Vehicle driving path planning method, device, equipment, medium and product

Abstract

The embodiment of the invention discloses a vehicle driving path planning method and device, equipment, a medium and a product, and the method comprises the steps: building a path planning model according to logistics distribution network information with the minimization of a vehicle distribution distance as a target function; solving the path planning model by using an African vulture optimization algorithm to determine an optimal distribution path of each vehicle; determining a first distribution path in the optimal distribution paths; the first distribution path is a path which does not meet the mileage constraint condition; and a charging pile is inserted into the optimal charging position in the first delivery path, so that a vehicle corresponding to the first delivery path performs order delivery on the first delivery path. According to the technical scheme, the optimal distribution path is determined by using the African vulture optimization algorithm, the first distribution path which does not meet the mileage constraint is determined in the optimal path, and the charging pile is inserted in the optimal charging position of the first distribution path, so that the problem that the distribution task cannot be completed due to insufficient electric quantity of the electric vehicle is solved, and the distribution efficiency is improved.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of path planning technology, and in particular to a method, device, equipment, medium and product for planning a vehicle driving path. Background Art

[0002] With its advantages of low noise and low emissions, electric vehicles have become an inevitable trend in the development of the logistics industry. However, electric vehicles often face the problems of limited driving range and inconvenient charging. In addition, the customer demand of e-commerce logistics companies is highly volatile, and the distribution of customer orders is highly uncertain, which leads to frequent changes in the driving routes of electric vehicles. In order to solve the above problems, charging pile suppliers have proposed a new model: e-commerce logistics companies can purchase mobile charging pile services. Charging pile suppliers will place charging piles in the location that best suits the vehicle route, and relocate them according to the new delivery route after each delivery task is completed. However, the site selection problem of mobile charging piles is interdependent with the vehicle route problem, which further increases the complexity of urban distribution problems. Summary of the invention

[0003] The embodiments of the present disclosure provide a vehicle driving path planning method, device, equipment, medium and product, which solve the problem that electric vehicles cannot complete delivery tasks due to insufficient power, and improve delivery efficiency.

[0004] In a first aspect, a vehicle driving path planning method is provided, comprising:

[0005] According to the logistics distribution network information, a path planning model is established with the minimization of vehicle distribution distance as the objective function;

[0006] The path planning model is solved using an African vulture optimization algorithm (AVOA) to determine the optimal delivery path for each vehicle;

[0007] Determine a first delivery path among the optimal delivery paths; the first delivery path is a path that does not satisfy the mileage constraint condition;

[0008] A charging pile is inserted at the best charging position in the first delivery path, so that the vehicle corresponding to the first delivery path can deliver the order on the first delivery path.

[0009] In a second aspect, a vehicle driving path planning device is provided, comprising:

[0010] The model building module is used to establish a path planning model based on the logistics distribution network information and taking the minimization of vehicle distribution distance as the objective function;

[0011] An optimal delivery path determination module is used to solve the path planning model using an African vulture optimization algorithm AVOA to determine the optimal delivery path for each vehicle;

[0012] A first delivery path determination module, used to determine a first delivery path in the optimal delivery path; the first delivery path is a path that does not meet the mileage constraint condition;

[0013] The charging pile insertion module is used to insert a charging pile at an optimal charging position in the first delivery path, so that the vehicle corresponding to the first delivery path can deliver the order on the first delivery path.

[0014] In a third aspect, an electronic device is provided, including:

[0015] at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle driving path planning method as described in the first aspect above.

[0018] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the vehicle driving path planning method as described in the first aspect above is implemented.

[0019] In a fifth aspect, a computer program product is provided, the computer program product comprising a computer program, and the computer program, when executed by a processor, implements the vehicle driving path planning method as described in the first aspect above.

[0020] The disclosed embodiment discloses a vehicle driving route planning method, device, equipment, medium and product, including: according to the logistics distribution network information, a route planning model is established with minimization of vehicle delivery distance as the objective function; the route planning model is solved by using the African vulture optimization algorithm AVOA to determine the optimal delivery route for each vehicle; the first delivery route in the optimal delivery route is determined; the first delivery route is a route that does not meet the mileage constraint; a charging pile is inserted at the best charging position in the first delivery route so that the vehicle corresponding to the first delivery route can deliver orders on the first delivery route. This technical solution uses the African vulture optimization algorithm to determine the optimal delivery route, and determines the first delivery route that does not meet the mileage constraint in the optimal route, and inserts a charging pile at the best charging position of the first delivery route, so as to solve the problem that electric vehicles cannot complete delivery tasks due to insufficient power, thereby improving delivery efficiency.

[0021] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the embodiments of the present disclosure. Other features of the embodiments of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 is a flow chart of a vehicle driving path planning method provided in Embodiment 1 of the present disclosure;

[0024] Figure 2 This is a schematic diagram of the steps of a vehicle driving path planning method provided in the first embodiment of the present disclosure;

[0025] Figure 3 It is a structural schematic diagram of a vehicle driving path planning device provided in Embodiment 2 of the present disclosure;

[0026] Figure 4 It is a structural diagram of an electronic device provided in Embodiment 3 of the present disclosure. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the solutions of the embodiments of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only embodiments of a part of the embodiments of the present disclosure, rather than all of the embodiments. Based on the embodiments in the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the embodiments of the present disclosure.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0029] Embodiment 1

[0030] Figure 1 This is a flow chart of a vehicle driving path planning method provided in the first embodiment of the present disclosure. This embodiment is applicable to the case of planning a vehicle driving path. The method can be executed by a vehicle driving path planning device. The vehicle driving path planning device can be implemented in the form of hardware and / or software. The vehicle driving path planning device can be configured in an electronic device, including but not limited to computers, computers, terminals, servers and other devices with data processing capabilities. Figure 1 As shown, the method includes:

[0031] S110. According to the logistics distribution network information, a path planning model is established with minimization of vehicle distribution distance as the objective function.

[0032] In this embodiment, the vehicle may be a delivery vehicle of a logistics company, the number of the vehicle is at least one, and the vehicle may provide delivery services to each customer node. A path planning model may be established based on the logistics distribution network information, with minimization of the vehicle delivery distance as the objective function. The logistics distribution network information may be information about the vehicle in the distribution network, and the logistics distribution network information may include: customer nodes, path decision variable information, vehicle attribute information, customer order information, driving path constraints, mileage constraints, and other information.

[0033] For example, the objective function can be expressed as:

[0034]

[0035] Among them, d ij represents the distance between node i and node j, x ij The path decision variable from node i to node j, 1 means selecting the path, 0 means not selecting it.

[0036] S120, solving the path planning model using the African vulture optimization algorithm AVOA to determine the optimal delivery path for each vehicle;

[0037] In this embodiment, the African Vultures Optimization Algorithm (AVOA) is inspired by the foraging behavior of African vultures, and performs path optimization by simulating the exploration of vultures in a wide area and the concentrated development behavior after finding food. After obtaining the path planning model, the AVOA algorithm can be used to solve the path planning model to determine the optimal delivery path for each vehicle, wherein the optimal delivery path can be the shortest driving distance of the vehicle when the delivery task is completed.

[0038] It should be noted that the number of vehicles is at least one, so the number of optimal delivery paths is also at least one. When there is one vehicle, the number of optimal delivery paths is also one; when there are multiple vehicles, the number of optimal delivery paths is also multiple.

[0039] S130. Determine a first delivery path among the optimal delivery paths; the first delivery path is a path that does not satisfy the mileage constraint condition.

[0040] Specifically, after the optimal delivery path is determined, it can be determined whether the optimal path satisfies a mileage constraint condition, and the mileage constraint condition can be a condition that constrains the mileage of the vehicle. Optionally, the mileage constraint condition can be: the current vehicle's cruising range is greater than the remaining delivery distance on the current delivery path.

[0041] Continuing with the above description, the first delivery path in the optimal path can be determined based on the mileage constraint condition, wherein the first delivery path is a path that does not meet the mileage constraint condition. That is, for the current delivery path, the vehicle's cruising range is less than the remaining delivery paths on the current delivery path.

[0042] S140: insert a charging pile at an optimal charging position in the first delivery path, so that vehicles corresponding to the first delivery path can deliver orders on the first delivery path.

[0043] It can be known that after the first delivery path is determined, a charging pile can be inserted at the best charging position in the first delivery path, wherein the best charging position can be a charging position that ensures that the vehicle can complete the delivery task after charging. After the charging pile is inserted at the best charging position of the first delivery path, the vehicle corresponding to the first delivery path can deliver orders on the first delivery path.

[0044] This embodiment provides a vehicle driving path planning method, including: establishing a path planning model based on logistics distribution network information and taking minimizing vehicle delivery distance as the objective function; solving the path planning model using the African vulture optimization algorithm AVOA to determine the optimal delivery path for each vehicle; determining a first delivery path in the optimal delivery path; the first delivery path is a path that does not meet mileage constraints; inserting a charging pile at the best charging position in the first delivery path, so that the vehicle corresponding to the first delivery path can deliver orders on the first delivery path, thereby solving the problem that electric vehicles cannot complete delivery tasks due to insufficient power, and improving delivery efficiency.

[0045] As an optional implementation of this embodiment, the vehicle driving path planning method provided in this embodiment further includes:

[0046] A second delivery path in the optimal delivery path is determined so that a vehicle corresponding to the second delivery path performs order delivery on the second delivery path; the second delivery path is a path that satisfies a mileage constraint condition.

[0047] It can be known that the second delivery path in the optimal delivery path can also be determined according to the mileage constraint condition, wherein the second delivery path is a path that satisfies the mileage constraint condition. For the second delivery path, it satisfies the mileage constraint condition, therefore, it is not necessary to install a charging pile in the second delivery path, and the vehicles in the second delivery path can deliver orders on the second delivery path.

[0048] As an optional implementation of this embodiment, before establishing a path planning model based on the logistics distribution network information and taking minimization of vehicle distribution distance as the objective function, the method further includes:

[0049] Determine the logistics distribution network information, the logistics distribution network information includes: customer nodes, path decision variable information, vehicle attribute information, the customer order information, driving path constraints and the mileage constraints;

[0050] Specifically, the logistics distribution network information can be determined according to the actual distribution situation of the present technical solution, and the logistics distribution network information may include: customer nodes, path decision variable information, vehicle attribute information, the customer order information, driving path constraints and the mileage constraints and other information.

[0051] The customer node may be a customer node to be delivered, and the path decision variable information may be a decision variable for whether a path between two customer nodes is selected. For example, the path decision variable information may be expressed as: ij, represents the path decision variable from node i to node j, 1 means selecting the path, 0 means not selecting. Vehicle attribute information may include: the range of each vehicle and the maximum load capacity of each vehicle; customer order information includes: customer order demand and the latest delivery time.

[0052] Optionally, the driving path constraint condition may include: each customer node is visited once, such as: customer node; the delivery time of each customer node is less than the latest delivery time; the current vehicle's load capacity is less than the current vehicle's maximum load capacity; the delivery volume of each customer node meets the customer's order demand. The mileage constraint condition can be a condition that constrains the vehicle's cruising range, such as: Among them, e ij represents the energy consumption between node i and node j, E max Indicates the maximum range of an electric vehicle.

[0053] As an optional implementation of this embodiment, the African vulture optimization algorithm AVOA is used to solve the path planning model to determine the optimal delivery path, including:

[0054] 1) Randomly generate a set number of candidate routes and calculate the fitness value of each candidate route, where the fitness value is the delivery distance of the candidate route;

[0055] Specifically, a set number of vulture individuals can be randomly generated, each vulture individual represents a candidate path. After the set number of candidate paths are randomly generated, the fitness value of each candidate path can be calculated, and the fitness value is the delivery distance of each candidate path.

[0056] 2) In the current iteration, the candidate paths are adjusted by random directions and random step sizes to obtain different path combinations, and the candidate paths are optimized according to the fitness values ​​corresponding to the different path combinations;

[0057] It can be known that the African vulture optimization algorithm only includes two stages: exploration and exploitation. In the exploration stage, the vulture will randomly search for food in a larger range according to its hunger level, which usually involves the determination of random directions and random steps. The hunger level can affect the vulture's search behavior, and the hunger level can be expressed as: Among them, F represents the hunger level of the vulture, rand1 can be a random number between 0 and 1, and z can be a random number between -1 and 1, which may change in each iteration. iIt can represent the current number of iterations, maxiterations can be used to represent the maximum number of iterations, and t can be an additional random factor that can be used to simulate the behavior of vultures under different hunger states.

[0058] Continuing from the above description, when the absolute value of F is greater than 1, the vulture will enter the exploration phase and randomly search for food in a larger range. This usually means that the algorithm will perform a global search in the hope of finding a better solution. If the absolute value of F is less than 1, the algorithm enters the development phase, and the vulture will perform a more detailed search near the known better solution, which helps the algorithm to perform local search around the good solution that has been found in order to find a better solution.

[0059] Specifically, during the exploration phase, the vultures will search according to randomly generated directions. This random direction simulates the flight path of the vultures in nature when there is no specific target. The random direction helps the algorithm avoid falling into the local optimal solution and increases the chance of finding the global optimal solution. The step size determines the distance the vulture moves during the search process. In AVOA, the step size is usually related to the hunger level of the vultures. Vultures with higher hunger levels may take larger step sizes for searching. The random step size helps the algorithm to search effectively in the solution space while maintaining the diversity of the search process.

[0060] Specifically, by adjusting the candidate paths by random directions and random step lengths, different path combinations can be obtained. After obtaining different path combinations, the candidate paths can be optimized according to the fitness values ​​corresponding to the different path combinations. For example, a candidate path can be optimized by selecting a value with a small fitness.

[0061] 3) If the current iteration does not meet the iteration stop condition, enter the next iteration;

[0062] In this embodiment, the iteration stop condition may be a condition for the AVOA algorithm to stop iterating, such as: the iteration stop condition may be that the change in the fitness value is less than a preset threshold, which can be understood as the distance of the delivery path obtained through iteration tends to be stable; the iteration stop condition may also be that the current number of iterations reaches the maximum number of iterations.

[0063] 4) If the current iteration satisfies the iteration stop condition, the optimized candidate path is determined as the optimal delivery path.

[0064] Specifically, if the current iteration meets the iteration stop condition, the currently optimized candidate path can be determined as the optimal delivery path.

[0065] In one embodiment, inserting a charging pile at an optimal charging position in the first delivery path includes:

[0066] 1) Determine a first installation position of the charging pile according to a preset charging threshold, and determine a second installation position of the charging pile according to a preset cruising range threshold of the current vehicle; the first installation position is the predicted position of the current vehicle when the power level is lower than the preset charging threshold; the second installation position is the predicted position of the current vehicle when the cruising range is lower than the preset cruising range threshold;

[0067] In this embodiment, after the first delivery route is determined, the first installation position of the charging pile can be determined according to the preset charging threshold, and the second installation position of the charging pile can be determined according to the preset cruising range threshold of the current vehicle, wherein the first installation position is the predicted position of the current vehicle when the power level is lower than the preset charging threshold; the second installation position is the predicted position of the current vehicle when the cruising range is lower than the preset cruising range threshold.

[0068] Continuing with the above description, the preset charging threshold may be a pre-set charging threshold, and an exemplary charging threshold may be half of the battery power. When the current power of the vehicle is lower than the preset charging threshold, the position at which the current power is lower than the preset charging threshold may be set as the first installation position. For example, the battery capacity is 100 kwh, the preset charging threshold is 50 kwh, and when the power of the vehicle is lower than 50 kwh, the position at which the current power of the vehicle is lower than 50 kwh is determined as the first installation position.

[0069] Continuing with the above description, the preset cruising range threshold may be a preset battery range threshold, such as: the preset cruising range is 50 kilometers. If the vehicle's cruising range is less than 50 kilometers, the position of the vehicle when the range is less than 50 kilometers may be determined as the second installation position.

[0070] 2) determining an initial installation range according to the first installation position and the second installation position;

[0071] Specifically, after the first installation position and the second installation position are determined, an initial installation range may be determined according to the first installation position and the second installation position, wherein the initial installation range may be an installation range between the first installation position and the second installation position.

[0072] 3) Within the initial installation range, determining the optimal charging position based on power constraint conditions, and inserting a charging pile at the optimal charging position.

[0073] It can be known that the best charging position can be determined within the initial installation range based on the power constraint condition, wherein the power constraint condition may include: the range of the vehicle after charging is greater than the remaining delivery range of the first delivery route. Using the power constraint condition to determine the best installation position of the charging pile can avoid the situation where the vehicle cannot be delivered even after charging due to the charging pile insertion position being too far from the end point of the delivery route.

[0074] Among them, the optimal charging position can be a charging position within the initial installation range to ensure that the vehicle can complete route delivery after charging. After the optimal charging position is determined, the charging pile can be inserted at the optimal charging position to enable the vehicle to complete charging.

[0075] Figure 2 A schematic diagram of the steps of a vehicle driving path planning method provided in this embodiment, such as Figure 2 As shown, according to the logistics distribution information, the path planning model is established with the minimization of vehicle delivery distance as the objective function, and the path planning model is solved by AVOA to determine the optimal delivery path for each vehicle and whether the optimal delivery path meets the mileage constraint. If so, the order can be delivered according to the optimal delivery path; if not, the initial installation range is determined according to the preset charging threshold and the preset cruising range threshold, and the optimal charging position is determined based on the power constraint, and the charging pile is inserted at the optimal charging position to complete the path delivery, thereby solving the problem that electric vehicles cannot complete the delivery task due to insufficient power, and improving the delivery efficiency.

[0076] Embodiment 2

[0077] Figure 3 is a schematic diagram of the structure of a vehicle driving path planning device provided in the second embodiment of the present disclosure; Figure 3 As shown, the device includes: a model building module 210, an optimal delivery path determination module 220, a first delivery path determination module 230, and a charging pile insertion module 240.

[0078] The model building module 210 is used to establish a path planning model based on the logistics distribution network information and taking minimization of vehicle distribution distance as the objective function;

[0079] The optimal delivery path determination module 220 is used to solve the path planning model using the African vulture optimization algorithm AVOA to determine the optimal delivery path for each vehicle;

[0080] A first delivery path determination module 230 is used to determine a first delivery path in the optimal delivery path; the first delivery path is a path that does not meet the mileage constraint condition;

[0081] The charging pile insertion module 240 is used to insert a charging pile at an optimal charging position in the first delivery path, so that the vehicle corresponding to the first delivery path can deliver the order on the first delivery path.

[0082] The second embodiment of the present disclosure provides a vehicle driving path planning device, which solves the problem that electric vehicles cannot complete delivery tasks due to insufficient power, thereby improving delivery efficiency.

[0083] Furthermore, the device also includes:

[0084] The second delivery path determination module is used to determine a second delivery path in the optimal delivery path so that the vehicle corresponding to the second delivery path performs order delivery on the second delivery path; the second delivery path is a path that meets the mileage constraint condition.

[0085] Furthermore, the device also includes:

[0086] A logistics distribution network information determination module, used to determine the logistics distribution network information, the logistics distribution network information includes: customer nodes, path decision variable information, vehicle attribute information, the customer order information, driving path constraints and mileage constraints;

[0087] The vehicle attribute information includes: the cruising range of each vehicle and the maximum load capacity of each vehicle;

[0088] The customer order information includes: customer order demand quantity and latest delivery time.

[0089] Furthermore, the optimal delivery path determination module 220 is also used to:

[0090] Randomly generate a set number of candidate routes, and calculate the fitness value of each candidate route, wherein the fitness value is the delivery distance of the candidate route;

[0091] In the current iteration, the candidate paths are adjusted by random directions and random step sizes to obtain different path combinations, and the candidate paths are optimized according to the fitness values ​​corresponding to the different path combinations;

[0092] If the current iteration does not meet the iteration stop condition, it will enter the next iteration;

[0093] If the current iteration meets the iteration stop condition, the optimized candidate path is determined as the optimal delivery path.

[0094] Furthermore, the mileage constraint condition is:

[0095] The current vehicle range is greater than the remaining delivery distance on the current delivery route;

[0096] The driving path constraints include:

[0097] Each client node is visited once;

[0098] The delivery time of each customer node is less than the latest delivery time;

[0099] The load capacity of the current vehicle is less than the maximum load capacity of the current vehicle;

[0100] The delivery quantity of each customer node meets the customer order demand.

[0101] Furthermore, the charging pile insertion module 240 is also used for:

[0102] Determine a first installation position of the charging pile according to a preset charging threshold, and determine a second installation position of the charging pile according to a preset cruising range threshold of the current vehicle; the first installation position is a predicted position of the current vehicle when the power level is lower than the preset charging threshold; the second installation position is a predicted position of the current vehicle when the cruising range is lower than the preset cruising range threshold;

[0103] determining an initial installation range according to the first installation position and the second installation position;

[0104] Within the initial installation range, determining the optimal charging position based on power constraint conditions, and inserting a charging pile at the optimal charging position;

[0105] The power constraint conditions include:

[0106] The cruising range of the vehicle after charging is greater than the remaining delivery range of the first delivery route.

[0107] The vehicle driving path planning device provided in the embodiments of the present disclosure can execute the vehicle driving path planning method provided in any embodiment of the embodiments of the present disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

[0108] Embodiment 3

[0109] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present disclosure described and / or claimed herein.

[0110] like Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0111] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0112] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microprocessor, etc. The processor 11 executes the various methods and processes described above, such as a method for planning a vehicle driving path.

[0113] In some embodiments, the vehicle travel path planning method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the vehicle travel path planning method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the vehicle travel path planning method in any other appropriate manner (e.g., by means of firmware).

[0114] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0115] The computer programs for implementing the methods of the embodiments of the present disclosure may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0116] In the context of the disclosed embodiments, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0117] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0118] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0119] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0120] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in the embodiments of the present disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions of the embodiments of the present disclosure can be achieved, and this document does not limit this.

[0121] The above specific implementations do not constitute a limitation on the protection scope of the embodiments of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the embodiments of the present disclosure shall be included in the protection scope of the embodiments of the present disclosure.

[0122] The embodiments of the present disclosure also provide a computer program product, including a computer program and / or instructions, which, when executed by a processor, implements a vehicle driving path planning method as provided in any embodiment of the present application.

[0123] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to write computer program codes for performing the operation of the disclosed embodiments, the programming languages ​​including object-oriented programming languages, such as Java, Smalltalk, C++, and also including conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0124] Note that the above are only preferred embodiments of the embodiments of the present disclosure and the technical principles used. Those skilled in the art will understand that the embodiments of the present disclosure are not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the protection scope of the embodiments of the present disclosure. Therefore, although the embodiments of the present disclosure are described in more detail through the above embodiments, the embodiments of the present disclosure are not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the embodiments of the present disclosure, and the scope of the embodiments of the present disclosure is determined by the scope of the attached claims.

Claims

1. A vehicle driving path planning method, characterized in that: include: According to the logistics distribution network information, a path planning model is established with the minimization of vehicle distribution distance as the objective function; The path planning model is solved using an African vulture optimization algorithm (AVOA) to determine the optimal delivery path for each vehicle; Determine a first delivery path among the optimal delivery paths; the first delivery path is a path that does not satisfy the mileage constraint condition; A charging pile is inserted at an optimal charging position in the first delivery path, so that a vehicle corresponding to the first delivery path can deliver an order on the first delivery path.

2. The method according to claim 1, characterized in that: The method further comprises: A second delivery path in the optimal delivery path is determined so that a vehicle corresponding to the second delivery path performs order delivery on the second delivery path; the second delivery path is a path that satisfies a mileage constraint condition.

3. The method according to claim 1, characterized in that Before establishing a path planning model based on the logistics distribution network information and taking minimization of vehicle distribution distance as the objective function, the method further includes: Determine the logistics distribution network information, the logistics distribution network information includes: customer nodes, path decision variable information, vehicle attribute information, the customer order information, driving path constraints and the mileage constraints; The vehicle attribute information includes: the cruising range of each vehicle and the maximum load capacity of each vehicle; The customer order information includes: customer order demand quantity and latest delivery time.

4. The method according to claim 1, characterized in that The method of solving the path planning model using the African vulture optimization algorithm AVOA to determine the optimal delivery path includes: Randomly generate a set number of candidate routes, and calculate the fitness value of each candidate route, wherein the fitness value is the delivery distance of the candidate route; In the current iteration, the candidate paths are adjusted by random directions and random step sizes to obtain different path combinations, and the candidate paths are optimized according to the fitness values ​​corresponding to the different path combinations; If the current iteration does not meet the iteration stop condition, it will enter the next iteration; If the current iteration meets the iteration stop condition, the optimized candidate path is determined as the optimal delivery path.

5. The method according to claim 3, characterized in that: The mileage constraints are: The current vehicle range is greater than the remaining delivery distance on the current delivery route; The driving path constraints include: Each client node is visited once; The delivery time of each customer node is less than the latest delivery time; The load capacity of the current vehicle is less than the maximum load capacity of the current vehicle; The delivery quantity of each customer node meets the customer order demand.

6. The method according to claim 5, characterized in that The inserting a charging pile at an optimal charging position in the first delivery path comprises: Determine a first installation position of the charging pile according to a preset charging threshold, and determine a second installation position of the charging pile according to a preset cruising range threshold of the current vehicle; the first installation position is a predicted position of the current vehicle when the power level is lower than the preset charging threshold; the second installation position is a predicted position of the current vehicle when the cruising range is lower than the preset cruising range threshold; determining an initial installation range according to the first installation position and the second installation position; Within the initial installation range, determining the optimal charging position based on power constraint conditions, and inserting a charging pile at the optimal charging position; The power constraint conditions include: The cruising range of the vehicle after charging is greater than the remaining delivery range of the first delivery route.

7. A vehicle driving path planning device, characterized in that: include: The model building module is used to establish a path planning model based on the logistics distribution network information and taking the minimization of vehicle distribution distance as the objective function; An optimal delivery path determination module is used to solve the path planning model using an African vulture optimization algorithm AVOA to determine the optimal delivery path for each vehicle; A first delivery path determination module, used to determine a first delivery path in the optimal delivery path; the first delivery path is a path that does not meet the mileage constraint condition; The charging pile insertion module is used to insert a charging pile at an optimal charging position in the first delivery path, so that the vehicle corresponding to the first delivery path can deliver the order on the first delivery path.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle driving path planning method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the vehicle driving path planning method as described in any one of claims 1-6 is implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the vehicle driving path planning method as described in any one of claims 1 to 6 is implemented.

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