Freight platform vehicle intelligent scheduling management method based on Internet of Things

Abstract

The invention relates to the technical field of freight scheduling management, in particular to a freight platform vehicle intelligent scheduling management method based on the Internet of Things. The method comprises the following steps: collecting vehicle-mounted terminal information and performing feature extraction, generating a vehicle comprehensive time sequence feature set, drawing a freight route according to the feature set, obtaining basic freight route data, reconstructing a real-time map by using the data, forming a freight map network, and performing uniform partitioning. Analyzing the intersection condition of regional vehicle tracks, generating intersection track data, predicting regional vehicle demands, simulating on-demand scheduling, generating related data, carrying out transport capacity resource comparison on a vehicle comprehensive time sequence feature set and the simulated scheduling data to identify transport capacity differences, designing a scheduling scheme based on the differences, and implementing intelligent scheduling. Therefore, optimal management of transport capacity is realized. According to the invention, a more efficient and more flexible freight platform vehicle intelligent scheduling management method is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of freight dispatch management, and in particular to a management method for intelligent dispatch of vehicles on a freight platform based on the Internet of Things. Background Art

[0002] Intelligent vehicle dispatching has become the key to improving freight efficiency and reducing operating costs. However, most freight platforms still rely on traditional dispatching methods and lack effective use of real-time data. This method not only fails to respond to changes in freight demand in a timely manner, but also leads to waste of resources and low dispatching efficiency, thus affecting the operation of the overall transportation chain. Especially during peak periods, the problem of unreasonable vehicle dispatching is particularly prominent, often leading to delivery delays and reduced customer satisfaction. In addition, the existing dispatching system often lacks real-time monitoring and analysis of vehicle operation status, resulting in the inability to accurately predict vehicle demand. This information lag makes it difficult for freight platforms to achieve refined management and unable to allocate resources according to actual needs. With the acceleration of urbanization, the volatility of freight demand has increased, and the traditional static dispatching method has become increasingly unsuitable. Summary of the invention

[0003] Based on this, it is necessary to provide a management method for intelligent scheduling of vehicles on a freight platform based on the Internet of Things to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a management method for intelligent dispatching of freight platform vehicles based on the Internet of Things includes the following steps: Step S1: collecting vehicle terminal information; extracting features from the vehicle terminal information to obtain vehicle feature information data; performing time series decomposition on the vehicle feature information data to generate a vehicle comprehensive time series feature set; Step S2: draw a freight route for the comprehensive time series feature set of the vehicle to obtain basic freight route data; perform real-time map reconstruction on the comprehensive time series feature set of the vehicle according to the basic freight route data to generate a freight map network; Step S3: evenly partitioning the freight map network to obtain a partitioned freight map network; performing regional vehicle trajectory intersection analysis on the vehicle comprehensive time series feature set based on the partitioned freight map network to generate intersection trajectory data; Step S4: predicting vehicle demand for the partitioned freight map network according to the intersection trajectory data and the vehicle comprehensive time series feature set to obtain regional vehicle demand data; performing on-demand scheduling simulation on the vehicle comprehensive time series feature set based on the regional vehicle demand data to generate simulated on-demand scheduling data; Step S5: Compare the vehicle comprehensive time series feature set and the simulated on-demand dispatching data with transport resources to obtain transport capacity difference data; design a dispatching plan for the simulated on-demand dispatching data based on the transport capacity difference data to generate a vehicle dispatching plan; Step S6: Intelligently dispatch the freight platform vehicles according to the vehicle dispatch plan to achieve a capacity optimization management method.

[0005] The present invention can obtain the real-time status and operation characteristics of the vehicle by collecting vehicle terminal information and performing feature extraction. The vehicle comprehensive time series feature set generated after time series decomposition provides rich basic data for subsequent data analysis, supports multi-dimensional information mining and analysis, and enhances the timeliness and accuracy of the data. The freight route drawing combines the basic freight route data to make the visualization of the transportation route more intuitive. The freight map network generated by real-time map reconstruction provides spatial information support for optimized scheduling and can effectively reflect the current traffic conditions and road usage. The freight map network after uniform partitioning provides a clear framework for regional vehicle trajectory intersection analysis. The generated intersection trajectory data can reveal the activity patterns of different vehicles in the region and support dynamic monitoring of vehicle flow. It has enhanced the sensitivity to regional freight demand. The vehicle demand forecast is based on the intersection trajectory data and the comprehensive time series feature set of the vehicle, which can accurately identify the fluctuations in transportation demand in different regions. The regional vehicle demand data formed helps the on-demand scheduling simulation, promotes the intelligence of the scheduling strategy, and optimizes resource allocation. The capacity resource comparison can clearly identify areas with insufficient and excess capacity by analyzing the comprehensive time series feature set of the vehicle and simulating the on-demand scheduling data. The generated capacity difference data provides a basis for the design of scheduling plans, ensuring the rational allocation and effective utilization of resources. Intelligent scheduling is implemented according to the vehicle scheduling plan, realizing the efficient management of freight platform vehicles, optimizing the capacity configuration, improving the overall scheduling efficiency, and enhancing the responsiveness and service quality of the freight platform.

[0006] Preferably, step S1 comprises the following steps: Step S11: real-time acquisition of vehicle location information to obtain original location data; monitoring and acquisition of vehicle operation status to obtain original status data; dynamic acquisition of vehicle load status to obtain original load data; Step S12: integrating the original position data, the original state data and the original load data to obtain vehicle terminal information; performing unified format conversion processing on the vehicle terminal information to obtain standard format information; Step S13: extracting features from the standard format information to obtain vehicle-borne feature information data; performing periodic analysis on the vehicle-borne feature information data to obtain periodic data; Step S14: performing change trend statistics on the periodic data to generate a periodic change trend; performing time series reorganization on the periodic data and the periodic change trend to obtain a comprehensive vehicle time series feature set.

[0007] The present invention collects vehicle location information, operating status and load conditions in real time, and the obtained raw data provides rich basic information for subsequent analysis, ensuring the accuracy and timeliness of the data. The dynamic monitoring capability improves the real-time control of the vehicle status and enhances the transparency of the transportation process. Data integration and unified format conversion enable data from different sources to be effectively combined to form vehicle-mounted terminal information. The generation of standard format information improves the standardization and consistency of data processing, simplifies the complexity of subsequent data analysis, and ensures the availability of data in all links. Feature extraction and periodic analysis provide an important basis for in-depth understanding of vehicle operation characteristics. The generated on-board feature information data and periodic data can reflect the performance of the vehicle under different conditions and support accurate evaluation and optimization of vehicle performance. Change trend statistics and time series reorganization can reveal the laws and potential problems of vehicle operation. The generated periodic change trend provides a scientific basis for dynamic scheduling decisions, promotes comprehensive monitoring of vehicle operation status, and realizes the sensitive capture and prediction capabilities of capacity changes.

[0008] Preferably, step S2 comprises the following steps: Step S21: extracting path points from the comprehensive time series feature set of the vehicle to obtain path point data; performing frequency statistics on the path point data to generate path frequency data; Step S22: Calculate the path density of the path point data based on the path frequency data to generate path density data; draw a freight route for the path point data according to the path density data to obtain basic freight route data; Step S23: gridding the basic freight route data to obtain a road network grid; identifying road network nodes on the road network grid to generate road network node data; Step S24: performing connectivity analysis on the road network node data to obtain connectivity data; performing road network weight calculation on the connectivity data based on the preset road network weight assignment to generate road network weight data; Step S25: constructing a topological relationship for the basic freight route data according to the road network weight data to obtain topological structure data; performing network integration on the topological structure data to generate a freight map network.

[0009] The present invention extracts path points from the comprehensive time series feature set of vehicles, and the generated path point data lays a foundation for subsequent path analysis. Frequency statistics provide a quantitative analysis of the use of each path point. The path frequency data reflects the use frequency of different paths. Path density calculation is performed based on the path frequency data, and the generated path density data reveals the transportation density of different areas. The basic freight route data obtained by freight route drawing provides an intuitive basis for effective transportation planning. The basic freight route data is gridded, and the generated road network grid provides a structured basis for subsequent analysis. Road network node identification can clarify the key nodes in the transportation network. Connectivity analysis can reveal the connection between different nodes in the road network. The generated connectivity data provides a basis for the overall performance evaluation of the transportation network. The road network weight calculation is based on the preset weight assignment. The road network weight data is combined with the basic freight route data. The constructed topological relationship lays a foundation for the structural optimization of the transportation network. The generation of topological structure data provides a clear framework for the integration of freight map networks, which is conducive to achieving efficient transportation management and scheduling decisions.

[0010] Preferably, step S3 comprises the following steps: Step S31: performing boundary identification on the freight map network to obtain the map network boundary; calculating the regional area of ​​the freight map network according to the map network boundary to generate the global map network area; Step S32: dividing the freight map network into four equal regions based on the area of ​​the global map network to obtain a partitioned freight map network; Step S33: mapping the comprehensive time series feature set of vehicles to each region according to the partitioned freight map network to obtain the partitioned vehicle time series feature set; extracting vehicle trajectories from the partitioned vehicle time series feature set to generate the vehicle trajectories belonging to the partition; Step S34: performing intersection point identification on the vehicle trajectories of the belonging partition to obtain vehicle intersection point data; performing intersection trajectory statistics on the vehicle trajectories of the belonging partition based on the vehicle intersection point data to generate intersection trajectory data.

[0011] The present invention identifies the boundaries of the freight map network, and the generated map network boundaries provide clear boundaries for subsequent regional analysis. The calculation of the global map network area lays the foundation for understanding the scale and characteristics of the entire transportation network. The global map network area is divided into four equal regions, and the partitioned freight map network obtained provides a clear structure for the optimized management of different transportation areas, ensuring the balance and coordination between the regions. The partitioned freight map network is used to map the comprehensive time series feature set of vehicles to each region. The generated partitioned vehicle time series feature set can accurately reflect the operating status of vehicles in each region, and the intersection points of the vehicle trajectories belonging to the partition are identified. The obtained vehicle intersection data reveals the interaction between different vehicles in the same area. The intersection trajectory statistics based on the vehicle intersection data provide a quantitative basis for analyzing the vehicle flow pattern, which promotes the insight into and improvement of transportation efficiency in the region.

[0012] Preferably, step S4 comprises the following steps: Step S41: performing attribution area aggregation calculation on the intersection trajectory data to obtain area density data; dividing the vehicle comprehensive time series feature set into time periods to generate time-divided vehicle feature data; Step S42: performing feature correlation analysis on the regional density data and the time-sharing vehicle feature data to obtain track density feature data; performing vehicle demand forecasting on the partitioned freight map network according to the track density feature data to obtain regional vehicle demand data; Step S43: performing resource allocation calculation on the regional vehicle demand data to obtain an on-demand resource allocation plan; Step S44: Perform on-demand scheduling simulation on the comprehensive time series feature set of the vehicle based on the on-demand resource allocation scheme to generate simulated on-demand scheduling data.

[0013] The invention performs regional aggregation calculation on the intersecting trajectory data, and the generated regional density data effectively reflects the concentration degree of vehicle activities in each area. The time-sharing vehicle characteristic data generated by time period division provides a detailed perspective for the vehicle operation status in different time periods. The characteristic correlation analysis between the regional density data and the time-sharing vehicle characteristic data, the trajectory density characteristic data obtained reveals the flow trend of vehicles in different time periods and regions, and provides a quantitative basis for the dynamic change of transportation demand. The vehicle demand forecast of the partitioned freight map network based on the trajectory density characteristic data, the resource allocation calculation of the regional vehicle demand data, and the generated on-demand resource allocation scheme provide scientific support for ensuring the efficient use of resources, can reasonably configure vehicle resources according to actual needs, optimize transportation efficiency and service quality, and perform on-demand scheduling simulation on the comprehensive time series feature set of vehicles based on the on-demand resource allocation scheme. The generated simulated on-demand scheduling data provides a reference for actual scheduling decisions, can realize dynamic adjustment and optimization of vehicle operation, and improve the flexibility and response speed of overall logistics operations.

[0014] Preferably, step S42 includes the following steps: Step S421: performing correlation analysis on the regional density data and the time-sharing vehicle characteristic data to obtain correlation data; performing feature combination processing on the correlation data to generate combined feature data; Step S422: sorting the combined feature data by relevance importance based on the relevance data to obtain sorted feature data; screening and integrating the sorted feature data to generate trajectory density feature data; Step S423: performing regional freight supply deduction on the trajectory density feature data to obtain predicted regional freight supply data; performing supply digestion calculation on the regional freight map network based on the predicted regional freight supply data to generate digestion supply and demand data; Step S424: Perform vehicle demand statistics on the regional freight map network based on the digested supply and demand data to obtain regional vehicle demand data.

[0015] The present invention performs correlation analysis on regional density data and time-sharing vehicle feature data, and the generated correlation data reveals the relationship between vehicle demand and regional characteristics. The combined feature data obtained after feature combination processing provides a multi-dimensional information perspective for subsequent analysis. The combined feature data is sorted by correlation importance based on the correlation data, and the obtained sorted feature data provides a basis for identifying key factors. The track density feature data after screening and integration further optimizes the feature set, ensuring the pertinence and effectiveness of subsequent analysis. The track density feature data is deduced for regional freight supply, and the generated predicted regional freight supply data provides an important basis for supply and demand balance. Based on these data, the supply digestion calculation is performed on the partitioned freight map network, and the generated digested supply and demand data provides a quantitative reference for resource allocation and scheduling decisions. According to the digested supply and demand data, the vehicle demand statistics of the partitioned freight map network are performed, and the obtained regional vehicle demand data provides a real-time basis for dynamic scheduling, which can timely reflect changes in freight demand, support the optimization and adjustment of scheduling plans, and improve the flexibility and responsiveness of logistics management.

[0016] Preferably, step S423 includes the following steps: Perform regional division calculation on the trajectory density feature data to obtain regional distribution data; Based on the trajectory density feature data, the trajectory density change trend of the regional distribution data is analyzed to generate the trajectory density change trend; Map the supply quantity to the trajectory density change trend to obtain the supply quantity change trend; The supply quantity is estimated based on the supply quantity change trend according to the trajectory density feature data, and the freight supply data of the forecast area is generated; Based on the forecasted regional freight supply data, the regional freight map network is matched with the road network mapping to obtain the road network matching data; Carry out freight supply and demand balance calculation on the road network matching data to obtain freight supply and demand balance data; Based on the freight supply and demand balance data, the supply digestion demand forecast is carried out on the freight supply data in the forecast area to generate digestion supply and demand data.

[0017] The present invention divides the trajectory density feature data into regions and calculates the generated regional distribution data, which provides a basis for analyzing the transportation demand of different regions and can clearly display the vehicle activities in each region. The trajectory density change trend is analyzed on the regional distribution data based on the trajectory density feature data, and the generated trajectory density change trend reveals the dynamic changes of vehicle activities, providing important information for understanding the evolution of transportation modes. The supply volume is mapped on the trajectory density change trend, and the obtained supply volume change trend provides an intuitive basis for predicting the future freight supply level. The road network mapping and matching of the partitioned freight map network is performed based on the predicted regional freight supply data, and the generated road network matching data provides support for the optimization and adjustment of the actual transportation path. The freight supply and demand balance calculation is performed on the road network matching data, and the obtained freight supply and demand balance data provides a quantitative basis for subsequent scheduling decisions. The supply digestion demand forecast is performed on the predicted regional freight supply data based on the freight supply and demand balance data, and the generated digestion supply and demand data provides an important reference for dynamic scheduling and real-time decision-making, thereby improving the flexibility and responsiveness of freight management.

[0018] Preferably, step S5 comprises the following steps: Step S51: Calculate the transport capacity of the comprehensive time series feature set of the vehicle to obtain the existing transport capacity data; calculate the dispatch capacity of the simulated on-demand dispatch data to obtain the dispatch capacity data; Step S52: Performing spatiotemporal distribution mapping on the existing transport capacity data to obtain existing transport capacity distribution data; performing regional dispatch demand analysis on the dispatch transport capacity data to obtain regional demand data; Step S53: Calculate the matching degree of the existing capacity distribution data and the regional demand data to obtain matching degree data; compare the capacity difference of the vehicle comprehensive time series feature set and the simulated on-demand scheduling data based on the matching degree data to obtain capacity difference data; Step S54: scheduling route planning for the simulated on-demand scheduling data according to the capacity difference data to obtain route planning data; performing conflict detection on the route planning data to obtain scheduling conflict data; Step S55: adjusting the route planning data based on the scheduling conflict data to generate an adjusted scheduling route; formulating a scheduling plan for the adjusted scheduling route to generate a vehicle scheduling plan.

[0019] The present invention calculates the capacity of the comprehensive time series feature set of vehicles, and the generated existing capacity data provides a basis for evaluating the current transportation capacity. The scheduling capacity calculation of the simulated on-demand scheduling data reveals the actual capacity demand in the scheduling process. The spatiotemporal distribution mapping of the existing capacity data generates the existing capacity distribution data, which can clearly show the capacity distribution of different regions. The regional scheduling demand analysis of the scheduling capacity data provides a basis for identifying key scheduling areas. The matching degree of the existing capacity distribution data and the regional demand data is calculated, and the obtained matching degree data provides a quantitative basis for evaluating the rationality of resource allocation. Based on the matching degree data, the vehicle comprehensive time series feature set and the simulated on-demand scheduling data are compared for capacity differences. The simulated on-demand scheduling data is scheduled according to the capacity difference data. The route planning data obtained provides a scientific basis for optimizing the vehicle driving path. The conflict detection can identify potential scheduling problems. The route planning data is adjusted based on the scheduling conflict data. The generated adjusted scheduling route provides a guarantee for ensuring the smoothness and efficiency of the transportation process. The formulation of the scheduling plan is combined with the adjustment of the scheduling route. The vehicle scheduling plan finally formed provides a systematic solution for realizing capacity optimization and efficient management.

[0020] Preferably, step S53 includes the following steps: Perform data alignment on existing capacity distribution data and regional demand data to obtain alignment basic data; Performing regional matching calculation on the aligned basic data to obtain regional matching data; Performing time matching calculation on the alignment basic data based on the regional matching data to obtain time matching data; Perform capacity matching calculation on the aligned basic data based on the time matching data to generate capacity matching data; Calculate the matching degree of the alignment basic data according to the capacity matching data to generate matching degree data; Perform threshold segmentation on the matching degree data to obtain threshold classification data; Based on the threshold classification data, the capacity difference is identified for the comprehensive time series feature set of vehicles and the simulated on-demand dispatch data to obtain the difference degree data; The difference degree data is integrated to generate capacity difference data.

[0021] The present invention performs data alignment processing on the existing capacity distribution data and regional demand data, and the generated alignment basic data provides a unified reference framework for subsequent analysis, ensuring the consistency and comparability between different data sources. The regional matching calculation is performed on the aligned basic data, and the obtained regional matching data provides a basis for identifying the effectiveness of resource allocation in each region, supporting the dynamic analysis of capacity and demand in a specific region. The time matching calculation is performed based on the regional matching data, and the generated time matching data provides support for analyzing the time characteristics of capacity and demand, ensuring the timely capture of transportation demand fluctuations. The capacity matching calculation is performed on the aligned basic data based on the time matching data, and the generated capacity matching data is used to evaluate the efficiency of capacity utilization in each region. It provides a basis for identifying areas with insufficient or excess capacity, calculates the matching degree of the aligned basic data based on the capacity matching data, and the generated matching degree data provides a quantitative basis for evaluating the matching degree between capacity and demand, supports the optimization and adjustment of scheduling strategies, and performs threshold segmentation on the matching degree data. The threshold classification data obtained provides a clear standard for the subsequent identification of capacity differences. Based on the threshold classification data, the capacity difference is identified for the vehicle comprehensive time series feature set and simulated on-demand scheduling data, and the difference degree data is integrated. The generated capacity difference data provides a basis for comprehensively evaluating the relationship between current transportation capacity and demand, promotes the accuracy and scientificity of scheduling decisions, and improves the efficiency and flexibility of overall logistics management.

[0022] Preferably, step S55 includes the following steps: Identify the conflicting sections of the dispatch conflict data to obtain the conflicting section data; retrieve the alternative routes of the route planning data based on the conflicting section data to generate the alternative route data; Allocate time windows for the alternative route data to obtain time allocation data; reorganize the route planning data based on the time allocation data to generate an adjusted scheduling route; Carry out vehicle allocation planning for the adjusted dispatch route and obtain vehicle allocation data; A scheduling plan is formulated for the vehicle allocation data based on the time allocation data to generate a vehicle scheduling plan.

[0023] The present invention identifies conflicting sections of dispatch conflict data, and the generated conflicting section data provides a basis for identifying potential transportation obstacles, can clearly define specific areas that affect vehicle travel, and support subsequent route optimization and adjustment. According to the conflicting section data, alternative routes are retrieved for route planning data, and the generated alternative route data provides diversified options for resolving conflicts, ensures flexible response capabilities in emergencies, and reduces the risk of transportation delays. Time windows are allocated to alternative route data, and the obtained time allocation data provides a scientific basis for the driving time of each alternative route, ensures the rationality and feasibility of the transportation plan, and promotes the improvement of overall dispatch efficiency. Route planning data is reorganized based on the time allocation data, and the generated adjusted dispatch route provides clear guidance for optimizing vehicle driving paths, can effectively reduce driving time and cost, and improve transportation efficiency. Vehicle allocation planning is performed on the adjusted dispatch route, and the obtained vehicle allocation data provides a basis for ensuring the rational use of resources, can reasonably allocate vehicle resources according to actual needs, and improves the flexibility and effectiveness of services. A dispatch plan is formulated for vehicle allocation data based on the time allocation data, and the generated vehicle dispatch plan provides a systematic solution for realizing capacity optimization and efficient management, and enhances the adaptability and response speed of the logistics platform in a dynamic environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the steps of a management method for intelligent dispatching of vehicles on a freight platform based on the Internet of Things; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0025] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 3 , a management method for intelligent dispatching of freight platform vehicles based on the Internet of Things, comprising the following steps: Step S1: collecting vehicle terminal information; extracting features from the vehicle terminal information to obtain vehicle feature information data; performing time series decomposition on the vehicle feature information data to generate a vehicle comprehensive time series feature set; Step S2: draw a freight route for the comprehensive time series feature set of the vehicle to obtain basic freight route data; perform real-time map reconstruction on the comprehensive time series feature set of the vehicle according to the basic freight route data to generate a freight map network; Step S3: evenly partitioning the freight map network to obtain a partitioned freight map network; performing regional vehicle trajectory intersection analysis on the vehicle comprehensive time series feature set based on the partitioned freight map network to generate intersection trajectory data; Step S4: predicting vehicle demand for the partitioned freight map network according to the intersection trajectory data and the vehicle comprehensive time series feature set to obtain regional vehicle demand data; performing on-demand scheduling simulation on the vehicle comprehensive time series feature set based on the regional vehicle demand data to generate simulated on-demand scheduling data; Step S5: Compare the vehicle comprehensive time series feature set and the simulated on-demand dispatching data with transport resources to obtain transport capacity difference data; design a dispatching plan for the simulated on-demand dispatching data based on the transport capacity difference data to generate a vehicle dispatching plan; Step S6: Intelligently dispatch the freight platform vehicles according to the vehicle dispatch plan to achieve a capacity optimization management method.

[0029] The present invention can obtain the real-time status and operation characteristics of the vehicle by collecting vehicle terminal information and performing feature extraction. The vehicle comprehensive time series feature set generated after time series decomposition provides rich basic data for subsequent data analysis, supports multi-dimensional information mining and analysis, and enhances the timeliness and accuracy of the data. The freight route drawing combines the basic freight route data to make the visualization of the transportation route more intuitive. The freight map network generated by real-time map reconstruction provides spatial information support for optimized scheduling and can effectively reflect the current traffic conditions and road usage. The freight map network after uniform partitioning provides a clear framework for regional vehicle trajectory intersection analysis. The generated intersection trajectory data can reveal the activity patterns of different vehicles in the region and support dynamic monitoring of vehicle flow. It has enhanced the sensitivity to regional freight demand. The vehicle demand forecast is based on the intersection trajectory data and the comprehensive time series feature set of the vehicle, which can accurately identify the fluctuations in transportation demand in different regions. The regional vehicle demand data formed helps the on-demand scheduling simulation, promotes the intelligence of the scheduling strategy, and optimizes resource allocation. The capacity resource comparison can clearly identify areas with insufficient and excess capacity by analyzing the comprehensive time series feature set of the vehicle and simulating the on-demand scheduling data. The generated capacity difference data provides a basis for the design of scheduling plans, ensuring the rational allocation and effective utilization of resources. Intelligent scheduling is implemented according to the vehicle scheduling plan, realizing the efficient management of freight platform vehicles, optimizing the capacity configuration, improving the overall scheduling efficiency, and enhancing the responsiveness and service quality of the freight platform.

[0030] In the embodiment of the present invention, refer to Figure 1 , which is a schematic flow chart of the steps of a management method for intelligent dispatching of freight platform vehicles based on the Internet of Things of the present invention. In this example, the management method for intelligent dispatching of freight platform vehicles based on the Internet of Things includes the following steps: Step S1: collecting vehicle terminal information; extracting features from the vehicle terminal information to obtain vehicle feature information data; performing time series decomposition on the vehicle feature information data to generate a vehicle comprehensive time series feature set; In this embodiment, a high-precision GPS module and a variety of sensors are installed in the vehicle terminal to collect the vehicle's location information, speed, acceleration, fuel consumption and other data in real time. The data collection frequency is set to once per second, and the data is transmitted to the central processing system via a wireless network (such as 4G / 5G). The central processing system receives and stores the original data, and uses data preprocessing technology to clean and denoise the data to ensure the accuracy of the information. Then, feature extraction is performed on the cleaned data, and key feature information is extracted from it using machine learning algorithms (such as decision trees and random forests). Finally, the extracted feature information is time-series decomposed to generate a comprehensive vehicle time-series feature set, which includes the dynamic state data of the vehicle in different time periods.

[0031] Step S2: draw a freight route for the comprehensive time series feature set of the vehicle to obtain basic freight route data; perform real-time map reconstruction on the comprehensive time series feature set of the vehicle according to the basic freight route data to generate a freight map network; In this embodiment, the location information in the comprehensive time series feature set of the vehicle is used to draw the freight route using GIS (geographic information system) software. First, each location point passed by the vehicle is connected to form a path line, and then the length and travel time of each route are calculated based on the path line to generate basic freight route data. Subsequently, based on these basic data, the real-time map API (application programming interface) is used to monitor the driving status of the vehicle in real time, reconstruct the real-time map, and generate a freight map network to ensure that the map network reflects the latest traffic conditions and route information, which is convenient for subsequent scheduling decisions.

[0032] Step S3: evenly partitioning the freight map network to obtain a partitioned freight map network; performing regional vehicle trajectory intersection analysis on the vehicle comprehensive time series feature set based on the partitioned freight map network to generate intersection trajectory data; In this embodiment, the generated freight map network is evenly partitioned, and a spatial segmentation algorithm (such as Voronoi diagram or K-means clustering) is used to divide the map area into several even partitions to ensure that the transportation demand and resource allocation of each partition are balanced. Subsequently, based on the partitioned map network, the vehicle comprehensive time series feature set is used to perform regional vehicle trajectory intersection analysis, and the time series data matching algorithm is used to identify the intersection of different vehicle trajectories, and generate intersection trajectory data to ensure comprehensive monitoring and recording of vehicle activities in each partition.

[0033] Step S4: predicting vehicle demand for the partitioned freight map network according to the intersection trajectory data and the vehicle comprehensive time series feature set to obtain regional vehicle demand data; performing on-demand scheduling simulation on the vehicle comprehensive time series feature set based on the regional vehicle demand data to generate simulated on-demand scheduling data; In this embodiment, based on the intersection trajectory data and the comprehensive time series feature set of vehicles, a time series analysis method (such as the ARIMA model) is used to predict the vehicle demand of the partitioned freight map network. First, seasonality and trend analysis is performed on the historical data to generate regional vehicle demand data. Then, based on the regional vehicle demand data, simulation tools (such as AnyLogic) are used to perform on-demand scheduling simulation to generate simulated on-demand scheduling data, thereby ensuring that vehicle scheduling can adapt to changes in demand in different time periods and regions, thereby improving the flexibility and accuracy of scheduling.

[0034] Step S5: Compare the vehicle comprehensive time series feature set and the simulated on-demand dispatching data with transport resources to obtain transport capacity difference data; design a dispatching plan for the simulated on-demand dispatching data based on the transport capacity difference data to generate a vehicle dispatching plan; In this embodiment, a capacity resource comparison is performed on the comprehensive vehicle time series feature set and the simulated on-demand scheduling data. First, a capacity resource database is established, which includes information such as the number, type, and load of vehicles. Then, a comparison algorithm (such as a difference analysis method) is used to calculate the difference between the existing capacity and demand, and capacity difference data is generated. Based on the capacity difference data, an optimization algorithm (such as a genetic algorithm) is used to design a scheduling plan to ensure that the generated vehicle scheduling plan can effectively match the current capacity demand and resource allocation, thereby improving the overall transportation efficiency.

[0035] Step S6: Intelligently dispatch the freight platform vehicles according to the vehicle dispatch plan to achieve a capacity optimization management method.

[0036] In this embodiment, the dispatch management system is used to intelligently dispatch the freight platform vehicles according to the generated vehicle dispatch plan. First, the dispatch plan is input into the dispatch system, and the system automatically assigns vehicles to specific transportation tasks through algorithms (such as the shortest path algorithm) to ensure that the vehicles can complete the freight tasks efficiently and timely. Then, the vehicle status during transportation is monitored in real time, and the Internet of Things (IoT) technology is used for data feedback and adjustment to ensure that the dispatch plan can be dynamically optimized according to the real-time situation, ultimately realizing capacity optimization management and improving the quality and efficiency of transportation services.

[0037] Preferably, step S1 comprises the following steps: Step S11: real-time acquisition of vehicle location information to obtain original location data; monitoring and acquisition of vehicle operation status to obtain original status data; dynamic acquisition of vehicle load status to obtain original load data; Step S12: integrating the original position data, the original state data and the original load data to obtain vehicle terminal information; performing unified format conversion processing on the vehicle terminal information to obtain standard format information; Step S13: extracting features from the standard format information to obtain vehicle-borne feature information data; performing periodic analysis on the vehicle-borne feature information data to obtain periodic data; Step S14: performing change trend statistics on the periodic data to generate a periodic change trend; performing time series reorganization on the periodic data and the periodic change trend to obtain a comprehensive vehicle time series feature set.

[0038] In this embodiment, the real-time position of the vehicle is collected through the vehicle-mounted GPS module (Global Positioning System), and the geographical coordinates of each vehicle are uploaded to the Internet of Things platform at a predetermined frequency to generate the original position data; at the same time, the OBD (On-Board Diagnostic System) is used to In addition, the vehicle's current load is measured by combining on-board sensors, and the cargo weight is measured and uploaded by pressure sensors to form the original load data. In this process, the data acquisition module in the on-board acquisition system continuously obtains and summarizes all monitoring information, and uses the cellular network to upload data in real time to ensure the timeliness of the data. The central processing system of the Internet of Things platform integrates the received vehicle position original data, status original data and load original data, and distributes them to the corresponding vehicle information database according to the vehicle number on the platform side through the data synchronization system to complete the aggregation of the on-board terminal information. In this step, the data integration module is used to summarize all information from multiple sources according to the label structure of location, status and load. At the same time, the data cleaning module is used to standardize the fields and data types of each type of data, and the data format is converted into the standard format information of the system. For example, the location data is unified into the latitude and longitude form, the status data is unified into integer or Boolean type, and the load data is converted into kilograms (kg). The Internet of Things platform uses feature extraction algorithms to standardize the standard format. The information is parsed and the main feature information of the vehicle is extracted from the data. The position feature extraction calculates the average speed of the vehicle through the relative change rate of the position coordinates. The state feature extraction uses the engine power and fuel consumption information provided by OBD to comprehensively calculate the fuel economy of the vehicle. The load feature extraction calculates the stability of the load through real-time load changes. The feature extraction system summarizes the extracted information into vehicle feature information data and performs periodic analysis based on this information. The platform's periodic analysis module collects statistics on vehicle driving distance, state change frequency, load fluctuation amplitude and other information at a fixed period (such as every 24 hours), so as to The periodic data is obtained. The periodic data consists of various statistical indicators of the vehicle so that it can be provided to the dispatching system for use. Statistical analysis is performed based on the periodic data to identify the changing trends of the vehicle in multiple periods. Moving average and regression analysis are performed on periodic data such as driving distance, fuel consumption, and load to obtain the periodic change trend. The generated periodic change trend is used to identify the long-term stability and reliability of vehicle operation. Subsequently, the timing reorganization module arranges the periodic data and the periodic change trend in time series, and reorganizes the time points of the periodic data into a continuous time series based on the time axis of each vehicle, thereby obtaining a comprehensive time series feature set for the vehicle.

[0039] Preferably, step S2 comprises the following steps: Step S21: extracting path points from the comprehensive time series feature set of the vehicle to obtain path point data; performing frequency statistics on the path point data to generate path frequency data; Step S22: Calculate the path density of the path point data based on the path frequency data to generate path density data; draw a freight route for the path point data according to the path density data to obtain basic freight route data; Step S23: gridding the basic freight route data to obtain a road network grid; identifying road network nodes on the road network grid to generate road network node data; Step S24: performing connectivity analysis on the road network node data to obtain connectivity data; performing road network weight calculation on the connectivity data based on the preset road network weight assignment to generate road network weight data; Step S25: constructing a topological relationship for the basic freight route data according to the road network weight data to obtain topological structure data; performing network integration on the topological structure data to generate a freight map network.

[0040] As an example of the present invention, see Figure 2 In this example, step S2 includes: Step S21: extracting path points from the comprehensive time series feature set of the vehicle to obtain path point data; performing frequency statistics on the path point data to generate path frequency data; In this embodiment, based on the comprehensive time series feature set of the vehicle, the system uses a path recognition algorithm to extract the main path points formed by the vehicle during driving one by one, and marks the geographic coordinates of each path point through the positioning module to obtain a path point data set containing information such as longitude and latitude; after the path point extraction is completed, the platform's frequency statistics module analyzes the path point data, identifies the frequency of appearance of the vehicle at different path points, calculates the number of times each path point is passed, and generates path frequency data. The frequency data is composed of the coordinates of the path points and the corresponding frequency values, providing data support for subsequent path optimization and scheduling planning.

[0041] Step S22: Calculate the path density of the path point data based on the path frequency data to generate path density data; draw a freight route for the path point data according to the path density data to obtain basic freight route data; In this embodiment, the platform uses a path density calculation algorithm to calculate the density of the frequency information in the path point data in the area, and generates path density data by dividing the area into equally spaced grids and treating the path points with high frequency as dense areas. The path density data contains the density value of each grid and is used to determine the high-frequency routes of freight vehicles. Subsequently, based on the path density data, the platform draws out the basic freight routes, and connects the high-density path points to mark the most commonly used freight routes. The basic freight route data is a complete linear structure, which includes information such as the sorting of the path points, the path direction, and the path density, providing a stable freight route network structure to facilitate subsequent route optimization and path planning.

[0042] Step S23: gridding the basic freight route data to obtain a road network grid; identifying road network nodes on the road network grid to generate road network node data; In this embodiment, the basic freight route data is grid-segmented by a gridding algorithm, and the coverage area of ​​the freight route is decomposed into interconnected small grid units to generate road network grid data. The data structures the spatial distribution of the entire freight route, so that each grid represents a corresponding geographical area and contains route point data within the grid; after the gridding process, the intersection points or key sections between the grids are further identified by the road network node identification module, and these intersection points are marked as road network nodes. Each road network node data contains the coordinates of the node, the number of the adjacent grid and the route information passed, providing basic data for subsequent road network analysis.

[0043] Step S24: performing connectivity analysis on the road network node data to obtain connectivity data; performing road network weight calculation on the connectivity data based on the preset road network weight assignment to generate road network weight data; In this embodiment, a connectivity analysis algorithm is applied to the road network node data to analyze the connection relationship between each road network node to determine the accessibility of the path between different grids, and the information such as the starting point, end point, and path length of the accessible path is converted into connectivity data; after the connectivity analysis is completed, a weight value is assigned to the connectivity data through a weight calculation module, and combined with a preset road network weight model, different weights are assigned to each path considering factors such as road length, traffic flow, and path stability to generate road network weight data. Each path weight data contains information such as the connection nodes of the path, the path weight value, etc., so that the path weight data can guide the vehicle to give priority to high-weight paths in path selection.

[0044] Step S25: constructing a topological relationship for the basic freight route data according to the road network weight data to obtain topological structure data; performing network integration on the topological structure data to generate a freight map network.

[0045] In this embodiment, a topological relationship construction algorithm is used to comprehensively process the basic freight route data and the road network weight data, and topological structure data is constructed based on the connection relationship between nodes and paths. The topological structure data describes the overall network relationship of the freight route through an ordered combination of nodes, paths and weights, so that the topological information of each node forms a close structured connection with its adjacent nodes and paths; after completing the topological structure data, the system's network integration module integrates the data and merges the topological structure data existing in all freight route networks into a complete freight map network. The freight map network is a multi-level structure, including the relationship between nodes, paths and weights, and provides complete freight route structure information.

[0046] Preferably, step S3 comprises the following steps: Step S31: performing boundary identification on the freight map network to obtain the map network boundary; calculating the regional area of ​​the freight map network according to the map network boundary to generate the global map network area; Step S32: dividing the freight map network into four equal regions based on the area of ​​the global map network to obtain a partitioned freight map network; Step S33: mapping the comprehensive time series feature set of vehicles to each region according to the partitioned freight map network to obtain the partitioned vehicle time series feature set; extracting vehicle trajectories from the partitioned vehicle time series feature set to generate the vehicle trajectories belonging to the partition; Step S34: performing intersection point identification on the vehicle trajectories of the belonging partition to obtain vehicle intersection point data; performing intersection trajectory statistics on the vehicle trajectories of the belonging partition based on the vehicle intersection point data to generate intersection trajectory data.

[0047] As an example of the present invention, see Figure 3 In this example, step S3 includes: Step S31: performing boundary identification on the freight map network to obtain the map network boundary; calculating the regional area of ​​the freight map network according to the map network boundary to generate the global map network area; In this embodiment, a boundary recognition algorithm is used to scan and identify the boundary area of ​​the freight map network. The outer boundaries of the nodes and path ranges of the map network are located, and a complete boundary line is calculated point by point to obtain the map network boundary data. After the boundary recognition is completed, the system uses an area calculation tool based on the boundary data, and uses the polygon area formula to sum up the area of ​​each segment to obtain the global map network area. In the area area calculation, the system multiplies the area of ​​each boundary path and adjacent nodes to ensure the data accuracy of the global map network area. The global map network area data includes area values ​​and boundary coordinate information.

[0048] Step S32: dividing the freight map network into four equal regions based on the area of ​​the global map network to obtain a partitioned freight map network; In this embodiment, the freight map network is divided into four equal parts based on the area of ​​the global map network through the map network segmentation tool. To achieve the four-partition effect, the system uses a regional segmentation algorithm to set the center point of the global map network as the starting point, and divides the global map into quadrants in turn. The boundary paths and nodes in each quadrant are assigned to the corresponding areas to obtain a partitioned freight map network. The partitioned freight map network contains the boundary, node and path information of each partition, and the partition structure ensures the reasonable layout of freight routes.

[0049] Step S33: mapping the comprehensive time series feature set of vehicles to each region according to the partitioned freight map network to obtain the partitioned vehicle time series feature set; extracting vehicle trajectories from the partitioned vehicle time series feature set to generate the vehicle trajectories belonging to the partition; In this embodiment, based on the data of the partitioned freight map network, the corresponding geographical location information in the vehicle comprehensive time series feature set is processed by partition mapping, and the coincidence of the location information and the partition boundary of each vehicle's running trajectory is detected in turn. The vehicle's time series features are mapped to the corresponding partition through geographic coordinate matching to obtain a partitioned vehicle time series feature set. The partitioned vehicle time series feature set contains the vehicle's driving data, stop points and time node information in each partition. After the mapping is completed, the system uses a trajectory extraction tool to extract trajectories from the partitioned vehicle time series feature set, and connects all trajectories belonging to the same area in the time series feature set to generate a partitioned vehicle trajectory. The partitioned vehicle trajectory data contains content such as the path node sequence and driving time sequence within the partition.

[0050] Step S34: performing intersection point identification on the vehicle trajectories of the belonging partition to obtain vehicle intersection point data; performing intersection trajectory statistics on the vehicle trajectories of the belonging partition based on the vehicle intersection point data to generate intersection trajectory data.

[0051] In this embodiment, an intersection point recognition algorithm is used to analyze the vehicle trajectories of the assigned partitions point by point to detect the intersection between the trajectories, and the node coordinates of each vehicle trajectory of the assigned partition are compared one by one to identify the trajectory intersection points and generate vehicle intersection data. The vehicle intersection data includes the coordinates of the intersection points, the identification of the involved trajectories and the number of intersections. After the identification is completed, the system performs intersection trajectory statistics based on the vehicle intersection data, calculates the intersection strength and frequency between each trajectory by counting the frequency of occurrence of intersections and the path connection between intersections, and generates intersection trajectory data. The intersection trajectory data includes the frequency of intersection trajectories, path coverage and time distribution.

[0052] Preferably, step S4 comprises the following steps: Step S41: performing attribution area aggregation calculation on the intersection trajectory data to obtain area density data; dividing the vehicle comprehensive time series feature set into time periods to generate time-divided vehicle feature data; Step S42: performing feature correlation analysis on the regional density data and the time-sharing vehicle feature data to obtain track density feature data; performing vehicle demand forecasting on the partitioned freight map network according to the track density feature data to obtain regional vehicle demand data; Step S43: performing resource allocation calculation on the regional vehicle demand data to obtain an on-demand resource allocation plan; Step S44: Perform on-demand scheduling simulation on the comprehensive time series feature set of the vehicle based on the on-demand resource allocation scheme to generate simulated on-demand scheduling data.

[0053] In this embodiment, an aggregation calculation algorithm is used to aggregate the intersection trajectory data according to the regional location, extract and count the intersection trajectory frequency of each region, and divide all the trajectory data in the region by the coordinate boundary of the belonging region. After completing the regional grouping, the trajectory number is accumulated to generate regional density data. The regional density data includes indicators such as the trajectory frequency and trajectory coverage rate of each region. On this basis, the time period division method is used for the vehicle comprehensive time series feature set, and the vehicle driving data is divided according to the set time period. The time-sharing vehicle feature data is generated through the segmented time window. The time-sharing vehicle feature data includes the vehicle driving path, stop point and driving time in each time period. The time period division is convenient for segmentation. Analyze the changes in vehicle flow in different time periods, perform correlation analysis on regional density data and time-sharing vehicle feature data through feature correlation analysis tools, use trajectory density calculation algorithm to calculate the vehicle density characteristics of each region in different time periods, perform trajectory density mapping on each path of the partitioned freight map network, and generate trajectory density feature data. The trajectory density feature data contains the vehicle flow density, path coverage and change trends in each time period of each region. By combining the trajectory density feature data with the historical vehicle demand situation, the system predicts the vehicle demand for each region in the partitioned freight map network based on linear regression and time series prediction models to obtain regional vehicle demand data. The regional vehicle demand data includes The estimated number of vehicle demands, peak demand periods and average demand change rates in each region are analyzed through the resource allocation calculation module. According to the vehicle demand, demand period and estimated peak demand situation in each region, the resource allocation algorithm is used to dynamically allocate and calculate the number of vehicles required for each region. The on-demand resource allocation plan includes the allocation of vehicle quantities, scheduling priorities and scheduling schedules for each region. In specific operations, the demand differences in each region are first calculated, and vehicle resources are scheduled through the optimization allocation strategy for the number of vehicles, so that the vehicle demand in each region matches the actual allocated quantity. The on-demand resource allocation plan ensures the reasonable allocation of vehicle resources among different regions to avoid Too many or too few vehicle resources are wasted. The on-demand scheduling simulation tool is used to perform scheduling simulation operations on the vehicle comprehensive time series feature set based on the on-demand resource allocation plan. During the simulation, the vehicle path and time are dynamically adjusted according to the vehicle resource allocation situation in each area. By mapping the vehicle allocation data in the on-demand resource allocation plan to the vehicle comprehensive time series feature set, the path and area of ​​the vehicle in each time period are re-planned. In the on-demand scheduling simulation, the vehicle's driving route, stop point and scheduling time are adjusted in turn to ensure that the vehicle resources in each area match its demand intensity, and generate simulated on-demand scheduling data. The simulated on-demand scheduling data includes the scheduling path, arrival time, stop area and scheduling number of each vehicle.

[0054] Preferably, step S42 includes the following steps: Step S421: performing correlation analysis on the regional density data and the time-sharing vehicle characteristic data to obtain correlation data; performing feature combination processing on the correlation data to generate combined feature data; Step S422: sorting the combined feature data by relevance importance based on the relevance data to obtain sorted feature data; screening and integrating the sorted feature data to generate trajectory density feature data; Step S423: performing regional freight supply deduction on the trajectory density feature data to obtain predicted regional freight supply data; performing supply digestion calculation on the regional freight map network based on the predicted regional freight supply data to generate digestion supply and demand data; Step S424: Perform vehicle demand statistics on the regional freight map network based on the digested supply and demand data to obtain regional vehicle demand data.

[0055] In this embodiment, the correlation analysis algorithm is used to quantitatively analyze the relationship between the regional density data and the time-sharing vehicle characteristic data. The correlation data is obtained by calculating the correlation degree between the two in different time periods and regions. The correlation data specifically includes the relationship weight value between the regional density and the vehicle flow and the correlation characteristics of the time period. The correlation analysis adopts Dynamic Time Warping (DTW) Warping) algorithm is used to extract the correlation index between regional density and vehicle feature data through similarity matching analysis of data curves, and then feature combination processing is performed on the correlation data. The multi-dimensional feature combination method is used to merge various correlation indicators to generate combined feature data. The combined feature data contains multi-dimensional correlation data points, and the weights of each dimension are normalized. Based on the correlation data, the various feature dimensions of the combined feature data are sorted by correlation importance. The feature importance sorting algorithm is used to prioritize the features by calculating the contribution value and correlation of each feature in different time periods to obtain sorted feature data. The sorted feature data contains the priority ranking and weight value of each feature. The sorted feature data is then screened and integrated. Low-weight data is screened out by setting a weight threshold, and feature data with high correlation and high weight are retained. The important features after screening are integrated to generate trajectory density feature data. The trajectory density feature data contains the density information in the screened high-correlation feature set and vehicle trajectory data, which is used for subsequent supply deduction. The trajectory density feature data is used to deduce regional freight supply through the regional supply deduction model, based on the historical freight volume and vehicle density in the region. Combined with the current trajectory density feature data, the future freight demand is predicted. The deduction model adopts a combination of time series analysis and regression analysis. The future freight supply of each region is predicted by segmented analysis of the time series characteristics, so as to obtain the predicted regional freight supply data. The predicted regional freight supply data contains the predicted value of future freight demand and the expected supply cycle of each region. Then, based on the predicted regional freight supply data, the supply digestion calculation is performed on the partitioned freight map network, and the supply is compared with the expected demand of the region. The supply and demand balance in each region is dynamically calculated through the supply digestion algorithm to generate digestion supply and demand data. The digestion supply and demand data includes the supply and demand matching situation and difference indicators in each region. According to the supply and demand difference values ​​of each region in the digestion supply and demand data, the specific statistics of vehicle demand are performed on the partitioned freight map network through the demand statistics module. In the case of insufficient regional supply, the number and time of vehicle scheduling demand are calculated based on the demand difference, and the number of vehicles is optimized for the supply and demand balanced area. The minimum vehicle scheduling algorithm is used to calculate the vehicle demand in each region and generate regional vehicle demand data. The regional vehicle demand data includes the total vehicle demand, demand distribution and specific scheduling demand in each region.

[0056] Preferably, step S423 includes the following steps: Perform regional division calculation on the trajectory density feature data to obtain regional distribution data; Based on the trajectory density feature data, the trajectory density change trend of the regional distribution data is analyzed to generate the trajectory density change trend; Map the supply quantity to the trajectory density change trend to obtain the supply quantity change trend; The supply quantity is estimated based on the supply quantity change trend according to the trajectory density feature data, and the freight supply data of the forecast area is generated; Based on the forecasted regional freight supply data, the regional freight map network is matched with the road network mapping to obtain the road network matching data; Carry out freight supply and demand balance calculation on the road network matching data to obtain freight supply and demand balance data; Based on the freight supply and demand balance data, the supply digestion demand forecast is carried out on the freight supply data in the forecast area to generate digestion supply and demand data.

[0057] In this embodiment, the trajectory density feature data is used to perform accurate regional division calculation on the freight map. First, based on the spatial coordinates of the vehicle position and trajectory density data on the freight platform, the freight map is gridded and partitioned according to the geographical boundaries, and the regional division is refined in combination with the vehicle trajectory density in the partition. The K-means clustering algorithm is used to perform cluster analysis on the density data points in the region to ensure that the divided regions do not overlap in space and have consistent density characteristics. The regional distribution data contains the spatial boundary information and trajectory density statistics of each region. The time series analysis method is used to perform change trend analysis on the trajectory density feature data of each region in the regional distribution data. First, the trajectory density change value of each region is extracted according to the time period (such as hours, days), and the weighted moving average (WMA) is used to calculate the change trend of the trajectory density feature data of each region. The track density change in different time periods is smoothed by the Average method to generate the track density change trend of each area. The track density change trend includes the density fluctuation of different areas in each time period, which provides basic data for the subsequent supply mapping. According to the track density change trend data, the supply demand of each area in each time period is calculated by the supply mapping model. The supply mapping model maps the increase and decrease of track density fluctuations with the corresponding relationship between vehicle supply demand, and obtains the specific demand reflection of track density change on supply. The quantitative analysis method based on linear regression is used to obtain the supply change trend value through regression calculation. The supply change trend data includes the predicted supply of each area in different time periods. The value provides the dynamic changes in the demand for regional freight vehicles. According to the supply change trend data generated in the previous stage, the forecast analysis model is used to estimate the regional freight supply. The specific steps are to linearly fit the supply change trend with the trajectory density feature data, and generate the supply value in a specific period of time in the future through the trend forecast model that increases hour by hour. The forecast regional freight supply data includes the supply and demand in each time period in the future. The forecast data is weighted to make the supply forecast value more dynamically responsive. The forecast regional freight supply data is mapped to the partitioned freight map network. First, the main freight routes in the map network are screened and matched according to the supply and demand and vehicle trajectory density in the region. The network mapping matching algorithm (Graph The road network matching data obtained contains the freight demand data of each road section in the freight map network and the predicted traffic pressure index, which provides a basis for the subsequent supply and demand balance calculation. The supply and demand balance calculation is performed on the road network matching data. First, the difference between supply and demand is calculated according to the supply and demand imbalance area in the road network matching data. The Lagrangian optimization algorithm is used to perform dynamic balance calculation on the supply and demand balance of each area. The calculation results sort the areas according to the balance value of supply and demand to obtain freight supply and demand balance data.The data includes the supply and demand balance difference of each region, the vehicle demand gap, and the freight supply and demand balance status of each road section, and provides data support for the supply and digestion demand forecast. Based on the freight supply and demand balance data, the supply and digestion forecast model is used to adjust the regional supply and demand. The model predicts the future supply and digestion demand by analyzing the balance of the supply and demand difference of each region. The specific operation includes fitting and adjusting the future supply data according to the current demand gap, and generating digestion supply and demand data. The digestion supply and demand data includes the supply and demand digestion demand of each region in the future time period and the specific number of vehicle allocations, which provides scheduling strategy support for the intelligent scheduling system.

[0058] Preferably, step S5 comprises the following steps: Step S51: Calculate the transport capacity of the comprehensive time series feature set of the vehicle to obtain the existing transport capacity data; calculate the dispatch capacity of the simulated on-demand dispatch data to obtain the dispatch capacity data; Step S52: Performing spatiotemporal distribution mapping on the existing transport capacity data to obtain existing transport capacity distribution data; performing regional dispatch demand analysis on the dispatch transport capacity data to obtain regional demand data; Step S53: Calculate the matching degree of the existing capacity distribution data and the regional demand data to obtain matching degree data; compare the capacity difference of the vehicle comprehensive time series feature set and the simulated on-demand scheduling data based on the matching degree data to obtain capacity difference data; Step S54: scheduling route planning for the simulated on-demand scheduling data according to the capacity difference data to obtain route planning data; performing conflict detection on the route planning data to obtain scheduling conflict data; Step S55: adjusting the route planning data based on the scheduling conflict data to generate an adjusted scheduling route; formulating a scheduling plan for the adjusted scheduling route to generate a vehicle scheduling plan.

[0059] In this embodiment, the transport capacity is calculated in detail for the comprehensive time series feature set of the vehicle. Specifically, by analyzing the characteristic data such as the working time, load condition, geographical location, and driving distance of the vehicle, the transport capacity that can be provided by the vehicle in different time periods is summarized by using the weighted average method and the peak analysis method to obtain the existing transport capacity data. The existing transport capacity data includes the specific transport capacity values ​​of each region in different time periods. At the same time, the dispatch capacity is calculated for the simulated on-demand dispatch data. The dispatch capacity data is obtained by dispatching simulation calculations of the demand and vehicle availability in each time period. The dispatch capacity data represents the expected demand transport capacity of each region in different time periods. Based on the existing transport capacity data, the spatiotemporal mapping model is first used to calculate the transport capacity of each region in different time periods. The capacity data of each time period is spatially marked and distributed on the map, and the existing capacity data is converted into the existing capacity distribution data in combination with the spatial coordinates. The existing capacity distribution data includes the capacity distribution of different regions in different time periods. The dispatch capacity data is analyzed for regional dispatch demand, and the dispatch demand values ​​of each region are classified according to the geographical location. The demand analysis tool is used to further refine the capacity demand in the region to obtain regional demand data. This data includes the vehicle demand of different regions in each time period. The matching degree calculation method (Cosine Similarity (CosineSimil arity), one-to-one matching of existing capacity distribution data and regional demand data is performed, and the capacity supply and demand are compared one by one to obtain matching data. The matching data includes the degree of matching between supply and demand in each region in different time periods. Then, based on the matching data, the capacity difference is compared between the comprehensive time series feature set of vehicles and the simulated on-demand scheduling data. By calculating the difference between supply and demand in each region, the balance state and deviation between supply and demand in each region are determined to obtain capacity difference data. The capacity difference data is used to clarify the capacity gap or surplus in each region in each time period. According to the capacity difference data, the optimal path algorithm is used to calculate the capacity difference between the vehicle and the demand. The scheduling conflict data is generated by using the scheduling conflict algorithm to plan the scheduling route for the simulated on-demand scheduling data. First, the optimal driving route is planned for each vehicle according to the vehicle location, demand area and road traffic conditions, and the route planning data is generated. The route planning data includes the driving route and estimated arrival time of each vehicle. Then, conflict detection is performed on the route planning data. The conflict detection determines the potential traffic conflict risk by calculating the intersection points and congestion points of each vehicle in the driving route, and generates scheduling conflict data. The scheduling conflict data includes the conflict area in each driving route and the time period of vehicle driving, which provides a basis for subsequent route adjustments. The original route planning data is adjusted according to the scheduling conflict data. The specific adjustment operation includes the time sequence arrangement of vehicles at the conflict points in the same route to avoid route congestion and conflict.The route re-selection algorithm is used to provide each vehicle with a conflict-free driving path and generate an adjusted scheduling route. The adjusted scheduling route includes the final driving route of each vehicle and the corresponding adjusted driving period. Then, based on the adjusted route and combined with the vehicle's scheduling demand data, a vehicle scheduling plan is formulated. The scheduling order, driving time and destination area of ​​the vehicle are determined through the scheduling strategy generation method. The generated vehicle scheduling plan includes the driving route and scheduling plan of each vehicle.

[0060] Preferably, step S53 includes the following steps: Perform data alignment on existing capacity distribution data and regional demand data to obtain alignment basic data; Performing regional matching calculation on the aligned basic data to obtain regional matching data; Performing time matching calculation on the alignment basic data based on the regional matching data to obtain time matching data; Perform capacity matching calculation on the aligned basic data based on the time matching data to generate capacity matching data; Calculate the matching degree of the alignment basic data according to the capacity matching data to generate matching degree data; Perform threshold segmentation on the matching degree data to obtain threshold classification data; Based on the threshold classification data, the capacity difference is identified for the comprehensive time series feature set of vehicles and the simulated on-demand dispatch data to obtain the difference degree data; The difference degree data is integrated to generate capacity difference data.

[0061] In this embodiment, the existing capacity distribution data and regional demand data are aligned through a data alignment algorithm to ensure the consistency of the two sets of data in time and space. The core of the data alignment process is to integrate the data information of different time points and spatial regions so that they can be compared and calculated in the same analysis dimension. In the specific implementation, the two data sources are divided according to the predetermined time period and geographic coordinates through the time-space mapping tool, and the discontinuous regional demand data are interpolated at the same time so that they can be matched to the same time period and spatial range. After the data alignment process is completed, the generated alignment basic data contains the unified alignment information of the existing capacity distribution and regional demand, and the regional matching calculation is performed. The capacity distribution information in the alignment basic data is compared with the regional demand information in the same geographical area through the matching algorithm (cosine similarity), and the capacity resources and demand are calculated for each region to generate regional matching data. The regional matching calculation is performed through the spatial matching model (spatial matching model). The time matching model is used to achieve this goal. By comparing supply and demand in each region, the supply and demand ratio of each region is calculated. The regional matching data records the matching ratio data of each region in the form of a table to facilitate the subsequent time matching operation. The alignment basic data in the region is used for time matching calculation to evaluate the matching degree of capacity supply and demand in different time periods. Through the time matching algorithm, the capacity distribution and demand distribution in each time period are compared one by one, and the supply and demand ratio in each time period is calculated to generate time matching data. The time matching data contains the supply and demand balance information of each region in different time periods. The demand peak and the maximum load point of capacity supply in each time period can be obtained through the time series analysis tool. Based on the time matching data, further capacity matching calculation is performed. Calculation, through the capacity analysis tool, the capacity data of each area in the alignment basic data is compared with the demand data, and the matching calculation is performed according to the actual capacity. The capacity matching data generated by the capacity matching calculation records the ratio of the actual capacity to the demand capacity in each time period and region, and the matching difference is marked with a numerical value. After the capacity matching data is formed, the overall matching degree calculation is performed in the alignment basic data according to the capacity matching data. Through the matching degree calculation model, the supply and demand data of each time period and region are comprehensively compared to generate matching degree data. The matching degree data is recorded in the form of supply and demand percentage in each time period and region. The closer the value is to 1, the more balanced the supply and demand are. After the matching degree data is generated, the data is threshold segmented by the threshold classification tool. The threshold segmentation classifies the matching degree data through the preset threshold range, and marks the area with high supply and demand matching as "balanced area", and the area with low matching as "need to be supplemented area", and marks the specific supply and demand level according to the high and low thresholds. The generated threshold classification data is used for the capacity difference identification step.The matching classification of different regions is presented in a tabular form. After the threshold classification data is generated, the threshold classification data is comprehensively analyzed with the vehicle comprehensive time series feature set and the simulated on-demand scheduling data through the capacity difference identification algorithm. By comparing the scheduling supply and demand gaps in different regions, the capacity differences in different time periods and regions are identified. The capacity difference identification specifically uses the feature extraction tool to perform difference calculations between the scheduling values ​​and the actual values ​​to generate difference degree data. The difference degree data displays the supply and demand gaps and surpluses in each time period in the form of difference values. The difference degree data is integrated and the difference degree data in different time periods and regions are summarized through the capacity analysis model to generate capacity difference data. The capacity difference data is used to describe the overall situation of supply and demand balance in each region and indicate the key capacity gap areas and times. The generated capacity difference data provides a basis for subsequent scheduling optimization.

[0062] Preferably, step S55 includes the following steps: Identify the conflicting sections of the dispatch conflict data to obtain the conflicting section data; retrieve the alternative routes of the route planning data based on the conflicting section data to generate the alternative route data; Allocate time windows for the alternative route data to obtain time allocation data; reorganize the route planning data based on the time allocation data to generate an adjusted scheduling route; Carry out vehicle allocation planning for the adjusted dispatch route and obtain vehicle allocation data; A scheduling plan is formulated for the vehicle allocation data based on the time allocation data to generate a vehicle scheduling plan.

[0063] In this embodiment, the dispatch conflict data is analyzed, and the road section conflict identification algorithm is used to extract the location of the road section with dispatch conflict. The conflict section identification is performed based on the road network topology data. First, the conflict point in the dispatch conflict data is matched with the road network. By analyzing the overlap with other dispatch routes, the location and range of the conflicting road section are further determined. The generated conflicting road section data includes the specific location, starting and ending point coordinates of each conflicting road section and the expected congestion time information of the road section. The conflicting road section data serves as the basis for subsequent alternative route retrieval. Based on the conflicting road section data, the conflicting road section in the route planning data is searched for alternative routes. Through the path planning tool, the shortest path algorithm (such as Dijkstra algorithm) is used. Or other intelligent planning algorithms are used to select alternative routes. The alternative route retrieval tries to select non-congested sections and fast-passing sections under the premise of ensuring driving safety. The alternative route is selected through the iterative calculation of the algorithm. The generated alternative route data records the sections passed by each path, the estimated driving time and related road condition information. The alternative route data is used for the time window allocation step. After the alternative route data is generated, the alternative route data is processed based on the time window allocation algorithm to match each alternative route with the time window expected to pass through its section to reduce vehicle scheduling during congestion. In the specific operation, the peak traffic period of each section is set as an unavailable window, the vehicle's passing time during non-peak hours is calculated, and a time window is generated. The corresponding time allocation data includes the travel time nodes and expected time periods of each alternative route. The time allocation data is used for the subsequent route reorganization step. The time allocation data is integrated with the route planning data through the route reorganization algorithm. First, the existing route is adjusted according to the feasible travel time period of each section to avoid the peak congestion period, and the conflicting sections are replaced by alternative routes. The adjusted scheduling route is formed by splicing and reorganizing the alternative routes. The adjusted scheduling route includes a reasonable driving path in both time and space to ensure the timeliness and smoothness of the entire scheduling. The generated adjusted scheduling route records the specific driving section, expected travel time and scheduling priority information. The existing The fleet resources are matched with the adjusted dispatch routes to optimize the vehicle configuration. By analyzing the driving time of each dispatch route, the fleet capacity and the scheduled dispatch tasks, the vehicle that best meets the task requirements is assigned to the designated dispatch route. In the specific allocation process, the vehicle allocation data is generated by considering factors such as the current status of the fleet, mileage and fuel consumption. The vehicle allocation data includes the route number, departure time and expected arrival time corresponding to each vehicle. The final vehicle dispatch plan is formulated through the dispatch plan generation module. The dispatch plan includes the specific driving route of the vehicle, the passing time nodes of each section and the interval time between vehicles. The dispatch plan records in detail the dispatch order, driving route and departure and arrival time nodes of each vehicle.Ensure that the dispatch optimization of transportation capacity is completed within a reasonable time window.

[0064] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0065] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A management method for intelligent dispatching of freight platform vehicles based on the Internet of Things, characterized in that: The following steps are involved: Step S1: Collect vehicle terminal information; Extracting features from vehicle terminal information to obtain vehicle feature information data; Perform time series decomposition on vehicle feature information data to generate a comprehensive vehicle time series feature set; Step S2: draw a freight route for the comprehensive time series feature set of the vehicle to obtain basic freight route data; Carry out real-time map reconstruction of the comprehensive time series feature set of vehicles based on basic freight route data to generate a freight map network; Step S3: evenly partition the freight map network to obtain a partitioned freight map network; Based on the partitioned freight map network, the regional vehicle trajectory intersection analysis is performed on the vehicle comprehensive time series feature set to generate intersection trajectory data; Step S4: predicting vehicle demand for the partitioned freight map network according to the intersection trajectory data and the vehicle comprehensive time series feature set to obtain regional vehicle demand data; performing on-demand scheduling simulation on the vehicle comprehensive time series feature set based on the regional vehicle demand data to generate simulated on-demand scheduling data; Step S5: Compare the vehicle comprehensive time series feature set and the simulated on-demand dispatching data with transport resources to obtain transport capacity difference data; design a dispatching plan for the simulated on-demand dispatching data based on the transport capacity difference data to generate a vehicle dispatching plan; Step S6: Intelligently dispatch the freight platform vehicles according to the vehicle dispatch plan to achieve a capacity optimization management method.

2. The management method for intelligent dispatching of freight platform vehicles based on the Internet of Things according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: real-time acquisition of vehicle location information to obtain original location data; monitoring and acquisition of vehicle operation status to obtain original status data; dynamic acquisition of vehicle load status to obtain original load data; Step S12: integrating the original position data, the original state data and the original load data to obtain vehicle terminal information; performing unified format conversion processing on the vehicle terminal information to obtain standard format information; Step S13: extracting features from the standard format information to obtain vehicle-borne feature information data; performing periodic analysis on the vehicle-borne feature information data to obtain periodic data; Step S14: performing change trend statistics on the periodic data to generate a periodic change trend; performing time series reorganization on the periodic data and the periodic change trend to obtain a comprehensive vehicle time series feature set.

3. The management method for intelligent dispatching of freight platform vehicles based on the Internet of Things according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: extracting path points from the comprehensive time series feature set of the vehicle to obtain path point data; performing frequency statistics on the path point data to generate path frequency data; Step S22: Calculate the path density of the path point data based on the path frequency data to generate path density data; draw a freight route for the path point data according to the path density data to obtain basic freight route data; Step S23: gridding the basic freight route data to obtain a road network grid; identifying road network nodes on the road network grid to generate road network node data; Step S24: performing connectivity analysis on the road network node data to obtain connectivity data; performing road network weight calculation on the connectivity data based on the preset road network weight assignment to generate road network weight data; Step S25: constructing a topological relationship for the basic freight route data according to the road network weight data to obtain topological structure data; performing network integration on the topological structure data to generate a freight map network.

4. The management method for intelligent dispatching of freight platform vehicles based on the Internet of Things according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: performing boundary identification on the freight map network to obtain the map network boundary; calculating the regional area of ​​the freight map network according to the map network boundary to generate the global map network area; Step S32: dividing the freight map network into four equal regions based on the area of ​​the global map network to obtain a partitioned freight map network; Step S33: mapping the comprehensive time series feature set of vehicles to each region according to the partitioned freight map network to obtain the partitioned vehicle time series feature set; extracting vehicle trajectories from the partitioned vehicle time series feature set to generate the vehicle trajectories belonging to the partition; Step S34: performing intersection point identification on the vehicle trajectories of the belonging partition to obtain vehicle intersection point data; performing intersection trajectory statistics on the vehicle trajectories of the belonging partition based on the vehicle intersection point data to generate intersection trajectory data.

5. The management method for intelligent dispatching of freight platform vehicles based on the Internet of Things according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: performing attribution area aggregation calculation on the intersection trajectory data to obtain area density data; dividing the vehicle comprehensive time series feature set into time periods to generate time-divided vehicle feature data; Step S42: performing feature correlation analysis on the regional density data and the time-sharing vehicle feature data to obtain track density feature data; performing vehicle demand forecasting on the partitioned freight map network according to the track density feature data to obtain regional vehicle demand data; Step S43: performing resource allocation calculation on the regional vehicle demand data to obtain an on-demand resource allocation plan; Step S44: Perform on-demand scheduling simulation on the comprehensive time series feature set of the vehicle based on the on-demand resource allocation scheme to generate simulated on-demand scheduling data.

6. The management method for intelligent dispatching of freight platform vehicles based on the Internet of Things according to claim 5 is characterized in that: Step S42 includes the following steps: Step S421: performing correlation analysis on the regional density data and the time-sharing vehicle characteristic data to obtain correlation data; performing feature combination processing on the correlation data to generate combined feature data; Step S422: sorting the combined feature data by relevance importance based on the relevance data to obtain sorted feature data; screening and integrating the sorted feature data to generate trajectory density feature data; Step S423: performing regional freight supply deduction on the trajectory density feature data to obtain predicted regional freight supply data; performing supply digestion calculation on the regional freight map network based on the predicted regional freight supply data to generate digestion supply and demand data; Step S424: Perform vehicle demand statistics on the regional freight map network based on the digested supply and demand data to obtain regional vehicle demand data.

7. The management method for intelligent dispatching of freight platform vehicles based on the Internet of Things according to claim 6 is characterized in that: Step S423 includes the following steps: Perform regional division calculation on the trajectory density feature data to obtain regional distribution data; Based on the trajectory density feature data, the trajectory density change trend of the regional distribution data is analyzed to generate the trajectory density change trend; Map the supply quantity to the trajectory density change trend to obtain the supply quantity change trend; The supply quantity is estimated based on the supply quantity change trend according to the trajectory density feature data, and the freight supply data of the forecast area is generated; Based on the forecasted regional freight supply data, the regional freight map network is matched with the road network mapping to obtain the road network matching data; Carry out freight supply and demand balance calculation on the road network matching data to obtain freight supply and demand balance data; Based on the freight supply and demand balance data, the supply digestion demand forecast is carried out on the freight supply data in the forecast area to generate digestion supply and demand data.

8. The management method for intelligent dispatching of freight platform vehicles based on the Internet of Things according to claim 1 is characterized in that: Step S5 includes the following steps: Step S51: Calculate the transport capacity of the comprehensive time series feature set of the vehicle to obtain the existing transport capacity data; calculate the dispatch capacity of the simulated on-demand dispatch data to obtain the dispatch capacity data; Step S52: Performing spatiotemporal distribution mapping on the existing transport capacity data to obtain existing transport capacity distribution data; performing regional dispatch demand analysis on the dispatch transport capacity data to obtain regional demand data; Step S53: Calculate the matching degree of the existing capacity distribution data and the regional demand data to obtain matching degree data; compare the capacity difference of the vehicle comprehensive time series feature set and the simulated on-demand scheduling data based on the matching degree data to obtain capacity difference data; Step S54: scheduling route planning for the simulated on-demand scheduling data according to the capacity difference data to obtain route planning data; performing conflict detection on the route planning data to obtain scheduling conflict data; Step S55: adjusting the route planning data based on the scheduling conflict data to generate an adjusted scheduling route; formulating a scheduling plan for the adjusted scheduling route to generate a vehicle scheduling plan.

9. The management method for intelligent dispatching of freight platform vehicles based on the Internet of Things according to claim 8 is characterized in that: Step S53 includes the following steps: Perform data alignment on existing capacity distribution data and regional demand data to obtain alignment basic data; Performing regional matching calculation on the aligned basic data to obtain regional matching data; Performing time matching calculation on the alignment basic data based on the regional matching data to obtain time matching data; Perform capacity matching calculation on the aligned basic data based on the time matching data to generate capacity matching data; Calculate the matching degree of the alignment basic data according to the capacity matching data to generate matching degree data; Perform threshold segmentation on the matching degree data to obtain threshold classification data; Based on the threshold classification data, the capacity difference is identified for the comprehensive time series feature set of vehicles and the simulated on-demand dispatch data to obtain the difference degree data; The difference degree data is integrated to generate capacity difference data.

10. The management method for intelligent dispatching of freight platform vehicles based on the Internet of Things according to claim 8 is characterized in that: Step S55 includes the following steps: Identify the conflicting sections of the dispatch conflict data to obtain the conflicting section data; retrieve the alternative routes of the route planning data based on the conflicting section data to generate the alternative route data; Allocate time windows for the alternative route data to obtain time allocation data; reorganize the route planning data based on the time allocation data to generate an adjusted scheduling route; Carry out vehicle allocation planning for the adjusted dispatch route and obtain vehicle allocation data; A scheduling plan is formulated for the vehicle allocation data based on the time allocation data to generate a vehicle scheduling plan.

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