Expressway vehicle charging service method and system based on Internet of Vehicles

Abstract

The invention provides an expressway vehicle charging service method based on the Internet of Vehicles. The expressway vehicle charging service method comprises the following steps: acquiring service area data and vehicle data of an expressway; based on the service area data and the vehicle data, constructing a service area load balancing model, and determining a constraint condition and a target function of the load balancing model; the load balancing model is solved, the optimal parking number of each service area and the ratio of the predicted waiting service time of the vehicles to the service time are obtained, and the charging fee price of the service area is adjusted according to the ratio of the predicted waiting service time of the vehicles to the service time. According to the invention, the current situation of traffic jam in a service area caused by difficulty in finding proper service resources when a user travels on a highway is solved, and the problem that the service efficiency of various services in the service area is reduced due to addition of a reservation function in the service area is solved.

Description

Technical Field

[0001] This invention relates to the field of highway vehicle charging technology, and in particular to a highway vehicle charging service method and system based on vehicle-to-everything (V2X) networks. Background Technology

[0002] With the improvement of living standards, the proportion of private car ownership has increased year by year, experiencing explosive growth. The corresponding infrastructure construction is lengthy and difficult, leading to increasingly common highway traffic congestion. Highway service areas, especially during peak periods and holidays, suffer from long waiting times for charging / refueling, parking shortages, rampant illegal parking, and chaotic management, frequently experiencing large-scale and prolonged congestion. This not only disrupts people's schedules but also easily causes traffic accidents. Furthermore, in recent years, 5G, vehicle-to-everything (V2X) technology, big data, cloud computing, and road electronic technologies have developed rapidly. V2X technology, employing wireless communication and next-generation internet technologies, enables comprehensive, real-time dynamic information interaction between vehicles and roads. Based on the collection and fusion of dynamic traffic information across all times and spaces, it conducts active vehicle safety control and road collaborative management, fully realizing effective coordination between people, vehicles, and roads, ensuring traffic safety, improving traffic efficiency and service quality, thus forming a safe, efficient, and environmentally friendly road traffic system. Therefore, there is an urgent need for a highway vehicle charging service method and system based on V2X to solve the existing technical problems. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art, and proposes a method and system for highway vehicle charging services based on the Internet of Vehicles.

[0004] In a first aspect, embodiments of the present invention provide a method for providing highway vehicle charging services based on the Internet of Vehicles, comprising:

[0005] Acquire highway service area data and vehicle data;

[0006] Based on the service area data and vehicle data, a service area load balancing model is constructed, and the constraints and objective function of the load balancing model are determined.

[0007] Solve the load balancing model to obtain the optimal number of parking spaces and the ratio of predicted waiting time to actual service time for each service area. Adjust the charging price for the service area based on the ratio of predicted waiting time to actual service time.

[0008] Furthermore, the service area data includes at least a service area set S. er ={er1, er2, ..., er n}, where er1, er2, ..., er nThese represent service areas numbered sequentially as 1, 2, ..., n, with K parking spaces available for vehicle charging in each service area. n .

[0009] Furthermore, the vehicle data includes at least the number of vehicles p traveling on the highway, the proportion u of vehicles with a need to enter the service area, and the distance er of vehicle m from the service area. n Distance d mn Vehicle speed V m .

[0010] Furthermore, based on the service area data and vehicle data, a service area load balancing model is constructed, specifically including:

[0011] The vehicles are categorized according to their accessibility to service areas, resulting in a set V of vehicles that require access to service areas. el ={V el1 V el2 ,.....V eln}, where V el1 This indicates that the vehicle can only reach the nth service area. el2 This indicates a vehicle that can reach the second service area. (V) eln The first type of vehicle represents the vehicle that can reach the nth service area;

[0012] Based on the service area data and vehicle data, calculate the average service area arrival rate within period T. Service intensity of service area ;

[0013] The service area is used as a queuing model with finite waiting time, where the vehicle entry process follows a Poisson distribution. The expected service time E of the service area is obtained. T and variance Calculate the average waiting time W for vehicles at the nth service area. N .

[0014] Furthermore, the constraints of the load balancing model include: any vehicle stops at only one service area, the time for a vehicle to arrive at a service area does not exceed the expected time, the number of vehicles stopping at each service area does not exceed the number of parking spaces available in the service area and the service area parking capacity limit threshold; the objective function of the load balancing model is to minimize the total waiting time of all vehicles.

[0015] Furthermore, the load balancing model is solved to obtain the optimal number of parking spaces for each service area. Specific methods include:

[0016] The specific conditions for setting the model should include at least the number of service areas n, the period time T, the number of vehicles to be assigned within the period up, and the distance d from the current location to each service area. mnNumber of available charging berths (K) in each service area n Parking capacity threshold M;

[0017] Based on the given conditions, a random vehicle speed and time distribution that conforms to the characteristics of highway vehicle operation is generated as the initial conditions for solving the problem.

[0018] The parameters required to calculate the objective function based on the given conditions include at least the expected arrival rate of each service area, the expected average parking time, and the average vehicle waiting time of each service area.

[0019] Solve the objective function, and based on the set objective function, decision variables and constraints, output the number of parking spaces in each service area and the service area selection of each vehicle.

[0020] Furthermore, the service area is evaluated based on the ratio of the predicted waiting time to the actual service time. The specific method includes: calculating the ratio of the predicted waiting time to the actual service time based on the average arrival rate of the service area, the standard deviation of the service time of the charging station, the number of parking spaces available for vehicle charging in each service area, and the average service rate of the charging station. When the ratio of the predicted waiting time to the actual service time is less than the ratio of the actual waiting time to the actual service time, the charging fee in the service area is increased; otherwise, the charging fee in the service area remains unchanged.

[0021] Secondly, this embodiment discloses a highway vehicle charging service system based on the Internet of Vehicles, including: a service area data and vehicle data acquisition unit, a service area load balancing model construction unit, and a service area load balancing model solving unit; wherein:

[0022] Service area data and vehicle data acquisition unit, used to acquire service area data and vehicle data of the highway;

[0023] The service area load balancing model construction unit is used to construct a service area load balancing model based on the service area data and vehicle data, and to determine the constraints and objective function of the load balancing model.

[0024] The service area load balancing model solving unit is used to solve the load balancing model to obtain the optimal number of parking spaces and the ratio of the predicted waiting time to the actual service time for each service area. The charging price for the service area is then adjusted based on the ratio of the predicted waiting time to the actual service time.

[0025] Thirdly, this invention also discloses an electronic device, comprising:

[0026] One or more processors;

[0027] Memory, used to store one or more programs;

[0028] When the one or more programs are executed by the one or more processors, the one or more processors are made to implement.

[0029] Fourthly, the present invention also discloses a computer-readable medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the steps in any of the highway vehicle charging service methods.

[0030] This invention provides a highway vehicle charging service method based on the Internet of Vehicles (IoV). It involves acquiring highway service area data and vehicle data; constructing a service area load balancing model based on this data; determining the constraints and objective function of the load balancing model; solving the load balancing model to obtain the optimal number of parking spaces and the ratio of predicted waiting time to actual service time for each service area; and adjusting the charging fee at the service area based on this ratio. This invention addresses the current situation where users face difficulties finding suitable service resources on highways, leading to traffic congestion at service areas, and the problem that adding reservation functions to service areas has actually reduced the efficiency of various services. Attached Figure Description

[0031] Figure 1 A schematic flowchart illustrating a highway vehicle charging service method based on the Internet of Vehicles (IoV) provided in an embodiment of the present invention;

[0032] Figure 2 This is a schematic diagram of the method for constructing a service area load balancing model in step S200 of this embodiment of the invention;

[0033] Figure 3 A structural block diagram of a highway vehicle charging service method based on the Internet of Vehicles provided in an embodiment of the present invention;

[0034] Figure 4 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0035] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0036] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0037] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0038] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0039] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0040] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.

[0041] Among related technologies, a smart parking management system is disclosed, including a mobile terminal management module, a license plate information recognition module, a parking space information management module, and a parking lock management module. The mobile terminal management module is used by users to reserve remaining parking spaces in the parking space information management module online through mobile devices and to pay parking fees online. The existing technology, by using mobile devices to search for and reserve parking spaces online, can facilitate finding available parking spaces in advance, so as to quickly find the designated parking space in the parking lot. Moreover, by using parking locks to manage parking spaces, it can prevent vehicles from parking randomly and conveniently realize the billing of parking time, thereby achieving the level of unattended parking lot management.

[0042] However, existing technologies have added online reservation and payment functions to traditional parking lots, but have not solved the problems of malicious parking space grabbing or unused reservations that may result from the online reservation function. For the upgraded smart parking lot, only the usage ideas and parking space finding ideas are given, but no specific methods are provided, so the practicality is relatively low.

[0043] Existing technology also discloses a smart parking management method and cloud platform based on hotspot positioning and information sharing. First, it acquires different traffic flow trajectory information from the smart parking lot. Second, it determines the comparison results of the hotspot positioning distribution of different traffic flow trajectory information and converts them into a hotspot sharing positioning list. Then, based on the parking reservation information in the hotspot sharing positioning list, it determines the congestion situation of the smart parking lot during the operating hours between different traffic flow trajectory information. Finally, based on the congestion situation, it issues a vehicle location information sharing instruction to the target terminal and performs parking scheduling based on real-time vehicle location sharing information. In this way, it can comprehensively analyze the hotspot positioning and information sharing of vehicles corresponding to the smart parking lot, thereby quickly and accurately determining the congestion situation of the smart parking lot. Then, based on real-time vehicle location sharing information, it can realize the internal and external management and scheduling of the smart parking lot, avoiding or reducing parking and exit congestion and ensuring the smooth operation of the smart parking lot.

[0044] However, existing technologies only analyze historical objective data recorded by the system, which does not take into account the habits of parking users, resulting in insufficient accuracy in dynamic scheduling. Existing technologies merely improve user travel efficiency by reducing vehicle entry and exit time in parking lots, without considering the parking lot's utilization efficiency and economic benefits.

[0045] To address at least one of the technical problems existing in the aforementioned related technologies, this embodiment provides a method and system for highway vehicle charging services based on the Internet of Vehicles.

[0046] This implementation discloses a method for providing highway vehicle charging services based on the Internet of Vehicles (IoV), such as... Figure 1 ,include:

[0047] S100. Obtain service area data and vehicle data for highways; through statistical analysis of the service area data and vehicle data, the regular relationship between highway service areas and high-speed vehicles can be obtained.

[0048] In this embodiment, the service area data includes at least a service area set S. er ={er1, er2, ..., er n}, where er1, er2, ..., er n These represent service areas numbered sequentially as 1, 2, ..., n, with K parking spaces available for vehicle charging in each service area. n The vehicle data includes at least the number of vehicles p traveling on the highway, the proportion u of vehicles with a need to enter the service area, and the distance er of vehicle m from the service area. n Distance d mn Vehicle speed V m .

[0049] Specifically, assume that during a period T, there are P vehicles traveling on the highway, and a proportion u of these vehicles need to enter a service area, V el Let S represent the set of vehicles that need to enter a service area, denoted by el. Assume there are n service areas downstream. er Let represent the set of n downstream service areas, denoted by er, with α parking spaces available for vehicle charging in each service area. Vehicle allocation is initiated after the nearest upstream entrance / exit of the first service area, with el parking spaces allocated to each service area. m Distance from this point to the service area n The distance is d mn The driving speed is V m It needs to be within the time limit t l The number of vehicles arriving at each service area is q. n .

[0050] S200. Based on the service area data and vehicle data, construct a service area load balancing model, and determine the constraints and objective function of the load balancing model;

[0051] In S200 of this embodiment, a service area load balancing model is constructed based on the service area data and vehicle data. The specific method includes:

[0052] S201. Classify vehicles according to their accessibility to service areas to obtain the set V of vehicles that need to enter service areas. el ={Vel1 V el2 ,.....V eln}, where V el1 This indicates that the vehicle can only reach the nth service area. el2 This indicates a vehicle that can reach the second service area. (V) eln The first type of vehicle represents the vehicle that can reach the nth service area;

[0053] The variables defined in the service area load balancing model are as follows:

[0054]

[0055] The objective function of the model is to minimize the total waiting time of all vehicles.

[0056]

[0057] in, The table represents the average waiting time for vehicles in the i-th service area. The number of vehicles entering the service area n.

[0058] S202. Based on the service area data and vehicle data, calculate the average service area arrival rate within period T. Service intensity of service area ;

[0059] In this embodiment, the number of vehicles entering the nth service area is: Y mn This indicates that vehicle m is parked at server n; the average arrival rate of the service area. Service area service intensity ;

[0060] S203. Treat the service area as a finite waiting queuing model, where the vehicle entry process follows a Poisson distribution, and obtain the expected service time E of the service area. T and variance Calculate the average waiting time W for vehicles at the nth service area. N .

[0061] In this embodiment, the service area parking lot is considered as a queuing system with limited waiting time. The customer input process in the queuing model follows the parameters: The Poisson distribution has an expected service time of E. T (Statistical value, preferably 0.5h), variance is ( The value is 6.74). The above parameters should be set according to specific requirements. The average waiting time W for vehicles in the nth service area is... n for:

[0062]

[0063] In this embodiment, the constraints of the service area load balancing model are as follows:

[0064] 1. Each vehicle may only stop at one service area: , ;

[0065] 2. The vehicle's arrival time at the service area did not exceed the expected time: ;

[0066] 3. The number of vehicles parked at each service area shall not exceed the number of vehicles that can be parked at the service area: , ;

[0067] 4. Service area parking capacity limit threshold: The value is determined based on traffic flow management needs, and M is set according to requirements.

[0068] After obtaining the service area load balancing model, the model will be validated. Since the objective function of this model includes the performance indicators of the queuing model, the model holds true when the system is not saturated, that is, it must satisfy: .

[0069] With the optimization objective of model optimization, and to avoid over-concentration of superior resources while ensuring vehicles can smoothly enter service areas, a constraint on the parking capacity of service areas is added to the model. This also takes into account the load balancing of the service area.

[0070] S300. Solve the load balancing model to obtain the optimal number of parking spaces and the ratio of predicted waiting time to service time for each service area. Adjust the charging price of the service area based on the ratio of predicted waiting time to service time.

[0071] In this embodiment, the load balancing model is solved to obtain the optimal number of parking spaces for each service area. The specific method includes:

[0072] The specific conditions for setting the model should include at least the number of service areas n, the period time T, the number of vehicles to be assigned within the period up, and the distance d from the current location to each service area. mn Number of available charging berths (K) in each service area n Parking capacity threshold M;

[0073] Based on the given conditions, a random vehicle speed and time distribution that conforms to the characteristics of highway vehicle operation is generated as the initial conditions for solving the problem.

[0074] The parameters required to calculate the objective function based on the given conditions include at least the expected arrival rate of each service area, the expected average parking time, and the average vehicle waiting time of each service area.

[0075] Solve the objective function, and based on the set objective function, decision variables and constraints, output the number of parking spaces in each service area and the service area selection of each vehicle.

[0076] Specifically, the Gurobi optimizer is used for solving the problem. The objective function of the Gurobi-solved model is:

[0077]

[0078] in: For the solution result, For the parameters of the objective function, The calculation formula is:

[0079]

[0080] First, define the specific conditions of the model, including the service area s. er The quantity, the value of the period time T, the number of vehicles to be allocated within the period up, and the distance d from the current location to each service area. mn Number of available charging stations (k) in each service area n Parking capacity threshold M, etc.

[0081] Based on given conditions, randomly generate vehicle v that conforms to the operating characteristics of vehicles on highways. m t l Distribution, as the initial condition for solving the problem.

[0082] Based on the given conditions, the parameters required for the objective function are calculated, including the expected arrival rate, the expected average parking time, and the average vehicle waiting time for each service area.

[0083] Solving the objective function: Based on the Anacoda3 environment and following the Gurobi modeling syntax, the objective function, decision variables, and constraints are set. The Gurobi 10.0.0 optimizer is then called to solve the optimization model, outputting the number of parking spaces for each service area. Additionally, the service area selection for each vehicle can also be output.

[0084] After solving the load balancing model, the ratio of the predicted waiting time to the actual service time is obtained. The charging price in the service area is then adjusted based on this ratio.

[0085] Specifically, the service area is evaluated based on the ratio of the predicted waiting time to the actual service time. The specific method includes: calculating the ratio of the predicted waiting time to the actual service time based on the average arrival rate of the service area, the standard deviation of the charging station service time, the number of available charging spaces in each service area, and the average service rate of the charging station. When the ratio of the predicted waiting time to the actual service time is less than the ratio of the actual waiting time to the actual service time, the charging fee in the service area is increased; otherwise, the charging fee remains unchanged.

[0086] This embodiment provides a highway vehicle charging service method based on the Internet of Vehicles (IoV). It involves acquiring highway service area data and vehicle data; constructing a service area load balancing model based on this data; determining the constraints and objective function of the load balancing model; solving the load balancing model to obtain the optimal number of parking spaces and the ratio of predicted waiting time to actual service time for each service area; and adjusting the charging fee at the service area based on this ratio. This invention addresses the current situation where users face difficulties finding suitable service resources on highways, leading to traffic congestion at service areas, and the problem that adding reservation functions to service areas has actually reduced the efficiency of various services.

[0087] Based on the same inventive concept, embodiments of the present invention also provide a highway vehicle charging service system based on the Internet of Vehicles, such as... Figure 2 It includes: a service area data and vehicle data acquisition unit, a service area load balancing model construction unit, and a service area load balancing model solution unit; wherein:

[0088] Service area data and vehicle data acquisition unit, used to acquire service area data and vehicle data of the highway;

[0089] The service area load balancing model construction unit is used to construct a service area load balancing model based on the service area data and vehicle data, and to determine the constraints and objective function of the load balancing model.

[0090] The service area load balancing model solving unit is used to solve the load balancing model to obtain the optimal number of parking spaces and the ratio of the predicted waiting time to the actual service time for each service area. The charging price for the service area is then adjusted based on the ratio of the predicted waiting time to the actual service time.

[0091] The specific working methods of the service area data and vehicle data acquisition unit, the service area load balancing model construction unit, and the service area load balancing model solving unit have been described in detail in the above embodiments, and will not be repeated here.

[0092] Based on the same inventive concept, embodiments of the present invention also provide an electronic device. Figure 3 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement any of the vehicle charging service methods described in the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0093] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).

[0094] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0095] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0096] This invention also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps of any of the vehicle charging service methods described in the above embodiments. The computer-readable storage medium may be volatile or non-volatile.

[0097] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-described vehicle charging service method.

[0098] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0099] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0100] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge service areas. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to computer-readable storage media in the respective computing / processing device.

[0101] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or service area. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0102] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0103] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0104] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0105] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0106] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0107] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A method for providing highway vehicle charging services based on the Internet of Vehicles, characterized in that, include: Acquire highway service area data and vehicle data; Based on the service area data and vehicle data, a service area load balancing model is constructed, and the constraints and objective function of the load balancing model are determined. Solve the load balancing model to obtain the optimal number of parking spaces and the ratio of predicted waiting time to actual service time for each service area. Adjust the charging price for the service area based on the ratio of predicted waiting time to actual service time.

2. The vehicle charging service method according to claim 1, characterized in that, The service area data includes at least a service area set S. er ={er1, er2, ..., er n }, where er1, er2, ..., er n These represent service areas numbered sequentially as 1, 2, ..., n, with K parking spaces available for vehicle charging in each service area. n .

3. The vehicle charging service method according to claim 1, characterized in that, The vehicle data includes at least the number of vehicles p traveling on the highway, the proportion u of vehicles with a need to enter the service area, and the distance er between vehicle m and the service area. n Distance d mn Vehicle speed V m .

4. The vehicle charging service method according to claim 3, characterized in that, Based on the service area data and vehicle data, a service area load balancing model is constructed. Specific methods include: The vehicles are categorized according to their accessibility to service areas, resulting in a set V of vehicles that require access to service areas. el ={V el1 V el2 ,.....V eln }, where V el1 This indicates that the vehicle can only reach the nth service area. el2 This indicates a vehicle that can reach the second service area. (V) eln The first type of vehicle represents the vehicle that can reach the nth service area; Based on the service area data and vehicle data, calculate the average service area arrival rate within period T. Service intensity of service area ; The service area is used as a queuing model with finite waiting time, where the vehicle entry process follows a Poisson distribution. The expected service time E of the service area is obtained. T and variance Calculate the average waiting time W for vehicles at the nth service area. N .

5. The vehicle charging service method according to claim 1, characterized in that, The constraints of the load balancing model include: any vehicle stops at only one service area, the time for a vehicle to arrive at a service area does not exceed the expected time, the number of vehicles stopping at each service area does not exceed the number of parking spaces available at the service area and the service area parking capacity limit threshold; the objective function of the load balancing model is to minimize the total waiting time of all vehicles.

6. The vehicle charging service method according to claim 3, characterized in that, Solving the load balancing model to obtain the optimal number of parking spaces for each service area involves the following methods: The specific conditions for setting the model should include at least the number of service areas n, the period time T, the number of vehicles to be assigned within the period up, and the distance d from the current location to each service area. mn Number of available charging berths (K) in each service area n Parking capacity threshold M; Based on the given conditions, a random vehicle speed and time distribution that conforms to the characteristics of highway vehicle operation is generated as the initial conditions for solving the problem. The parameters required to calculate the objective function based on the given conditions include at least the expected arrival rate of each service area, the expected average parking time, and the average vehicle waiting time of each service area. Solve the objective function, and based on the set objective function, decision variables and constraints, output the number of parking spaces in each service area and the service area selection of each vehicle.

7. The vehicle charging service method according to claim 1, characterized in that, The service area is evaluated based on the ratio of the predicted waiting time to the actual service time. The specific method includes: calculating the ratio of the predicted waiting time to the actual service time based on the average arrival rate of the service area, the standard deviation of the service time of the charging station, the number of parking spaces available for vehicle charging in each service area, and the average service rate of the charging station. When the ratio of the predicted waiting time to the actual service time is less than the ratio of the actual waiting time to the actual service time, the charging fee in the service area is increased; otherwise, the charging fee in the service area remains unchanged.

8. A highway vehicle charging service system based on vehicle-to-everything (V2X) communication, employing any one of the methods described in claims 1-7, characterized in that, include: Service area data and vehicle data acquisition unit, service area load balancing model construction unit, and service area load balancing model solution unit; wherein: Service area data and vehicle data acquisition unit, used to acquire service area data and vehicle data of the highway; The service area load balancing model construction unit is used to construct a service area load balancing model based on the service area data and vehicle data, and to determine the constraints and objective function of the load balancing model. The service area load balancing model solving unit is used to solve the load balancing model to obtain the optimal number of parking spaces and the ratio of the predicted waiting time to the actual service time for each service area. The charging price for the service area is then adjusted based on the ratio of the predicted waiting time to the actual service time.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the highway vehicle charging service method as described in any one of claims 1 to 7.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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