Method and system for optimizing conversion and connection of bulk logistics in multimodal transport

Abstract

The invention provides a method and system for optimizing conversion and connection of bulk logistics in multimodal transport, and relates to the technical field of transport optimization. A plurality of total transport paths are extracted, and expected transport paths and information are obtained; obtaining a transfer node according to the expected transportation path information, obtaining a stage transportation path, and carrying out the comprehensive evaluation of the stage transportation path and the expected transportation path; performing path comprehensive evaluation result comparison on the transportation path to obtain an evaluation comparison result, performing path replacement according to the evaluation comparison result to obtain a replacement result, and obtaining an optimal expected path according to the replacement result; the stage transportation paths are subjected to abnormity comparison, abnormal transportation paths are obtained, normal transportation paths for replacement are obtained according to the path similarity, path replacement is carried out, and then the updated expected path is obtained.According to the method, complex and changeable cargo transportation requirements can be effectively met, the transportation efficiency is improved, and the transportation cost is reduced; and the demand optimization individuation degree is improved.

Description

Technical Field

[0001] The present invention proposes a method and system for optimizing the conversion and connection of bulk logistics in multimodal transport, which relates to the technical field of multimodal transport optimization, and specifically to the technical field of optimizing the conversion and connection of bulk logistics in multimodal transport. Background Art

[0002] With the rapid development of the logistics industry and the popularization of e-commerce, the demand for cargo transportation has become increasingly complex and diversified, which has put forward higher requirements for the optimization and selection of transportation routes. Traditional cargo transportation route planning methods often rely on manual experience and simple rule judgments, which are difficult to cope with large-scale, multi-variable, and dynamically changing cargo transportation needs.

[0003] Existing cargo transportation route optimization technologies are difficult to obtain ideal transportation routes, and it is difficult to perform exception optimization on each part of the total transportation route, which makes it difficult to match and optimize the transportation routes obtained by existing technologies with actual needs. At the same time, the route planning is not detailed enough, the exception handling is not timely enough, and the optimization effect is limited. Summary of the invention

[0004] The present invention provides a method and system for optimizing the conversion and connection of bulk logistics in multimodal transport, which is used to solve the problems that the existing cargo transportation path optimization technology is difficult to obtain the ideal transportation path, and it is difficult to optimize the various parts of the total transportation path abnormally, so that the transportation path obtained by the existing technology is often difficult to match and optimize with the actual demand, and the path planning is not precise enough, the abnormal processing is not timely enough, and the optimization effect is limited. The present invention proposes a method and system for optimizing the conversion and connection of bulk logistics in multimodal transport, the method comprising: S1. Obtain cargo transportation demand information and a preset transportation path network, extract multiple total transportation paths, obtain a desired transportation path from the multiple total transportation paths according to a preset optimization target, and obtain information on the desired transportation path; S2. Obtaining the transfer node according to the expected transportation path information, and then obtaining the stage transportation path, collecting and comparing the path evaluation indicators of each type for the stage transportation path, and then making a comprehensive evaluation of the stage transportation path and the expected transportation path; S3, comparing the comprehensive evaluation results of the transport paths to obtain an evaluation comparison result, performing path replacement according to the evaluation comparison result to obtain a replacement result, and obtaining the optimal expected path according to the replacement result; S4. Perform abnormal comparison on the stage transportation paths to obtain abnormal transportation paths, obtain normal transportation paths for replacement according to path similarity, perform path replacement, and then obtain updated expected paths.

[0005] Furthermore, the S1 includes: Obtain cargo transportation demand information and preset transportation route network for each cargo in bulk logistics; Extracting a total transportation route from a preset transportation route network according to the cargo transportation demand information; The preset optimization target in the transportation demand information is obtained, and a total transportation path that meets most preset optimization targets among multiple transportation paths is obtained as an expected transportation path, and the expected transportation path information is obtained.

[0006] Further, the S2 includes: Acquire a transfer node of the expected transportation path according to the expected transportation path information, and acquire multiple stage transportation paths according to the transfer node; Acquire multiple types of preset path evaluation indicators, collect data of multiple types of preset path evaluation indicators for each stage of transportation path, and obtain multiple types of indicator data for each stage of transportation path; Compare the indicator data of each category with the corresponding indicator threshold to obtain the indicator comparison result; According to the indicator comparison result, the number of abnormal indicator types of the stage transportation path is obtained, and the number of abnormal indicator types is compared with the preset indicator number threshold, and the indicator number comparison result is obtained to obtain the abnormal stage transportation number; According to the number of abnormal indicator types of the stage transportation path, the stage transportation path is comprehensively evaluated to obtain the stage comprehensive evaluation result; Compare the number of transports in the abnormal stage with a preset stage number threshold to obtain an abnormal comparison result; According to the number of abnormal stage transportations of the expected transportation path, the expected transportation path is comprehensively evaluated to obtain the comprehensive evaluation result of the path.

[0007] Further, the S3 includes: Obtain all comprehensive evaluation results of the expected transportation route and the total transportation route; Comparing the comprehensive evaluation results of the expected transport path with the comprehensive evaluation results of the paths of the remaining total transport paths in sequence to obtain multiple evaluation comparison results; Performing path replacement according to the evaluation and comparison results, replacing the transportation path with a small comprehensive evaluation result with the transportation path with a large comprehensive evaluation result, and obtaining a replacement result; The transportation path corresponding to the final replacement result is obtained from all replacement results as the optimal expected path.

[0008] Further, the S4 includes: Obtain multiple stage transportation paths of the optimal expected path, compare the stage comprehensive evaluation result of each stage transportation path with the preset abnormal evaluation threshold, and obtain the stage path comparison result; Annotating abnormal transport paths according to the stage path comparison results to obtain abnormal transport paths; A normal transport path with the highest path similarity to the abnormal transport path is obtained, the abnormal transport path is replaced by the normal transport path to obtain a replacement path, and an updated expected path is generated until no abnormal transport path exists in the updated expected path.

[0009] Furthermore, the system comprises: A path planning module is used to obtain cargo transportation demand information and a preset transportation path network, extract multiple total transportation paths, obtain a desired transportation path from the multiple total transportation paths according to a preset optimization target, and obtain the desired transportation path information; A comprehensive evaluation module is used to obtain the transfer node according to the expected transportation path information, and then obtain the stage transportation path, collect and compare the path evaluation indicators of each type for the stage transportation path, and then conduct a comprehensive evaluation of the stage transportation path and the expected transportation path; An optimal acquisition module, used to compare the comprehensive evaluation results of the transport paths, obtain the evaluation comparison results, replace the paths according to the evaluation comparison results, obtain the replacement results, and obtain the optimal expected path according to the replacement results; The path updating module is used to compare the stage transportation paths abnormally, obtain the abnormal transportation paths, obtain the normal transportation paths for replacement according to the path similarity, perform path replacement, and then obtain the updated expected path.

[0010] Furthermore, the path planning module includes: An information acquisition module is used to obtain cargo transportation demand information and a preset transportation route network for each cargo of bulk logistics; A path extraction module, used to extract a total transportation path in a preset transportation path network according to the cargo transportation demand information; The optimization extraction module is used to obtain the preset optimization target in the transportation demand information, obtain the total transportation path that meets the most preset optimization targets among multiple transportation paths as the expected transportation path, and obtain the expected transportation path information.

[0011] Furthermore, the comprehensive evaluation module includes: A segmentation module, used for obtaining a transfer node of the expected transportation path according to the expected transportation path information, and obtaining a plurality of stage transportation paths according to the transfer node; An index collection module is used to obtain multiple types of preset path evaluation indicators, collect data of multiple types of preset path evaluation indicators for each stage of transportation path, and obtain multiple types of indicator data for each stage of transportation path; The abnormal comparison module is used to compare the indicator data of each type with the corresponding indicator threshold to obtain the indicator comparison result; The abnormality determination module is used to obtain the number of abnormal indicator types of the stage transportation path according to the indicator comparison result, compare the number of abnormal indicator types with the preset indicator number threshold, obtain the indicator number comparison result to obtain the abnormal stage transportation number; The stage evaluation module is used to comprehensively evaluate the stage transportation path according to the number of abnormal indicator types of the stage transportation path to obtain the stage comprehensive evaluation result; A path evaluation module is used to compare the number of transportations in the abnormal stage with a preset stage number threshold to obtain an abnormal comparison result; According to the number of abnormal stage transportations of the expected transportation path, the expected transportation path is comprehensively evaluated to obtain the comprehensive evaluation result of the path.

[0012] Furthermore, the optimal acquisition module includes: A path comparison module is used to obtain all comprehensive evaluation results of the expected transportation path and the total transportation path; Comparing the comprehensive evaluation results of the expected transport path with the comprehensive evaluation results of the paths of the remaining total transport paths in sequence to obtain multiple evaluation comparison results; A path replacement module is used to replace the path according to the evaluation and comparison results, and replace the transportation path with a small comprehensive evaluation result with the transportation path with a large comprehensive evaluation result to obtain a replacement result; The transportation path corresponding to the final replacement result is obtained from all replacement results as the optimal expected path.

[0013] Furthermore, the path updating module includes: The phase comparison module is used to obtain multiple phase transportation paths of the optimal expected path, compare the phase comprehensive evaluation result of each phase transportation path with the preset abnormal evaluation threshold, and obtain the phase path comparison result; A stage replacement module, used for marking abnormal transportation paths according to the stage path comparison result to obtain abnormal transportation paths; A normal transport path with the highest path similarity to the abnormal transport path is obtained, the abnormal transport path is replaced by the normal transport path to obtain a replacement path, and an updated expected path is generated until no abnormal transport path exists in the updated expected path.

[0014] Beneficial effects of the present invention: By optimizing the transportation route and reducing the transit time, the overall efficiency of cargo transportation is significantly improved. The costs of various modes of transportation are comprehensively considered, and the transportation route with the best cost is selected to reduce the logistics costs of the enterprise. Through anomaly detection and path replacement, potential safety risks are avoided and the safety of cargo transportation is ensured. By shortening the transportation time and improving transportation reliability, customer satisfaction with logistics services is improved. Considering environmental factors such as carbon emissions during the optimization process will help enterprises achieve green logistics and promote sustainable development. Using big data and intelligent algorithms for path optimization and exception handling provides enterprises with intelligent decision-making support and improves the scientificity and accuracy of logistics management. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of a method for optimizing the conversion and connection of bulk logistics in multimodal transport. DETAILED DESCRIPTION

[0016] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0017] In one embodiment of the present invention, a method and system for optimizing the conversion and connection of bulk logistics in multimodal transport is proposed by the present invention, and the method comprises: S1. Obtain cargo transportation demand information and a preset transportation path network, extract multiple total transportation paths, obtain a desired transportation path from the multiple total transportation paths according to a preset optimization target, and obtain information on the desired transportation path; S2. Obtaining the transfer node according to the expected transportation path information, and then obtaining the stage transportation path, collecting and comparing the path evaluation indicators of each type for the stage transportation path, and then making a comprehensive evaluation of the stage transportation path and the expected transportation path; S3, comparing the comprehensive evaluation results of the transport paths to obtain an evaluation comparison result, performing path replacement according to the evaluation comparison result to obtain a replacement result, and obtaining the optimal expected path according to the replacement result; S4. Perform abnormal comparison on the stage transportation paths to obtain abnormal transportation paths, obtain normal transportation paths for replacement according to path similarity, perform path replacement, and then obtain updated expected paths.

[0018] The working principle of the above technical solution is as follows: the system collects specific demand information for cargo transportation, such as cargo type, quantity, origin, destination, etc. Based on the preset transportation path network (including multiple transportation modes such as road, rail, water, aviation and their connection points), the system extracts multiple possible total transportation paths. According to the preset optimization goals (such as lowest cost, shortest time, least carbon emissions, etc.), the desired transportation path is selected from multiple total transportation paths. The expected transportation path is subdivided into multiple stage transportation paths, and each stage may involve different transportation modes and transfer nodes. Detailed path evaluation indicators are collected for each stage transportation path, such as transportation time, cost, safety, reliability, etc. By comparing abnormalities with preset standards or historical data, possible abnormal transportation paths (such as too long transportation time, too high cost, high safety risks, etc.) are identified. Comprehensive evaluation is performed on all stage transportation paths and the overall expected transportation path, considering the mutual influence of each stage and the overall optimization goal. According to the comprehensive evaluation results, the poorly performing path segments are replaced to find better alternatives to obtain the optimal expected path. For abnormal transport routes detected, the system uses a path similarity algorithm to find similar but normal transport routes as replacement options. After implementing the path replacement, the expected path is re-evaluated and updated to ensure the efficiency, safety and cost-effectiveness of the entire transportation process.

[0019] The technical effects of the above technical solutions are: by optimizing the transportation route and reducing the transit time, the overall efficiency of cargo transportation is significantly improved. The cost of various modes of transportation is comprehensively considered, and the transportation route with the best cost is selected to reduce the logistics cost of the enterprise. Through anomaly detection and path replacement, potential safety risks are avoided and the safety of cargo transportation is ensured. By shortening the transportation time and improving the reliability of transportation, customer satisfaction with logistics services is improved. Considering environmental factors such as carbon emissions during the optimization process will help enterprises achieve green logistics and promote sustainable development. Using big data and intelligent algorithms for path optimization and exception handling provides enterprises with intelligent decision-making support and improves the scientificity and accuracy of logistics management.

[0020] In one embodiment of the present invention, the S1 includes: Obtain cargo transportation demand information (transportation starting point, destination point, expected transportation time, etc.) and preset transportation route network for each cargo in bulk logistics; Extracting a total transportation path (all paths that can reach the destination from the transportation starting point) from the preset transportation path network according to the cargo transportation demand information, the total transportation path includes multiple stage transportation paths and transfer nodes; Obtain the preset optimization goals (lowest cost, shortest time, etc.) in the transportation demand information, and obtain the total transportation path that meets the most preset optimization goals among multiple transportation paths as the expected transportation path (if there is no total transportation path that meets the most preset optimization goals, the preset weights of the goals shall prevail), and obtain the expected transportation path information.

[0021] The working principle of the above technical solution is as follows: the system first collects detailed cargo transportation demand information for each cargo, which includes but is not limited to key information such as the cargo transportation starting point, destination point, and expected transportation time. This information is the basis for subsequent path planning and optimization. The system also loads a preset transportation path network, which includes multiple modes of transportation (such as road, rail, water, and aviation) and their connection points (transfer nodes), constituting a potential path set for cargo transportation. Based on the starting point and destination point of cargo transportation, the system uses graph theory or path search algorithms (such as Dijkstra algorithm, A* algorithm, etc.) to search for all possible total transportation paths in the preset transportation path network. These paths start from the starting point, pass through a series of transfer nodes, and finally reach the destination point. Each total transportation path is further subdivided into multiple stage transportation paths, and each stage may involve different modes of transportation and different transfer nodes. This subdivision helps to conduct detailed analysis and optimization of the path later.

[0022] The system analyzes the preset optimization goals in the transportation demand information, such as the lowest cost, the shortest time, etc. These optimization goals may be single or a combination of multiple goals with certain weights. The system evaluates the extracted multiple total transportation routes and calculates the performance of each route in meeting the preset optimization goals. If no path can fully meet all the optimization goals, the system weighs the preset weights of the goals and selects the path with the highest comprehensive score as the expected transportation path. The system obtains and outputs detailed information on the expected transportation path, including the transportation methods, transfer nodes, and estimated transportation time at each stage.

[0023] The technical effects of the above technical solutions are: through automated path search and optimization algorithms, the best path to meet transportation needs can be quickly found, reducing the time and error of manual planning and improving logistics efficiency. Considering cost factors in multiple optimization objectives, it is possible to select the transportation path with the lowest cost or the highest cost-effectiveness, which helps enterprises reduce logistics costs. When the expected transportation time becomes the main optimization goal, the system can give priority to the path with the shortest time to ensure timely delivery of goods and improve customer satisfaction. The system supports the combination and weight adjustment of multiple optimization goals, and can be flexibly configured according to different transportation needs and business scenarios to meet the personalized needs of different customers. Through refined path planning and stage division, it helps enterprises to better utilize transportation resources (such as vehicles, ships, aircraft, etc.), reduce empty driving and waiting time, and improve resource utilization. The expected transportation path information output by the system provides enterprises with rich data support, which helps enterprises to conduct logistics decision analysis and optimize supply chain management processes.

[0024] In one embodiment of the present invention, S2 includes: Acquire a transfer node of the expected transportation path according to the expected transportation path information, and acquire multiple stage transportation paths according to the transfer node; Obtain multiple types of preset path evaluation indicators (transportation distance, transportation time, transportation weight range, transportation cost and transportation weather, etc.), collect data of multiple types of preset path evaluation indicators for each stage of the transportation path, and obtain multiple types of indicator data for each stage of the transportation path; Compare the indicator data of each category with the corresponding indicator threshold to obtain the indicator comparison result; According to the indicator comparison result, the number of abnormal indicator types of the stage transportation path is obtained, and the number of abnormal indicator types is compared with the preset indicator number threshold, and the indicator number comparison result is obtained to obtain the abnormal stage transportation number; According to the number of abnormal indicator types of the stage transportation path, the stage transportation path is comprehensively evaluated to obtain the stage comprehensive evaluation result (assuming that there are N types of abnormal indicators in the stage transportation path, the evaluation of the stage transportation path is N levels of abnormality, N = 1-∞); Obtaining the stage comprehensive evaluation information of the stage transportation path, and calculating the stage comprehensive evaluation coefficient of each stage transportation path according to the stage comprehensive evaluation information; The calculation formula of the comprehensive evaluation coefficient of the stage is:

[0025] Among them, Z jd is the comprehensive evaluation coefficient of the stage transportation path, o is the total number of preset path evaluation indicators, h bsi is the actual collected data of the i-th preset path evaluation index, hbyi is the index threshold of the i-th preset path evaluation index, YB is the preset index number threshold, J q is the preset weight value of the stage transportation path, h bsi -h byi When it is a positive number, set it to 1, h bsi -h byi When it is a negative number, set it to 0; the larger the evaluation coefficient, the more abnormal it is.

[0026] Compare the number of transports in the abnormal stage with a preset stage number threshold to obtain an abnormal comparison result; According to the number of abnormal stage transportations of the expected transportation path, the expected transportation path is comprehensively evaluated to obtain the comprehensive evaluation result of the path (assuming that the expected transportation path has N abnormal stage transportations, the evaluation of the expected transportation path is N levels of abnormality, N = 1-∞); Obtaining expected path comprehensive evaluation information of the expected transportation path, and calculating an expected comprehensive evaluation coefficient of each expected transportation path according to the expected path comprehensive evaluation information; The calculation formula of the expected comprehensive evaluation coefficient is:

[0027] Among them, Q zh is the expected path comprehensive evaluation coefficient of the expected transportation path, c is the total number of stage transportation paths, Z jda is the comprehensive evaluation coefficient of the transport path in the ath stage, Z yda is the preset stage coefficient threshold of the transport path in the ath stage, YZ is the preset abnormal stage number threshold, W q is the preset weight value of the expected transportation path, Z jda -Z yda When it is a positive number, set it to 1, Z jda -Z yda If it is negative, set it to 0.

[0028] The working principle of the above technical solution is as follows: according to the expected transportation path information, the system first identifies all the transfer nodes. Based on these transfer nodes, the expected transportation path is divided into multiple stage transportation paths, and each stage corresponds to the transportation process from one transfer node to the next transfer node (or from the starting point to the first transfer node, or from the last transfer node to the destination point). The system presets a variety of path evaluation indicators, such as transportation distance, transportation time, transportation cost and transportation weather, which are used to comprehensively evaluate the performance of the stage transportation path. For each stage transportation path, the system collects the actual data of these preset indicators. The collected indicator data is compared with the corresponding indicator threshold to identify the indicator types that are beyond the normal range (i.e., abnormal). The number of abnormal indicator types in each stage transportation path is counted and compared with the preset indicator number threshold. If the number of abnormal indicators exceeds the threshold, the stage is regarded as an abnormal stage. According to the number of abnormal indicator types, the stage transportation path is graded and evaluated. For example, if there are N abnormal indicator types, it is evaluated as N-level abnormality (the larger N is, the higher the degree of abnormality). The number of abnormal stages in the entire expected transportation path is counted and compared with the preset stage number threshold. According to the number of abnormal stages, the expected transportation path is comprehensively evaluated. Similarly, a hierarchical evaluation method is adopted. If there are N abnormal stages, it is evaluated as N levels of abnormality.

[0029] The technical effect of the above technical solution is as follows: by presetting a variety of path evaluation indicators and collecting actual data, a refined evaluation of the stage transportation path is achieved, which is helpful to discover potential problems and risk points. By counting and comparing the number of abnormal indicator types, the system can automatically identify abnormal stage transportation paths, provide timely early warning information for enterprises, and facilitate the adoption of countermeasures. The use of a hierarchical evaluation method to comprehensively evaluate the stage transportation paths and the overall expected transportation paths makes the evaluation results more intuitive and easy to understand, which helps enterprises to formulate corresponding optimization strategies based on the evaluation results. The comprehensive evaluation results provide strong data support for enterprises, help enterprises to make logistics decisions more scientific and reasonable, and improve the efficiency and level of logistics management. By identifying abnormal stage transportation paths, enterprises can optimize resource allocation, reduce investment in inefficient or high-risk paths, and improve resource utilization and overall benefits. By promptly identifying and solving potential problems, delays and losses in the transportation process can be reduced, and customer satisfaction and loyalty can be improved. Through the above formula The number of abnormal indicator types can be calculated by using the formula The stage comprehensive evaluation coefficient of the corresponding weight of the stage transportation path can be calculated. To calculate the number of transport paths in the abnormal stage, the formula The expected path comprehensive evaluation coefficient corresponding to the weight of the expected transportation path can be calculated. In one embodiment of the present invention, S3 includes: Obtain all comprehensive evaluation results of the expected transportation route and the total transportation route; The comprehensive evaluation results of the expected transport path are compared with the comprehensive evaluation results of the paths of the remaining total transport paths in turn (pairwise comparison, when the comprehensive evaluation result of the expected transport path is less than the comprehensive evaluation result of the total transport path, the total transport path is replaced with the expected transport path, and the replaced total transport path is compared with the other total transport paths for comprehensive evaluation results. Obtaining the expected transport path can reduce the number of replacements and save computing resources), and multiple evaluation comparison results are obtained; the comprehensive evaluation coefficients of the remaining total transport paths are calculated in the same way as the expected comprehensive evaluation coefficients. The comparison of comprehensive evaluation results can also be a comparison of comprehensive evaluation coefficients.

[0030] Performing path replacement according to the evaluation and comparison results, replacing the transportation path with a small comprehensive evaluation result with the transportation path with a large comprehensive evaluation result, and obtaining a replacement result; The transportation path corresponding to the final replacement result is obtained from all replacement results as the optimal expected path.

[0031] The working principle of the above technical solution is as follows: the system obtains the comprehensive evaluation results of the expected transportation path and all the total transportation paths. These evaluation results are calculated through the steps described above (including the division of stage transportation paths, data collection, anomaly detection and comprehensive evaluation). The system starts to execute an iterative comparison process. In this process, the path comprehensive evaluation result of the expected transportation path is compared with the path comprehensive evaluation result of each of the remaining total transportation paths in turn. The comparison adopts the "two-by-two comparison" method, that is, only two paths are compared each time. If the evaluation result of the expected transportation path is less than the evaluation result of a total transportation path, a replacement operation occurs, and the total transportation path replaces the current expected transportation path. The replaced "new" expected transportation path continues to compare the comprehensive evaluation results with other uncompared total transportation paths until all total transportation paths have been compared. In the comparison process, the replacement operation is guided by the comprehensive evaluation result (i.e., a quantitative representation of the comprehensive evaluation result). The transportation path with a large comprehensive evaluation result (indicating better) is used to replace the transportation path with a small comprehensive evaluation result. This replacement strategy aims to gradually optimize the expected transportation path while minimizing the number of replacements to save computing resources and time. After the above comparison and replacement process, the system finds the transportation path corresponding to the final replacement result among all the replacement results, and this path is determined as the optimal expected path.

[0032] The technical effect of the above technical solution is: through the iterative comparison and replacement process, the system can gradually approach and find the optimal expected transportation path, thereby improving the efficiency of logistics path optimization. Reduce unnecessary calculation and resource consumption. Through gradual replacement and comparison, the system can minimize the number of replacements and calculations while achieving the optimization goal. The comparison and replacement strategy based on the comprehensive evaluation results makes the decision-making process more scientific and reasonable. The system can objectively evaluate the pros and cons of each path and make the best choice accordingly. By optimizing the expected transportation path, the transportation time is shortened, the cost is reduced, and the reliability and safety of transportation are improved, which helps to improve customer satisfaction. During the optimization process, the system can identify and avoid potential risk points (such as abnormal transportation paths), reducing uncertainty and risks in the transportation process. This method has high flexibility and adaptability, and can be customized and optimized according to different transportation needs and business scenarios to meet the personalized needs of different enterprises.

[0033] In one embodiment of the present invention, the S4 includes: Obtain multiple stage transportation paths of the optimal expected path, compare the stage comprehensive evaluation result of each stage transportation path with the preset abnormal evaluation threshold, and obtain the stage path comparison result; Annotating abnormal transport paths according to the stage path comparison results to obtain abnormal transport paths; A normal transport path with the highest path similarity to the abnormal transport path is obtained, the abnormal transport path is replaced by the normal transport path to obtain a replacement path, and an updated expected path is generated until no abnormal transport path exists in the updated expected path.

[0034] The calculation formula of the path similarity is:

[0035] Among them, LS is the path similarity, p is the number of path similarity indicators, S us is the actual data of the u-th indicator, S uy is the preset data of the u-th indicator. The path similarity indicators include path length, transportation preset weight and transportation time, etc.

[0036] The working principle of the above technical solution is as follows: the optimal expected path is split into multiple stage transportation paths. This is based on the transfer nodes or other logical breakpoints in the path. A comprehensive evaluation is performed on each stage transportation path to obtain its stage comprehensive evaluation results. These results reflect the comprehensive performance of the stage in terms of transportation efficiency, cost, risk, etc. The stage comprehensive evaluation results of each stage transportation path are compared with the preset abnormal evaluation threshold. If the evaluation result of a stage exceeds the threshold (that is, it means that there is an abnormality in the stage), the stage is marked as an abnormal transportation path. For each transportation path marked as abnormal, the system needs to find the normal transportation path with the highest similarity. This usually involves comparing and matching various features of the transportation path (such as starting point, end point, waypoint, transportation mode, road conditions, etc.). The abnormal transportation path is replaced with the normal transportation path found to generate a new replacement path. This replacement process needs to ensure that the replaced path is logically coherent and meets the actual transportation needs. The replaced path is reassembled into an updated expected path. At this time, the system needs to check again whether there are still abnormal stages in the updated path. If so, the above anomaly detection, replacement and path updating process is repeated until there is no abnormal transportation path in the updated expected path.

[0037] The technical effects of the above technical solution are: by eliminating abnormal stages in the path, the efficiency loss caused by transportation interruption, delay or high cost is reduced, thereby improving the overall transportation efficiency. Replacing abnormal paths with normal paths with lower costs helps to reduce transportation costs and improve the economic benefits of enterprises. The transportation risks caused by path abnormalities are reduced, and the reliability and stability of transportation are improved. By providing better transportation paths and more reliable transportation services, it helps to improve customer satisfaction and loyalty. This process can be achieved through an automated and intelligent system, which reduces manual intervention and errors and improves the accuracy and efficiency of decision-making. The system can flexibly adjust and optimize the transportation path according to the actual situation and changes in transportation needs to ensure a smooth and efficient transportation process.

[0038] In one embodiment of the present invention, the system comprises: A path planning module is used to obtain cargo transportation demand information and a preset transportation path network, extract multiple total transportation paths, obtain a desired transportation path from the multiple total transportation paths according to a preset optimization target, and obtain the desired transportation path information; A comprehensive evaluation module is used to obtain the transfer node according to the expected transportation path information, and then obtain the stage transportation path, collect and compare the path evaluation indicators of each type for the stage transportation path, and then conduct a comprehensive evaluation of the stage transportation path and the expected transportation path; An optimal acquisition module, used to compare the comprehensive evaluation results of the transport paths, obtain the evaluation comparison results, replace the paths according to the evaluation comparison results, obtain the replacement results, and obtain the optimal expected path according to the replacement results; The path updating module is used to compare the stage transportation paths abnormally, obtain the abnormal transportation paths, obtain the normal transportation paths for replacement according to the path similarity, perform path replacement, and then obtain the updated expected path.

[0039] The working principle of the above technical solution is as follows: the system collects specific demand information for cargo transportation, such as cargo type, quantity, origin, destination, etc. Based on the preset transportation path network (including multiple transportation modes such as road, rail, water, aviation and their connection points), the system extracts multiple possible total transportation paths. According to the preset optimization goals (such as lowest cost, shortest time, least carbon emissions, etc.), the desired transportation path is selected from multiple total transportation paths. The expected transportation path is subdivided into multiple stage transportation paths, and each stage may involve different transportation modes and transfer nodes. Detailed path evaluation indicators are collected for each stage transportation path, such as transportation time, cost, safety, reliability, etc. By comparing abnormalities with preset standards or historical data, possible abnormal transportation paths (such as too long transportation time, too high cost, high safety risks, etc.) are identified. Comprehensive evaluation is performed on all stage transportation paths and the overall expected transportation path, considering the mutual influence of each stage and the overall optimization goal. According to the comprehensive evaluation results, the poorly performing path segments are replaced to find better alternatives to obtain the optimal expected path. For abnormal transport routes detected, the system uses a path similarity algorithm to find similar but normal transport routes as replacement options. After implementing the path replacement, the expected path is re-evaluated and updated to ensure the efficiency, safety and cost-effectiveness of the entire transportation process.

[0040] The technical effects of the above technical solutions are: by optimizing the transportation route and reducing the transit time, the overall efficiency of cargo transportation is significantly improved. The cost of various modes of transportation is comprehensively considered, and the transportation route with the best cost is selected to reduce the logistics cost of the enterprise. Through anomaly detection and path replacement, potential safety risks are avoided and the safety of cargo transportation is ensured. By shortening the transportation time and improving the reliability of transportation, customer satisfaction with logistics services is improved. Considering environmental factors such as carbon emissions during the optimization process will help enterprises achieve green logistics and promote sustainable development. Using big data and intelligent algorithms for path optimization and exception handling provides enterprises with intelligent decision-making support and improves the scientificity and accuracy of logistics management.

[0041] In one embodiment of the present invention, the path planning module includes: The information acquisition module is used to obtain the cargo transportation demand information (transportation starting point, destination point, expected transportation time, etc.) of each cargo in bulk logistics, and the preset transportation route network; A path extraction module is used to extract a total transportation path (all paths that can reach the destination from the transportation starting point, and the total transportation path includes multiple stage transportation paths and transfer nodes) from a preset transportation path network according to the cargo transportation demand information; The optimization extraction module is used to obtain the preset optimization goals (lowest cost, shortest time) in the transportation demand information, and obtain the total transportation path that meets the most preset optimization goals among multiple transportation paths as the expected transportation path (if there is no total transportation path that meets the most preset optimization goals, the preset weight of the goal shall prevail), and obtain the expected transportation path information.

[0042] The working principle of the above technical solution is as follows: the system first collects detailed cargo transportation demand information for each cargo, which includes but is not limited to key information such as the cargo transportation starting point, destination point, and expected transportation time. This information is the basis for subsequent path planning and optimization. The system also loads a preset transportation path network, which includes multiple modes of transportation (such as road, rail, water, and aviation) and their connection points (transfer nodes), constituting a potential path set for cargo transportation. Based on the starting point and destination point of cargo transportation, the system uses graph theory or path search algorithms (such as Dijkstra algorithm, A* algorithm, etc.) to search for all possible total transportation paths in the preset transportation path network. These paths start from the starting point, pass through a series of transfer nodes, and finally reach the destination point. Each total transportation path is further subdivided into multiple stage transportation paths, and each stage may involve different modes of transportation and different transfer nodes. This subdivision helps to conduct detailed analysis and optimization of the path later.

[0043] The system analyzes the preset optimization goals in the transportation demand information, such as the lowest cost, the shortest time, etc. These optimization goals may be single or a combination of multiple goals with certain weights. The system evaluates the extracted multiple total transportation routes and calculates the performance of each route in meeting the preset optimization goals. If no path can fully meet all the optimization goals, the system weighs the preset weights of the goals and selects the path with the highest comprehensive score as the expected transportation path. The system obtains and outputs detailed information on the expected transportation path, including the transportation methods, transfer nodes, and estimated transportation time at each stage.

[0044] The technical effects of the above technical solutions are: through automated path search and optimization algorithms, the best path to meet transportation needs can be quickly found, reducing the time and error of manual planning and improving logistics efficiency. Considering cost factors in multiple optimization objectives, it is possible to select the transportation path with the lowest cost or the highest cost-effectiveness, which helps enterprises reduce logistics costs. When the expected transportation time becomes the main optimization goal, the system can give priority to the path with the shortest time to ensure timely delivery of goods and improve customer satisfaction. The system supports the combination and weight adjustment of multiple optimization goals, and can be flexibly configured according to different transportation needs and business scenarios to meet the personalized needs of different customers. Through refined path planning and stage division, it helps enterprises to better utilize transportation resources (such as vehicles, ships, aircraft, etc.), reduce empty driving and waiting time, and improve resource utilization. The expected transportation path information output by the system provides enterprises with rich data support, which helps enterprises to conduct logistics decision analysis and optimize supply chain management processes.

[0045] In one embodiment of the present invention, the comprehensive evaluation module includes: A segmentation module, used for obtaining a transfer node of the expected transportation path according to the expected transportation path information, and obtaining a plurality of stage transportation paths according to the transfer node; The indicator collection module is used to obtain multiple types of preset path evaluation indicators (transportation distance, transportation time, transportation cost and transportation weather, etc.), collect data of multiple types of preset path evaluation indicators for each stage of the transportation path, and obtain multiple types of indicator data for each stage of the transportation path; The abnormal comparison module is used to compare the indicator data of each type with the corresponding indicator threshold to obtain the indicator comparison result; The abnormality determination module is used to obtain the number of abnormal indicator types of the stage transportation path according to the indicator comparison result, compare the number of abnormal indicator types with the preset indicator number threshold, obtain the indicator number comparison result to obtain the abnormal stage transportation number; The stage evaluation module is used to comprehensively evaluate the stage transportation path according to the number of abnormal indicator types of the stage transportation path, and obtain the stage comprehensive evaluation result (assuming that there are N types of abnormal indicators in the stage transportation path, the evaluation of the stage transportation path is N levels of abnormality, N = 1-∞); Obtaining the stage comprehensive evaluation information of the stage transportation path, and calculating the stage comprehensive evaluation coefficient of each stage transportation path according to the stage comprehensive evaluation information; The calculation formula of the comprehensive evaluation coefficient of the stage is:

[0046] Among them, Z jd is the comprehensive evaluation coefficient of the stage transportation path, o is the total number of preset path evaluation indicators, hbsi is the actual collected data of the i-th preset path evaluation index, h byi is the index threshold of the i-th preset path evaluation index, YB is the preset index number threshold, J q is the preset weight value of the stage transportation path, h bsi -h byi When it is a positive number, set it to 1, h bsi -h byi If it is a negative number, set it to 0; A path evaluation module is used to compare the number of transportations in the abnormal stage with a preset stage number threshold to obtain an abnormal comparison result; According to the number of abnormal stage transportations of the expected transportation path, the expected transportation path is comprehensively evaluated to obtain the comprehensive evaluation result of the path (assuming that the expected transportation path has N abnormal stage transportations, the evaluation of the expected transportation path is N levels of abnormality, N = 1-∞); Obtaining expected path comprehensive evaluation information of the expected transportation path, and calculating an expected comprehensive evaluation coefficient of each expected transportation path according to the expected path comprehensive evaluation information; The calculation formula of the expected comprehensive evaluation coefficient is:

[0047] Among them, Q zh is the expected path comprehensive evaluation coefficient of the expected transportation path, c is the total number of stage transportation paths, Z jda is the comprehensive evaluation coefficient of the transport path in the ath stage, Z yda is the preset stage coefficient threshold of the transport path in the ath stage, YZ is the preset abnormal stage number threshold, W q is the preset weight value of the expected transportation path, Z jda -Z yda When it is a positive number, set it to 1, Z jda -Z yda If it is negative, set it to 0.

[0048] The working principle of the above technical solution is as follows: according to the expected transportation path information, the system first identifies all the transfer nodes. Based on these transfer nodes, the expected transportation path is divided into multiple stage transportation paths, and each stage corresponds to the transportation process from one transfer node to the next transfer node (or from the starting point to the first transfer node, or from the last transfer node to the destination point). The system presets a variety of path evaluation indicators, such as transportation distance, transportation time, transportation cost and transportation weather, which are used to comprehensively evaluate the performance of the stage transportation path. For each stage transportation path, the system collects the actual data of these preset indicators. The collected indicator data is compared with the corresponding indicator threshold to identify the indicator types that are beyond the normal range (i.e., abnormal). The number of abnormal indicator types in each stage transportation path is counted and compared with the preset indicator number threshold. If the number of abnormal indicators exceeds the threshold, the stage is regarded as an abnormal stage. According to the number of abnormal indicator types, the stage transportation path is graded and evaluated. For example, if there are N abnormal indicator types, it is evaluated as N-level abnormality (the larger N is, the higher the degree of abnormality). The number of abnormal stages in the entire expected transportation path is counted and compared with the preset stage number threshold. According to the number of abnormal stages, the expected transportation path is comprehensively evaluated. Similarly, a hierarchical evaluation method is adopted. If there are N abnormal stages, it is evaluated as N levels of abnormality.

[0049] The technical effect of the above technical solution is: by presetting multiple path evaluation indicators and collecting actual data, a refined evaluation of the stage transportation path is achieved, which helps to discover potential problems and risk points. By counting and comparing the number of abnormal indicator types, the system can automatically identify abnormal stage transportation paths, provide timely early warning information for enterprises, and facilitate the adoption of countermeasures. The use of a hierarchical evaluation method to comprehensively evaluate the stage transportation path and the overall expected transportation path makes the evaluation results more intuitive and easy to understand, which helps enterprises to formulate corresponding optimization strategies based on the evaluation results. The comprehensive evaluation results provide strong data support for enterprises, which helps enterprises to make logistics decisions more scientifically and reasonably, and improve the efficiency and level of logistics management. By identifying abnormal stage transportation paths, enterprises can optimize resource allocation, reduce investment in inefficient or high-risk paths, and improve resource utilization and overall benefits. By timely identifying and solving potential problems, delays and losses in the transportation process can be reduced, and customer satisfaction and loyalty can be improved.

[0050] In one embodiment of the present invention, the optimal acquisition module includes: A path comparison module is used to obtain all comprehensive evaluation results of the expected transportation path and the total transportation path; Compare the comprehensive evaluation results of the expected transport path with the comprehensive evaluation results of the paths of the remaining total transport paths in turn (pairwise comparison, when the comprehensive evaluation result of the expected transport path is less than the comprehensive evaluation result of the total transport path, replace the expected transport path with the total transport path, and continue to compare the comprehensive evaluation results of the replaced total transport path with other total transport paths. Obtaining the expected transport path can reduce the number of replacements and save computing resources), and obtain multiple evaluation comparison results; A path replacement module is used to replace the path according to the evaluation and comparison results, and replace the transportation path with a small comprehensive evaluation result with the transportation path with a large comprehensive evaluation result to obtain a replacement result; The transportation path corresponding to the final replacement result is obtained from all replacement results as the optimal expected path.

[0051] The working principle of the above technical solution is as follows: the system obtains the comprehensive evaluation results of the expected transportation path and all the total transportation paths. These evaluation results are calculated through the steps described above (including the division of stage transportation paths, data collection, anomaly detection and comprehensive evaluation). The system starts to execute an iterative comparison process. In this process, the path comprehensive evaluation result of the expected transportation path is compared with the path comprehensive evaluation result of each of the remaining total transportation paths in turn. The comparison adopts the "two-by-two comparison" method, that is, only two paths are compared each time. If the evaluation result of the expected transportation path is less than the evaluation result of a total transportation path, a replacement operation occurs, and the total transportation path replaces the current expected transportation path. The replaced "new" expected transportation path continues to compare the comprehensive evaluation results with other uncompared total transportation paths until all total transportation paths have been compared. In the comparison process, the replacement operation is guided by the comprehensive evaluation result (i.e., a quantitative representation of the comprehensive evaluation result). The transportation path with a large comprehensive evaluation result (indicating better) is used to replace the transportation path with a small comprehensive evaluation result. This replacement strategy aims to gradually optimize the expected transportation path while minimizing the number of replacements to save computing resources and time. After the above comparison and replacement process, the system finds the transportation path corresponding to the final replacement result among all the replacement results, and this path is determined as the optimal expected path.

[0052] The technical effect of the above technical solution is: through the iterative comparison and replacement process, the system can gradually approach and find the optimal expected transportation path, thereby improving the efficiency of logistics path optimization. Reduce unnecessary calculation and resource consumption. Through gradual replacement and comparison, the system can minimize the number of replacements and calculations while achieving the optimization goal. The comparison and replacement strategy based on the comprehensive evaluation results makes the decision-making process more scientific and reasonable. The system can objectively evaluate the pros and cons of each path and make the best choice accordingly. By optimizing the expected transportation path, the transportation time is shortened, the cost is reduced, and the reliability and safety of transportation are improved, which helps to improve customer satisfaction. During the optimization process, the system can identify and avoid potential risk points (such as abnormal transportation paths), reducing uncertainty and risks in the transportation process. This method has high flexibility and adaptability, and can be customized and optimized according to different transportation needs and business scenarios to meet the personalized needs of different enterprises.

[0053] In one embodiment of the present invention, the path updating module includes: The phase comparison module is used to obtain multiple phase transportation paths of the optimal expected path, compare the phase comprehensive evaluation result of each phase transportation path with the preset abnormal evaluation threshold, and obtain the phase path comparison result; A stage replacement module, used for marking abnormal transportation paths according to the stage path comparison result to obtain abnormal transportation paths; A normal transport path with the highest path similarity to the abnormal transport path is obtained, the abnormal transport path is replaced by the normal transport path to obtain a replacement path, and an updated expected path is generated until no abnormal transport path exists in the updated expected path.

[0054] The calculation formula of the path similarity is:

[0055] Among them, LS is the path similarity, p is the number of path similarity indicators, S us is the actual data of the u-th indicator, S uy is the preset data of the u-th indicator. The path similarity indicators include path length, transportation preset weight and transportation time, etc.

[0056] The working principle of the above technical solution is as follows: the optimal expected path is split into multiple stage transportation paths. This is based on the transfer nodes or other logical breakpoints in the path. A comprehensive evaluation is performed on each stage transportation path to obtain its stage comprehensive evaluation results. These results reflect the comprehensive performance of the stage in terms of transportation efficiency, cost, risk, etc. The stage comprehensive evaluation results of each stage transportation path are compared with the preset abnormal evaluation threshold. If the evaluation result of a stage exceeds the threshold (that is, it means that there is an abnormality in the stage), the stage is marked as an abnormal transportation path. For each transportation path marked as abnormal, the system needs to find the normal transportation path with the highest similarity. This usually involves comparing and matching various features of the transportation path (such as starting point, end point, waypoint, transportation mode, road conditions, etc.). The abnormal transportation path is replaced with the normal transportation path found to generate a new replacement path. This replacement process needs to ensure that the replaced path is logically coherent and meets the actual transportation needs. The replaced path is reassembled into an updated expected path. At this time, the system needs to check again whether there are still abnormal stages in the updated path. If so, the above anomaly detection, replacement and path updating process is repeated until there is no abnormal transportation path in the updated expected path.

[0057] The technical effects of the above technical solution are: by eliminating abnormal stages in the path, the efficiency loss caused by transportation interruption, delay or high cost is reduced, thereby improving the overall transportation efficiency. Replacing abnormal paths with normal paths with lower costs helps to reduce transportation costs and improve the economic benefits of enterprises. The transportation risks caused by path abnormalities are reduced, and the reliability and stability of transportation are improved. By providing better transportation paths and more reliable transportation services, it helps to improve customer satisfaction and loyalty. This process can be achieved through an automated and intelligent system, which reduces manual intervention and errors and improves the accuracy and efficiency of decision-making. The system can flexibly adjust and optimize the transportation path according to the actual situation and changes in transportation needs to ensure a smooth and efficient transportation process.

[0058] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing the conversion and connection of bulk logistics in multimodal transport, characterized in that: The method comprises: S1. Obtain cargo transportation demand information and a preset transportation path network, extract multiple total transportation paths, obtain a desired transportation path from the multiple total transportation paths according to a preset optimization target, and obtain the desired transportation path information; S2. Obtaining the transfer node according to the expected transportation path information, and then obtaining the stage transportation path, collecting and comparing the path evaluation indicators of each type for the stage transportation path, and then making a comprehensive evaluation of the stage transportation path and the expected transportation path; S3, comparing the comprehensive evaluation results of the transport paths to obtain an evaluation comparison result, performing path replacement according to the evaluation comparison result to obtain a replacement result, and obtaining the optimal expected path according to the replacement result; S4. Perform abnormal comparison on the stage transportation paths to obtain abnormal transportation paths, obtain normal transportation paths for replacement according to the path similarity, perform path replacement, and then obtain an updated expected path.

2. According to claim 1, a method for optimizing bulk logistics in multimodal transport conversion and connection, characterized in that: The S1 includes: Obtain cargo transportation demand information and preset transportation route network for each cargo in bulk logistics; Extracting a total transportation route from a preset transportation route network according to the cargo transportation demand information; The preset optimization target in the transportation demand information is obtained, and a total transportation path that meets most preset optimization targets among multiple transportation paths is obtained as an expected transportation path, and the expected transportation path information is obtained.

3. According to claim 1, a method for optimizing bulk logistics in multimodal transport conversion and connection, characterized in that: The S2 includes: Acquire a transfer node of the expected transportation path according to the expected transportation path information, and acquire multiple stage transportation paths according to the transfer node; Acquire multiple types of preset path evaluation indicators, collect data of multiple types of preset path evaluation indicators for each stage of transportation path, and obtain multiple types of indicator data for each stage of transportation path; Compare the indicator data of each category with the corresponding indicator threshold to obtain the indicator comparison result; According to the indicator comparison result, the number of abnormal indicator types of the stage transportation path is obtained, and the number of abnormal indicator types is compared with the preset indicator number threshold, and the indicator number comparison result is obtained to obtain the abnormal stage transportation number; According to the number of abnormal indicator types of the stage transportation path, the stage transportation path is comprehensively evaluated to obtain the stage comprehensive evaluation result; Compare the number of transports in the abnormal stage with a preset stage number threshold to obtain an abnormal comparison result; According to the number of abnormal stage transportations of the expected transportation path, the expected transportation path is comprehensively evaluated to obtain the comprehensive evaluation result of the path.

4. According to claim 1, a method for optimizing bulk logistics in multimodal transport conversion and connection, characterized in that: The S3 includes: Obtain all comprehensive evaluation results of the expected transportation route and the total transportation route; Comparing the comprehensive evaluation results of the expected transport path with the comprehensive evaluation results of the paths of the remaining total transport paths in sequence to obtain multiple evaluation comparison results; Performing path replacement according to the evaluation and comparison results, replacing the transportation path with a small comprehensive evaluation result with the transportation path with a large comprehensive evaluation result, and obtaining a replacement result; The transportation path corresponding to the final replacement result is obtained from all replacement results as the optimal expected path.

5. According to claim 1, a method for optimizing bulk logistics in multimodal transport conversion and connection, characterized in that: The S4 includes: Obtain multiple stage transportation paths of the optimal expected path, compare the stage comprehensive evaluation result of each stage transportation path with the preset abnormal evaluation threshold, and obtain the stage path comparison result; An abnormal transport path is marked according to the stage path comparison result to obtain the abnormal transport path; A normal transport path with the highest path similarity to the abnormal transport path is obtained, the abnormal transport path is replaced by the normal transport path to obtain a replacement path, and an updated expected path is generated until no abnormal transport path exists in the updated expected path.

6. A bulk logistics multimodal transport conversion and connection optimization system, characterized in that: The system comprises: A path planning module is used to obtain cargo transportation demand information and a preset transportation path network, extract multiple total transportation paths, obtain a desired transportation path from the multiple total transportation paths according to a preset optimization target, and obtain the desired transportation path information; A comprehensive evaluation module is used to obtain the transfer node according to the expected transportation path information, and then obtain the stage transportation path, collect and compare the path evaluation indicators of each type for the stage transportation path, and then conduct a comprehensive evaluation of the stage transportation path and the expected transportation path; An optimal acquisition module, used to compare the comprehensive evaluation results of the transport paths, obtain the evaluation comparison results, replace the paths according to the evaluation comparison results, obtain the replacement results, and obtain the optimal expected path according to the replacement results; The path updating module is used to compare the stage transportation paths abnormally, obtain the abnormal transportation paths, obtain the normal transportation paths for replacement according to the path similarity, perform path replacement, and then obtain the updated expected path.

7. According to claim 6, a bulk logistics multimodal transport conversion and connection optimization system is characterized in that: The path planning module includes: An information acquisition module is used to obtain cargo transportation demand information and a preset transportation route network for each cargo of bulk logistics; A path extraction module, used to extract a total transportation path in a preset transportation path network according to the cargo transportation demand information; The optimization extraction module is used to obtain the preset optimization target in the transportation demand information, obtain the total transportation path that meets the most preset optimization targets among multiple transportation paths as the expected transportation path, and obtain the expected transportation path information.

8. According to claim 6, a bulk logistics multimodal transport conversion and connection optimization system is characterized in that: The comprehensive evaluation module includes: A segmentation module, used for obtaining a transfer node of the expected transportation path according to the expected transportation path information, and obtaining a plurality of stage transportation paths according to the transfer node; An index collection module is used to obtain multiple types of preset path evaluation indicators, collect data of multiple types of preset path evaluation indicators for each stage of transportation path, and obtain multiple types of indicator data for each stage of transportation path; The abnormal comparison module is used to compare the indicator data of each type with the corresponding indicator threshold to obtain the indicator comparison result; The abnormality determination module is used to obtain the number of abnormal indicator types of the stage transportation path according to the indicator comparison result, compare the number of abnormal indicator types with the preset indicator number threshold, obtain the indicator number comparison result to obtain the abnormal stage transportation number; The stage evaluation module is used to comprehensively evaluate the stage transportation path according to the number of abnormal indicator types of the stage transportation path to obtain the stage comprehensive evaluation result; A path evaluation module is used to compare the number of transportations in the abnormal stage with a preset stage number threshold to obtain an abnormal comparison result; According to the number of abnormal stage transportations of the expected transportation path, the expected transportation path is comprehensively evaluated to obtain the comprehensive evaluation result of the path.

9. The system for optimizing the conversion and connection of bulk logistics in multimodal transport according to claim 6, characterized in that: The optimal acquisition module comprises: A path comparison module is used to obtain all comprehensive evaluation results of the expected transportation path and the total transportation path; Comparing the comprehensive evaluation results of the expected transport path with the comprehensive evaluation results of the paths of the remaining total transport paths in sequence to obtain multiple evaluation comparison results; A path replacement module is used to replace the path according to the evaluation and comparison results, and replace the transportation path with a small comprehensive evaluation result with the transportation path with a large comprehensive evaluation result to obtain a replacement result; The transportation path corresponding to the final replacement result is obtained from all replacement results as the optimal expected path.

10. The system for optimizing the conversion and connection of bulk logistics in multimodal transport according to claim 6, characterized in that: The path updating module comprises: The phase comparison module is used to obtain multiple phase transportation paths of the optimal expected path, compare the phase comprehensive evaluation result of each phase transportation path with the preset abnormal evaluation threshold, and obtain the phase path comparison result; A stage replacement module, used for marking abnormal transportation paths according to the stage path comparison result to obtain abnormal transportation paths; A normal transport path with the highest path similarity to the abnormal transport path is obtained, the abnormal transport path is replaced by the normal transport path to obtain a replacement path, and an updated expected path is generated until no abnormal transport path exists in the updated expected path.

Images