to recalculate the path optimization for incomplete customs clearance points or transit nodes. Preferably, the preset threshold is used to characterize the maximum allowable deviation between the actual customs clearance progress and the predicted customs clearance time. The threshold can be preset according to the needs of cross-border logistics operations, specifically using an absolute time deviation threshold, a relative time ratio threshold, or a deviation threshold based on historical statistical distribution. Specifically, the absolute time deviation threshold limits the maximum allowable time difference between the actual customs clearance time and the predicted customs clearance time; the relative time ratio threshold limits the proportional deviation between the actual customs clearance time and the predicted customs clearance time; and the deviation threshold based on historical statistical distribution determines the conditions for triggering path reconstruction based on the fluctuation range of historical customs clearance times. Preferably, the path reconstruction submodule only adjusts the incomplete part of the path to avoid affecting the completed transportation links, thereby ensuring the continuity and stability of the cross-border logistics process. Beneficial effects

[0006] First, by performing structured modeling on historical customs clearance data and predicting customs clearance timeliness under different ports and conditions, the customs clearance process, which originally relied on experience and was difficult to quantify, is transformed into calculable and comparable technical parameters. These parameters are then introduced into the cross-border logistics route planning process, thereby significantly improving the scientific nature of cross-border logistics route selection and the controllability of overall timeliness.

[0007] Second, by continuously monitoring the actual customs clearance progress during the cross-border logistics process, and dynamically reconstructing only the unfinished parts of the route when the clearance progress deviates from the predicted results, real-time optimization of the logistics route is achieved without affecting the completed transportation links. This effectively reduces the risk of logistics delays caused by customs clearance uncertainties and improves the stability and continuity of cross-border logistics operations. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the overall system structure of the cross-border logistics route optimization system based on customs clearance timeliness prediction of the present invention; Figure 2 This is a structural diagram of the customs clearance time prediction module of the cross-border logistics route optimization system based on customs clearance time prediction of the present invention; Figure 3 This is a schematic diagram illustrating the logic of cross-border logistics route generation and optimization in the cross-border logistics route optimization system based on customs clearance timeliness prediction of the present invention. Figure 4 This is a schematic diagram of the dynamic adjustment process of the cross-border logistics route optimization system based on customs clearance timeliness prediction according to the present invention. Detailed Implementation Example 1

[0009] The cross-border logistics route optimization system based on customs clearance timeliness prediction of this invention is implemented as follows: The cross-border logistics route optimization system based on customs clearance timeliness prediction of this invention includes a customs clearance timeliness prediction module, a cross-border logistics route generation module, a route comprehensive evaluation and optimization module, and a route dynamic adjustment module. The feature is that the customs clearance timeliness prediction module establishes signal interaction with the cross-border logistics route generation module, the route comprehensive evaluation and optimization module, and the route dynamic adjustment module; the cross-border logistics route generation module establishes signal interaction with the route comprehensive evaluation and optimization module and the route dynamic adjustment module; and the route comprehensive evaluation and optimization module establishes signal interaction with the route dynamic adjustment module. The customs clearance timeliness prediction module includes a customs clearance data acquisition submodule, a customs clearance feature construction submodule, a customs clearance timeliness prediction submodule, and a prediction result correction submodule. The customs clearance data acquisition submodule is used to collect historical and operational data related to the customs clearance process. Preferably, the historical data and operational data include at least the exporting country's customs clearance port identifier and the importing country's customs clearance port identifier, the goods category or commodity code information for identifying the goods' attributes, the transportation mode information for identifying the transportation mode, and the customs clearance declaration start time, customs clearance release completion time, and customs clearance status change records for calculating customs clearance timeliness. The customs clearance data collection submodule calculates the time difference between the start time of the customs clearance declaration and the completion time of customs clearance release to obtain historical customs clearance time sample data under different customs clearance ports and different cargo conditions. Preferably, the customs clearance data collection submodule marks atypical customs clearance data caused by declaration errors, document corrections, or abnormal inspections, and reduces the weight of such data or removes it during subsequent prediction processes to avoid extreme cases interfering with the prediction results. The customs clearance feature construction submodule is used to perform structured processing and feature extraction on the collected customs clearance data, converting factors affecting customs clearance timeliness into feature parameters that can be used for calculation. Preferably, the structured processing includes encoding discrete data such as customs clearance port, cargo category, and mode of transport, and standardizing time-based data such as customs declaration start time and customs clearance release completion time. Preferably, the feature extraction includes statistical analysis of clearance times for different ports of entry, different cargo categories, and different modes of transport based on historical clearance data, to extract statistical features characterizing clearance efficiency and stability. Preferably, the characteristic parameters include at least the following: customs clearance port characteristic parameters reflecting the regulatory intensity and historical processing efficiency of different customs clearance ports; cargo category characteristic parameters reflecting the differences in regulatory requirements for different goods; transportation mode characteristic parameters reflecting the differences in customs clearance procedures under different modes of transportation; and statistical characteristic parameters reflecting historical customs clearance time. Preferably, the customs clearance feature construction submodule encodes and normalizes various feature parameters to construct feature vectors in a unified format, thereby improving the stability and comparability of subsequent prediction calculations. The customs clearance time prediction submodule, based on the feature vector, uses time series analysis to model the correspondence between historical customs clearance time data and the feature vector. Based on the modeling results, it predicts the time required for goods to complete customs clearance under designated customs ports and given declaration conditions, and outputs the predicted customs clearance time result. Preferably, the customs clearance time prediction submodule can also employ a statistical regression model or a machine learning model to model the relationship between the feature vector and historical customs clearance time, thereby improving the prediction accuracy under complex customs clearance conditions. Preferably, the predicted customs clearance timeliness results include estimated customs clearance time values, customs clearance time intervals, or customs clearance timeliness stability indicators, used to reflect the time cost and uncertainty level of the customs clearance process. The prediction result correction submodule is used to compare and analyze the actual customs clearance time with the predicted customs clearance time. When there is a deviation between the predicted and actual results, the deviation distribution is statistically analyzed based on the customs clearance time samples over a recent period, and the statistical parameters or weight parameters of the prediction model are updated accordingly. This allows the prediction results to be dynamically corrected as port clearance efficiency changes, thereby improving the accuracy and adaptability of subsequent customs clearance time predictions. The cross-border logistics route generation module includes a route element definition submodule, a route combination rule submodule, and a candidate route construction submodule. The route element definition submodule is used to determine the optional basic route elements in cross-border logistics routes. Preferably, the route elements include at least the export customs clearance port of the country of origin, the mode of transport, the transit country or transit node, and the import customs clearance port of the destination country. Each route element corresponds to a key node at a different stage in the cross-border logistics process. The route combination rules submodule is used to define the combination constraints between different route elements to ensure that the generated cross-border logistics routes meet the actual transportation and customs clearance conditions. Preferably, the combined constraint relationship includes at least transportation mode compatibility constraints, customs clearance port adaptation constraints, and transit node reachability constraints. The transportation mode compatibility constraints limit the executable relationship between transportation modes and customs clearance ports; the customs clearance port adaptation constraints limit the adaptation relationship between different cargo categories and customs clearance ports; and the transit node reachability constraints limit whether there are executable transportation and customs clearance connection conditions between transit nodes, thereby filtering out path combinations that do not meet actual execution conditions during the path generation stage. Preferably, the path combination rule submodule can dynamically adjust the combination constraint relationship based on historical path execution results. These historical execution results include records of different path element combinations being determined as unexecutable or failing during actual execution. When the proportion of an unexecutable path element combination in historical execution exceeds a preset threshold, the system marks the corresponding combination constraint as a high-risk constraint and increases its removal priority in subsequent path generation. When an unexecutable path element combination remains executable in multiple consecutive historical executions, the system reduces the constraint strength of the corresponding constraint, thereby reducing the proportion of unfeasible paths generated. The candidate route construction submodule constructs multiple cross-border logistics candidate routes based on the route elements and route combination rules, according to a preset route generation logic. Each candidate route corresponds to a unique sequence of transportation nodes and a combination of clearance nodes, which is used to provide a set of routes for subsequent route evaluation and optimization. The route comprehensive evaluation and optimization module includes a route mapping submodule, a route timeliness calculation submodule, and a route optimization decision submodule. The path mapping submodule is used to automatically associate the predicted clearance time output by the clearance time prediction module with the clearance points in the corresponding candidate paths based on the identification information of the clearance points in the candidate paths, thereby realizing the node-level mapping of the predicted clearance time in the path structure. The route timeliness calculation submodule is used to perform segmented cumulative calculations of the predicted customs clearance timeliness of each clearance point and the transportation time of each transportation node according to the sequential structure of each node in the candidate route, and generate a comprehensive route timeliness parameter to characterize the overall time cost of the candidate route. Preferably, during the calculation process, the route timeliness calculation submodule assigns a higher weight to clearance points than to transportation nodes based on historical timeliness fluctuation characteristics or predicted stability parameters, in order to reduce the impact of customs clearance uncertainty on the overall route timeliness assessment results. The route optimization decision-making submodule compares, ranks, or filters multiple candidate routes based on the comprehensive timeliness parameters of the routes, and outputs the cross-border logistics route with the best comprehensive timeliness or the lowest timeliness risk under preset technical constraints, thereby achieving the optimal selection of cross-border logistics routes. The path dynamic adjustment module includes a path status monitoring submodule and a path reconstruction submodule. The path status monitoring submodule is used to acquire customs clearance status information at each clearance point in real time or periodically during the cross-border logistics process, and compare the actual customs clearance progress with the corresponding predicted customs clearance time to determine whether there are any deviations. The route reconstruction submodule is used to reassess and adjust the cross-border logistics route when the customs clearance progress deviates from the predicted customs clearance time. When the deviation between the actual customs clearance progress and the predicted result exceeds a preset threshold, the path reconstruction submodule triggers a path reassessment process to recalculate the path optimization for incomplete customs clearance points or transit nodes. Preferably, the preset threshold is used to characterize the maximum allowable deviation between the actual customs clearance progress and the predicted customs clearance time. The threshold can be preset according to the needs of cross-border logistics operations, specifically using an absolute time deviation threshold, a relative time ratio threshold, or a deviation threshold based on historical statistical distribution. Specifically, the absolute time deviation threshold limits the maximum allowable time difference between the actual customs clearance time and the predicted customs clearance time; the relative time ratio threshold limits the proportional deviation between the actual customs clearance time and the predicted customs clearance time; and the deviation threshold based on historical statistical distribution determines the conditions for triggering path reconstruction based on the fluctuation range of historical customs clearance times. Preferably, the route reconstruction submodule only adjusts the incomplete parts of the route to avoid affecting completed transportation links, thereby ensuring the continuity and stability of the cross-border logistics process. In practice, during system deployment or initial operation, backend operators first initialize the cross-border logistics route optimization system, importing historical cross-border logistics data and historical customs clearance data. This historical data includes records of customs declaration and release times for different export and import ports, cargo categories, and modes of transport. The system uses a customs clearance data collection submodule to uniformly organize and store this data, forming a foundational dataset for subsequent analysis and modeling. Subsequently, the system initiates the customs clearance feature construction process. The customs clearance feature construction submodule performs structured processing on the collected customs clearance data, encoding discrete information such as customs ports, cargo categories, and modes of transport, and recording customs declaration start time and customs release time. Time-related information such as completion time is standardized. Simultaneously, based on historical customs clearance data, statistical analysis is performed on the customs clearance time corresponding to different ports, cargo categories, and transportation modes. Feature parameters characterizing customs clearance efficiency and stability are extracted, and a unified-format feature vector is constructed. After feature construction, the system enters the customs clearance timeliness prediction stage. The customs clearance timeliness prediction submodule, based on the feature vector, models the correspondence between historical customs clearance time data and the feature vector using time series analysis methods. Based on the modeling results, it predicts the time required for the planned cargo to complete customs clearance under specified ports and given declaration conditions, outputting the predicted customs clearance timeliness result. The prediction result can be an estimated customs clearance time value, a customs clearance time interval, or... After obtaining the predicted customs clearance timeliness, the system initiates the cross-border logistics route generation process. The route element definition submodule determines the basic route elements available in the cross-border logistics route, including the export customs clearance port in the country of origin, mode of transport, transit country or transit node, and import customs clearance port in the destination country. The route combination rule submodule filters the combination relationships between different route elements based on transport mode compatibility constraints, customs clearance port adaptation constraints, and transit node accessibility constraints. The candidate route construction submodule then constructs multiple cross-border logistics candidate routes with practical execution conditions. Subsequently, the system enters the route comprehensive evaluation and optimization stage. The route mapping submodule maps the predicted customs clearance timeliness output by the customs clearance timeliness prediction module to the candidate routes. The system automatically associates clearance checkpoints to achieve node-level mapping of predicted clearance time in the route structure. The route timeliness calculation submodule calculates the predicted clearance time of each clearance checkpoint and the transportation time of each transportation node in segments according to the sequential structure of the nodes in the candidate routes. This generates a comprehensive route timeliness parameter characterizing the overall time cost of each candidate route. During the calculation process, clearance checkpoints are assigned a higher weight than transportation nodes to reduce the impact of clearance uncertainty on the route evaluation results. After completing the comprehensive route evaluation, the route optimization decision submodule compares, ranks, or filters multiple candidate routes based on the comprehensive route timeliness parameter, outputting the cross-border logistics route with the optimal comprehensive timeliness or the lowest timeliness risk under preset technical constraints.As the recommended cross-border logistics route, the system continuously runs a dynamic route adjustment process during actual cross-border logistics execution. The route status monitoring submodule acquires the customs clearance status information of each clearance point in real time or periodically, and compares the actual customs clearance progress with the corresponding predicted customs clearance time. When the deviation between the actual customs clearance progress and the predicted result exceeds a preset threshold, the route reconstruction submodule triggers a route reassessment process. This recalculates the route for incomplete clearance points or transit nodes and generates an adjusted route plan. After the route adjustment is completed, the system synchronously updates the adjusted route information to the route comprehensive assessment and optimization module and the back-end operation management module for subsequent logistics execution and status monitoring. This achieves dynamic optimization of the cross-border logistics route without affecting completed transportation stages, ensuring the continuity of the cross-border logistics process and the stability of overall timeliness. The customs clearance data collection submodule calculates the time difference between the start time of customs clearance declaration and the completion time of customs clearance release to obtain historical customs clearance time sample data under different customs clearance ports and different cargo conditions. This design can transform the customs clearance process into quantifiable time characteristics, providing a reliable data foundation for subsequent customs clearance timeliness modeling. The design of the customs clearance feature construction submodule, which encodes discrete data such as customs clearance ports, cargo categories and transportation methods, and standardizes time-related data, can eliminate the differences in the dimensions and forms of different types of data, and improve the feasibility and stability of joint calculation of multi-source customs clearance data in the same prediction model. The route combination rule submodule is used to define the design of transportation mode compatibility constraints, customs clearance port adaptation constraints, and transit node reachability constraints. It can screen out route combinations that do not have actual execution conditions in advance during the route generation stage, reduce invalid routes from entering the subsequent evaluation process, and thus improve the efficiency and reliability of route optimization calculation. The route timeliness calculation submodule assigns a higher weight to clearance nodes than to transportation nodes during the calculation process. This design highlights the key impact of customs clearance on the overall timeliness of cross-border logistics, reduces route assessment bias caused by customs clearance uncertainties, and improves the actual feasibility of cross-border logistics route selection. When the deviation between the actual customs clearance progress and the predicted result exceeds a preset threshold, the route reconstruction is triggered. The design only adjusts the unfinished part of the route, which can achieve dynamic optimization of the route without affecting the completed transportation links, thus ensuring the continuity and stability of the cross-border logistics process. The goal is to achieve quantitative modeling of customs clearance timeliness through customs clearance data collection and feature construction, to predict customs clearance time in advance under different customs clearance ports and conditions through the customs clearance timeliness prediction module, to incorporate customs clearance timeliness into route optimization decision-making through the cross-border logistics route generation and comprehensive evaluation module, and to reconstruct subsequent routes in real time when customs clearance deviates through the route dynamic adjustment module. This will enable intelligent optimization of cross-border logistics routes, controllable timeliness, and reduced risk.

[0010] Other similar embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art that are not disclosed herein.

[0011] The above embodiments are preferred embodiments of the present invention. Due to space limitations, the applicant has not used other embodiments, but this is not intended to limit the scope of the present invention. Any person skilled in the art can make some modifications without departing from the scope of the present invention; that is, all equivalent modifications made in accordance with the present invention should be covered by the scope of the present invention.

Claims

1. A system for cross-border logistics path optimization based on clearance time prediction, comprising a clearance time prediction module, a cross-border logistics path generation module, a path comprehensive evaluation and optimization module, and a path dynamic adjustment module, characterized in that: The clearance time prediction module comprises a clearance data acquisition submodule, a clearance feature construction submodule and a clearance time prediction submodule. The clearance data acquisition submodule is configured to collect historical data and operation data related to the clearance process. The clearance feature construction submodule is configured to perform structured processing and feature extraction on the collected clearance data, and convert factors affecting the clearance time into feature parameters that can be used for calculation. The clearance time prediction submodule is configured to model the correspondence between historical clearance time consumption data and the feature vector based on the feature vector through a time series analysis method, and predict the time required for the goods to complete clearance at a specified clearance port and under given declaration conditions based on the modeling result, and output a predicted clearance time result. The cross-border logistics path generation module comprises a path element definition submodule, a path combination rule submodule and a candidate path construction submodule. The path element definition submodule is configured to determine selectable basic path elements in the cross-border logistics path. The path combination rule submodule is configured to define combination constraint relationships between different path elements. The candidate path construction submodule is configured to construct multiple cross-border logistics candidate paths according to a preset path generation logic based on the path elements and the path combination rules. The path comprehensive evaluation and optimization module comprises a path mapping submodule, a path time calculation submodule and a path optimization decision submodule. The path mapping submodule is configured to automatically associate the predicted clearance time result output by the clearance time prediction module with the clearance nodes in the corresponding candidate path based on the identification information of the clearance nodes in the candidate path, so as to realize node-level mapping of the predicted clearance time in the path structure. The path time calculation submodule is configured to perform segmented cumulative calculation on the predicted clearance time of each clearance node and the transportation time of each transportation node according to the sequential structure of the nodes in the candidate path, and generate a path comprehensive time parameter for representing the overall time cost of the candidate path. The path optimization decision submodule is configured to compare, sort or filter multiple candidate paths based on the path comprehensive time parameter, and output a cross-border logistics path with optimal comprehensive time or lowest time risk under the condition of meeting the preset technical constraints. The path dynamic adjustment module comprises a path state monitoring submodule and a path reconstruction submodule. The path state monitoring submodule is configured to acquire clearance state information of each clearance node in real time or periodically during the execution of the cross-border logistics, and compare the actual clearance progress with the corresponding predicted clearance time result to determine whether there is a deviation. The path reconstruction submodule is configured to reevaluate and adjust the cross-border logistics path when a clearance progress deviation from the predicted clearance time result is detected.

2. The cross-border logistics path optimization system based on the time limit for clearance prediction according to claim 1, characterized in that The clearance data acquisition submodule obtains historical clearance time consumption sample data under different clearance ports and different goods conditions by calculating the time difference between the clearance declaration start time and the clearance release completion time, each candidate path corresponds to a unique combination of transportation nodes and clearance nodes, which provides a path set for subsequent path evaluation and optimization, the clearance time prediction module introduces a prediction result correction submodule, which is used to compare and analyze the actual clearance time consumption with the predicted clearance time result, when there is a deviation between the prediction result and the actual result, based on the statistical deviation distribution of the clearance time consumption sample in the recent period, the statistical parameters or weight parameters of the prediction model are updated accordingly, so that the prediction result is dynamically corrected with the changes of port clearance efficiency, to improve the accuracy and adaptive ability of subsequent clearance time prediction.

3. The cross-border logistics path optimization system based on the time limit for clearance prediction according to claim 1, characterized in that The path elements at least include the export clearance port of the country of origin, the transportation mode, the transit country or node, and the import clearance port of the destination country, each path element corresponds to a key node in different stages of the cross-border logistics process, the path combination rule submodule dynamically adjusts the combination constraint relationship based on the historical path execution results, the historical path execution results include records of path element combinations that are judged as unexecutable or failed in actual execution, when the unexecutable proportion of a certain type of path element combination in historical execution exceeds the preset threshold, the system marks the corresponding combination constraint condition as a high-risk constraint, and increases the exclusion priority of this constraint condition in the subsequent path generation process, when a certain type of path element combination remains executable in continuous multiple historical executions, the system reduces the restriction strength of the corresponding constraint condition, thereby reducing the generation proportion of infeasible paths, the combination constraint relationship at least includes transportation mode compatibility constraint, clearance port adaptation constraint and transit node accessibility constraint, wherein the transportation mode compatibility constraint is used to limit the executable relationship between the transportation mode and the clearance port, the clearance port adaptation constraint is used to limit the adaptation relationship between different goods categories and clearance ports, and the transit node accessibility constraint is used to limit whether the transit nodes have executable transportation and clearance connection conditions, thereby excluding path combinations that do not have actual execution conditions in the path generation stage.

4. The cross-border logistics path optimization system based on the time limit for clearance prediction according to claim 1, characterized in that In the calculation process of the path time calculation submodule, according to the historical time fluctuation characteristics or prediction stability parameters of the clearance nodes, the clearance nodes are given a higher weight than the transportation nodes, so as to reduce the influence of clearance uncertainty on the overall path time evaluation result.

5. The cross-border logistics path optimization system based on the time limit for clearance prediction according to claim 1, characterized in that When the deviation between the actual clearance progress and the prediction result exceeds a preset threshold, the path reconstruction submodule triggers a path reevaluation process, and reperforms path optimization calculation on the clearance nodes or transit nodes that have not been completed, and the preset threshold is used to represent the maximum deviation allowed between the actual clearance progress and the predicted clearance time limit, and the threshold can be preset according to the cross-border logistics business demand, and specifically, an absolute time deviation threshold, a relative time proportion threshold or a deviation threshold based on historical statistical distribution can be used, wherein the absolute time deviation threshold is used to limit the maximum allowed time difference of the actual clearance time relative to the predicted clearance time limit, the relative time proportion threshold is used to limit the proportional deviation of the actual clearance time relative to the predicted clearance time limit, and the deviation threshold based on historical statistical distribution is used to determine the condition for triggering path reconstruction according to the fluctuation range of historical clearance time, and the path reconstruction submodule only adjusts the path part that has not been executed to avoid affecting the completed transportation link, thereby ensuring the continuity and stability of the cross-border logistics process.

6. The cross-border logistics path optimization system based on the time limit for clearance prediction according to claim 1, characterized in that The clearance time limit prediction module, the cross-border logistics path generation module, the path comprehensive evaluation and optimization module and the path dynamic adjustment module establish signal interaction, the cross-border logistics path generation module, the path comprehensive evaluation and optimization module and the path dynamic adjustment module establish signal interaction, and the path comprehensive evaluation and optimization module and the path dynamic adjustment module establish signal interaction.

7. The cross-border logistics path optimization system based on the time limit for clearance prediction according to claim 2, characterized in that The clearance feature construction submodule encodes and normalizes various feature parameters to construct a feature vector in a unified format, thereby improving the stability and comparability of subsequent prediction calculation, and the clearance time limit prediction submodule can also use a statistical regression model or a machine learning model to model the relationship between the feature vector and historical clearance time, thereby improving the prediction accuracy under complex clearance conditions, and the predicted clearance time limit result includes a predicted clearance time value, a clearance time interval or a clearance time limit stability index, which is used to reflect the time cost and uncertainty level of the clearance process.

8. The cross-border logistics path optimization system based on the time limit for clearance prediction according to claim 2, characterized in that The clearance data acquisition submodule performs marking processing on atypical clearance data caused by declaration errors, data correction or abnormal inspection, reduces the weight of such data or excludes it in the subsequent prediction process, so as to avoid interference of extreme cases on the prediction result, and the structured processing includes encoding processing on discrete data such as clearance port, cargo category and transportation mode, and standardization processing on time data such as clearance declaration start time and clearance release completion time.

9. The cross-border logistics path optimization system based on the time limit for clearance prediction according to claim 2, characterized in that The feature extraction includes statistical analysis of clearance time corresponding to different clearance ports, different cargo categories and different transportation modes based on historical clearance data, and extraction of statistical features for representing clearance efficiency and stability, and the feature parameters at least include clearance port feature parameters reflecting the supervision intensity and historical processing efficiency of different clearance ports, cargo category feature parameters reflecting the differences in supervision requirements of different cargos, transportation mode feature parameters reflecting the differences in clearance process under different transportation modes, and statistical feature parameters of historical clearance time. The feature extraction includes statistical analysis of clearance time corresponding to different clearance ports, different cargo categories and different transportation modes based on historical clearance data, and extraction of statistical features for representing clearance efficiency and stability, and the feature parameters at least include clearance port feature parameters reflecting the supervision intensity and historical processing efficiency of different clearance ports, cargo category feature parameters reflecting the differences in supervision requirements of different cargos, transportation mode feature parameters reflecting the differences in clearance process under different transportation modes, and statistical feature parameters of historical clearance time.

10. The cross-border logistics path optimization system based on the time limit for clearance prediction according to claim 2, characterized in that The historical data and operation data at least include an export country customs port identifier and an import country customs port identifier for identifying the clearance port, cargo category or commodity code information for identifying the cargo attribute, transportation mode information for identifying the transportation mode, and clearance declaration start time, clearance release completion time and clearance status change record for calculating the clearance time limit.

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