Electric vehicle charging reminder method, device, electronic device and storage medium

Abstract

The present invention discloses an electric vehicle charging reminder method, device, electronic device and storage medium. The method comprises: determining the user's charging habits according to the historical charging information of the vehicle and the historical user behavior information corresponding to the historical charging behavior; determining the current vehicle state of the vehicle according to the current power information of the vehicle, the current user behavior information and the historical user behavior information; if the current vehicle state of the vehicle satisfies the user's charging habits, then issuing an electric vehicle charging reminder to the user. The technical solution of the embodiment of the present invention is adopted, so that the determination of the user's charging habits can no longer be judged only by the remaining power of the vehicle and the charging time, so that the determination of the user's charging habits is more accurate, so that when reminding the user that the electric vehicle needs to be charged, it is more in line with the user's own user charging habits, so that the reminder is more accurate and can better meet the needs of different users.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control technology, and in particular to an electric vehicle charging reminder method, device, electronic equipment and storage medium. Background Art

[0002] With the rapid development of science and technology, more and more people are responding to green and environmentally friendly travel modes, and electric vehicles, as the most important part of them, are also received great attention.

[0003] However, compared with traditional energy vehicles, electric vehicle battery packs have limited power capacity and need to be charged frequently, which means that electric vehicles often need to find charging piles to recharge during driving. The process of finding charging piles and charging will inevitably generate extra time and extra driving routes. Therefore, there is an urgent need for a method that can remind users to charge in time according to their own charging habits and provide charging stations that meet their habits. Summary of the invention

[0004] The present invention provides an electric vehicle charging reminder method, device, electronic equipment and storage medium to solve the problem that when charging reminders are given to users, the real needs of users cannot be met.

[0005] According to one aspect of the present invention, a method for reminding an electric vehicle to charge is provided, the method comprising:

[0006] Determine the user's charging habits based on the vehicle's historical charging information and historical user behavior information corresponding to the historical charging behavior;

[0007] Determine the current vehicle state of the vehicle based on the current power information of the vehicle, the current user behavior information, and the historical user behavior information;

[0008] If the current vehicle status of the vehicle meets the user's charging habits, an electric vehicle charging reminder is issued to the user.

[0009] According to another aspect of the present invention, a device for reminding an electric vehicle to charge is provided, the device comprising:

[0010] A charging behavior determination module, used to determine the user's charging habits based on the historical charging information of the vehicle and the historical user behavior information corresponding to the historical charging behavior;

[0011] A vehicle state determination module, used to determine the current vehicle state of the vehicle based on the current power information of the vehicle, the current user behavior information and the historical user behavior information;

[0012] The charging reminder module is used to send an electric vehicle charging reminder to the user if the current vehicle status meets the user's charging habits.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the electric vehicle charging reminder method of any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the electric vehicle charging reminder method of any embodiment of the present invention when executed.

[0018] By adopting the technical solution of the embodiment of the present invention, the user's charging habits are determined based on the historical charging information of the vehicle and the historical user behavior information corresponding to the historical charging behavior, so that the determination of the user's charging habits can no longer be judged only by the remaining power of the vehicle and the charging time, but by comprehensively considering the user's own user behavior information and the historical charging information of the vehicle, so that the determination of the user's charging habits is more accurate. If the current vehicle state of the vehicle meets the user's charging habits, an electric vehicle charging reminder is issued to the user, so that when the user is reminded that the electric vehicle needs to be charged, it is more in line with the user's own user charging habits, so that the reminder is more accurate and can better meet the needs of different users.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 This is a flow chart of an electric vehicle charging reminder method provided according to Embodiment 1 of the present invention;

[0022] Figure 2 is a flow chart of another electric vehicle charging reminder method provided according to the first embodiment of the present invention;

[0023] Figure 3 This is a structural schematic diagram of an electric vehicle charging reminder device provided according to Embodiment 3 of the present invention;

[0024] Figure 4 It is a structural schematic diagram of an electronic device for implementing the electric vehicle charging reminder method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention 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 interchanged where appropriate, so that the embodiments of the present invention 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.

[0027] Embodiment 1

[0028] Figure 1 A flowchart of an electric vehicle charging reminder method is provided for the first embodiment of the present invention. This embodiment can be applied to provide electric vehicle charging reminders according to the user's own habits. The method can be executed by an electric vehicle charging reminder device. The electric vehicle charging reminder device can be implemented in the form of hardware and / or software. The electric vehicle charging reminder device can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0029] S110 . Determine the user's charging habits based on the historical charging information of the vehicle and the historical user behavior information corresponding to the historical charging behavior.

[0030] Historical charging information may include the remaining power of the vehicle, charging time, charging duration of the charging process, and charging power when the user charged in the past. Historical charging behavior may be the charging behavior of the user in the past. Historical user behavior information may be the behavior that causes the user to charge. For example, the user regularly drives for a long time every Saturday, so he charges every Friday night regardless of the remaining power. The user's charging habits may be the user's judgment that the vehicle should be charged, for example, if the power is less than 50%, the user determines that it should be charged, or if the vehicle has traveled more than 200 kilometers, the user believes that it should be charged, etc.

[0031] When users use electric vehicles, different users have different criteria for determining whether charging is necessary, which leads to certain differences in the judgments of different users on whether the vehicle needs to be charged.

[0032] Therefore, it is necessary to use the historical charging information of the vehicle and the historical user behavior information corresponding to the historical charging behavior to determine under what circumstances the user believes that the electric vehicle needs to be charged, so as to obtain the user's charging habits.

[0033] By determining the user's charging habits based on the vehicle's historical charging information and historical user behavior information corresponding to the historical charging behavior, the user's charging habits are no longer determined solely by the vehicle's remaining power and charging time, but by comprehensively considering the user's own user behavior information and the vehicle's historical charging information, thereby making the determination of the user's charging habits more accurate.

[0034] In an optional solution, determining the user's charging habits based on the historical charging information of the vehicle and the historical user behavior information corresponding to the historical charging behavior may include steps A1-A4:

[0035] Step A1: Determine the user's charging habits based on the user's historical charging information.

[0036] Step A2: Determine the user's charging behavior based on historical user behavior information.

[0037] Step A3: Determine the weight coefficient of the user's charging habit of power and the user's charging habit of behavior based on the historical charging information.

[0038] Step A4: Based on the weight coefficient, determine the user's charging habits according to the user's power charging habits and the user's behavioral charging habits.

[0039] The user's power charging habit can be the user's habit of determining whether to charge the electric vehicle based on information such as the remaining power of the electric vehicle. The user's behavior charging habit can be the user's habit of determining whether to charge the electric vehicle based on previous charging behaviors. The weight coefficient can be the user's power charging habit and the degree of influence of the user's behavior charging habit on the user's charging habit.

[0040] After determining the historical charging information, the user's charging habits, that is, the user's charging habits, can be determined through the historical charging information when the remaining power of the electric vehicle is in what state.

[0041] After determining the historical user behavior information, the user's habit of charging the electric vehicle before performing or about to perform a certain operation, that is, the user's behavior charging habit, can be determined through the historical user behavior information.

[0042] After determining the user's power charging habits and the user's behavioral charging habits, the weight coefficients of the user's power charging habits and the user's behavioral charging habits each time the user charges can be determined based on historical charging information.

[0043] According to their respective weight coefficients, the user's power charging habits and user's behavior charging habits can be processed to obtain the user's charging habits.

[0044] Optionally, exemplarily, when it is determined that the weight coefficient of the user's power charging habits is 0.5 and the weight coefficient of the user's behavior charging habits is also 0.5, the user's charging habits are equal to 0.5*user power charging habits+0.5*user behavior charging habits.

[0045] S120: Determine a current vehicle state of the vehicle based on current battery information of the vehicle, current user behavior information, and historical user behavior information.

[0046] The current power information may be information such as the remaining power of the electric vehicle at the current moment, etc. The current user behavior information may be the user's current operation behavior on the electric vehicle, including but not limited to driving, preheating, and precooling.

[0047] After determining the user's charging habits, the current vehicle state of the electric vehicle can be determined based on the vehicle's current power information, current user behavior information, and historical user behavior information.

[0048] In an optional solution, determining the current vehicle state of the vehicle according to the current power information of the vehicle, the current behavior information and the historical user behavior information may include steps B1-B2:

[0049] Step B1: input current power information, current behavior information, and historical user behavior information into a pre-trained XGboost model.

[0050] Step B2: Determine the current vehicle state through the pre-trained XGboost model.

[0051] Among them, the pre-trained XGboost model is trained by the historical charging information of the vehicle and the historical user behavior information before the historical charging behavior.

[0052] After obtaining the current power information, current behavior information and historical user behavior information, the current power information, current behavior information and historical user behavior information can be calculated through a neural network model to determine the current vehicle state of the vehicle.

[0053] By inputting the current power information, current behavior information and historical user behavior information into the pre-trained XGboost model, the pre-trained XGboost model operates on the input data to obtain the current vehicle state of the electric vehicle.

[0054] In an optional solution, the training process of the XGboost model may include steps C1-C3:

[0055] Step C1, determining a first training data set and a second training data set according to historical charging information, historical user behavior information before historical charging behavior, and current behavior information.

[0056] Step C2: input the first training data set into the first XGboost model for training to obtain a second XGboost model.

[0057] Step C3: Correct the second XGboost model according to the second training data set to obtain an XGboost model.

[0058] The first training data set may be data used to train the XGboost model. The second training data set may be data used to correct the trained XGboost model. The first XGboost model may be an untrained XGboost model. The second XGboost model may be an XGboost model that has been initially trained but not corrected.

[0059] For the training of the XGBoost model, it is necessary to divide the historical charging information, the historical user behavior information before the historical charging behavior, and the current behavior information, and determine the first training data set for model training and the second training data set for correcting the model calculation results.

[0060] The first training data set is input into the first XGboost model, so that the first XGboost model can determine the current vehicle state of the vehicle according to the historical charging information, the historical user behavior information before the historical charging behavior, and the current behavior information.

[0061] The second training data set is then input into the second XGboost model to correct the accuracy of the calculation results of the first XGboost model, thereby obtaining the second XGboost model.

[0062] S130: If the current vehicle status of the vehicle meets the user's charging habits, a reminder to charge the electric vehicle is issued to the user.

[0063] After determining the current vehicle state of the vehicle and the user's charging habits, it can be determined whether the current vehicle state satisfies the user's charging habits.

[0064] If it is satisfied, it indicates that according to the user's habits, the electric vehicle should be charged at this time, so an electric vehicle charging reminder will be sent to the user to remind the user that the electric vehicle needs to be charged.

[0065] By sending an electric vehicle charging reminder to the user if the current vehicle status meets the user's charging habits, when reminding the user that the electric vehicle needs to be charged, it is more in line with the user's own charging habits, making the reminder more accurate and better able to meet the needs of different users.

[0066] Optionally, in addition to using the neural network model to determine the current vehicle status, the neural network model can also be used to directly judge the vehicle status to determine whether the current vehicle meets the user's charging habits. If so, an electric vehicle charging reminder is directly sent to the user.

[0067] Exemplarily, the historical charging information and the historical user behavior information before the historical charging behavior are merged and mapped to obtain a first training data set and a second training data set.

[0068] Among them, the first training data set T can be expressed as:

[0069]

[0070] Where X represents the historical charging information corresponding to the electric vehicle and the historical user behavior information before the historical charging behavior. y represents whether the current vehicle status of the current vehicle meets the user's charging habits, 0 means not satisfied, and 1 means satisfied.

[0071] The loss function is the squared error loss function (MSE):

[0072] L(y,f(x))=(yf(x))2

[0073] Get the boosted tree f M (x)

[0074] Initialize f0(x)=0;

[0075] For m=1,2,3,...M., train the mth CART regression tree in sequence

[0076] Calculate the residual: r mi =y i -f m-1 (x i ),i=1,2,...N

[0077] Fitting residual r mi Learn a regression tree and get

[0078]

[0079] R j is the J regions j=1,2,...,J that the regression tree divides in the input space, which is the number of leaf nodes. So c j It means that each node is a constant output in the region. m ={(R1,c1).(R1,c2),...,(R J ,c J )}, representing the region division of the tree and the constants on each region.

[0080] c: Update: f m =f m-1 +T(x;θ m )

[0081] Get the boosted tree for regression problem:

[0082]

[0083] At this time, the output f M (x) will directly indicate whether to send electric vehicle charging reminders to users.

[0084] By adopting the technical solution of the embodiment of the present invention, the user's charging habits are determined based on the historical charging information of the vehicle and the historical user behavior information corresponding to the historical charging behavior, so that the determination of the user's charging habits can no longer be judged only by the remaining power of the vehicle and the charging time, but by comprehensively considering the user's own user behavior information and the historical charging information of the vehicle, so that the determination of the user's charging habits is more accurate. If the current vehicle state of the vehicle meets the user's charging habits, an electric vehicle charging reminder is issued to the user, so that when the user is reminded that the electric vehicle needs to be charged, it is more in line with the user's own user charging habits, so that the reminder is more accurate and can better meet the needs of different users.

[0085] Embodiment 2

[0086] Figure 2 This is a flow chart of another electric vehicle charging reminder method provided in the second embodiment of the present invention. Based on the above embodiment, this embodiment further optimizes the process after the user is issued an electric vehicle charging reminder if the current vehicle status of the vehicle meets the user's charging habits in the above embodiment. This embodiment can be combined with various optional solutions in one or more of the above embodiments. Figure 2 As shown, the method includes:

[0087] S210: Determine the user's charging habits based on the historical charging information of the vehicle and the historical user behavior information corresponding to the historical charging behavior.

[0088] S220: Determine the current vehicle state of the vehicle according to the current power information of the vehicle, the current user behavior information, and the historical user behavior information.

[0089] S230: If the current vehicle status of the vehicle meets the user's charging habits, a reminder to charge the electric vehicle is issued to the user.

[0090] S240: Determine at least two frequently used charging stations for the vehicle based on historical charging information of the vehicle.

[0091] The commonly used charging stations may be at least two charging stations with the highest selection rate when users charge their electric vehicles.

[0092] After the user is reminded to charge the electric vehicle, the charging station selected by the user for each charging recorded in the historical charging information can be determined through the historical charging information, and each charging station is arranged according to the number of times selected to determine at least two charging stations.

[0093] By determining at least two frequently used charging stations for the vehicle based on the historical charging information of the vehicle, the determined charging stations are more consistent with the frequently used charging stations of the user, thereby improving the convenience of charging for the user.

[0094] Optionally, after determining the commonly used charging stations, one or more charging stations with the lowest electricity prices may be determined for push notification based on the push information of each charging station.

[0095] S250: Determine charging station prompt information based on the predicted working status of common charging stations, and send it to the user.

[0096] The predicted working status may be information such as the queue time at the commonly used charging station when the user's vehicle arrives at the commonly used charging station. The charging station prompt information may be information for recommending a charging station to the user.

[0097] After the commonly used charging stations are determined, the commonly used charging stations can be sorted according to their predicted working states, and the commonly used charging stations with the best predicted working states can be determined for priority prompting.

[0098] By determining the charging station prompt information based on the predicted working status of the commonly used charging stations and sending it to the user, the commonly used charging stations prompted to the user can provide charging services to the user more quickly, thereby reducing the user's waiting time and improving the user's charging experience.

[0099] In an optional solution, the predicted working state of the common charging station is used, and before determining the charging station prompt information, the method further includes steps D1-D3:

[0100] Step D1: Obtain the current vehicle status of other vehicles in the target detection area at the current moment.

[0101] Step D2: Determine the queue time of the commonly used charging stations according to the current vehicle status of other vehicles.

[0102] Step D3: Determine the predicted working status of the commonly used charging stations according to the queuing time.

[0103] The target detection area may be a pre-set area centered on the user's electric vehicle.

[0104] After determining at least two commonly used charging stations for the vehicle, other electric vehicles that need to be charged at the current moment can be judged based on the current vehicle status of other vehicles in the target detection area at the current moment, and the commonly used charging stations corresponding to other charging vehicles can be determined.

[0105] According to the current vehicle status of other vehicles, the queue length of the user's electric vehicle when it arrives at the commonly used charging station is judged, and the queue time is determined according to the queue length, thereby determining the predicted working status of the commonly used charging station.

[0106] In an optional solution, determining the queue time of a common charging station according to the current vehicle status of other vehicles may include steps E1-E4:

[0107] Step E1: Determine a first distance between the other vehicle and a common charging station according to the current vehicle status of the other vehicle and the location information of the other vehicle.

[0108] Step E2: Determine a second distance from the vehicle to a common charging station based on the location information of the vehicle.

[0109] Step E3: Determine the number of charging station queues of the commonly used charging stations according to the first distance and the second distance.

[0110] Step E4: Determine the queue time of the commonly used charging stations according to the number of charging station queues.

[0111] The first distance may be the distance between other vehicles and the user's commonly used charging station. The second distance may be the distance between the user's vehicle and the commonly used charging station.

[0112] The location information of other vehicles is obtained, and a first distance from the other vehicles to the common charging station is determined based on the location information. The location information of the user vehicle is obtained, and a second distance from the user vehicle to the common charging station is determined based on the location information of the user vehicle.

[0113] After obtaining the first distance and the second distance, the number of other vehicles that arrive at the common charging station faster than the user's vehicle can be determined, thereby determining the number of charging station queues and further determining the queue time of the common charging station.

[0114] By adopting the technical solution of the embodiment of the present application, at least two commonly used charging stations for the vehicle are determined based on the historical charging information of the vehicle, so that the determined charging stations are more consistent with the user's commonly used charging stations, thereby improving the convenience of charging for the user. By determining the charging station prompt information based on the predicted working status of the commonly used charging stations and sending it to the user, the commonly used charging stations prompted to the user can provide charging services to the user more quickly, thereby reducing the user's queuing time and improving the user's charging experience.

[0115] Embodiment 3

[0116] Figure 3 The present invention provides a structural block diagram of an electric vehicle charging reminder device. This embodiment can be applied to situations where an electric vehicle charging reminder is performed according to the user's own habits. The electric vehicle charging reminder device can be implemented in the form of hardware and / or software, and the electric vehicle charging reminder device can be configured in an electronic device with data processing capabilities. Figure 3 As shown, the electric vehicle charging reminder device of this embodiment may include: a charging behavior determination module 310, a vehicle state determination module 320 and a charging reminder module 330. Among them:

[0117] A charging behavior determination module 310, for determining a user's charging habits based on the historical charging information of the vehicle and the historical user behavior information corresponding to the historical charging behavior;

[0118] The vehicle state determination module 320 is used to determine the current vehicle state of the vehicle according to the current power information of the vehicle, the current user behavior information and the historical user behavior information;

[0119] The charging reminder module 330 is used to send an electric vehicle charging reminder to the user if the current vehicle status of the vehicle meets the user's charging habits.

[0120] Based on the above embodiment, optionally, the charging behavior determination module 310 includes:

[0121] A power habit determination unit, used to determine the user's power charging habits based on the user's historical charging information;

[0122] A behavior habit determination unit, used to determine the user's charging behavior habits based on historical user behavior information;

[0123] A weight coefficient determination unit, used to determine the weight coefficient of the user's power charging habit and the user's behavior charging habit based on historical charging information;

[0124] The user habit determination unit is used to determine the user's charging habit based on a weight coefficient according to the user's power charging habit and the user's behavior charging habit.

[0125] Based on the above embodiment, optionally, the charging behavior determination module 310 includes:

[0126] A model input unit, used to input current power information, current behavior information, and historical user behavior information into a pre-trained XGboost model;

[0127] The model operation unit is used to determine the current vehicle state through the pre-trained XGboost model;

[0128] Among them, the pre-trained XGboost model is trained by the historical charging information of the vehicle and the historical user behavior information before the historical charging behavior.

[0129] Based on the above embodiment, optionally, the training process of the XGboost model includes:

[0130] A training data set determination subunit, configured to determine a first training data set and a second training data set according to historical charging information, historical user behavior information before historical charging behavior, and current behavior information;

[0131] A second model determination subunit is used to input the first training data set into the first XGboost model for training to obtain a second XGboost model;

[0132] The model correction subunit is used to correct the second XGboost model according to the second training data set to obtain the XGboost model.

[0133] Based on the above embodiment, optionally, after the charging reminder module 330, the device further includes:

[0134] A commonly used charging station determination module, used to determine at least two commonly used charging stations for the vehicle based on historical charging information of the vehicle;

[0135] The prompt information determination module is used to determine the charging station prompt information according to the predicted working status of the commonly used charging stations and send it to the user.

[0136] Based on the above embodiment, optionally, before the prompt information determination module, the device further includes:

[0137] Other vehicle status acquisition module, used to obtain the current vehicle status of other vehicles in the target detection area at the current moment;

[0138] A queue time determination module, used to determine the queue time of a commonly used charging station based on the current vehicle status of other vehicles;

[0139] The working status prediction module is used to determine the predicted working status of common charging stations based on the queuing time.

[0140] Based on the above embodiment, optionally, the queuing time determination module includes:

[0141] a first distance determination unit, configured to determine a first distance between the other vehicle and the common charging station according to the current vehicle state of the other vehicle and the position information of the other vehicle;

[0142] a second distance determining unit, configured to determine a second distance from the vehicle to a common charging station according to the location information of the vehicle;

[0143] a queue quantity determination unit, configured to determine a charging station queue quantity of a commonly used charging station according to the first distance and the second distance;

[0144] The queuing time calculation unit is used to determine the queuing time of the commonly used charging stations according to the number of charging station queues.

[0145] The electric vehicle charging reminder device provided in the embodiment of the present invention can execute the electric vehicle charging reminder method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0146] Embodiment 4

[0147] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention 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 electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0148] like Figure 4 As 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.

[0149] 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.

[0150] 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 dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as an electric vehicle charging reminder method.

[0151] In some embodiments, the electric vehicle charging reminder 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 electric vehicle charging reminder method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the electric vehicle charging reminder method in any other appropriate manner (e.g., by means of firmware).

[0152] 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.

[0153] Computer programs for implementing the methods of the present invention 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 and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0154] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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 described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0159] The above specific implementations do not constitute a limitation on the protection scope of the present invention. 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 modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for reminding an electric vehicle to charge, characterized in that: include: Determine the user's charging habits based on the vehicle's historical charging information and historical user behavior information corresponding to the historical charging behavior; Determining a current vehicle state of the vehicle according to the current power information of the vehicle, the current user behavior information, and the historical user behavior information; If the current vehicle state of the vehicle meets the charging habits of the user, a reminder to charge the electric vehicle is issued to the user; Wherein, if the current vehicle state of the vehicle satisfies the charging habit of the user, then the current vehicle state of the vehicle is modified to a pre-charging state, and after issuing an electric vehicle charging reminder to the user, the method further includes: Determining at least two common charging stations for the vehicle based on the historical charging information of the vehicle; Determine charging station prompt information according to the predicted working status of the commonly used charging stations, and send it to the user; Wherein, before determining the charging station prompt information according to the predicted working status of the commonly used charging station, the method further includes: Obtaining the current vehicle status of other vehicles in the target detection area at the current moment; Determining a queue time of the common charging station according to the current vehicle status of the other vehicles; Determining the predicted working status of the common charging station according to the queuing time; After determining the commonly used charging stations, one or more charging stations with the lowest electricity prices are determined and pushed according to the push information of each charging station; The determining of the user's charging habits based on the historical charging information of the vehicle and the historical user behavior information corresponding to the historical charging behavior includes: Determining the user's charging habits based on the user's historical charging information; Determining the user's charging behavior habits based on the historical user behavior information; Determining a weight coefficient between the user's power charging habit and the user's behavior charging habit based on the historical charging information; Based on the weight coefficient, determining the user charging habit according to the user power charging habit and the user behavior charging habit; Wherein, determining the current vehicle state of the vehicle according to the current power information, current behavior information and historical user behavior information of the vehicle includes: Inputting the current power information, the current behavior information, and the historical user behavior information into a pre-trained XGboost model; Determining the current vehicle state through the pre-trained XGboost model; Wherein, the pre-trained XGboost model is trained by the historical charging information of the vehicle and the historical user behavior information before the historical charging behavior; The training process of the XGboost model is as follows: Determine a first training data set and a second training data set according to the historical charging information, the historical user behavior information before the historical charging behavior, and the current behavior information; Input the first training data set into the first XGboost model for training to obtain a second XGboost model; Correcting the second XGboost model according to the second training data set to obtain the XGboost model; Wherein, determining the queue time of the common charging station according to the current vehicle status of the other vehicles includes: Determining a first distance between the other vehicle and the common charging station according to the current vehicle state of the other vehicle and the position information of the other vehicle; Determining a second distance from the vehicle to the common charging station according to the location information of the vehicle; determining the number of charging station queues of the commonly used charging station according to the first distance and the second distance; The queue time of the commonly used charging stations is determined according to the queue number of the charging stations.

2. An electric vehicle charging reminder device, characterized in that: include: A charging behavior determination module, used to determine the user's charging habits based on the historical charging information of the vehicle and the historical user behavior information corresponding to the historical charging behavior; A vehicle state determination module, used to determine the current vehicle state of the vehicle according to the current power information of the vehicle, the current user behavior information and the historical user behavior information; A charging reminder module, used to send an electric vehicle charging reminder to the user if the current vehicle status of the vehicle meets the user's charging habits; Among them, after the charging reminder module, it also includes: Determining at least two common charging stations for the vehicle based on the historical charging information of the vehicle; Determine charging station prompt information according to the predicted working status of the commonly used charging stations, and send it to the user; Wherein, before determining the charging station prompt information according to the predicted working status of the commonly used charging station, the method further includes: Obtaining the current vehicle status of other vehicles in the target detection area at the current moment; Determining a queue time of the common charging station according to the current vehicle status of the other vehicles; Determining the predicted working status of the common charging station according to the queuing time; After determining the commonly used charging stations, one or more charging stations with the lowest electricity prices are determined and pushed according to the push information of each charging station; The charging behavior determination module includes: A power habit determination unit, used to determine the user's power charging habits based on the user's historical charging information; A behavior habit determination unit, used to determine the user's charging behavior habits based on historical user behavior information; A weight coefficient determination unit, used to determine the weight coefficient of the user's power charging habit and the user's behavior charging habit based on historical charging information; A user habit determination unit, configured to determine the user's charging habit based on a weight coefficient according to the user's charging habit of electric quantity and the user's charging habit of behavior; The charging behavior determination module includes: A model input unit, used to input current power information, current behavior information, and historical user behavior information into a pre-trained XGboost model; The model operation unit is used to determine the current vehicle state through the pre-trained XGboost model; Among them, the pre-trained XGboost model is trained by the historical charging information of the vehicle and the historical user behavior information before the historical charging behavior; Among them, the training process of the XGboost model includes: A training data set determination subunit, configured to determine a first training data set and a second training data set according to historical charging information, historical user behavior information before historical charging behavior, and current behavior information; A second model determination subunit is used to input the first training data set into the first XGboost model for training to obtain a second XGboost model; A model correction subunit, used to correct the second XGboost model according to the second training data set to obtain an XGboost model; Wherein, after the charging reminder module (330), the device further comprises: A commonly used charging station determination module, used to determine at least two commonly used charging stations for the vehicle based on historical charging information of the vehicle; A prompt information determination module, used to determine the charging station prompt information according to the predicted working status of the commonly used charging stations, and send it to the user; Before the prompt information determination module is provided, the device further includes: Other vehicle status acquisition module, used to obtain the current vehicle status of other vehicles in the target detection area at the current moment; A queue time determination module, used to determine the queue time of a commonly used charging station based on the current vehicle status of other vehicles; A working status prediction module is used to determine the predicted working status of common charging stations based on the queuing time; Among them, the queuing time determination module includes: a first distance determination unit, configured to determine a first distance between the other vehicle and the common charging station according to the current vehicle state of the other vehicle and the position information of the other vehicle; a second distance determining unit, configured to determine a second distance from the vehicle to a common charging station according to the location information of the vehicle; a queue quantity determination unit, configured to determine a charging station queue quantity of a commonly used charging station according to the first distance and the second distance; The queuing time calculation unit is used to determine the queuing time of the commonly used charging stations according to the number of charging station queues.

3. An electronic device, characterized in that: The electronic device comprises: at least one processor; and 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 electric vehicle charging reminder method described in claim 1.

4. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the electric vehicle charging reminder method described in claim 1 when executed.

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