Mobile charging vehicle scheduling method and device, computer readable medium and electronic equipment

Abstract

The invention provides a mobile charging vehicle scheduling method, a mobile charging vehicle scheduling device, a computer readable medium and electronic equipment, and relates to the technical field of vehicle scheduling processing. The method comprises the following steps: collecting vehicle-mounted state data of the mobile charging vehicle and charging demand data uploaded by a user; determining a target matching relationship between the mobile charging vehicle and the charging demand according to the vehicle-mounted state data and the charging demand data, and generating a scheduling instruction based on the target matching relationship; and calculating a driving path according to the scheduling instruction and a preset station map so as to control the mobile charging vehicle to drive to the position of the to-be-charged vehicle corresponding to the charging demand. According to the invention, the distribution of the mobile charging vehicles can be more suitable for the state of each mobile charging vehicle and the requirement of each charging demand data, and the scheduling intelligence and the scheduling efficiency are improved; and meanwhile, the mobile charging vehicle can move more reasonably and efficiently.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle dispatching and processing technology, specifically to a mobile charging vehicle dispatching method, a mobile charging vehicle dispatching device, a computer-readable medium, and an electronic device. Background Technology

[0002] While the new energy vehicle industry is developing rapidly, the problem of insufficient charging infrastructure supply is becoming increasingly prominent. Currently, electric vehicles typically need to be charged at public charging stations or fixed charging piles in residential areas. However, fixed charging piles have significant drawbacks: first, they are expensive to build, limiting large-scale deployment; second, their coverage is insufficient, making it difficult to meet the needs of all-area travel; and third, queues are severe during peak hours, with users at highway service areas waiting an average of 47 minutes, resulting in low service efficiency. To address these shortcomings, mobile charging solutions have emerged. However, existing mobile charging solutions typically rely on manual dispatching, meaning each charging request requires manual dispatch of a mobile charging vehicle, which can lead to low dispatching efficiency and unreasonable dispatching results. Summary of the Invention

[0003] The purpose of this disclosure is to provide a mobile charging vehicle scheduling method, a mobile charging vehicle scheduling device, a computer-readable medium, and an electronic device, thereby improving the intelligence of mobile charging vehicle scheduling and increasing scheduling efficiency to at least a certain extent.

[0004] According to a first aspect of this disclosure, a mobile charging vehicle scheduling method is provided, comprising: collecting on-board status data of the mobile charging vehicle and charging demand data uploaded by users; determining a target matching relationship between the mobile charging vehicle and the charging demand based on the on-board status data and the charging demand data, and generating a scheduling instruction based on the target matching relationship; calculating a driving path based on the scheduling instruction and a preset station map, so as to control the mobile charging vehicle to drive to the location of the vehicle to be charged corresponding to the charging demand.

[0005] According to a second aspect of this disclosure, a mobile charging vehicle dispatching device is provided, comprising: a data acquisition module for acquiring onboard status data of the mobile charging vehicle and charging demand data uploaded by users; an instruction generation module for determining a target matching relationship between the mobile charging vehicle and the charging demand based on the onboard status data and the charging demand data, and generating a dispatching instruction based on the target matching relationship; and a mobility control module for calculating a driving path based on the dispatching instruction and a preset station map, so as to control the mobile charging vehicle to drive to the location of the vehicle to be charged corresponding to the charging demand.

[0006] According to a third aspect of this disclosure, a computer-readable medium is provided that stores a computer program thereon, which, when executed by a processor, implements the method described above.

[0007] According to a fourth aspect of this disclosure, an electronic device is provided, characterized in that it includes: a processor; and a memory for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method described above.

[0008] One embodiment of this disclosure provides a mobile charging vehicle scheduling method that determines the target matching relationship between mobile charging vehicles and charging demands based on collected vehicle status data and user-uploaded charging demand data. This makes the allocation of mobile charging vehicles more suitable for the status of each mobile charging vehicle and the requirements of each charging demand data, improving the intelligence and efficiency of scheduling. At the same time, scheduling instructions are generated based on the target matching relationship, and the driving path of the mobile charging vehicles is planned based on the scheduling instructions and a preset station map, making the movement of mobile charging vehicles more reasonable and efficient.

[0009] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0010] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings: Figure 1 A schematic diagram of an exemplary system architecture to which embodiments of the present disclosure may be applied is shown; Figure 2 A flowchart illustrating a mobile charging vehicle scheduling method in an exemplary embodiment of this disclosure is shown schematically. Figure 3 The flowchart schematically illustrates a pre-training method for a scheduling model in an exemplary embodiment of the present disclosure; Figure 4 This schematically illustrates a flowchart of a method for creating a preset station map in an exemplary embodiment of the present disclosure; Figure 5 This schematic diagram illustrates a mobile charging vehicle architecture according to an exemplary embodiment of the present disclosure; Figure 6 This illustration schematically depicts a server architecture diagram according to an exemplary embodiment of the present disclosure; Figure 7 This illustration schematically depicts a software system architecture diagram according to an exemplary embodiment of the present disclosure; Figure 8 This schematic diagram illustrates the composition of a mobile charging vehicle scheduling device in an exemplary embodiment of the present disclosure. Figure 9 A schematic diagram of an electronic device to which embodiments of the present disclosure may be applied is shown. Detailed Implementation

[0011] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0012] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0013] Figure 1 A schematic diagram of the system architecture of an exemplary application environment in which a mobile charging vehicle scheduling method and apparatus according to embodiments of the present disclosure can be applied is shown.

[0014] like Figure 1 As shown, system architecture 100 may include one or more of terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables. Terminal devices 101, 102, and 103 may be various electronic devices with data processing capabilities, including but not limited to desktop computers, portable computers, smartphones, and tablets. It should be understood that... Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, there can be any number of terminal devices, networks, and servers. For example, server 105 could be a server cluster composed of multiple servers.

[0015] The mobile charging vehicle scheduling method provided in this embodiment is generally executed by server 105, and correspondingly, the mobile charging vehicle scheduling device can also be set in server 105. However, those skilled in the art will readily understand that the mobile charging vehicle scheduling method provided in this embodiment can also be executed by terminal devices 101, 102, and 103, and correspondingly, the mobile charging vehicle scheduling device is generally set in terminal devices 101, 102, and 103. This exemplary embodiment does not impose any special limitations on this. For example, in one exemplary embodiment, terminal devices 101, 102, and 103 may collect on-board status data of the mobile charging vehicle and charging demand data uploaded by users through network 104, then determine the target matching relationship between the mobile charging vehicle and the charging demand based on the on-board status data and the charging demand data, and generate a scheduling instruction based on the target matching relationship. After that, a driving route is planned according to the scheduling instruction and a preset station map, and the scheduling instruction and the planned driving route are sent to the corresponding mobile charging vehicle through network 104 to control the mobile charging vehicle to drive to the location of the vehicle to be charged corresponding to the charging demand.

[0016] In related technologies, mobile charging solutions typically rely on manual dispatching, resulting in inefficient scheduling with an average response time exceeding 30 minutes. The lack of scientific matching and route optimization leads to a charging vehicle empty-run rate as high as 40%, causing significant resource waste. Furthermore, users must manually operate the charging equipment, making the interaction cumbersome.

[0017] To address one or more of the aforementioned problems, this exemplary embodiment provides a mobile charging vehicle scheduling method. This mobile charging vehicle scheduling method can be applied to the server 105, or to one or more of the terminal devices 101, 102, and 103, and is not specifically limited in this exemplary embodiment. (See reference...) Figure 2 As shown, the mobile charging vehicle scheduling method may include the following steps S210 to S230: In step S210, the vehicle status data of the mobile charging vehicle and the charging demand data uploaded by the user are collected.

[0018] Among them, the vehicle status data is used to characterize the current status of the mobile charging vehicle, which may include data such as the location, voltage, current, speed, tire angle, and whether it is currently charging; the charging demand data is used to characterize relevant data of the vehicle to be charged, which may include user information bound to the vehicle to be charged, the current location of the vehicle to be charged, and the amount of electricity required by the vehicle to be charged.

[0019] In step S220, the target matching relationship between the mobile charging vehicle and the charging demand is determined based on the vehicle status data and charging demand data, and a scheduling instruction is generated based on the target matching relationship.

[0020] Among them, the target matching relationship refers to the matching relationship between each charging demand and the mobile charging vehicle suitable for handling the charging demand, which is determined based on the on-board status data and charging demand data of each mobile charging vehicle; the scheduling instruction refers to the instruction generated based on the target matching relationship, which is used to schedule a mobile charging vehicle to move to the location of the corresponding vehicle to be charged.

[0021] In one exemplary embodiment, after obtaining vehicle status data and charging demand data, the vehicle status data and charging demand data can be input into a pre-trained scheduling model to output the matching relationship between the mobile charging vehicle with the highest matching degree and the charging demand, thereby obtaining the target matching relationship.

[0022] In one exemplary embodiment, reference is made to Figure 3 As shown, the pre-training process of the scheduling model may include the following steps S310 and S320: Step S310: Establish an objective function based on the weights of grid load cost, arrival time, and urgency level.

[0023] Among them, grid load cost refers to the cost estimated by the time-of-use electricity price model when each mobile charging vehicle provides charging services for charging demand, including but not limited to the empty driving energy consumption and charging cost of the mobile charging vehicle; arrival time refers to the time taken from when the user uploads the demand data to when the mobile charging vehicle travels to the location of the vehicle to be charged corresponding to the matching charging demand; urgency weight refers to the weight set based on the urgency of each charging demand.

[0024] In one exemplary embodiment, the urgency weight can be set based on the battery level of the vehicle to be charged, or based on the profit corresponding to the charging demand, or a combination of one or more parameters; this disclosure does not impose any particular limitation on this. Furthermore, when the number of charging demand data points exceeds the number of mobile charging vehicles that can be dispatched, the urgency weight can also be set based on the waiting time corresponding to the charging demand data to avoid the problem of excessively long waiting times for a particular charging demand.

[0025] In one exemplary embodiment, the objective function may be as follows: Formula 1 Where tij represents the estimated arrival time of mobile charging vehicle i to the location of the vehicle to be charged corresponding to charging demand j; xij represents the binary decision variable of whether mobile charging vehicle i serves charging demand j, with a value of 1 indicating that mobile charging vehicle i serves charging demand j and a value of 0 indicating that it does not serve; Egrid represents the estimated grid load cost when mobile charging vehicle i provides charging service to charging demand; Upriority represents the urgency weight corresponding to charging demand j, with higher values ​​for urgent demands; a, b, and c are non-negative weight coefficients used to balance the optimization priority of estimated arrival time, grid load cost, and urgency weight.

[0026] The constraints are as follows: This means that each charging demand j can only be served by one mobile charging vehicle i. Where Ej represents the amount of charging required by the vehicle to be charged for charging demand j, and Bi represents the remaining output power of mobile charging vehicle i. This constraint means that the total amount of charging provided by mobile charging vehicle i for the assigned charging demand does not exceed its remaining output power.

[0027] It should be noted that the non-negative weight coefficients a, b, and c can be set according to the emphasis of the scheduling scheme. The higher the emphasis on a certain direction, the larger the weight coefficient value corresponding to that direction.

[0028] Step S320: Using the optimal solution of the objective function as the training label, train the preset model based on the preset training dataset to obtain the pre-trained scheduling model.

[0029] The preset model can be any type of machine learning model, such as a deep learning model or a reinforcement learning model, and this disclosure does not impose any special restrictions on it.

[0030] Specifically, based on the objective function, the optimal solution corresponding to each group of data in the preset training dataset can be calculated first. Then, the preset training dataset can be used as input, and the corresponding optimal solution can be used as training labels to train the preset model and obtain the pre-trained scheduling model.

[0031] It should be noted that during peak charging periods, when the number of charging demand data exceeds the number of mobile charging vehicles that can be dispatched, multiple dispatch instructions can be issued to the same mobile charging vehicle to control the mobile charging vehicle to complete the charging tasks corresponding to multiple charging demand data in sequence.

[0032] In step S230, a driving route is planned according to the dispatch instructions and the preset station map to control the mobile charging vehicle to drive to the location of the vehicle to be charged corresponding to the charging demand.

[0033] The scheduling instruction may include an instruction identifier, the executing entity (i.e., the identifier of the mobile charging vehicle that performs the scheduling task), the instruction type (specifically, the charging service instruction), the target location, and the matching charging demand data.

[0034] In some embodiments, the scheduling instruction may also include a parameter for characterizing priority, that is, when the same mobile charging vehicle receives multiple scheduling instructions, the scheduling task to be executed first can be determined according to the parameter characterizing priority.

[0035] It should be noted that the dispatching instructions may also include parameters for constraining the operation of mobile charging vehicles, such as arrival time requirements and prohibited areas. This disclosure does not impose any special restrictions on the constraint parameters.

[0036] In one exemplary embodiment, after receiving a scheduling instruction and a driving route, the mobile charging vehicle can move according to the scheduling instruction and the driving route. During the movement, visual data and echo data collected by cameras and radar installed on the mobile charging vehicle can be analyzed and combined with driving algorithms to drive the vehicle.

[0037] In one exemplary embodiment, before performing scheduling, a preset station map can be created based on data acquired by cameras and radar deployed at the fixed station. Specifically, refer to... Figure 4 As shown, the steps S410 to S420 may be included: Step S410: Deploy cameras and radar at the fixed site, and collect visual data of the fixed site based on the cameras and echo data of the fixed site based on the radar.

[0038] Step S420: Visual data and echo data are fused using a fusion algorithm to obtain multimodal fused data.

[0039] Step S430: Construct a preset site map based on the multimodal fusion data.

[0040] Specifically, cameras and radar can be deployed at fixed charging stations based on their actual environment. The cameras collect visual data, while the radar collects echo data. A fusion algorithm then combines the visual and echo data to create a pre-defined station map. By fusing visual and radar data, and then constructing a map based on this multimodal fusion data, a higher-precision map can be obtained, allowing mobile charging vehicles to move based on this high-precision map.

[0041] It should be noted that data fusion can be performed in various ways. For example, a data layer fusion algorithm can be used to directly fuse the raw data from the camera and the radar. Alternatively, a feature layer fusion algorithm can be used to extract features from the visual data and the echo data respectively, and then fuse the extracted features. This disclosure does not impose any special limitations on the data fusion method.

[0042] In an exemplary embodiment, after scheduling the mobile charging vehicle, the pre-trained scheduling model can be iteratively optimized based on the scheduling results. Specifically, a reward function can be established based on demand waiting time, grid procurement costs, and the empty-running energy consumption of the mobile charging vehicle. Then, the parameters in the pre-trained scheduling model can be iteratively optimized based on the reward and penalty values ​​fed back by the reward function.

[0043] The reward function can be as follows: Formula 2 Among them, T wait This indicates the actual waiting time for charging demand data; P grid E represents the actual power grid procurement cost; empty This represents the actual empty-run energy consumption of the mobile charging vehicle; d, e, and f are non-negative weighting coefficients used to balance the optimization priority of demand waiting time, grid procurement costs, and the empty-run energy consumption of the mobile charging vehicle.

[0044] It should be noted that the non-negative weighting coefficients d, e, and f can be set according to the actual optimization focus. The higher the emphasis on a certain direction, the larger the weighting coefficient value corresponding to that direction. For example, if the focus is more on waiting time, the non-negative weighting coefficients d, e, and f can be 1, 0.5, and 0.2 respectively; if the focus is more on actual grid procurement costs, they can be 0.3, 1, and 0.5 respectively; and if the focus is more on actual empty-running energy consumption, they can be 0.1, 0.1, and 1 respectively.

[0045] In addition, in some embodiments, a camera can be installed on the robotic arm of the mobile charging vehicle, and the visual data collected by the camera can be processed using a visual algorithm to capture the charging port of the vehicle to be charged, and then the automatic insertion of the charging gun can be achieved by controlling the movement of the robotic arm.

[0046] The following is for reference Figures 5 to 7 The method for dispatching mobile charging vehicles is explained in detail.

[0047] Reference Figure 5The schematic diagram of the mobile charging vehicle architecture shown indicates that the mobile charging vehicle 500 may include a sensing unit 510, a power unit 520, and an execution unit 530. The sensing unit 510 is used to collect real-time visual and echo data (distance data of obstacles, etc.) of the surrounding environment; the power unit 520 is used to control the movement of the mobile charging vehicle; and the execution unit 530 is used to control the movement of the robotic arm to establish a connection between the charging gun and the vehicle to be charged.

[0048] Reference Figure 6 The server architecture diagram shown indicates that server 600 may include a data processing unit 610 and a database 620. The data processing unit 610 can be used for background data calculations, including but not limited to driving route planning and dispatch instruction generation; the database 620 can be used to cache real-time data or store historical data.

[0049] Reference Figure 7 The illustrated software system architecture diagram shows that software system 700 may include a data acquisition unit 710 and a decision optimization unit 720. The data acquisition unit 710 is located on the mobile charging vehicle and is used to collect onboard status data of the mobile charging vehicle. The decision optimization unit 720 is deployed on a server and may include a scheduling subunit 721 and a planning subunit 722. The scheduling subunit 721 is used to schedule the mobile charging vehicle according to a mobile charging vehicle scheduling method; the planning subunit 722 is used to plan the driving path of the mobile charging vehicle.

[0050] Based on the above exemplary embodiment, the objective function is Equation 1. An experiment was conducted by deploying 3 charging vehicles and 5 user requests. The output scheduling instructions include: scheduling a vehicle with 95% of its power to serve the farthest emergency user; and scheduling a vehicle with 50% of its power to replenish power nearby. The results are: the average response time is reduced from 32 minutes to 9 minutes; and the peak load of the power grid is reduced by 17%.

[0051] In summary, this exemplary embodiment provides an intelligent mobile charging vehicle scheduling method that enables fully automatic charging without manual operation, greatly improving the convenience of charging for users. Furthermore, by setting the objective function, the response time and charging cost of the mobile charging vehicle are significantly reduced.

[0052] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this disclosure, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0053] Further reference Figure 8As shown, an exemplary embodiment of this disclosure provides a mobile charging vehicle scheduling device 800, including a data acquisition module 810, an instruction generation module 820, and a mobility control module 830. Wherein: The data acquisition module 810 can be used to collect on-board status data of the mobile charging vehicle and charging demand data uploaded by users.

[0054] The instruction generation module 820 can be used to determine the target matching relationship between the mobile charging vehicle and the charging demand based on the vehicle status data and charging demand data, and generate scheduling instructions based on the target matching relationship.

[0055] The mobile control module 830 can be used to calculate the driving route according to the dispatch instructions and the preset station map, so as to control the mobile charging vehicle to drive to the location of the vehicle to be charged corresponding to the charging demand.

[0056] The specific details of each module in the above-mentioned device have been described in detail in the method section of the implementation. For any undisclosed details, please refer to the implementation content of the method section, and therefore will not be repeated here.

[0057] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0058] An exemplary embodiment of this disclosure also provides an electronic device for implementing a mobile charging vehicle scheduling method, which may be... Figure 1 The terminal devices 101, 102, 103, or server 105 are included. The electronic device includes at least a processor and a memory, the memory being used to store executable instructions of the processor, and the processor being configured to execute the mobile charging vehicle scheduling method by executing the executable instructions.

[0059] The following is based on Figure 9 Taking a mobile terminal 900 as an example, the construction of the electronic device in this disclosure embodiment will be described by way of example. Those skilled in the art will understand that, apart from components specifically designed for mobile purposes, Figure 9 The structure shown can also be applied to fixed-type devices. In other embodiments, the mobile terminal 900 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The components illustrated can be implemented in hardware, software, or a combination of software and hardware. The interface connections between the components are only schematic and do not constitute a limitation on the structure of the mobile terminal 900. In other embodiments, the mobile terminal 900 may also adopt a similar design to... Figure 9 Different interface connection methods, or combinations of multiple interface connection methods.

[0060] like Figure 9 As shown, the mobile terminal 900 may specifically include: a processor 910, internal memory 921, external memory interface 922, Universal Serial Bus (USB) interface 930, charging management module 940, power management module 941, battery 942, antenna 1, antenna 2, mobile communication module 950, wireless communication module 960, audio module 970, speaker 971, receiver 972, microphone 973, headphone jack 974, sensor module 980, display screen 990, camera module 991, indicator 992, motor 993, buttons 994, and subscriber identification module (SIM) card interface 995, etc. The sensor module 980 may include a depth sensor 9801, a pressure sensor 9802, a gyroscope sensor 9803, etc.

[0061] The processor 910 may include one or more processing units, such as an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). These different processing units may be independent devices or integrated into one or more processors.

[0062] NPU stands for Neural Network (NN) computing processor. By borrowing the structure of biological neural networks, such as the transmission patterns between neurons in the human brain, it can rapidly process input information and continuously learn on its own. NPUs can enable intelligent cognitive applications in mobile terminals, such as image recognition, facial recognition, speech recognition, and text understanding. In some embodiments, NPUs can be used to train preset models and output target matching relationships based on vehicle status data and charging demand data.

[0063] The processor 910 includes a memory. The memory can store instructions for implementing six modular functions: detection instructions, link instructions, information management instructions, analysis instructions, data transfer instructions, and notification instructions, which are executed under the control of the processor 910. In some embodiments, scheduling instructions can be stored in the memory for later querying or iterative optimization.

[0064] The wireless communication function of the mobile terminal 900 can be implemented through antenna 1, antenna 2, mobile communication module 950, wireless communication module 960, modem processor, and baseband processor. Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals; mobile communication module 950 can provide wireless communication solutions including 2G / 3G / 4G / 5G for use on the mobile terminal 900; modem processor can include modulator and demodulator; wireless communication module 960 can provide wireless communication solutions including Wireless Local Area Networks (WLAN) (such as Wireless Fidelity (Wi-Fi) networks) and Bluetooth (BT) for use on the mobile terminal 900. In some embodiments, antenna 1 of the mobile terminal 900 is coupled to mobile communication module 950, and antenna 2 is coupled to wireless communication module 960, enabling the mobile terminal 900 to communicate with networks and devices such as mobile charging vehicles via wireless communication technology.

[0065] The internal memory 921 can be used to store executable program code, including instructions. The internal memory 921 may include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback, image playback, etc.), etc. The data storage area may store data created during the use of the mobile terminal 900 (such as audio data, phonebook, etc.). Furthermore, the internal memory 921 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, Universal Flash Storage (UFS), etc. The processor 910 executes various functional applications and data processing of the mobile terminal 900 by running instructions stored in the internal memory 921 and / or instructions stored in memory located in the processor.

[0066] Furthermore, exemplary embodiments of this disclosure also provide a computer-readable storage medium storing a program product capable of implementing the methods described above. In some possible embodiments, various aspects of this disclosure can also be implemented as a program product including program code, which, when run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure, such as executing... Figures 2 to 4 Any one or more steps in the process.

[0067] It should be noted that the computer-readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0068] In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wireline, optical fiber, RF, etc., or any suitable combination thereof.

[0069] Furthermore, program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0070] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0071] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for scheduling mobile charging vehicles, characterized in that, include: Collect onboard status data from mobile charging vehicles and charging demand data uploaded by users; The target matching relationship between the mobile charging vehicle and the charging demand is determined based on the vehicle status data and the charging demand data, and a scheduling instruction is generated based on the target matching relationship. The driving path is calculated based on the dispatch instructions and the preset station map to control the mobile charging vehicle to drive to the location of the vehicle to be charged corresponding to the charging demand.

2. The method according to claim 1, characterized in that, Determining the target matching relationship between the mobile charging vehicle and the charging demand includes: The vehicle status data and the charging demand data are input into a pre-trained scheduling model to output the matching relationship between the mobile charging vehicle with the highest matching degree and the charging demand, thus obtaining the target matching relationship.

3. The method according to claim 2, characterized in that, The method further includes: An objective function is established based on the weights of grid load cost, arrival time, and urgency. Using the optimal solution of the objective function as the training label, a pre-trained scheduling model is trained on a pre-set training dataset to obtain a pre-trained scheduling model.

4. The method according to claim 3, characterized in that, The objective function is as follows: Among them, t ij This represents the estimated arrival time of mobile charging vehicle i to the location of the vehicle to be charged corresponding to charging demand j; x ij A binary decision variable representing whether mobile charging vehicle i serves charging demand j; a value of 1 indicates that mobile charging vehicle i serves charging demand j, and a value of 0 indicates that it does not serve; E grid This represents the estimated grid load cost when mobile charging vehicle i provides charging services to meet charging demands; U priority This represents the urgency weight corresponding to charging demand j, with higher values ​​for urgent demands; a, b, and c are non-negative weight coefficients used to balance the optimization priority of estimated arrival time, grid load cost, and urgency weight. The constraints are as follows: This means that each charging demand j can only be served by one mobile charging vehicle i. E j B represents the amount of charge required by the vehicle to be charged, corresponding to the charging demand j. i This represents the remaining output capacity of mobile charging vehicle i. This constraint means that the total amount of charging provided by mobile charging vehicle i for the assigned charging demand does not exceed its remaining output capacity.

5. The method according to claim 2, characterized in that, The method further includes: A reward function is established based on demand waiting time, grid procurement cost, and mobile charging vehicle empty-run energy consumption, and the parameters of the pre-trained scheduling model are optimized based on the reward and penalty values ​​fed back by the reward function.

6. The method according to claim 5, characterized in that, The reward function is as follows: Among them, T wait This indicates the actual waiting time for charging demand data; P grid E represents the actual power grid procurement cost; empty This represents the actual empty-run energy consumption of the mobile charging vehicle; d, e, and f are non-negative weighting coefficients used to balance the optimization priority of demand waiting time, grid procurement costs, and the empty-run energy consumption of the mobile charging vehicle.

7. The method according to claim 1, characterized in that, The method further includes: Cameras and radars are deployed at fixed sites, and visual data of the fixed sites is collected based on the cameras, and echo data of the fixed sites is collected based on the radars. The visual data and the echo data are fused using a fusion algorithm to obtain multimodal fused data; The preset site map is constructed based on the multimodal fusion data.

8. A mobile charging vehicle dispatching device, characterized in that, include: The data acquisition module is used to collect onboard status data of the mobile charging vehicle and charging demand data uploaded by users; The instruction generation module is used to determine the target matching relationship between the mobile charging vehicle and the charging demand based on the vehicle status data and the charging demand data, and to generate a scheduling instruction based on the target matching relationship. The mobile control module is used to calculate the driving path according to the scheduling instructions and the preset station map, so as to control the mobile charging vehicle to drive to the location of the vehicle to be charged corresponding to the charging demand.

9. 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 method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, include: processor; and memory for storing the executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 7 by executing the executable instructions.

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