Highway transportation track visualization and intelligent monitoring method for multimodal transportation

Abstract

The invention provides a highway transportation track visualization and intelligent monitoring method for multimodal transportation, and relates to the technical field of intelligent monitoring. The method comprises the following steps: collecting historical multimodal transport data, extracting line information aiming at a transport transfer point, and forming transport line feature data; obtaining target intermodal transportation information, and carrying out line calibration in combination with the intermodal line feature data to form target visual line data; and acquiring real-time combined transportation information, and performing transportation monitoring analysis in combination with the combined transportation line characteristic data and the target visual line data to form target real-time transportation monitoring visual data. According to the method, real-time monitoring of the transportation condition is more intuitively and effectively realized through reasonable transportation data acquisition and processing.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology, and more specifically, to a method for visualizing and intelligently monitoring road transport trajectories for multimodal transport. Background Technology

[0002] Multimodal transport is a transportation process involving the connection and transfer of two or more modes of transport, collectively known as combined transport. The advantage of multimodal transport is that customers do not need to consider changes in the modes of transport throughout the entire process, providing them with more convenient service.

[0003] Currently, because multimodal transport involves different modes of transportation on different routes, it cannot obtain transport information as quickly and effectively as a single mode of transport. Operators need to rationally integrate data from the entire transport process. However, most operators currently rely on simple manual monitoring to segment transport conditions on different routes, increasing labor costs and failing to obtain real-time transport information quickly and effectively. This creates difficulties for real-time and effective monitoring of the entire transport process, potentially impacting the timely completion of orders.

[0004] Therefore, there is an urgent need for a method for visualizing and intelligently monitoring road transport trajectories for multimodal transport, which can achieve more intuitive and effective real-time monitoring of transport conditions through reasonable transport data collection and processing. Summary of the Invention

[0005] The purpose of this invention is to provide a method for visualizing and intelligently monitoring road transport trajectories for multimodal transport. By acquiring historical intermodal transport data from multimodal transport operators, the method can extract data on each segment of the operator's intermodal transport routes and determine the specific transport parameters of the routes based on historical data. This provides fundamental reference information for subsequent analysis and judgment of real-time intermodal transport conditions. Based on the extraction of information from the signed target intermodal transport data, the method can display the route selection of the tracked orders on a visualized transport route map. Combined with real-time location data, it enables visualized location tracking and judgment of the actual transport status. The visualized display of transport routes and real-time locations allows for more intuitive and clear monitoring of the transport trajectory of target orders. Simultaneously, by combining historical data, it allows for reasonable prediction and judgment of the transport status to ensure that orders are completed within the stipulated timeframe. This method not only clearly and effectively displays the real-time status of transport routes but also reasonably ensures the monitoring of transport conditions, significantly strengthening transport security.

[0006] In a first aspect, the present invention provides a method for visualizing and intelligently monitoring road transport trajectories for multimodal transport, comprising: collecting historical multimodal transport data, extracting route information for intermodal transport junctions to form intermodal transport route feature data; acquiring target intermodal transport information, combining the intermodal transport route feature data to perform route calibration, forming target visualized route data; acquiring real-time intermodal transport information, combining the intermodal transport route feature data and the target visualized route data to perform transport monitoring and analysis, forming target real-time transport monitoring visualized data.

[0007] In this invention, the method acquires historical intermodal transport data from multimodal transport operators, extracting data from each segment of the operator's intermodal transport routes. Based on this historical data, specific transport parameters for each route are determined, providing fundamental reference information for subsequent analysis and judgment of real-time intermodal transport conditions. By extracting information from the target intermodal transport data, the method displays the route selection for tracked orders on a visualized transport route map. Real-time location data is used for visualized location tracking and assessment of the actual transport status. This visualized display of transport routes and real-time locations allows for more intuitive and clear monitoring of the target waybill's transport trajectory. Combined with historical data, reasonable predictions and judgments of the transport status are made to ensure orders are completed within the stipulated timeframe. This clear and effective display of the real-time status of the transport route also ensures reasonable monitoring of the transport situation, significantly strengthening transport assurance.

[0008] One possible approach is to collect historical multimodal transport data, extract route information for intermodal transport hubs, and form intermodal route feature data. This includes: extracting routes based on transport modes from historical multimodal transport data to form visualized multimodal transport route data; performing transport efficiency feature analysis based on historical multimodal transport data and combined with visualized multimodal transport route data to form multimodal transport route feature data; and combining visualized multimodal transport route data and multimodal transport route feature data to form intermodal transport route feature data.

[0009] In this invention, the main objectives, based on historical multimodal transport data, are twofold. First, to identify the intermodal transport routes operated by the operators, specifically including different road transport vehicles and all corresponding transport routes. Second, to extract features from the relevant transport information of different transport routes. This ensures that subsequent transport monitoring of different routes provides fundamental reference data for predictive analysis.

[0010] As one possible approach, based on historical multimodal transport data, route extraction based on transport mode is performed to form visualized multimodal transport route data. This includes: extracting all different transport route information corresponding to different transport modes based on historical multimodal transport data to form transport mode route data corresponding to different transport modes; acquiring road network map data, and marking routes on the road network map based on the transport mode route data corresponding to different transport modes to form visualized multimodal transport route data.

[0011] In this invention, historical data can be used to fully obtain the transportation routes operated by multimodal transport operators and the means of transportation that can be used. By mapping the routes that can be executed by different means of transportation onto the road network map, a clear and comprehensive route visualization is achieved, which provides an important data foundation for subsequent real-time and effective monitoring of the transportation process based on the visualized route map.

[0012] One possible approach is to analyze transportation efficiency characteristics based on historical multimodal transport data and visualized multimodal transport route data to form multimodal transport route characteristic data. This includes: identifying all transshipment points for different modes of transport based on their corresponding route data; extracting transshipment efficiency data for each transshipment point based on historical multimodal transport data to analyze transshipment efficiency characteristics, resulting in transshipment point transshipment efficiency characteristic data for each transshipment point; identifying all route segments for each mode of transport based on their corresponding route data; extracting transportation efficiency data for each route segment based on historical multimodal transport data to analyze transportation efficiency characteristics, resulting in route segment transportation efficiency characteristic data for each route segment; combining transshipment efficiency characteristic data for each transshipment point and route segment transportation efficiency characteristic data for each mode of transport to form corresponding multimodal transport route characteristic data; and finally, combining the multimodal transport route characteristic data for each mode of transport to form the overall multimodal transport route characteristic data.

[0013] In this invention, to achieve real-time monitoring of the transportation process based on visualized road network map information, it is also necessary to acquire information related to transportation efficiency along the route. After all, the purpose of transportation is to complete the transportation task within the time limit required by the customer, so information on time parameters can serve as necessary reference data for real-time monitoring. Of course, for multimodal transport, since different means of transport are used on different transport routes, two important operations affecting transportation efficiency are involved. One is the transfer between different route segments, especially when different means of transport are selected for different route segments. This transfer requires transshipment operations, including but not limited to inspection, unloading, and loading. The time of these operations has a significant impact on transportation efficiency and needs to be fully considered. Moreover, the transshipment efficiency of different means of transport is different. Therefore, the time data at the transshipment point is analyzed with reference to different means of transport. The other is that the transportation efficiency of different route segments varies due to different road conditions, and even on the same route segment, the efficiency of different means of transport is different. Therefore, it is also necessary to perform feature analysis and extraction of transportation efficiency for different routes based on the means of transport, providing important basic data for subsequent real-time monitoring.

[0014] As one possible approach, for different modes of transport, based on historical multimodal transport data, transshipment efficiency data of corresponding transshipment points for different modes of transport is extracted for transshipment efficiency feature analysis, forming transshipment efficiency feature data for transshipment points of different modes of transport. This includes: for different transshipment points of different modes of transport, based on historical multimodal transport data, extracting the periodic transshipment time variation data of transshipment points within multiple transshipment analysis periods; for different transshipment analysis periods, determining the minimum transshipment rate per unit transshipment point among different transshipment unit analysis times within the transshipment analysis period based on the periodic transshipment time variation data of transshipment points. and the unit transfer rate of the maximum transfer point Where n represents the number of the transfer point for different modes of transport, m represents the number of the different transfer analysis periods extracted, and k represents the number of the analysis duration of different transfer units determined within the transfer analysis period numbered n; for different transfer points of different modes of transport, the minimum transfer rate per transfer point is determined based on the analysis duration of different transfer units within the corresponding different transfer analysis periods. The minimum periodic transshipment rate change function for different modes of transport at transshipment points within the transshipment analysis period was determined. For different transit points of different modes of transport, the maximum transit rate per unit is calculated based on the analysis duration of different transit units within the corresponding different transit analysis periods. The maximum periodic transfer rate variation function of different transport modes at transfer points within the transfer analysis period was determined. For different transit points of different modes of transport, the minimum periodic transit rate change function is calculated based on the corresponding transit analysis period. Determine the minimum transshipment rate change function ,in, , This represents the function for obtaining the change in the minimum periodic transport rate at the corresponding time points. The minimum value; for different transit points of different modes of transport, based on the maximum periodic transit rate change function of the corresponding different transit analysis periods. Determine the function of maximum transfer rate change. ,in, , This represents the function for obtaining the change in maximum periodic transport rate at corresponding time points. The maximum value; the minimum transfer rate change function corresponding to the set of different transfer points for different modes of transport. and the function of maximum transfer rate change This generates corresponding transfer efficiency characteristic data for transfer points.

[0015] In this invention, the extraction of transshipment efficiency features at transshipment points primarily provides basic efficiency reference data for monitoring and analysis during transshipment. This enables the prediction and extraction of response analysis based on transportation time information. Considering that different transshipment points will have different transshipment efficiencies due to varying modes of transportation and transshipment times—because different modes of transportation involve different inspection, unloading, and transfer processes, and different modes of transportation handle different volumes of transshipment tasks—the analysis is conducted by performing corresponding transshipment efficiency analyses for different modes of transportation at different transshipment points. It should be noted that if time is directly considered as the indicator of efficiency in transshipment, the parameter would lose its representativeness due to incomplete consideration. The most important factor affecting transshipment efficiency is the volume of the goods. The larger the volume of the goods, the longer the transshipment takes. Of course, the volume of the goods also determines the means of transshipment, and the use of the means of transportation also takes time. However, for the type of goods transported, which is determined by the means of transportation, the type of means of transportation can remain unchanged or there is no significant difference in the time required to use the means of transportation. Therefore, this application uses weight as the parameter that needs to be considered in terms of the impact on transshipment efficiency, and uses the weight transshipped per unit time as the transshipment rate to reflect the transshipment efficiency of the transshipment point. It is understandable that freight transportation itself has a cyclical nature, with low transportation volume during the off-season and high volume during peak season. This directly determines the transshipment efficiency of transfer points, especially during peak season when waiting times for transfers are longer. Therefore, efficiency analysis also needs to consider the cyclical nature of transportation. Thus, reasonable efficiency characteristic data can be obtained by analyzing the transshipment efficiency of recent cycles. The analysis period and the unit duration within the analysis period can be set according to the actual situation. Generally, the analysis period can be set to an annual unit, and the analysis duration can be set to a daily unit. That is, the minimum and maximum values ​​of transshipment efficiency completed each day within the analysis period are extracted for representative analysis. Finally, a corresponding transshipment rate change function is fitted to define the range and pattern of transshipment efficiency changes of the transfer point within a complete transportation cycle.

[0016] As one possible approach, for different modes of transport, based on historical multimodal transport data, transport efficiency data for corresponding route segments of different modes of transport is extracted for transport efficiency feature analysis, forming route segment transport efficiency feature data for different transport segments. This includes: for different transport segments of different modes of transport, based on historical multimodal transport data, extracting the periodic transport time variation data of the route segment within multiple transport analysis periods; and for different transport analysis periods, determining the minimum route segment unit transport rate among different transport unit analysis times within the transport analysis period based on the periodic transport time variation data of the route segment. and the maximum unit transport rate of the line segment Where x represents the number of the route segment for different modes of transport, y represents the number of the different transport analysis periods extracted, and z represents the number of the analysis duration of different transport units determined within the transport analysis period numbered x; for different route segments of different modes of transport, the minimum unit transport rate of the route segment within the corresponding different transport analysis period is used. The minimum periodic transport rate change function for different transport modes and route segments within the transport analysis period was determined. For different transportation modes and route segments, the maximum unit transportation rate of the route segment is determined based on the analysis time of different transportation units within the corresponding different transportation analysis periods. The maximum periodic transport rate change function for different transport modes and route segments within the transport analysis period was determined. For different modes of transport and different transport route segments, the minimum periodic transport rate change function is used based on the corresponding different transport analysis periods. Determine the minimum transport rate change function ,in, , This represents the function of obtaining different minimum periodic transport rate changes at corresponding time points. The minimum value; for different transportation routes of different modes of transport, based on the maximum periodic transport rate change function of the corresponding different transport analysis periods. Determine the function of maximum transport rate change. ,in, , This represents the function for obtaining different maximum periodic transport rate changes at corresponding time points. The maximum value; the minimum transport rate change function corresponding to the set for different transport routes of different transport modes. and maximum transport rate change function This generates corresponding data on the transportation efficiency characteristics of the line segments.

[0017] Similarly, in this invention, the extraction of efficiency characteristic information for different transportation segments considers the periodicity of transportation and factors affecting transportation time. Transportation time per unit weight is used as representative data for the transportation rate. Minimum and maximum values ​​are obtained within multiple nearby analysis periods to define the range and variation pattern of the transportation rate for each segment. This provides important basic reference data for subsequent real-time monitoring of transportation segments.

[0018] One possible approach is to acquire target intermodal transport information, combine it with intermodal route characteristic data to perform route calibration, and form target visualized route data. This includes: determining real-time location coordinates based on the target intermodal transport information; determining completed transport trajectories based on the target intermodal transport information; calibrating the location in the road network map data based on the real-time location coordinates; and delineating the trajectory in the road network map data based on the completed transport trajectory information.

[0019] In this invention, the purpose of route marking is primarily to visualize the real-time location of the transportation process and display the completed transportation route trajectory, providing a more intuitive perspective for real-time monitoring. Route marking includes two parts: marking the current transportation location information and delineating the completed transportation trajectory. Both pieces of information provide crucial foundational data for subsequent real-time monitoring. It should be noted that route marking and delineation can be distinguished by different colors on a road network map. Similarly, routes accessible by different modes of transport can also be distinguished by different colors. This facilitates rapid subsequent judgment on whether the correct and appropriate transportation tools and corresponding routes are being used.

[0020] One possible approach is to acquire real-time intermodal transport information, combine intermodal route characteristic data and target visualized route data for transport monitoring and analysis, and form target real-time transport monitoring and visualization data. This includes: extracting planned transport route data and intermodal efficiency data based on real-time intermodal transport information; conducting route correctness monitoring and analysis based on planned transport route data and target visualized route data to form route correctness analysis results; and conducting efficiency monitoring and analysis based on route correctness analysis results, combined with intermodal efficiency data, planned transport route data, intermodal route characteristic data, and target visualized route data to form efficiency monitoring and analysis results.

[0021] In this invention, transportation monitoring and analysis mainly includes two aspects: one is determining whether the cargo transportation is on the planned and normal transportation route, and the other is determining whether the current transportation progress can meet the customer's transportation requirements. This fully achieves real-time monitoring of the correctness of the transportation route and the transportation efficiency throughout the entire transportation process, ensuring that transportation operations can be completed smoothly and efficiently. It improves transportation operation efficiency while providing more intuitive and timely monitoring of the transportation process, thus strengthening the protection of the transportation process.

[0022] As one possible implementation, route correctness monitoring and analysis is performed based on planned transportation route data and target visualized route data to generate route correctness analysis results. These results include: determining the planned route for the target transportation based on the planned transportation route data; comparing the completed transportation trajectory information in the target visualized route data with the planned route: if the completed transportation route delineated by the completed transportation trajectory information completely overlaps with the planned route, and the actual transportation vehicles used on each route segment are consistent with the planned transportation vehicles, then a normal route transportation vehicle result is generated; if the completed transportation route delineated by the completed transportation trajectory information does not completely overlap with the planned route, then a normal route transportation vehicle result is generated. If the routes completely overlap, but the actual vehicles used on some segments are different from the planned vehicles, then a "normal route, abnormal vehicle" result is generated. If the completed transportation routes delineated by the completed transportation trajectory information do not completely overlap with the planned routes, but the actual vehicles used on each segment are the same as the planned vehicles, then a "route deviates from normal vehicle" result is generated. If the completed transportation routes delineated by the completed transportation trajectory information do not completely overlap with the planned routes, and the actual vehicles used on some segments are different from the planned vehicles, then a "route deviates from normal vehicle" result is generated.

[0023] In this invention, the analysis of route accuracy must include whether the transportation route matches the planned route, and whether the transportation vehicles used on different route segments match the planned transportation vehicles. Only when both pieces of information are completely consistent can it be concluded that the transportation is proceeding normally according to plan. Any anomalies, whether inconsistencies in transportation vehicles or routes, may lead to reduced transportation efficiency or damage to transportation vehicles and goods, thereby affecting the entire transportation plan and causing losses. By monitoring and comparing routes and transportation vehicles in real time, effective end-to-end monitoring can be achieved, and the need for temporary changes to routes and transportation vehicles mid-journey can be avoided, ensuring that transportation operations are executed according to plan.

[0024] As one possible implementation, based on the route correctness analysis results, combined with intermodal transport efficiency data, planned transport route data, intermodal transport route characteristic data, and target visualized route data, efficiency monitoring and analysis results are generated. These results include: when the route correctness analysis results output a normal result for the transport vehicle, the completed transport time corresponding to the completed transport route is determined based on the intermodal transport efficiency data. The current route segment has been transported for a certain amount of time. Net weight of transported goods and the total planned transportation completion time Based on planned transport route data, intermodal transport route characteristic data, and target visualized route data, the transport efficiency characteristic data of the route segment, including all different route segments not yet traversed by the current route segment, are determined, and the corresponding minimum transfer rate change function is sequentially labeled. and the function that did not experience the maximum translocation rate change Where u is the numbering of transfer points for different modes of transport according to the transport sequence of the transport plan; based on the planned transport route data, intermodal transport route characteristic data, and target visualized route data, the transfer efficiency characteristic data of transfer points including all transfer points of different modes of transport not yet passed through in the current route segment are determined, and the corresponding minimum transport rate change function is sequentially labeled. and the function of no maximum transport rate change Where v is the numbering of different route segments according to the transportation sequence of the transportation plan; based on the current transportation cycle time point and different minimum transfer rate change functions. No maximum transshipment rate change function No minimum transport rate change function occurred and the function of no maximum transport rate change Determine the longest estimated travel time for different transport segments of the route. Shortest estimated travel time for the line segment Longest transit time and the shortest transit time ,in, , , , , This represents the current point in the transportation cycle before the maximum change in transportation rate occurs. The corresponding maximum transport rate did not occur. This represents the current point in the transportation cycle before the minimum transport rate change occurs. The corresponding minimum transport rate did not occur. The current transportation cycle time point has not yet experienced the maximum transshipment rate change function. The corresponding maximum transfer rate did not occur. The current transportation cycle time point has not experienced a minimum transshipment rate change function. The corresponding minimum transfer rate did not occur; the longest transfer time for different modes of transport transfer points. Shortest transit time The longest estimated travel time for each transport segment corresponding to different route segments. Shortest estimated travel time for the line segment Completed transportation time The current route segment has been transported for a certain amount of time. and the total planned transportation completion time The following monitoring and analysis will be conducted: If This results in a tense outcome for the plan; if If so, the plan will succeed; if This results in a plan being postponed; among them, This indicates the estimated longest total time. This indicates the estimated shortest total time. , U represents the total number of all unpassed route segments, including the current route segment, and V represents the total number of all unpassed transfer points. This indicates the longest estimated travel time for line segment numbered 1. This indicates the shortest estimated travel time for the line segment corresponding to line segment number 1.

[0025] In this invention, the purpose of efficiency monitoring and analysis is to analyze in advance whether the transportation task can be completed within the required transportation time. It utilizes feature data to determine the efficiency characteristics of each untraveled route segment and transfer point, and uses the current time point as a reference to determine the corresponding planned travel time. It should be noted that this application considers short-term transportation when determining the planned travel time for untraveled route segments and transfer points. That is, the travel time obtained from the feature data based on the current time point within the transportation period matches the travel time corresponding to the actual passage time of the goods. For long-term transportation, the planned time points of passing through these currently untraveled route segments and transfer points in sequence must be considered, and then the corresponding planned travel time is obtained from the feature data based on the corresponding planned time points. Since short-distance transportation is not long, the shortest unit of time for feature data extraction is in days. Therefore, it can be considered that the deviation of the estimated travel time for subsequent different route segments and transfer points based on the current time point for short-distance transportation will not be too large or will remain basically unchanged, especially for transportation that can be completed within one day. After obtaining the planned time for each transfer point and route segment, the estimated longest and shortest total route time can be determined. If the planned time is longer than the longest estimated time, the transportation time is considered sufficient. If the planned time is between the longest and shortest estimated times, the transportation time is considered tight, requiring expedited transportation. If the planned time is shorter than the shortest estimated time, transportation is considered to be delayed. Therefore, by analyzing and predicting the execution of transportation efficiency in advance, proactive measures can be taken to ensure that transportation arrives on schedule.

[0026] The beneficial effects of the road transport trajectory visualization and intelligent monitoring method for multimodal transport provided by this invention are as follows:

[0027] This method, by acquiring historical intermodal transport data from multimodal transport operators, can extract data on each segment of the operator's intermodal transport routes and determine specific transport parameters based on historical data. This provides fundamental reference information for subsequent analysis and judgment of real-time intermodal transport conditions. Based on the extraction of information from the target intermodal transport data, the method can display the route selection of the tracked orders on a visualized transport route map, and combine real-time location data for visualized location tracking and judgment of the actual transport status. The visualized display of transport routes and real-time locations allows for more intuitive and clear monitoring of the transport trajectory of target orders. Simultaneously, by combining historical data, reasonable predictions and judgments of the transport status can be made to ensure that orders are completed within the stipulated timeframe. This method not only clearly and effectively displays the real-time status of transport routes but also reasonably ensures monitoring of transport conditions, significantly strengthening transport assurance. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 A flowchart illustrating the steps of a road transport trajectory visualization and intelligent monitoring method for multimodal transport provided in an embodiment of the present invention;

[0030] Figure 2 This is a schematic diagram of the structure of the road transport trajectory visualization and intelligent monitoring method for multimodal transport provided in an embodiment of the present invention. Detailed Implementation

[0031] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention.

[0032] Multimodal transport is a transportation process involving the connection and transfer of two or more modes of transport, collectively known as combined transport. The advantage of multimodal transport is that customers do not need to consider changes in the modes of transport throughout the entire process, providing them with more convenient service.

[0033] Currently, because multimodal transport involves different modes of transportation on different routes, it cannot obtain transport information as quickly and effectively as a single mode of transport. Operators need to rationally integrate data from the entire transport process. However, most operators currently rely on simple manual monitoring to segment transport conditions on different routes, increasing labor costs and failing to obtain real-time transport information quickly and effectively. This creates difficulties for real-time and effective monitoring of the entire transport process, potentially impacting the timely completion of orders.

[0034] refer to Figures 1-2 This invention provides a method for visualizing and intelligently monitoring road transport trajectories for multimodal transport. This method acquires historical intermodal transport data from multimodal transport operators, extracting data from each segment of the operator's intermodal transport routes. Based on this historical data, it determines the specific transport parameters of the routes, providing fundamental reference information for subsequent analysis and judgment of real-time intermodal transport conditions. Based on the extraction of information from the signed target intermodal transport data, the method displays the route selection of the tracked orders on a visualized transport route map. It also combines real-time location data for visualized location tracking and judgment of the actual transport status. The visualized display of transport routes and real-time locations allows for more intuitive and clear monitoring of the transport trajectory of target orders. Simultaneously, by combining historical data, it makes reasonable predictions and judgments about the transport status to ensure that orders are completed within the stipulated timeframe. This method clearly and effectively displays the real-time status of transport routes while ensuring reasonable monitoring of transport conditions, significantly strengthening transport assurance.

[0035] The method for visualizing and intelligently monitoring road transport trajectories for multimodal transport includes the following steps:

[0036] S1: Collect historical multimodal transport data, extract route information for intermodal transport junctions, and form intermodal route feature data.

[0037] Historical multimodal transport data is collected, and route information for intermodal transport hubs is extracted to form intermodal route feature data. This includes: extracting routes based on transport modes from historical multimodal transport data to form visualized multimodal transport route data; conducting transport efficiency feature analysis based on historical multimodal transport data and combined with visualized multimodal transport route data to form multimodal transport route feature data; and combining visualized multimodal transport route data and multimodal transport route feature data to form intermodal transport route feature data.

[0038] The main objectives of combining historical multimodal transport data are twofold. First, to identify the intermodal transport routes operated by the operators, specifically including different road transport modes and all corresponding transport routes. Second, to extract features from the transport information of different routes. This ensures that subsequent transport monitoring of different routes provides a foundation for predictive analysis.

[0039] Based on historical multimodal transport data, routes are extracted based on transport modes to form visualized multimodal transport route data. This includes: extracting all different transport route information corresponding to different transport modes from historical multimodal transport data to form transport mode route data corresponding to different transport modes; acquiring road network map data and marking routes on the road network map according to the transport mode route data corresponding to different transport modes to form visualized multimodal transport route data.

[0040] By leveraging historical data, we can fully obtain the transportation routes operated by multimodal transport operators and the means of transportation they can use. By mapping the routes that different means of transportation can perform onto the road network map, we can achieve clear and comprehensive route visualization, which provides an important data foundation for subsequent real-time and effective monitoring of the transportation process based on the visualized route map.

[0041] Based on historical multimodal transport data and combined with visualized multimodal transport route data, transport efficiency characteristic analysis is performed to form multimodal transport route characteristic data. This includes: identifying all transshipment points for different modes of transport based on their corresponding route data; for different modes of transport, extracting transshipment efficiency data for each transshipment point based on historical multimodal transport data and performing transshipment efficiency characteristic analysis to form transshipment point transshipment efficiency characteristic data for each transshipment point; identifying all route segments for different modes of transport based on their corresponding route data; for different modes of transport, extracting transport efficiency data for each route segment based on historical multimodal transport data and performing transport efficiency characteristic analysis to form route segment transport efficiency characteristic data for each route segment; combining transshipment efficiency characteristic data for different transshipment points and route segment transport efficiency characteristic data for different modes of transport to form corresponding transport route characteristic data; and combining the transport route characteristic data for different modes of transport to form multimodal transport route characteristic data.

[0042] To achieve real-time monitoring of the transportation process based on visualized road network map information, it is also necessary to acquire information related to transportation efficiency along the route. After all, the purpose of transportation is to complete the task within the time frame required by the customer, so information on time parameters is essential reference data for real-time monitoring. Of course, for multimodal transport, since different modes of transport are used on different transport segments, two important operations affecting transportation efficiency are involved. One is the transfer between different route segments, especially when different modes of transport are chosen. This transfer requires transshipment operations, including but not limited to inspection, unloading, and loading. The time of these operations has a significant impact on transportation efficiency and needs to be fully considered. Furthermore, the transshipment efficiency varies between different modes of transport; therefore, time data at transshipment points is analyzed with reference to different modes of transport. The other is that the transportation efficiency varies between different route segments due to different road conditions, and even on the same route segment, the efficiency varies depending on the mode of transport. Therefore, it is also necessary to perform feature analysis and extraction of transportation efficiency for different routes based on the mode of transport, providing important basic data for subsequent real-time monitoring.

[0043] For different modes of transport, based on historical multimodal transport data, transshipment efficiency data of corresponding transshipment points for different modes of transport are extracted for transshipment efficiency feature analysis, forming transshipment efficiency feature data for transshipment points of different modes of transport. This includes: for different transshipment points of different modes of transport, based on historical multimodal transport data, extracting the periodic transshipment time variation data of transshipment points within multiple transshipment analysis periods; for different transshipment analysis periods, determining the minimum transshipment rate per unit transshipment point within different transshipment unit analysis times within the transshipment analysis period based on the periodic transshipment time variation data of transshipment points. and the unit transfer rate of the maximum transfer point Where n represents the number of the transfer point for different modes of transport, m represents the number of the different transfer analysis periods extracted, and k represents the number of the analysis duration of different transfer units determined within the transfer analysis period numbered n; for different transfer points of different modes of transport, the minimum transfer rate per transfer point is determined based on the analysis duration of different transfer units within the corresponding different transfer analysis periods. The minimum periodic transshipment rate change function for different modes of transport at transshipment points within the transshipment analysis period was determined. For different transit points of different modes of transport, the maximum transit rate per unit is calculated based on the analysis duration of different transit units within the corresponding different transit analysis periods. The maximum periodic transfer rate variation function of different transport modes at transfer points within the transfer analysis period was determined. For different transit points of different modes of transport, the minimum periodic transit rate change function is calculated based on the corresponding transit analysis period. Determine the minimum transshipment rate change function ,in, , This represents the function for obtaining the change in the minimum periodic transport rate at the corresponding time points. The minimum value; for different transit points of different modes of transport, based on the maximum periodic transit rate change function of the corresponding different transit analysis periods. Determine the function of maximum transfer rate change. ,in, , This represents the function for obtaining the change in maximum periodic transport rate at corresponding time points. The maximum value; the minimum transfer rate change function corresponding to the set of different transfer points for different modes of transport. and the function of maximum transfer rate change This generates corresponding transfer efficiency characteristic data for transfer points.

[0044] Extracting transshipment efficiency features at transshipment points primarily provides baseline data for monitoring and analysis during transshipment. This enables prediction and analysis based on transportation time information. Different transshipment points will have varying transshipment efficiencies due to differences in transportation methods and transshipment times. Different modes of transport involve different inspection, unloading, and transfer processes, and each mode of transport handles different volumes of transshipment tasks. Therefore, the analysis will be conducted using different modes of transport at different transshipment points to determine corresponding transshipment efficiency. It should be noted that if time is directly considered as the indicator of efficiency in transshipment, the parameter would lose its representativeness due to incomplete consideration. The most important factor affecting transshipment efficiency is the volume of the goods. The larger the volume of the goods, the longer the transshipment takes. Of course, the volume of the goods also determines the means of transshipment, and the use of the means of transportation also takes time. However, for the type of goods transported, which is determined by the means of transportation, the type of means of transportation can remain unchanged or there is no significant difference in the time required to use the means of transportation. Therefore, this application uses weight as the parameter that needs to be considered in terms of the impact on transshipment efficiency, and uses the weight transshipped per unit time as the transshipment rate to reflect the transshipment efficiency of the transshipment point. It is understandable that freight transportation itself has a cyclical nature, with low transportation volume during the off-season and high volume during peak season. This directly determines the transshipment efficiency of transfer points, especially during peak season when waiting times for transfers are longer. Therefore, efficiency analysis also needs to consider the cyclical nature of transportation. Thus, reasonable efficiency characteristic data can be obtained by analyzing the transshipment efficiency of recent cycles. The analysis period and the unit duration within the analysis period can be set according to the actual situation. Generally, the analysis period can be set to an annual unit, and the analysis duration can be set to a daily unit. That is, the minimum and maximum values ​​of transshipment efficiency completed each day within the analysis period are extracted for representative analysis. Finally, a corresponding transshipment rate change function is fitted to define the range and pattern of transshipment efficiency changes of the transfer point within a complete transportation cycle.

[0045] For different modes of transport, based on historical multimodal transport data, transport efficiency data for corresponding route segments of different modes of transport are extracted for transport efficiency feature analysis, forming route segment transport efficiency feature data for different modes of transport. This includes: for different routes of different modes of transport, based on historical multimodal transport data, extracting the periodic transport time variation data of the route segment within multiple transport analysis periods; and for different transport analysis periods, determining the minimum route segment unit transport rate among different transport unit analysis times within the transport analysis period based on the periodic transport time variation data of the route segment. and the maximum unit transport rate of the line segment Where x represents the number of the route segment for different modes of transport, y represents the number of the different transport analysis periods extracted, and z represents the number of the analysis duration of different transport units determined within the transport analysis period numbered x; for different route segments of different modes of transport, the minimum unit transport rate of the route segment within the corresponding different transport analysis period is used. The minimum periodic transport rate change function for different transport modes and route segments within the transport analysis period was determined. For different transportation modes and route segments, the maximum unit transportation rate of the route segment is determined based on the analysis time of different transportation units within the corresponding different transportation analysis periods. The maximum periodic transport rate change function for different transport modes and route segments within the transport analysis period was determined. For different modes of transport and different transport route segments, the minimum periodic transport rate change function is used based on the corresponding different transport analysis periods. Determine the minimum transport rate change function ,in, , This represents the function of obtaining different minimum periodic transport rate changes at corresponding time points. The minimum value; for different transportation routes of different modes of transport, based on the maximum periodic transport rate change function of the corresponding different transport analysis periods. Determine the function of maximum transport rate change. ,in, , This represents the function for obtaining different maximum periodic transport rate changes at corresponding time points. The maximum value; the minimum transport rate change function corresponding to the set for different transport routes of different transport modes. and maximum transport rate change function This generates corresponding data on the transportation efficiency characteristics of the line segments.

[0046] Similarly, for the extraction of efficiency characteristic information on different transportation segments, considering the periodicity of transportation and factors affecting transportation time, the transportation time per unit weight is used as representative data of the transportation rate. Minimum and maximum values ​​are obtained within multiple nearby analysis periods to define the range and variation pattern of the transportation rate for each segment. This provides important basic reference data for subsequent real-time monitoring of transportation on the route segments.

[0047] S2: Obtain target intermodal transport information, combine intermodal route characteristic data to perform route calibration, and form target visualized route data.

[0048] Obtain target intermodal transport information, combine it with intermodal route characteristic data to perform route marking, and form target visualized route data, including: determining real-time location coordinates based on target intermodal transport information; determining completed transport trajectory information based on target intermodal transport information; performing location marking in road network map data based on real-time location coordinates; and delineating trajectories in road network map data based on completed transport trajectory information.

[0049] The purpose of route marking is primarily to visualize the real-time location of the transportation process and display the completed transportation route trajectory, providing a more intuitive perspective for real-time monitoring. Route marking consists of two parts: marking the current transportation location information and delineating the completed transportation trajectory. Both pieces of information provide crucial foundational data for subsequent real-time monitoring. It should be noted that route marking and delineation can be distinguished by different colors on the road network map. Similarly, routes accessible by different modes of transport can also be differentiated by different colors. This facilitates rapid assessment of whether the correct and appropriate transportation tools and routes are being used.

[0050] S3: Obtain real-time intermodal transport information, combine intermodal route characteristic data and target visualized route data to conduct transport monitoring and analysis, and form target real-time transport monitoring and visualization data.

[0051] Real-time intermodal transport information is acquired, and transport monitoring and analysis are performed by combining intermodal route characteristic data and target visualized route data to form target real-time transport monitoring and visualization data. This includes: extracting planned transport route data and intermodal efficiency data based on real-time intermodal transport information; performing route correctness monitoring and analysis based on planned transport route data and target visualized route data to form route correctness analysis results; and performing efficiency monitoring and analysis based on route correctness analysis results, combined with intermodal efficiency data, planned transport route data, intermodal route characteristic data, and target visualized route data to form efficiency monitoring and analysis results.

[0052] Transportation monitoring and analysis mainly includes two aspects: first, determining whether the cargo transportation is on the planned and normal transportation route; and second, determining whether the current transportation progress can meet the customer's transportation requirements. It fully achieves real-time monitoring of the correctness of the transportation route and the transportation efficiency throughout the entire transportation process, ensuring that transportation operations can be completed smoothly and efficiently. This improves transportation efficiency while providing more intuitive and timely monitoring of the transportation process, strengthening the guarantee of the transportation process.

[0053] Based on the planned transportation route data and combined with the target visualized route data, route correctness monitoring and analysis are conducted to form route correctness analysis results, including: determining the planned route for target transportation based on the planned transportation route data; comparing the completed transportation trajectory information in the target visualized route data with the planned route: if the completed transportation route delineated by the completed transportation trajectory information completely overlaps with the planned route, and the actual transportation vehicles used on each route segment are consistent with the planned transportation vehicles, then a normal route transportation vehicle result is formed; if the completed transportation route delineated by the completed transportation trajectory information completely overlaps with the planned route, but there are instances where the actual transportation vehicles used on some route segments are inconsistent with the planned transportation vehicles, then a normal route transportation vehicle abnormality result is formed; if the completed transportation route delineated by the completed transportation trajectory information does not completely overlap with the planned route, but the actual transportation vehicles used on each route segment are consistent with the planned transportation vehicles, then a route deviation transportation vehicle normal result is formed; if the completed transportation route delineated by the completed transportation trajectory information does not completely overlap with the planned route, and there are instances where the actual transportation vehicles used on some route segments are inconsistent with the planned transportation vehicles, then a route deviation transportation vehicle abnormality result is formed.

[0054] The analysis of route accuracy must include whether the transportation route matches the planned route, and whether the transportation vehicles used on different segments of the route are consistent with the planned vehicles. Only when both pieces of information are completely consistent can it be concluded that the transportation is proceeding normally according to plan. Any anomalies, whether inconsistencies in transportation vehicles or routes, may lead to reduced transportation efficiency or damage to transportation vehicles and goods, thereby affecting the entire transportation plan and causing losses. By monitoring and comparing routes and transportation vehicles in real time, effective end-to-end monitoring can be achieved, and the need for temporary changes to routes and transportation vehicles midway can be avoided, ensuring that transportation operations are executed according to plan.

[0055] Based on the route accuracy analysis results, combined with intermodal transport efficiency data, planned transport route data, intermodal transport route characteristic data, and target visualized route data, efficiency monitoring and analysis are conducted to form efficiency monitoring and analysis results. These results include: when the route accuracy analysis results output a normal transport vehicle status signal, the completed transport time corresponding to the completed transport route is determined based on the intermodal transport efficiency data. The current route segment has been transported for a certain amount of time. Net weight of transported goods and the total planned transportation completion time Based on planned transport route data, intermodal transport route characteristic data, and target visualized route data, the transport efficiency characteristic data of the route segment, including all different route segments not yet traversed by the current route segment, are determined, and the corresponding minimum transfer rate change function is sequentially labeled. and the function that did not experience the maximum translocation rate change Where u is the numbering of transfer points for different modes of transport according to the transport sequence of the transport plan; based on the planned transport route data, intermodal transport route characteristic data, and target visualized route data, the transfer efficiency characteristic data of transfer points including all transfer points of different modes of transport not yet passed through in the current route segment are determined, and the corresponding minimum transport rate change function is sequentially labeled. and the function of no maximum transport rate change Where v is the numbering of different route segments according to the transportation sequence of the transportation plan; based on the current transportation cycle time point and different minimum transfer rate change functions. No maximum transshipment rate change function No minimum transport rate change function occurred and the function of no maximum transport rate change Determine the longest estimated travel time for different transport segments of the route. Shortest estimated travel time for the line segment Longest transit time and the shortest transit time ,in, , , , , This represents the current point in the transportation cycle before the maximum change in transportation rate occurs. The corresponding maximum transport rate did not occur. This represents the current point in the transportation cycle before the minimum transport rate change occurs. The corresponding minimum transport rate did not occur. The current transportation cycle time point has not yet experienced the maximum transshipment rate change function. The corresponding maximum transfer rate did not occur. The current transportation cycle time point has not experienced a minimum transshipment rate change function. The corresponding minimum transfer rate did not occur; the longest transfer time for different modes of transport transfer points. Shortest transit time The longest estimated travel time for each transport segment corresponding to different route segments. Shortest estimated travel time for the line segment Completed transportation time The current route segment has been transported for a certain amount of time. and the total planned transportation completion time The following monitoring and analysis will be conducted: If This results in a tense outcome for the plan; if If so, the plan will succeed; if This results in a plan being postponed; among them, This indicates the estimated longest total time. This indicates the estimated shortest total time. , U represents the total number of all unpassed route segments, including the current route segment, and V represents the total number of all unpassed transfer points. This indicates the longest estimated travel time for line segment numbered 1. This indicates the shortest estimated travel time for the line segment corresponding to line segment number 1.

[0056] The purpose of efficiency monitoring and analysis is to analyze in advance whether the transportation task can be completed within the required transportation time. It uses feature data to determine the efficiency characteristics of each untraveled route segment and transfer point, and uses the current time as a reference to determine the corresponding planned travel time. It should be noted that this application considers short-term transportation when determining the planned travel time for untraveled route segments and transfer points. That is, the travel time obtained from the feature data based on the current time point within the transportation period matches the actual travel time of the goods. For long-term transportation, the planned time points of each untraveled route segment and transfer point must be considered, and the corresponding planned travel time is obtained from the feature data based on these planned time points. Since short-distance transportation is not long, the shortest unit of feature data extraction is in days. Therefore, it can be assumed that the deviation of the estimated travel time for subsequent different route segments and transfer points based on the current time point for short-distance transportation will not be too large or will remain basically unchanged, especially for transportation that can be completed within one day. After obtaining the planned time for each transfer point and route segment, the estimated longest and shortest total route time can be determined. If the planned time is longer than the longest estimated time, the transportation time is considered sufficient. If the planned time is between the longest and shortest estimated times, the transportation time is considered tight, requiring expedited transportation. If the planned time is shorter than the shortest estimated time, transportation is considered to be delayed. Therefore, by analyzing and predicting the execution of transportation efficiency in advance, proactive measures can be taken to ensure that transportation arrives on schedule.

[0057] This invention also provides a road transport trajectory visualization and intelligent monitoring system for multimodal transport. The system includes a data acquisition unit for acquiring historical multimodal transport data, target multimodal transport information, and real-time multimodal transport information; a visualization feature processing unit for extracting route information from the historical multimodal transport data acquired by the data acquisition unit, targeting intermodal transport junctions, to form intermodal route feature data, and combining this with the target intermodal transport information to calibrate the route, forming target visualized route data; and a monitoring and analysis unit for performing transport monitoring and analysis on the real-time multimodal transport information and target intermodal transport information acquired by the data acquisition unit, based on the intermodal route feature data and target visualized route data formed by the visualization feature processing unit, to form target real-time transport monitoring visualization data.

[0058] This system acquires historical data on multimodal transport through a data acquisition unit, thereby forming the operator's operational route data. Then, it uses a visualization feature processing unit to display the transport route information and corresponding transport efficiency characteristics in a more intuitive form. Thus, when acquiring real-time intermodal transport information, the monitoring and analysis unit enables real-time monitoring of the entire transport process and prediction of transport efficiency, effectively ensuring the efficient completion of transport operations and providing full-process visualization assurance for transport. It is an important material foundation for realizing visualized real-time monitoring of multimodal transport.

[0059] In summary, the beneficial effects of the road transport trajectory visualization and intelligent monitoring method for multimodal transport provided by the embodiments of the present invention are as follows:

[0060] This method, by acquiring historical intermodal transport data from multimodal transport operators, can extract data on each segment of the operator's intermodal transport routes and determine specific transport parameters based on historical data. This provides fundamental reference information for subsequent analysis and judgment of real-time intermodal transport conditions. Based on the extraction of information from the target intermodal transport data, the method can display the route selection of the tracked orders on a visualized transport route map, and combine real-time location data for visualized location tracking and judgment of the actual transport status. The visualized display of transport routes and real-time locations allows for more intuitive and clear monitoring of the transport trajectory of target orders. Simultaneously, by combining historical data, reasonable predictions and judgments of the transport status can be made to ensure that orders are completed within the stipulated timeframe. This method not only clearly and effectively displays the real-time status of transport routes but also reasonably ensures monitoring of transport conditions, significantly strengthening transport assurance.

[0061] In the embodiments of this application, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information is called the information to be instructed. In the specific implementation process, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a relationship between the other information and the information to be instructed. It can also instruct only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. At the same time, common parts of various pieces of information can be identified and uniformly indicated to reduce the instruction overhead caused by individually indicating the same information.

[0062] Furthermore, the specific indication method can also be any existing indication method, such as, but not limited to, the above-mentioned indication methods and their various combinations. Specific details of various indication methods can be found in existing technologies, and will not be repeated here. As described above, for example, when multiple pieces of information of the same type need to be indicated, the indication methods for different pieces of information may differ. In specific implementation, the required indication method can be selected according to specific needs. This application embodiment does not limit the selected indication method; therefore, the indication methods involved in this application embodiment should be understood to cover various methods that enable the party to be indicated to obtain the information to be indicated.

[0063] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information messages sent separately, and the sending period and / or timing of these sub-information messages can be the same or different. The specific sending method is not limited in this application embodiment. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the sending device by sending configuration information to the receiving device.

[0064] "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This application does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or communication device. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or communication device. The type of memory can be any form of storage medium, and this application does not limit this.

[0065] The “protocol” mentioned in the embodiments of this application may refer to a protocol family in the field of communication, a standard protocol with a similar protocol family frame structure, or a related protocol applied to future communication systems. The embodiments of this application do not specifically limit this.

[0066] In the embodiments of this application, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.

[0067] In the description of the embodiments of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0068] It should be understood that the processor in the embodiments of this application can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0069] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0070] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0071] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0072] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0073] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0074] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0075] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0076] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0077] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0078] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0079] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0080] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for visualizing and intelligently monitoring road transport trajectories for multimodal transport, characterized in that, include: Collect historical multimodal transport data, extract route information for intermodal transport junctions, and form intermodal route feature data. Obtain target intermodal transport information, and combine the intermodal route feature data to perform route calibration, thereby forming target visualized route data; Real-time intermodal transport information is obtained, and transport monitoring and analysis are performed by combining the intermodal transport route feature data and the target visualized route data to form target real-time transport monitoring and visualization data.

2. The method for visualizing and intelligently monitoring road transport trajectories for multimodal transport according to claim 1, characterized in that, The process involves collecting historical multimodal transport data, extracting route information for intermodal transport junctions, and forming intermodal route feature data, including: Based on the historical multimodal transport data, routes are extracted based on the mode of transport to form visualized multimodal transport route data; Based on the historical multimodal transport data and combined with the visualized multimodal transport route data, transport efficiency characteristic analysis is performed to form multimodal transport route characteristic data. The multimodal transport visualization route data and the multimodal transport route feature data are combined to form the multimodal transport route feature data.

3. The method for visualizing and intelligently monitoring road transport trajectories for multimodal transport according to claim 2, characterized in that, The step of extracting routes based on transportation modes from the historical multimodal transport data to form visualized multimodal transport route data includes: Based on the historical multimodal transport data, extract all different transport route information corresponding to different transport vehicles to form transport vehicle route data corresponding to different transport vehicles. Obtain road network map data, and mark the routes on the road network map according to the route data of different modes of transport to form the multimodal transport visualization route data.

4. The method for visualizing and intelligently monitoring road transport trajectories for multimodal transport according to claim 3, characterized in that, The process involves analyzing transport efficiency characteristics based on the historical multimodal transport data and the visualized multimodal transport route data to generate multimodal transport route characteristic data, including: Based on the route data of different modes of transport, all transfer points for different modes of transport are determined. For different modes of transport, based on the historical multimodal transport data, the transfer efficiency data of the corresponding transfer points of different modes of transport are extracted and the transfer efficiency feature data of the transfer points corresponding to different modes of transport are analyzed to form the transfer efficiency feature data of the transfer points of different modes of transport. For the route data of different modes of transport, determine all the route segments corresponding to different modes of transport; For different modes of transport, based on the historical multimodal transport data, the transport efficiency data of the corresponding route segments of the different modes of transport are extracted and transport efficiency feature analysis is performed to form the route segment transport efficiency feature data corresponding to the different route segments of the different modes of transport. For different modes of transport, the corresponding transfer efficiency characteristic data of different transfer points and the transport efficiency characteristic data of different route segments are collected to form the corresponding transport route characteristic data; The multimodal transport route feature data is formed by aggregating the route feature data of different transport vehicles.

5. The method for visualizing and intelligently monitoring road transport trajectories for multimodal transport according to claim 4, characterized in that, For different modes of transport, based on the historical multimodal transport data, the transshipment efficiency data of the corresponding transshipment points for different modes of transport is extracted and transshipment efficiency feature analysis is performed to form transshipment efficiency feature data for transshipment points corresponding to different modes of transport, including: For different transshipment points of different modes of transport, based on the historical multimodal transport data, extract the data on the change of transshipment time of the transshipment points in multiple transshipment analysis periods; For different transshipment analysis periods, based on the data on the periodic transshipment duration changes at the transshipment points, the minimum transshipment rate per unit at the transshipment point is determined within the different transshipment unit analysis durations of the transshipment analysis period. and the unit transfer rate of the maximum transfer point Where n represents the number of the transfer point of different means of transport, m represents the number of the different transfer analysis cycles extracted, and k represents the number of the analysis duration of different transfer units determined within the transfer analysis cycle numbered n. For different transit points of different modes of transport, the minimum transit point unit transit rate is determined based on the corresponding transit unit analysis duration within different transit analysis periods. The minimum periodic transfer rate variation function of the transfer points for different modes of transport within the transfer analysis period is determined. ; For different transit points of different modes of transport, the maximum transit point unit transit rate is determined based on the analysis duration of different transit units within the corresponding different transit analysis periods. The maximum periodic transfer rate variation function of the transfer points for different modes of transport within the transfer analysis period was determined. ; For different transit points of different modes of transport, the minimum periodic transit rate change function is calculated based on the corresponding different transit analysis periods. Determine the minimum transshipment rate change function ,in, , This represents the function of obtaining different minimum periodic transport rate changes at the corresponding time points. The minimum value; For different transit points of different modes of transport, the maximum periodic transit rate variation function is calculated based on the corresponding different transit analysis periods. Determine the function of maximum transfer rate change. ,in, , This represents the function for obtaining the maximum periodic transport rate change at different points in time. The maximum value; For different transit points of different modes of transport, the set corresponds to the minimum transit rate variation function. and the maximum transshipment rate change function This generates corresponding transfer efficiency characteristic data for transfer points.

6. The method for visualizing and intelligently monitoring road transport trajectories for multimodal transport according to claim 5, characterized in that, For different modes of transport, based on the historical multimodal transport data, the transport efficiency data of the corresponding route segments of the different modes of transport are extracted for transport efficiency feature analysis, forming route segment transport efficiency feature data corresponding to the different route segments of the different modes of transport, including: For different transportation routes of different modes of transport, based on the historical multimodal transport data, extract the route segment periodic transport duration variation data within multiple transport analysis periods; For different transportation analysis periods, based on the periodic transportation time variation data of the route segments, the minimum route segment unit transportation rate among the different transportation unit analysis times within the transportation analysis period is determined. and the maximum unit transport rate of the line segment Where x represents the number of the different transportation vehicle route segments, y represents the number of the different transportation analysis cycles extracted, and z represents the number of the different transportation unit analysis duration determined within the transportation analysis cycle numbered x. For different transportation routes of different modes of transport, the minimum unit transportation rate of the route segment is determined based on the analysis time of different transportation units within the corresponding different transportation analysis periods. Determine the minimum periodic transport rate change function for different transport vehicle segments within the transport analysis period. ; For different transportation routes of different modes of transport, the maximum unit transportation rate of the route segment is determined based on the analysis duration of different transportation units within the corresponding different transportation analysis periods. The maximum periodic transport rate variation function for different transport vehicle segments within the transport analysis period is determined. ; For different transportation routes of different modes of transport, the minimum periodic transport rate change function is used according to the corresponding different transport analysis periods. Determine the minimum transport rate change function ,in, , This represents the function of obtaining different minimum periodic transport rate changes at the corresponding time points. The minimum value; For different transportation routes of different modes of transport, the maximum periodic transport rate change function is calculated based on the corresponding different transportation analysis periods. Determine the function of maximum transport rate change. ,in, , This represents the function of obtaining different maximum periodic transport rate changes at corresponding time points. The maximum value; For different transport routes of different transport vehicles, the set corresponds to the minimum transport rate change function. and the maximum transport rate change function This generates corresponding data on the transportation efficiency characteristics of the line segments.

7. The method for visualizing and intelligently monitoring road transport trajectories for multimodal transport according to claim 6, characterized in that, The process of acquiring target intermodal transport information and combining it with intermodal route feature data to perform route calibration and form target visualized route data includes: Based on the target intermodal transport information, the real-time location coordinates are determined; Based on the target intermodal transport information, the completed transport trajectory information that has already occurred is determined; Based on the real-time location coordinates, the location is calibrated in the road network map data; Based on the completed transportation trajectory information, the trajectory is delineated in the road network map data.

8. The method for visualizing and intelligently monitoring road transport trajectories for multimodal transport according to claim 7, characterized in that, The process of acquiring real-time intermodal transport information, combining the intermodal transport route characteristic data and the target visualized route data for transport monitoring and analysis, and forming target real-time transport monitoring and visualization data includes: Based on the real-time intermodal transport information, extract planned transport route data and intermodal transport efficiency data; Based on the planned transportation route data and combined with the target visualized route data, route correctness monitoring and analysis are performed to form route correctness analysis results; Based on the route correctness analysis results, combined with the intermodal transport efficiency data, the planned transport route data, the intermodal transport route characteristic data, and the target visualized route data, efficiency monitoring and analysis results are generated.

9. The method for visualizing and intelligently monitoring road transport trajectories for multimodal transport according to claim 8, characterized in that, The step of performing route correctness monitoring and analysis based on the planned transportation route data and the target visualized route data to form route correctness analysis results includes: Based on the planned transportation route data, the planned route for the target transportation is determined; The completed transportation trajectory information in the target visualized route data is compared with the planned route: If the completed transportation route delineated by the completed transportation trajectory information completely overlaps with the planned route, and the actual transportation vehicle used on each route segment is consistent with the planned transportation vehicle, then normal result information for the route transportation vehicle is formed. If the completed transportation route delineated by the completed transportation trajectory information completely overlaps with the planned route, but there are discrepancies between the actual transportation vehicles used on the route segment and the planned transportation vehicles, then abnormal transportation vehicle information is generated for the normal route. If the completed transportation route delineated by the completed transportation trajectory information does not completely overlap with the planned route, but the actual transportation vehicle used on each route segment is the same as the planned transportation vehicle, then the route deviation from the normal transportation vehicle result information is formed. If the completed transportation route delineated by the completed transportation trajectory information does not completely overlap with the planned route, and there are discrepancies between the actual transportation vehicles used on the route segment and the planned transportation vehicles, then an abnormal result information of route deviation from transportation vehicle is generated.

10. The method for visualizing and intelligently monitoring road transport trajectories for multimodal transport according to claim 9, characterized in that, The efficiency monitoring and analysis is performed based on the route correctness analysis results, combined with the intermodal transport efficiency data, the planned transport route data, the intermodal transport route characteristic data, and the target visualized route data, to form efficiency monitoring and analysis results, including: When the route correctness analysis outputs a normal result for the transportation vehicle on the route, the completed transportation time corresponding to the completed transportation route is determined based on the intermodal efficiency data. The current route segment has been transported for a certain amount of time. Net weight of transported goods and the total planned transportation completion time ; Based on the planned transport route data, the intermodal transport route characteristic data, and the target visualized route data, the transport efficiency characteristic data of the route segment, including all different route segments not yet traversed by the current route segment, are determined, and the corresponding minimum transfer rate change function is sequentially calibrated. and the function that did not experience the maximum translocation rate change Where u is the numbering of the transfer points for different means of transport according to the transport sequence of the transport plan; Based on the planned transportation route data, the intermodal route characteristic data, and the target visualized route data, the transfer point transfer efficiency characteristic data, including all transfer points of different modes of transport not yet passed through the current route segment, is determined, and the corresponding minimum transport rate change function is sequentially calibrated. and the function of no maximum transport rate change , where v is the numbering of different route segments according to the transportation sequence of the transportation plan; Based on the current transportation cycle time and the different minimum transshipment rate change functions mentioned above. The function that did not experience the maximum transfer rate change The function that did not result in a minimum transport rate change and the function that did not experience the maximum transport rate change Determine the longest estimated travel time for different transport segments of the route. Shortest estimated travel time for the line segment Longest transit time and the shortest transit time ,in, , , , , This indicates the current transportation cycle time point in the function where the maximum transportation rate change has not occurred. The corresponding maximum transport rate did not occur. This indicates the current point in the transportation cycle in the function where the minimum transportation rate change has not occurred. The corresponding minimum transport rate did not occur. The current transportation cycle time point is in the function where the maximum transshipment rate change has not occurred. The corresponding maximum transfer rate did not occur. The current transportation cycle time point is in the function where the minimum transshipment rate change has not occurred. The corresponding minimum transfer rate did not occur; Based on the longest transit time at the transit point corresponding to different modes of transportation. The shortest transit time at the aforementioned transfer point The longest estimated travel time for the transport segment corresponding to different route segments. The shortest estimated travel time for the aforementioned line segment The completed transportation time The current line segment has been transported for a certain amount of time. and the total time required to complete the planned transportation. The following monitoring and analysis were conducted: like This results in a situation where the plan is under strain. like This will lead to a successful outcome for the plan; like This results in a plan being postponed; in, This indicates the estimated longest total time. This indicates the estimated shortest total time. , U represents the total number of all unpassed route segments, including the current route segment, and V represents the total number of all unpassed transfer points. This indicates the longest estimated travel time for line segment numbered 1. This indicates the shortest estimated travel time for the line segment corresponding to line segment number 1.

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