Enterprise vehicle resource dynamic management and optimization platform

Abstract

The invention discloses an enterprise vehicle resource dynamic management and optimization platform, and relates to the technical field of vehicle resource management, and the platform comprises a data collection module which is used for collecting historical multi-dimensional data and processing the historical multi-dimensional data to form a unified time series data set; the order prediction module constructs a time sequence density field based on the time sequence data set, and predicts order request distribution in a future time window; the path optimization module is used for constructing a comprehensive cost function based on the predicted order request distribution, the calculation path complexity and the delay potential item, and dynamically optimizing the vehicle path by adopting a multi-target nonlinear optimization algorithm; and the dynamic adjustment module is used for updating the order prediction result and the path optimization scheme according to the real-time data so as to realize dynamic management and optimization of enterprise vehicle resources. Accurate order prediction is realized by constructing a time sequence density field, path length complexity and delay potential items are comprehensively considered, and comprehensiveness and diversity of optimization results are improved, so that efficient dynamic management and optimization of enterprise vehicle resources are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle resource management, and in particular to a dynamic management and optimization platform for enterprise vehicle resources. Background Art

[0002] With the acceleration of urbanization and the continuous growth of logistics demand, enterprise vehicle resource management and optimization has become an important part of improving operational efficiency and competitiveness. Traditional vehicle scheduling methods mostly rely on manual experience and static planning, which is difficult to cope with real-time changes in order demand, traffic conditions and environmental factors. This not only leads to low resource utilization, but also may cause service delays and operational risks, thus affecting the overall performance of the enterprise.

[0003] Existing vehicle management systems usually include basic functions such as data collection, order processing, and route planning. However, these systems have shortcomings in the following aspects: Inaccurate order prediction: Traditional systems often use simple statistical analysis or linear models based on historical data to predict orders, which is difficult to capture complex spatiotemporal patterns and dynamic changes, resulting in insufficient prediction results and affecting subsequent resource scheduling and route planning. Single route optimization: Existing route planning algorithms often only consider single factors such as route length or travel time, ignoring multi-dimensional factors such as route complexity, service delay potential, and operational risks. This single optimization strategy is difficult to achieve comprehensive optimization in a changing environment, which may lead to resource waste and reduced service quality. Limited dynamic adjustment capabilities: Many existing systems lack effective dynamic adjustment mechanisms when facing real-time data changes, and cannot respond to fluctuations and emergencies in order demand in a timely manner. This static scheduling method performs poorly in complex and dynamic environments, making it difficult to maintain the efficiency and flexibility of the system. Insufficient multi-objective optimization: Traditional optimization methods usually focus on a single objective, such as the shortest path or the lowest cost, and ignore the trade-off relationship between multiple objectives. As enterprises' demands for operational efficiency, service quality, risk control and other aspects increase, single-objective optimization methods can no longer meet the requirements of comprehensive optimization. Summary of the invention

[0004] Based on the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide an enterprise vehicle resource dynamic management and optimization platform to solve the above-mentioned technical problems.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: a dynamic management and optimization platform for enterprise vehicle resources, comprising: The data collection module is used to collect historical order data, vehicle operation data and road network information, and clean, align and fuse the collected data to form a unified time series data set; An order prediction module, based on the time series data set, constructs a time series density field to predict the order request distribution in a future time window; The route optimization module calculates the route complexity and delay potential terms based on the predicted order request distribution, constructs a comprehensive cost function based on the route complexity and delay potential terms, and uses a multi-objective nonlinear optimization algorithm to dynamically optimize the vehicle route; The dynamic adjustment module is used to update order prediction results and route optimization plans based on real-time data, thereby achieving dynamic management and optimization of enterprise vehicle resources.

[0006] The present invention is further configured that, in the time series data set, the order data , each order Include timestamp ,Location , order type and order amount, the vehicle operation data , per vehicle Including location, load and status information, the road network information Includes road nodes, edges, distances, and road levels.

[0007] The present invention is further configured that the order prediction module includes: a time series density field construction unit and an order request distribution prediction unit; The time series density field construction unit is used to map the order position into a density field according to the order data through a preset time-space density field function; The order request distribution prediction unit predicts the order distribution in a future time window according to the density field.

[0008] The present invention is further configured to map the order position into a density field through a preset spatiotemporal density field function, and the calculation logic is: ,in, For historical time slices Inside, Location The order density field at is the kernel function used to smooth the contribution of order positions to the density field, , is a variable, is the diffusion parameter, which is used to control the decay rate and diffusion range of the kernel function.

[0009] The present invention is further configured to predict the order distribution in the future time window according to the density field, and the calculation logic is: ,in, For future time window Internal position The predicted result of probability density distribution of order requests at is the amplification parameter, is the sensitivity parameter, is the morphology adjustment parameter.

[0010] The present invention is further configured such that the path optimization module includes: a path complexity calculation unit, a delay potential term calculation unit, a comprehensive cost function construction unit and an optimization algorithm unit; The path complexity calculation unit is used to calculate the path complexity according to the tortuosity of the path and the rate of change of the road grade; The delay potential term calculation unit is used to accumulate the delay potential on the path to obtain the delay potential term according to the order distribution in the future time window predicted by the density field; The comprehensive cost function construction unit is used to construct a comprehensive cost function according to the path complexity and delay potential terms; The optimization algorithm unit is used to dynamically optimize the vehicle path by adopting a multi-objective nonlinear optimization algorithm.

[0011] The present invention is further configured such that the calculation logic of the path complexity is: ,in, is the path length The path complexity of is the path parameter, which identifies the position ratio on the path, and its value range is [0,1]. To control the exponential weight of the tortuosity of the path to the complexity, The arc length of the path The tortuosity of , The arc length of the path Previous Node The tortuosity of , For Node The two-dimensional coordinates of The arc length of the path The road grade change rate at , The arc length of the path Previous Node The rate of change of road grade, The arc length of the path Previous Node The road grade.

[0012] The present invention is further configured such that the calculation logic of the delay potential term is: ,in, is the path length The delayed potential term, is the amplification parameter, The arc length of the path The potential for delay accumulation at ,in, For path parameters The potential for delay accumulation at is a two-dimensional real number space, For future time window Internal position The predicted result of probability density distribution of order requests at For path parameters The corresponding position, is the Gaussian kernel width parameter, which is used to control the diffusion range and influence range of the kernel function.

[0013] The present invention is further configured such that the construction logic of the comprehensive cost function is: ,in, is the comprehensive cost function, To adjust the parameters, is the nonlinear power parameter.

[0014] The present invention is further configured such that the multi-objective nonlinear optimization algorithm includes: a non-dominated sorting genetic algorithm, a multi-objective differential evolution algorithm, and a mixture of a genetic algorithm and a local search.

[0015] The present invention provides a dynamic management and optimization platform for enterprise vehicle resources, comprising: a data collection module, which is used to collect historical order data, vehicle operation data and road network information, clean, align and fuse the collected data to form a unified time series data set; an order prediction module, which constructs a time series density field based on the time series data set to predict the order request distribution in the future time window; a path optimization module, which calculates the path complexity and delay potential items based on the predicted order request distribution, constructs a comprehensive cost function according to the path complexity and delay potential items, and dynamically optimizes the vehicle path using a multi-objective nonlinear optimization algorithm; a dynamic adjustment module, which is used to update the order prediction results and path optimization scheme according to real-time data, and realize the dynamic management and optimization of enterprise vehicle resources, and the beneficial effects produced include: 1. Accurate order prediction capability: By constructing a time series density field and applying advanced spatiotemporal density field functions, historical order data is accurately mapped into a density field. Combined with nonlinear aggregation methods, it effectively captures complex spatiotemporal patterns and dynamic changes, thereby improving the accuracy of future order request distribution predictions; 2. Comprehensively consider multi-dimensional cost factors: In the process of path optimization, not only the complexity of path length is considered, but also the delay potential item is considered to fully reflect the multi-faceted characteristics of the path. By accurately defining the path complexity and delay potential, it is ensured that the optimization result achieves the best balance between the path geometry complexity and the service delay potential, avoiding the waste of resources and the decline of service quality caused by single-objective optimization; 3. Efficient multi-objective nonlinear optimization algorithm: A multi-objective nonlinear optimization algorithm is used to effectively handle multiple conflicting optimization objectives and improve the comprehensiveness and diversity of the optimization results. The optimization algorithm has good global search capabilities and population diversity maintenance mechanisms, and can quickly converge in complex search spaces to find high-quality optimal solutions.

[0016] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 The present invention is a schematic structural diagram of an enterprise vehicle resource dynamic management and optimization platform according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0019] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0020] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0021] A dynamic management and optimization platform for enterprise vehicle resources, such as Figure 1 As shown, including: The data collection module is used to collect historical order data, vehicle operation data and road network information, and clean, align and fuse the collected data to form a unified time series data set; An order prediction module, based on the time series data set, constructs a time series density field to predict the order request distribution in a future time window; The route optimization module calculates the route complexity and delay potential terms based on the predicted order request distribution, constructs a comprehensive cost function based on the route complexity and delay potential terms, and uses a multi-objective nonlinear optimization algorithm to dynamically optimize the vehicle route; The dynamic adjustment module is used to update order prediction results and route optimization plans based on real-time data, thereby achieving dynamic management and optimization of enterprise vehicle resources.

[0022] The present invention is further configured that, in the time series data set, the order data , each order Include timestamp ,Location , order type and order amount, the vehicle operation data , per vehicle Including location, load and status information, the road network information Including road nodes, edges, distances and road levels. Specifically, in the order data, the timestamp The meaning is to record the specific time when an order is generated or requested, which is used for timing analysis to capture the time pattern and trend of order demand, location location The meaning of is the geographic coordinates of the order request, which is usually expressed in a two-dimensional Cartesian coordinate system and is used for spatial analysis to determine the geographic distribution and concentration area of ​​the order. The meaning of order type is to describe the nature or category of the order, such as delivery, pickup, service request, etc., to help classify order requirements and formulate optimization strategies for different types of orders. The meaning of order amount is the economic value or transaction amount of the order, which is used to measure the importance and priority of the order and optimize resource allocation to maximize economic benefits. In vehicle operation data, the meaning of location is the current geographic location of the vehicle, which is expressed in a two-dimensional Cartesian coordinate system to monitor vehicle distribution in real time and optimize vehicle scheduling and path planning. The meaning of load is the current load status of the vehicle, which is usually expressed in weight units and is used to ensure that the load of the vehicle is reasonable during transportation to avoid overloading or waste of resources. The meaning of status information is to describe the current status of the vehicle, such as idle, driving, maintenance, etc., which is used to dynamically manage the vehicle status and optimize resource utilization and response speed. In road network information, the meaning of road node is the geographic coordinates of each key point in the road network, such as intersections, turning points, etc., which is used to build the basis of the road network map and support path search and optimization algorithms. Edges refer to road segments connecting road nodes, usually represented by directed or undirected edges, and are used to define the connectivity of the road network, supporting path calculation and traffic flow analysis. Distance refers to the length of road edges, usually in kilometers or meters, and is used to calculate the total length and travel time of a path, supporting path optimization and cost evaluation. Road grades refer to the importance and capacity of roads, such as expressways, main roads, and secondary roads, and are used to influence vehicle speeds and priorities, and optimize path selection to improve efficiency and safety.

[0023] The present invention is further configured that the order prediction module includes: a time series density field construction unit and an order request distribution prediction unit; The time series density field construction unit is used to map the order position into a density field through a preset time-space density field function according to the order data; the present invention is further configured to map the order position into a density field through a preset time-space density field function, and the calculation logic is: ,in, For historical time slices Inside, Location The order density field at is the kernel function used to smooth the contribution of order positions to the density field, , is a variable, is a diffusion parameter, which is used to control the decay rate and diffusion range of the kernel function. Specifically, first obtain the enterprise’s historical time slice All order data in , each order Include timestamp ,Location , order type and order amount; Gaussian kernel function is used ,in Indicates waypoints With order Location The square of the Euclidean distance between is the diffusion parameter, which is used to control the decay rate and diffusion range of the kernel function. Each order within , calculate its position The contribution of the order density field at , all orders About Location The contributions at the locations are added up to obtain the original density accumulation value , by exponentially transforming the cumulative value, a smooth and continuous order density field is obtained ; The above calculation logic accurately maps historical order data into a smooth order density field by constructing a time series density field and applying advanced spatiotemporal density field functions, and combines nonlinear aggregation methods to accurately capture complex spatiotemporal patterns and dynamic changes. This calculation logic not only improves the accuracy and stability of order forecasts, but also provides high-quality data support for multi-objective nonlinear optimization algorithms, ensuring efficient dynamic management and optimization of enterprise vehicle resources. By flexibly adjusting key parameters, it can adapt to different business needs and operating environments, significantly improving the operational efficiency and resource utilization of enterprises.

[0024] The order request distribution prediction unit predicts the order distribution in the future time window according to the density field. The present invention is further configured to predict the order distribution in the future time window according to the density field, and the calculation logic is: ,in, For future time window Internal position The predicted result of probability density distribution of order requests at is the amplification parameter, is the sensitivity parameter, is the morphological adjustment parameter. Specifically, after obtaining the historical time slice Inside, Location Order density field Then, through the sensitivity parameter , , used to adjust the impact of historical order density on the forecast results, morphological adjustment parameters , , a nonlinear aggregation method used to control order density, weights the order density of all historical time slices and performs power transformation to accumulate the comprehensive impact value , by analyzing the comprehensive impact value The power is raised and the natural logarithm is taken to extract nonlinear aggregation features. , by enlarging the parameter , , which is used to adjust the influence of the aggregated features on the final prediction results. The nonlinear aggregated features are exponentially transformed and 1 is subtracted to obtain the future time window Internal position The predicted results of the probability density distribution of order requests at The above calculation logic accurately maps historical order data into a continuous and smooth order density field by constructing a time series density field and applying advanced spatiotemporal density field functions. It combines nonlinear aggregation methods and exponential transformations to achieve accurate prediction of order request distribution in future time windows. By flexibly adjusting key parameters, the platform can adapt to different business needs and operating environments, significantly improving the accuracy of order predictions, optimizing the comprehensiveness and diversity of path selection, and ultimately achieving efficient dynamic management and optimization of enterprise vehicle resources, improving the enterprise's operating efficiency and resource utilization.

[0025] The present invention is further configured such that the path optimization module includes: a path complexity calculation unit, a delay potential term calculation unit, a comprehensive cost function construction unit and an optimization algorithm unit; The path complexity calculation unit is used to calculate the path complexity according to the tortuosity of the path and the road grade change rate; the present invention is further configured such that the calculation logic of the path complexity is: ,in, is the path length The path complexity of is the path parameter, which identifies the position ratio on the path, and its value range is [0,1]. To control the exponential weight of the tortuosity of the path to the complexity, The arc length of the path The tortuosity of , The arc length of the path Previous Node The tortuosity of , For Node The two-dimensional coordinates of The arc length of the path The road grade change rate at , The arc length of the path Previous Node The rate of change of road grade, The arc length of the path Previous Node Specifically, the vehicle driving path is discretized into a series of continuous nodes , get each node Road grade , through three consecutive nodes , and The coordinates of the calculation node The tortuosity at the point is calculated, and the rate of change of road grade between two adjacent nodes is calculated. The tortuosity and the rate of change of road grade are used to quantify the complexity of the path. The tortuosity is a geometric feature that describes the degree of curvature of the path at a certain point. The greater the tortuosity, the more curved the path is at that point; the rate of change of road grade is used to measure the degree of fluctuation of the road grade at a certain point on the path; the path complexity comprehensively evaluates the tortuosity and the rate of change of road grade of the path, quantifies the geometric complexity of the path, and controls the exponential weight of the tortuosity of the path on the complexity. The value of is greater than 0. Through the path complexity calculation unit, based on the tortuosity of the path and the rate of change of road grade, the geometric complexity of the path is quantified by the integral method. This method not only accurately reflects the driving difficulty and resource consumption of the path, but also supports the efficient operation of the multi-objective nonlinear optimization algorithm through flexible parameter adjustment, and realizes the intelligent dynamic management and optimization of enterprise vehicle resources.

[0026] The delay potential term calculation unit is used to accumulate the delay potential on the path to obtain the delay potential term according to the order distribution in the future time window predicted by the density field; the present invention is further configured that the calculation logic of the delay potential term is: ,in, is the path length The delayed potential term, is the amplification parameter, The arc length of the path The potential for delay accumulation at ,in, For path parameters The potential for delay accumulation at is a two-dimensional real number space, For future time window Internal position The predicted result of probability density distribution of order requests at For path parameters The corresponding position, is the Gaussian kernel width parameter, which is used to control the diffusion range and influence range of the kernel function. Internal position The predicted results of the probability density distribution of order requests at , the path parameter Corresponding position With all locations The distance between the two locations is weighted by the Gaussian kernel function to generate the path parameters The potential for delay accumulation at , the order request distribution is smoothed by the Gaussian kernel function to ensure that the order density in the area around the path point gradually attenuates the impact of the delay potential, and the path length from the starting point to the parameter The potential delay of all path points at Integrate and combine the amplification parameters , a term that quantifies the delay potential of the entire path segment , zoom parameter The value of is greater than 0. Through the delay potential calculation unit, based on the prediction of order distribution in the future time window, the Gaussian kernel function and nonlinear integration method are used to accurately quantify the service delay potential on the path. This method not only improves the accuracy and sensitivity of delay risk assessment, but also ensures efficient dynamic management and optimization of enterprise vehicle resources through flexible parameter adjustment and comprehensive multi-objective optimization strategy. As a result, the enterprise's operating efficiency and service quality are significantly improved, operating costs are reduced, and the system's adaptability and competitiveness are enhanced.

[0027] The comprehensive cost function construction unit is used to construct a comprehensive cost function according to the path complexity and the delay potential term; the present invention is further configured that the construction logic of the comprehensive cost function is: ,in, is the comprehensive cost function, To adjust the parameters, is a nonlinear power parameter. Specifically, the comprehensive cost function The construction of is to integrate multiple optimization objectives (path complexity and delay potential) into a unified evaluation index, so that these factors can be considered simultaneously in the path optimization process to achieve multi-objective balanced optimization. and delayed potential , respectively introduce adjustment parameters and nonlinear power parameters , in order to adjust its weight and sensitivity in the comprehensive cost function, use the multiplication operation to combine the cost terms of each single objective, and realize the nonlinear integration of multiple objectives through power transformation to ensure the balance and coordination between different objectives. By integrating from 0 to 1 and accumulating the comprehensive cost on the entire path, we get the final comprehensive cost function. By constructing the comprehensive cost function, we integrate multi-dimensional cost factors such as path complexity and delay potential into a unified optimization index, and use a multi-objective nonlinear optimization algorithm for dynamic optimization, thus achieving efficient dynamic management and optimization of enterprise vehicle resources.

[0028] The optimization algorithm unit is used to dynamically optimize the vehicle path by adopting a multi-objective nonlinear optimization algorithm. The present invention is further configured that the multi-objective nonlinear optimization algorithm includes: a non-dominated sorting genetic algorithm, a multi-objective differential evolution algorithm, and a hybrid of a genetic algorithm and a local search. Specifically, the optimization algorithm unit is used to dynamically optimize the driving path of the enterprise vehicle by adopting a multi-objective nonlinear optimization algorithm. The optimization algorithm unit integrates a variety of advanced multi-objective optimization algorithms, including a non-dominated sorting genetic algorithm (NSGA-II), a multi-objective differential evolution algorithm (MODE), and a hybrid method of a genetic algorithm and a local search (GA-LS). Through the application of these algorithms, the system can efficiently balance and optimize multiple conflicting objectives in a complex and changeable operating environment, thereby realizing the intelligent management and optimization of the enterprise vehicle resources. The non-dominated sorting genetic algorithm II (NSGA-II) is an evolutionary algorithm widely used in multi-objective optimization problems, and is known for its efficient non-dominated sorting mechanism and congestion distance sorting method. It aims to optimize multiple objective functions at the same time and generate a high-quality solution set covering the Pareto frontier; the multi-objective differential evolution algorithm (MODE) is a multi-objective extension of the differential evolution algorithm (DE), combining non-dominated sorting and diversity maintenance strategies, and is suitable for dealing with multiple conflicting optimization objectives. Its main features are efficient search capabilities and good population diversity; the hybrid of genetic algorithm and local search (GA-LS) is a hybrid optimization method that combines genetic algorithm (GA) with local search (LS). It combines the global search capability of genetic algorithms and the fine optimization capability of local searches, aiming to improve the quality and convergence speed of optimization results; the above-mentioned optimization algorithms are all existing technologies and will not be elaborated here. The optimization algorithm unit adopts a multi-objective nonlinear optimization algorithm, including a non-dominated sorting genetic algorithm (NSGA-II), a multi-objective differential evolution algorithm (MODE) and a hybrid method of genetic algorithm and local search (GA-LS), to achieve a comprehensive, flexible and efficient optimization of the enterprise vehicle path. By comprehensively considering multiple key factors such as path complexity and delay potential, and using advanced optimization algorithms to deal with conflicting objectives, the present invention significantly improves the dynamic management and optimization capabilities of the enterprise vehicle resources, enhances the adaptability and operational efficiency of the system, and has broad application prospects and significant economic benefits.

[0029] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0030] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0031] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0032] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0033] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0034] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0035] In the several embodiments provided in the present application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0036] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0037] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0038] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0039] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A dynamic management and optimization platform for enterprise vehicle resources, characterized in that: include: The data collection module is used to collect historical order data, vehicle operation data and road network information, and clean, align and fuse the collected data to form a unified time series data set; An order prediction module, based on the time series data set, constructs a time series density field to predict the order request distribution in a future time window; The route optimization module calculates the route complexity and delay potential terms based on the predicted order request distribution, constructs a comprehensive cost function based on the route complexity and delay potential terms, and uses a multi-objective nonlinear optimization algorithm to dynamically optimize the vehicle route; The dynamic adjustment module is used to update order prediction results and route optimization plans based on real-time data, thereby achieving dynamic management and optimization of enterprise vehicle resources.

2. According to claim 1, a dynamic management and optimization platform for enterprise vehicle resources is characterized in that: In the time series data set, the order data , each order Include timestamp ,Location , order type and order amount, the vehicle operation data , per vehicle Including location, load and status information, the road network information Includes road nodes, edges, distances, and road levels.

3. According to claim 2, a dynamic management and optimization platform for enterprise vehicle resources is characterized in that: The order prediction module includes: a time series density field construction unit and an order request distribution prediction unit; The time series density field construction unit is used to map the order position into a density field according to the order data through a preset time-space density field function; The order request distribution prediction unit predicts the order distribution in a future time window according to the density field.

4. According to claim 3, a dynamic management and optimization platform for enterprise vehicle resources is characterized in that: The order position is mapped to a density field through the preset space-time density field function. The calculation logic is: ,in, For historical time slices Inside, Location The order density field at is the kernel function used to smooth the contribution of order positions to the density field, , is a variable, is the diffusion parameter, which is used to control the decay rate and diffusion range of the kernel function.

5. The enterprise vehicle resource dynamic management and optimization platform according to claim 4 is characterized in that: According to the density field, the order distribution in the future time window is predicted, and the calculation logic is: ,in, For future time window Internal position The predicted result of probability density distribution of order requests at is the amplification parameter, is the sensitivity parameter, is the morphology adjustment parameter.

6. The enterprise vehicle resource dynamic management and optimization platform according to claim 1 is characterized in that: The path optimization module includes: a path complexity calculation unit, a delay potential term calculation unit, a comprehensive cost function construction unit and an optimization algorithm unit; The path complexity calculation unit is used to calculate the path complexity according to the tortuosity of the path and the rate of change of the road grade; The delay potential term calculation unit is used to accumulate the delay potential on the path to obtain the delay potential term according to the order distribution in the future time window predicted by the density field; The comprehensive cost function construction unit is used to construct a comprehensive cost function according to the path complexity and delay potential terms; The optimization algorithm unit is used to dynamically optimize the vehicle path by adopting a multi-objective nonlinear optimization algorithm.

7. The enterprise vehicle resource dynamic management and optimization platform according to claim 6 is characterized in that: The calculation logic of the path complexity is: ,in, is the path length The path complexity of is the path parameter, which identifies the position ratio on the path, and its value range is [0,1]. To control the exponential weight of the tortuosity of the path to the complexity, The arc length of the path The tortuosity of , is the arc length of the path Previous Node The tortuosity of , For Node The two-dimensional coordinates of The arc length of the path The road grade change rate at , The arc length of the path Previous Node The rate of change of road grade, is the arc length of the path Previous Node The road grade.

8. The enterprise vehicle resource dynamic management and optimization platform according to claim 7 is characterized in that: The calculation logic of the delay potential term is: ,in, is the path length The delayed potential term, is the amplification parameter, is the arc length of the path The potential for delay accumulation at ,in, For path parameters The potential for delay accumulation at is a two-dimensional real number space, For future time window Internal position The predicted result of probability density distribution of order requests at For path parameters The corresponding position, is the Gaussian kernel width parameter, which is used to control the diffusion range and influence range of the kernel function.

9. The enterprise vehicle resource dynamic management and optimization platform according to claim 8 is characterized in that: The construction logic of the comprehensive cost function is: ,in, is the comprehensive cost function, To adjust the parameters, is the nonlinear power parameter.

10. The enterprise vehicle resource dynamic management and optimization platform according to claim 9, characterized in that: Multi-objective nonlinear optimization algorithms include: non-dominated sorting genetic algorithm, multi-objective differential evolution algorithm and a hybrid of genetic algorithm and local search.

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