CN111949875B - Vehicle recommendation method and device, electronic equipment and storage medium - Google Patents

Vehicle recommendation method and device, electronic equipment and storage medium Download PDF

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CN111949875B
CN111949875B CN202010814357.9A CN202010814357A CN111949875B CN 111949875 B CN111949875 B CN 111949875B CN 202010814357 A CN202010814357 A CN 202010814357A CN 111949875 B CN111949875 B CN 111949875B
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vehicle
user
recommended
human body
posture
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CN111949875A (en
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余源
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BAIC Motor Co Ltd
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BAIC Motor Co Ltd
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    • G06Q30/06—Buying, selling or leasing transactions
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    • G06Q30/0631—Item recommendations
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0641—Shopping interfaces
    • G06Q30/0643—Graphical representation of items or shoppers

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Abstract

The invention discloses a vehicle recommendation method, a vehicle recommendation device, electronic equipment and a storage medium. The vehicle recommendation method comprises the following steps: acquiring attribute information and demand information of a user to be recommended; according to the demand information, inquiring a vehicle database, and acquiring vehicle data of a candidate vehicle matched with the demand information; generating simulated driving data of the user to be recommended in the candidate vehicle according to the attribute information and the vehicle data of the candidate vehicle; and determining a vehicle to be recommended according to the simulated driving data in the candidate vehicle, and recommending the vehicle to the user to be recommended. Therefore, the vehicle meeting the user requirements can be intelligently recommended to the user, the time and energy of the user are saved, and the accuracy of the vehicle recommended to the user is improved.

Description

Vehicle recommendation method and device, electronic equipment and storage medium
Technical Field
The present invention relates to the field of vehicle technologies, and in particular, to a vehicle recommendation method, device, electronic apparatus, and computer readable storage medium.
Background
With the development of the living standard of people and the automobile industry, the consumption demands of people on automobiles are also more and more vigorous. At present, people usually need to go to a car physical store to test driving of various interested vehicles to select vehicles meeting own demands according to driving experience when buying cars, and in this way, a long time is required to select the vehicles satisfying users, and the user's effort is consumed.
Disclosure of Invention
The present invention aims to solve at least to some extent one of the technical problems in the above-described technology. Therefore, an object of the present invention is to provide a vehicle recommendation method, which can intelligently recommend vehicles meeting user requirements to users, save time and effort of users, and improve accuracy of recommended vehicles to users.
A second object of the present invention is to provide a vehicle recommendation device.
A third object of the present invention is to propose an electronic device.
A fourth object of the present invention is to propose a computer readable storage medium.
To achieve the above object, an embodiment of a first aspect of the present invention provides a vehicle recommendation method, including the following steps: acquiring attribute information and demand information of a user to be recommended; according to the demand information, inquiring a vehicle database, and acquiring vehicle data of a candidate vehicle matched with the demand information; generating simulated driving data of the user to be recommended in the candidate vehicle according to the attribute information and the vehicle data of the candidate vehicle; and determining a vehicle to be recommended according to the simulated driving data in the candidate vehicle, and recommending the vehicle to the user to be recommended.
According to the vehicle recommendation method provided by the embodiment of the invention, the vehicle meeting the user requirement can be intelligently recommended to the user, so that the time and energy of the user are saved, and the accuracy of the vehicle recommended to the user is improved.
In addition, the vehicle recommendation method according to the above embodiment of the present invention may further have the following additional technical features:
in one embodiment of the present invention, the generating the simulated driving data of the user to be recommended in the candidate vehicle according to the attribute information and the vehicle data of the candidate vehicle includes: generating a three-dimensional human body model of the user to be recommended according to the attribute information of the user to be recommended; and generating the simulated driving data of the user to be recommended in the candidate vehicle according to the three-dimensional human body model of the user to be recommended and the vehicle data of the candidate vehicle.
In one embodiment of the present invention, the three-dimensional mannequin of the user to be recommended is a three-dimensional mannequin in a first posture; the generating the simulated driving data of the user to be recommended in the candidate vehicle according to the three-dimensional human model of the user to be recommended and the vehicle data of the candidate vehicle further comprises: and transforming the three-dimensional human body model in the first posture according to the posture mapping relation to obtain a three-dimensional human body model in a second posture, wherein the second posture is a sitting posture.
In one embodiment of the present invention, before transforming the three-dimensional mannequin in the first posture according to the posture mapping relationship to obtain the three-dimensional mannequin in the second posture, the method further includes: acquiring a plurality of human body samples and characteristic point information of each human body sample under a plurality of postures; aiming at each human body sample, determining an attitude mapping sub-relationship between any two attitudes according to characteristic point information of the human body sample under a plurality of attitudes; for each two gestures, determining a gesture mapping relation between the two gestures according to gesture mapping sub-relations of the plurality of human body samples between the two gestures; the transforming the three-dimensional human body model under the first gesture according to the gesture mapping relation to obtain the three-dimensional human body model under the second gesture comprises the following steps: and transforming the three-dimensional human body model in the first posture according to the posture mapping relation between the first posture and the second posture to obtain the three-dimensional human body model in the second posture.
In one embodiment of the invention, the simulated driving data includes at least one of the following information: the distance between the head of the user to be recommended and the roof of the candidate vehicle, the size of the activity space of the legs of the user to be recommended in the candidate vehicle, the seat adjusting position of the candidate vehicle, and the size of the rear evacuation space of the candidate vehicle when the user to be recommended is in the candidate vehicle.
In one embodiment of the present invention, the number of the candidate vehicles is a plurality; the determining the vehicle to be recommended according to the simulated driving data in the candidate vehicle comprises the following steps: displaying the simulated driving data in the plurality of candidate vehicles so that a user selects a vehicle according to the simulated driving data; determining the vehicle selected by the user as the vehicle to be recommended; or, acquiring first simulated driving data meeting preset conditions in the simulated driving data in the plurality of candidate vehicles, and determining the vehicle corresponding to the first simulated driving data as the vehicle to be recommended.
In one embodiment of the invention, the simulated driving data comprises simulated driving video; the presenting simulated driving data within the plurality of candidate vehicles includes: and displaying the simulated driving video so that a user can select a vehicle according to the simulated driving video.
To achieve the above object, a second aspect of the present invention provides a vehicle recommendation device, including: the first acquisition module is used for acquiring attribute information and demand information of the user to be recommended; the second acquisition module is used for inquiring a vehicle database according to the demand information and acquiring vehicle data of the candidate vehicles matched with the demand information; the generation module is used for generating simulated driving data of the user to be recommended in the candidate vehicle according to the attribute information and the vehicle data of the candidate vehicle; and the recommending module is used for determining a vehicle to be recommended according to the simulated driving data in the candidate vehicle and recommending the vehicle to the user to be recommended.
The vehicle recommendation device provided by the embodiment of the invention can intelligently recommend the vehicle meeting the user requirement for the user, so that the time and energy of the user are saved, and the accuracy of the vehicle recommended to the user is improved.
To achieve the above object, an embodiment of a third aspect of the present invention provides an electronic device, including a memory, and a processor; the processor executes a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the vehicle recommendation method according to the embodiment of the first aspect of the present invention.
According to the electronic equipment provided by the embodiment of the invention, the computer program stored in the memory is executed by the processor, so that the vehicle meeting the user requirement can be intelligently recommended to the user, the time and energy of the user are saved, and the accuracy of the vehicle recommended to the user is improved.
To achieve the above object, an embodiment of a fourth aspect of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the vehicle recommendation method according to the embodiment of the first aspect of the present invention.
The computer readable storage medium of the embodiment of the invention can intelligently recommend the vehicles meeting the user demands for the user by storing the computer program and executing the computer program by the processor, thereby saving the time and energy of the user and improving the accuracy of the vehicles recommended to the user.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flow chart of a vehicle recommendation method according to one embodiment of the invention;
FIG. 2 is a flow chart of a vehicle recommendation method according to another embodiment of the present invention;
FIG. 3 is a schematic representation of a standing position of a human body sample according to one embodiment of the present invention;
FIG. 4 is a schematic representation of a sitting posture of a human body sample according to one embodiment of the present invention;
FIG. 5 is a block diagram of a vehicle recommendation device according to one embodiment of the present invention;
FIG. 6 is a schematic view of a vehicle recommendation device according to an embodiment of the present invention;
FIG. 7 is a schematic view showing a structure of a vehicle recommendation apparatus according to another embodiment of the present invention; and
fig. 8 is a schematic structural view of an electronic device according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
Aiming at the problems that in the related art, people need to test driving to various interested vehicles in an automobile entity store when buying the automobile, the vehicles meeting the requirements of the users can be selected only by consuming a long time according to the mode of selecting the vehicles meeting the requirements of the users through driving experience, and the user energy is consumed, the embodiment of the application provides a vehicle recommendation method.
According to the vehicle recommendation method, firstly, attribute information and demand information of a user to be recommended are obtained, then, a vehicle database is queried according to the demand information, vehicle data of a candidate vehicle matched with the demand information is obtained, and then, according to the attribute information and the vehicle data of the candidate vehicle, simulated driving data of the user to be recommended in the candidate vehicle is generated, so that the vehicle to be recommended is determined according to the simulated driving data in the candidate vehicle, and is recommended to the user to be recommended. Therefore, the vehicle meeting the user requirements is intelligently recommended to the user, the time and energy of the user are saved, and the vehicle to be recommended is determined according to the simulated driving data of the user to be recommended in the candidate vehicle meeting the user requirements, so that the vehicle can be accurately recommended to the user, and the user experience is improved.
The vehicle recommendation method, apparatus, electronic device, and computer-readable storage medium of the embodiments of the present invention are described below with reference to the accompanying drawings.
The vehicle recommendation method provided in the present application is described below with reference to fig. 1. Fig. 1 is a flowchart of a vehicle recommendation method according to an embodiment of the present invention.
As shown in fig. 1, the vehicle recommendation method according to the embodiment of the present invention may include the following steps:
and step 101, acquiring attribute information and demand information of a user to be recommended.
Specifically, the vehicle recommendation method provided by the application can be executed by the vehicle recommendation device provided by the application, wherein the vehicle recommendation device can be configured in the electronic equipment to intelligently recommend the vehicle meeting the user requirement for the user. The electronic device may be any device capable of performing data processing, such as a smart phone, a computer, and the like.
The attribute information is data related to the attribute of the user to be recommended, and may include, for example, the height, weight, sex, body type, and the like of the user to be recommended. The demand information, which is information about the requirement of the user to be recommended on the desired vehicle, may include, for example, the price of the desired vehicle, the number of seats, the vehicle type, and the like.
In an exemplary embodiment, a user interaction interface of the electronic device may provide a window for enabling a user to input attribute information or demand information, so that the vehicle recommendation device may obtain the attribute information and the demand information of the user to be recommended through text or voice information that is directly input in the window provided by the user interaction interface by the user to be recommended; or, the user interaction interface of the electronic device can provide options related to the attribute information or the requirement information, so that the vehicle recommendation device can obtain the attribute information and the requirement information of the user to be recommended through the selection of the options related to the attribute information and the requirement information by the user to be recommended; or the user can upload the file such as the picture containing the attribute information or the demand information of the user to the electronic equipment, so that the vehicle recommendation device can acquire the attribute information and the demand information of the user to be recommended through the file such as the picture uploaded by the user to be recommended. For example, the user to be recommended may upload his own photo to the electronic device, so that the vehicle recommendation apparatus may obtain attribute information such as height and body type of the user according to the photo uploaded by the user.
Of course, the vehicle recommendation device may also acquire the attribute information and the requirement information of the user to be recommended in other manners, which is not limited in this application.
And 102, inquiring a vehicle database according to the demand information, and acquiring vehicle data of the candidate vehicles matched with the demand information.
The vehicle data may include any data related to the vehicle, such as the size of the internal space of the vehicle, the size and number of seats of the vehicle, the positions of the respective seats, and the like.
Specifically, a vehicle database may be pre-established, where the vehicle database includes vehicle data corresponding to vehicles of various vehicle types, so that the vehicle recommendation device may match, according to the requirement information, the vehicle data corresponding to the vehicles of various vehicle types in the vehicle database, so as to obtain the vehicle data of the candidate vehicle matched with the requirement information.
It should be noted that the number of candidate vehicles may be one or more, which is not limited in this application.
And step 103, generating simulated driving data of the user to be recommended in the candidate vehicle according to the attribute information and the vehicle data of the candidate vehicle.
The driving data is data for simulating driving feeling when the user to be recommended is actually sitting in the candidate vehicle, and may include a distance between a head of the user to be recommended and a roof of the candidate vehicle, an active space size of legs of the user to be recommended in the candidate vehicle, a seat adjustment position of the candidate vehicle, a rear evacuation space size of the vehicle when the user to be recommended is in the candidate vehicle, and the like.
Specifically, step 103 may be implemented by way of the following steps 101a-101 b:
step 101a, generating a three-dimensional human model of the user to be recommended according to the attribute information of the user to be recommended.
Step 101b, generating simulated driving data of the user to be recommended in the candidate vehicle according to the three-dimensional human body model of the user to be recommended and the vehicle data of the candidate vehicle.
In an exemplary embodiment, for example, the vehicle recommendation device obtains the height and the weight of the user to be recommended and the front and side whole body illumination of the user to be recommended, and then the three-dimensional mannequin of the user to be recommended can be generated according to the front and side whole body illumination and the height and the weight of the user to be recommended. Then, a scene of the three-dimensional mannequin of the user to be recommended when the user sits on the driving position of the candidate vehicle can be simulated, and according to the height, the weight, the size and other information of each part of the three-dimensional mannequin and the vehicle data of the candidate vehicle, the distance between the head of the three-dimensional mannequin and the roof of the candidate vehicle when the three-dimensional mannequin is in the scene, the size of the movable space of the legs of the three-dimensional mannequin in the candidate vehicle, the seat adjusting position of the candidate vehicle, the size of the rear emptying space of the selected vehicle when the three-dimensional mannequin is in the candidate vehicle and the like, the simulated driving data of the three-dimensional mannequin in the candidate vehicle are the simulated driving data of the user to be recommended in the candidate vehicle.
It should be noted that, in the exemplary embodiment, the vehicle recommendation device may directly generate a three-dimensional mannequin of the user to be recommended in a sitting position according to the attribute information of the user to be recommended, for example, the user to be recommended may upload a whole body of the user to be recommended in the sitting position, and then the vehicle recommendation device may directly use the three-dimensional mannequin of the user to be recommended to simulate a scene of the user to be recommended when sitting on a driving position of the candidate vehicle, so as to obtain simulated driving data of the user to be recommended in the candidate vehicle. Or, in the exemplary embodiment, the vehicle recommendation device may generate, according to attribute information of the user to be recommended, a three-dimensional mannequin of the user to be recommended in other postures except the sitting posture, for example, a three-dimensional mannequin in a standing posture, and after acquiring the three-dimensional mannequin of the user to be recommended, the vehicle recommendation device further needs to convert the three-dimensional mannequin into the three-dimensional mannequin in the sitting posture, and then simulate a scene of the user to be recommended when sitting on a driving position of the candidate vehicle by using the converted three-dimensional mannequin in the sitting posture, so as to acquire simulated driving data of the user to be recommended in the candidate vehicle.
In addition, the attribute information of the user to be recommended may be acquired simultaneously with the requirement information of the user to be recommended, or may not be acquired simultaneously, which is not limited in the application.
And 104, determining a vehicle to be recommended according to the simulated driving data in the candidate vehicle, and recommending the vehicle to the user to be recommended.
In an exemplary embodiment, when the number of candidate vehicles is 1, the 1 candidate vehicles may be directly determined as vehicles to be recommended, and the vehicles to be recommended may be recommended to users to be recommended.
In an exemplary embodiment, when the number of candidate vehicles is plural, the vehicle to be recommended may be determined in the following plural ways.
Mode one
Displaying the simulated driving data in the plurality of candidate vehicles so that a user selects a vehicle according to the simulated driving data; and determining the vehicle selected by the user as the vehicle to be recommended.
Specifically, the simulated driving data of the user to be recommended in the plurality of candidate vehicles can be displayed on the user interaction interface of the electronic device, so that the user to be recommended selects a vehicle which is satisfied with the user to be recommended according to the simulated driving data, and after the user to be recommended selects the vehicle which is satisfied with the user to be recommended through touching the simulated driving data of the vehicle which is satisfied with the user to be recommended or through other modes, the vehicle recommendation device can determine the vehicle selected by the user to be recommended as the vehicle to be recommended and recommend the vehicle to the user to be recommended.
When the user interaction interface of the electronic device displays the simulated driving data in the plurality of candidate vehicles, the simulated driving data in the form of characters can be displayed, so that a user to be recommended can know the driving feeling of the user, such as the distance between the head and the roof of the candidate vehicle, the size of the movable space of the leg in the candidate vehicle, the seat adjusting position of the candidate vehicle, the size of the rear evacuation space of the user in the candidate vehicle, and the like, according to the displayed simulated driving data, so as to select the user to be satisfied.
Or, in order to enable the user to be recommended to intuitively see the simulation situation of the user when the user drives in the candidate vehicle, so as to more accurately select the vehicle which is satisfied by the user, in the example embodiment, a picture simulating the scene of the three-dimensional mannequin of the user to be recommended when the user sits on the driving position in the candidate vehicle can be displayed, so that the user intuitively knows the distance between the head of the user and the roof of the candidate vehicle when the user sits in the candidate vehicle, the size of the movable space of the legs of the user in the candidate vehicle, the seat adjusting position of the candidate vehicle, the size of the rear-end space of the vehicle when the user sits in the candidate vehicle, and the like, so as to select the vehicle which is satisfied by the user.
Alternatively, the simulated driving data may be marked at the corresponding position of the picture while the picture simulating the scene where the three-dimensional mannequin of the user to be recommended sits on the driving position in the candidate vehicle is displayed, for example, the distance between the head of the user and the roof of the candidate vehicle is marked between the head of the three-dimensional mannequin and the roof of the candidate vehicle, the size of the rear space of the candidate vehicle when the user is in the candidate vehicle is marked at the rear-row position of the candidate vehicle, and the like, so that the user can more accurately select the vehicle satisfying himself in combination with the visual feeling of the simulated situation and the data in the text form of the driving of himself in the candidate vehicle obtained from the picture.
Through the display of the simulated driving data, a user can obtain the real feeling when simulating the scene of sitting on the driving position in the candidate vehicle, and the immersive driving experience is obtained, so that the accuracy of the selected vehicle is improved.
In addition, in order to further improve the accuracy of the vehicle selected by the user, in an exemplary embodiment, the simulated driving data may include a simulated driving video, where the simulated driving video, when simulating a scene in which the user to be recommended sits on the driving location of the candidate vehicle, is a simulated video of a front view seen from the perspective of the user to be recommended, and displaying the simulated driving data in a plurality of candidate vehicles, accordingly, may include:
and displaying the simulated driving video so that the user can select the vehicle according to the simulated driving video.
Through the display of the simulated driving video, a user can acquire more immersive driving experience, and therefore the accuracy of the selected vehicle is further improved.
In an exemplary embodiment, the simulated driving videos in various driving scenes can be displayed, for example, the simulated driving videos in driving scenes such as driving in urban roads, driving on rural roads, ascending slopes, descending slopes and the like are displayed, so that a user can know driving experiences when driving candidate vehicles in various driving scenes according to the simulated driving videos, and the user can select the satisfactory vehicles more accurately.
Mode two
And acquiring first simulated driving data meeting preset conditions in the simulated driving data in the plurality of candidate vehicles, and determining the vehicle corresponding to the first simulated driving data as the vehicle to be recommended.
Wherein, the preset conditions can be set according to the needs.
For example, the preset condition may be set such that the distance between the head of the user to be recommended and the roof of the candidate vehicle is greater than a first threshold, the active space of the leg of the user to be recommended within the candidate vehicle is greater than a second threshold, and so on. The first threshold and the second threshold may be set as needed. For example, when the number of candidate vehicles is small, the first threshold and the second threshold may be set small, when the number of candidate vehicles is large, the first threshold and the second threshold may be set large, and so on. Or, in the requirement information of the user to be recommended, including that the activity space of the leg is large during driving, the second threshold value may be set to be large, and so on.
Specifically, after the simulated driving data in the plurality of candidate vehicles are obtained, first simulated driving data meeting preset conditions can be selected from the plurality of candidate vehicles, the vehicle corresponding to the first simulated driving data is determined to be the vehicle to be recommended, and the vehicle to be recommended is recommended to the user to be recommended.
By determining the vehicle corresponding to the first simulated driving data meeting the preset conditions as the vehicle to be recommended to the user to be recommended, the accuracy of the recommended vehicle can be improved.
It should be noted that, when the vehicle to be recommended is recommended to the user to be recommended, the vehicle to be recommended may be displayed to the user in various manners, for example, the vehicle type of the vehicle to be recommended may be displayed to the user to be recommended, or the vehicle type of the vehicle to be recommended and the photo of the vehicle to be recommended may be displayed to the user to be recommended, or the vehicle type of the vehicle to be recommended, the photo of the vehicle to be recommended, and the simulated driving data of the user to be recommended in the vehicle to be recommended may be displayed to the user to be recommended, or the vehicle type of the vehicle to be recommended, the photo of the vehicle to be recommended, the simulated driving video of the user to be recommended in the vehicle to be recommended, and the like.
According to the attribute information and the demand information of the user to be recommended, the vehicle meeting the user demand can be intelligently recommended to the user, so that the user does not need to spend time and effort to go to an automobile entity store for trial driving, the satisfactory vehicle is determined according to the driving feeling of each vehicle, the time and effort of the user are saved, and the vehicle to be recommended is determined according to the simulated driving data simulating the driving feeling of the user to be recommended when the user to be recommended is actually seated in the candidate vehicle.
According to the vehicle recommendation method, firstly, attribute information and demand information of a user to be recommended are obtained, then, a vehicle database is queried according to the demand information, vehicle data of a candidate vehicle matched with the demand information is obtained, and then, according to the attribute information and the vehicle data of the candidate vehicle, simulated driving data of the user to be recommended in the candidate vehicle is generated, so that the vehicle to be recommended is determined according to the simulated driving data in the candidate vehicle, and is recommended to the user to be recommended. Therefore, the vehicle meeting the user requirements is intelligently recommended to the user, the time and energy of the user are saved, and the vehicle to be recommended is determined according to the simulated driving data of the user to be recommended in the candidate vehicle meeting the user requirements, so that the accuracy of the vehicle recommended to the user is improved, and the user experience is improved.
As can be seen from the above analysis, in the exemplary embodiment, the vehicle recommendation device may generate, according to the attribute information of the user to be recommended, a three-dimensional mannequin of the user to be recommended in a posture other than sitting posture, for example, a standing posture, and then after acquiring the three-dimensional mannequin of the user to be recommended, the vehicle recommendation device further needs to convert the three-dimensional mannequin into a sitting posture three-dimensional mannequin, and then simulate the scene of the user sitting in the driving position of the candidate vehicle by using the converted sitting posture three-dimensional mannequin to acquire simulated driving data of the user to be recommended in the candidate vehicle, so as to determine the vehicle to be recommended, and the vehicle recommendation method provided by the invention is further described below with reference to fig. 2.
Fig. 2 is a flowchart of a vehicle recommendation method according to another embodiment of the present invention. As shown in fig. 2, the vehicle recommendation method according to the embodiment of the invention may include the following steps:
step 201, obtaining attribute information and demand information of a user to be recommended.
And 202, inquiring a vehicle database according to the demand information, and acquiring vehicle data of the candidate vehicles matched with the demand information.
The specific implementation process and principle of the steps 201 to 202 may refer to the detailed description of the foregoing embodiments, which is not repeated herein.
And 203, generating a three-dimensional human body model of the user to be recommended in the first gesture according to the attribute information of the user to be recommended.
The first posture can be any posture out of sitting posture such as standing posture, lying posture and the like.
And 204, transforming the three-dimensional human body model in the first posture according to the posture mapping relation to obtain the three-dimensional human body model in the second posture, wherein the second posture is a sitting posture.
It will be appreciated that in order to transform the three-dimensional manikin in the first pose according to the pose mapping relationship, the pose mapping relationship between the first pose and the second pose needs to be predetermined.
In an exemplary embodiment, the posture mapping relationship between any two postures, for example, the posture mapping relationship between the sitting posture and the standing posture, the posture mapping relationship between the sitting posture and the lying posture, and the like, may be predetermined, so that when the three-dimensional human model under the second posture needs to be obtained according to the three-dimensional human model under the first posture, the three-dimensional human model under the first posture may be transformed according to the predetermined posture mapping relationship between the first posture and the second posture, to obtain the three-dimensional human model under the second posture.
That is, prior to step 204, the pose mapping relationship between any two poses may also be determined by:
acquiring a plurality of human body samples and characteristic point information of each human body sample under a plurality of postures; aiming at each human body sample, determining an attitude mapping sub-relationship between any two attitudes according to characteristic point information of the human body sample under a plurality of attitudes; for every two gestures, determining the gesture mapping relation between the two gestures according to the gesture mapping sub-relation of the plurality of human body samples between the two gestures.
Accordingly, step 204 may be specifically implemented by:
and transforming the three-dimensional human body model in the first posture according to the posture mapping relation between the first posture and the second posture to obtain the three-dimensional human body model in the second posture.
The number of human body samples can be set according to the requirement.
The feature point information may include position information of each feature point.
The above method of determining the posture mapping relationship between the two postures will be described below by taking the process of determining the posture mapping relationship between the standing posture and the sitting posture as an example.
First, a plurality of human body samples and characteristic point information of each human body sample in a standing posture and a sitting posture can be acquired, wherein the number of the human body samples and the number of the characteristic points of each human body sample in the standing posture and the sitting posture can be set according to requirements. It should be noted that the number of feature points of the human body sample in different postures is equal.
In order to avoid duplication of feature points, feature points should be selected as widely as possible, and feature points such as leg bending boundaries, head, chest, etc. should be selected at key points.
For example, a sample library may be established in advance, where the sample library includes a plurality of human samples and attribute information such as height, weight, etc. of each human sample. Then 200 human samples are obtained from the sample library, and 200 feature points of the top of the head, the left shoulder, the right shoulder, the left wrist, the right wrist and the like are selected. The attribute information of the 200 human samples may include various kinds of human samples including various kinds of attribute information such as tall, short, fat, thin, male, female, and the like. For each human body sample, position information corresponding to 200 characteristic points when the human body sample is in a standing position and a sitting position respectively can be obtained.
Then, for each human body sample, determining an attitude mapping sub-relationship between the standing and sitting postures according to characteristic point information of the human body sample under the standing and sitting postures respectively.
In combination with the schematic view of the standing posture human body sample shown in fig. 3 and the schematic view of the sitting posture human body sample shown in fig. 4, wherein black dots in fig. 3 and fig. 4 are characteristic points, assuming that positions of respective characteristic points of the standing posture human body sample in three-dimensional space satisfy a function I (x, Y, z), positions of respective characteristic points of the sitting posture human body sample in three-dimensional space satisfy a function Y (x, Y, z), then the following relationship exists between I (x, Y, z), Y (x, Y, z) and a posture mapping sub-relationship F (x, Y, z) between the standing posture and the sitting posture of each human body sample to be determined in the present application:
Y(x,y,z)＝I(x,y,z)*F(x,y,z) (1)
Since the polynomial can exhibit characteristics of translation, rotation, magnification, reduction, etc. in image transformation, it can be assumed that F (x, y, z) is a polynomial, i.e
Wherein (x ', y ', z ') is a coordinate value corresponding to each feature point of the human body sample under sitting posture, and (x, y, z) is a coordinate value corresponding to each feature point of the human body sample under standing posture, and n is a polynomial order.
Since the 4 th order polynomial can meet the requirement of analog conversion, n=3 can be substituted into the above formulas (2), (3) and (4), resulting in the following results:
x'＝a 000 +a 100 x+a 111 xyz+a 110 xy+……+a 333 x 3 y 3 z 3 (5)
y'＝b 000 +b 010 y+b 111 xyz+b 110 xy+……+b 333 x 3 y 3 z 3 (6)
z'＝c 000 +c 001 z+c 111 xyz+c 110 xy+……+c 333 x 3 y 3 z 3 (7)
in the present application, for each human body sample, the position information of a plurality of feature points of the human body sample in the standing posture and the position information of a plurality of feature points in the sitting posture are substituted into the above (5), (6) and (7), respectively, so that a can be solved 000 、a 001 、……a 333 、b 000 、b 001 、……b 333 、c 000 、c 001 、……c 333 192 parameters, etc., so that the posture mapping sub-relationship F (x, y, z) between the standing posture and the sitting posture of the human body sample can be determined according to the parameters.
Wherein F (x, y, z) =a 000 +b 000 +c 000 +a 001 z+a 111 xyz+a 110 xy+…+a 333 x 3 y 3 z 3 +…+c 333 z 3 y 3 z 3 。
After determining the posture mapping sub-relationship between the standing posture and the sitting posture for each human body sample, the posture mapping relationship between the standing posture and the sitting posture can be further determined according to the posture mapping sub-relationship between the standing posture and the sitting posture of a plurality of human body samples.
It will be appreciated that for each human sample, a mapping sub-relationship between any standing and sitting posture is determined, i.e. for each human sample, a set of a is determined 000 、a 001 、……a 333 、b 000 、b 001 、……b 333 、c 000 、c 001 、……c 333 After the parameters, the average value of a plurality of groups of parameters can be calculated to obtain a' 000 、a' 001 、……a' 333 、b' 000 、b' 001 、……b' 333 、c' 000 、c' 001 、……c' 333 To determine the posture mapping relation F' (x, y, z) between the standing posture and the sitting posture according to the averaged parameters.
Wherein F '(x, y, z) =a' 000 +b' 000 +c' 000 +a' 001 z+a' 111 xyz+a' 110 xy+…+a' 333 x 3 y 3 z 3 +…+c' 333 z 3 y 3 z 3 。
In an exemplary embodiment, in order to improve the accuracy of the determined posture mapping relationship, the posture mapping relationship between the standing posture and the sitting posture may also be determined according to the posture mapping sub-relationship between the standing posture and the sitting posture of the plurality of human body samples in the following manner.
Specifically, each parameter corresponding to the posture mapping relationship between standing posture and sitting posture of the male and female can be determined respectively, and when the posture mapping relationship between standing posture and sitting posture of the male and female is determined respectively, the average value of each parameter can be calculated by using a weighted average method.
More specifically, the posture mapping relationship between the standing posture and the sitting posture of the male can be determined by using the posture mapping sub-relationship between the standing posture and the sitting posture of the plurality of human body samples of the male, and the posture mapping relationship between the standing posture and the sitting posture of the female can be determined by using the posture mapping sub-relationship between the standing posture and the sitting posture of the plurality of human body samples of the female.
Taking the example of determining the posture mapping relationship between the standing posture and the sitting posture of the male, assuming that the average height of the male is 167.1cm, the weight of each parameter corresponding to the human body sample of each male can be determined by using an inverse proportion method shown in the following formula.
W=10/| (167.1—height of human sample) | (8)
For example, for a human sample of a man of 167.1cm height, the weight of each parameter corresponding to the human sample is 10/| (167.1-167.1) |=10. In the formula (8), when the denominator is 0, the denominator may be calculated as 1.
For a human sample of a man 226cm in height, the weight of each parameter corresponding to the human sample is 10/| (167.1-226) |=0.17.
From the above examples, it can be seen that the closer the height of the human body sample is to 167.1cm, the greater the weight of each parameter corresponding to the human body sample.
Further, after the weights of the parameters corresponding to the human body samples of the male are determined, the parameters a' corresponding to the posture mapping relationship between the standing posture and the sitting posture of the male can be determined " 000 、……c” 333 . Wherein, a is " 000 、c” 333 Two parameters are taken as an example, and assuming that the number of human body samples of a male is 100, a' can be determined by the following formula " 000 、c” 333 。
By the above parameters a' 000 、……c” 333 The posture mapping relation F "(x, y, z) between the standing posture and the sitting posture of the male can be determined, and n' is the number of human body samples of the male.
It can be understood that by a similar manner to the above process, the posture mapping relationship between the standing posture and the sitting posture of the female can be determined, so that when the attribute information of the user to be recommended is male or female and the first posture is the standing posture, the three-dimensional human model of the standing posture can be transformed according to the posture mapping relationship between the standing posture and the sitting posture of the corresponding gender, and the three-dimensional human model under the sitting posture is obtained.
Similar to the process of determining the posture mapping relationship between the standing posture and the sitting posture, the posture mapping relationship between various postures can be determined, so that when the first posture is any posture and the three-dimensional human body model under the second posture is required to be obtained according to the three-dimensional human body model under the first posture, the three-dimensional human body model under the first posture can be transformed according to the preset posture mapping relationship between the first posture and the second posture, and the three-dimensional human body model under the second posture is obtained.
Step 205, generating simulated driving data of the user to be recommended in the candidate vehicle according to the three-dimensional human body model in the second gesture and the vehicle data of the candidate vehicle.
Wherein the simulated driving data includes at least one of the following information: the distance between the head of the user to be recommended and the roof of the candidate vehicle, the size of the active space of the legs of the user to be recommended in the candidate vehicle, the seat adjusting position of the candidate vehicle, and the size of the rear evacuation space of the vehicle selected by the user to be recommended when the user is in the candidate vehicle.
Step 206, displaying the simulated driving data in the plurality of candidate vehicles so that the user selects the vehicle according to the simulated driving data.
In step 207, the vehicle selected by the user is determined as the vehicle to be recommended, and recommended to the user to be recommended.
The specific implementation process and principle of the steps 205-207 may refer to the detailed description of the embodiments, and will not be repeated here.
The vehicle recommendation method provided in the present application is further described below with reference to the architecture diagram of the vehicle recommendation apparatus shown in fig. 5.
As shown in fig. 5, parameters of a plurality of vehicles such as a vehicle a and a vehicle B … … and a vehicle N may be collected in advance, and a vehicle database may be established, where the vehicle database includes analysis data on driving dynamics, space, science and technology senses of the plurality of vehicles (step 1). After the user inputs the data about the attribute of the user, the vehicle recommendation device may generate a three-dimensional mannequin of the user to be recommended according to the information such as the user head, height, weight, etc. of the user to be recommended (step 2). After the user inputs the demand information (step 3), the vehicle recommendation device may combine the vehicle database and the user demand information to obtain vehicle data of the candidate vehicle matched with the demand information, and then generate simulated driving data of the user to be recommended in the candidate vehicle according to the three-dimensional human model of the user to be recommended and the vehicle data of the candidate vehicle, determine the vehicle to be recommended according to the simulated driving data, and recommend the vehicle to the user to be recommended (step 4). When the vehicle to be recommended is recommended to the user to be recommended, the simulated driving data of the user to be recommended in the vehicle to be recommended can be simultaneously derived and pushed to the user to be recommended, so that the user to be recommended can know parameters such as technological sense, power, comfort, stability and space of the vehicle to be recommended. In addition, a simulated driving video of the front view seen by the user to be recommended in the view angle of the user to be recommended when the user to be recommended is simulated to sit in the vehicle to be recommended on the driving position can be generated, and the simulated driving video is pushed to the user to be recommended, so that the user to be recommended can watch the simulated driving video (step 5).
It should be noted that, through the vehicle recommendation method provided by the application, attribute information and demand information of a plurality of users can be obtained, and users suitable for each vehicle type are determined through the attribute information and demand information of the plurality of users and the vehicle database, so as to determine vehicle purchasing groups corresponding to the various vehicle types, and further, vehicle research and development is continued based on the determined vehicle types corresponding to the different vehicle purchasing groups, so as to research and develop new-generation vehicles more matched with the different vehicle purchasing groups.
According to the vehicle recommendation method, firstly, attribute information and demand information of a user to be recommended are acquired, then, a vehicle database is queried according to the demand information, vehicle data of a candidate vehicle matched with the demand information is acquired, then, according to the attribute information of the user to be recommended, a three-dimensional human body model of the user to be recommended in a first posture is generated, then, the three-dimensional human body model of the user to be recommended in the first posture is converted according to a posture mapping relation, a three-dimensional human body model of the user to be recommended in a second posture is obtained, wherein the second posture is a sitting posture, then, according to the three-dimensional human body model of the user to be recommended and the vehicle data of the candidate vehicle, simulated driving data of the user to be recommended in the candidate vehicle are generated, and then, the simulated driving data of the candidate vehicle are displayed, so that the user can select the vehicle according to the simulated driving data, finally, the vehicle selected by the user is determined as the vehicle to be recommended, and the user to be recommended, therefore, the vehicle meeting the user's demand for intelligent recommendation of the user is realized, the time and energy of the user are saved, and the user experience is improved.
Fig. 6 is a schematic structural view of a vehicle recommendation apparatus according to an embodiment of the present invention.
As shown in fig. 6, the vehicle recommendation device 100 according to the embodiment of the present invention includes a first acquisition module 11, a second acquisition module 12, a generation module 13, and a recommendation module 14.
The first acquiring module 11 is configured to acquire attribute information and demand information of a user to be recommended;
a second obtaining module 12, configured to query a vehicle database according to the requirement information, and obtain vehicle data of a candidate vehicle matched with the requirement information;
a generating module 13, configured to generate simulated driving data of a user to be recommended in a candidate vehicle according to attribute information and vehicle data of the candidate vehicle;
the recommending module 14 is configured to determine a vehicle to be recommended according to the simulated driving data in the candidate vehicle, and recommend the vehicle to the user to be recommended.
Specifically, the vehicle recommendation device provided by the application can execute the vehicle recommendation method provided by the application. The vehicle recommending device can be configured in the electronic equipment to intelligently recommend the vehicle meeting the user requirement for the user. The electronic device may be any device capable of performing data processing, such as a smart phone, a computer, and the like.
In one embodiment of the present invention, the above-described simulated driving data includes at least one of the following information: the distance between the head of the user to be recommended and the roof of the candidate vehicle, the size of the active space of the legs of the user to be recommended in the candidate vehicle, the seat adjusting position of the candidate vehicle, and the size of the rear evacuation space of the vehicle selected by the user to be recommended when the user is in the candidate vehicle.
It should be noted that, for details not disclosed in the vehicle recommendation device according to the embodiment of the present invention, please refer to details disclosed in the vehicle recommendation method according to the above embodiment of the present invention, and details are not described here again.
According to the vehicle recommending device, firstly, attribute information and demand information of a user to be recommended are obtained, then, a vehicle database is queried according to the demand information, vehicle data of a candidate vehicle matched with the demand information is obtained, and then, according to the attribute information and the vehicle data of the candidate vehicle, simulated driving data of the user to be recommended in the candidate vehicle is generated, so that the vehicle to be recommended is determined according to the simulated driving data in the candidate vehicle, and is recommended to the user to be recommended. Therefore, the vehicle meeting the user requirements is intelligently recommended to the user, the time and energy of the user are saved, and the vehicle to be recommended is determined according to the simulated driving data of the user to be recommended in the candidate vehicle meeting the user requirements, so that the accuracy of the vehicle recommended to the user is improved, and the user experience is improved.
The vehicle recommendation device 100 disclosed in the present application is further described below with reference to fig. 7.
Fig. 7 is a schematic structural view of a vehicle recommendation apparatus according to another embodiment of the present invention.
As shown in fig. 7, the generating module 13 in the vehicle recommendation device 100 may include:
a first generating unit 131, configured to generate a three-dimensional human model of the user to be recommended according to attribute information of the user to be recommended;
the second generating unit 132 is configured to generate simulated driving data of the user to be recommended in the candidate vehicle according to the three-dimensional mannequin of the user to be recommended and the vehicle data of the candidate vehicle.
In one possible implementation form, the three-dimensional mannequin of the user to be recommended is the three-dimensional mannequin in the first pose; correspondingly, the generating module 13 may further include:
the transformation unit is used for transforming the three-dimensional human body model under the first gesture according to the gesture mapping relation to obtain the three-dimensional human body model under the second gesture, wherein the second gesture is a sitting gesture.
In one possible implementation form, the generating module 13 may further include:
the acquisition unit is used for acquiring a plurality of human body samples and characteristic point information of each human body sample under a plurality of postures;
The first determining unit is used for determining an attitude mapping sub-relationship between any two attitudes according to characteristic point information of the human body sample under a plurality of attitudes aiming at each human body sample;
the second determining unit is used for determining an attitude mapping relation between two attitudes according to the attitude mapping sub-relation of the plurality of human body samples between the two attitudes for each two attitudes;
correspondingly, the transformation unit is specifically configured to:
and transforming the three-dimensional human body model in the first posture according to the posture mapping relation between the first posture and the second posture to obtain the three-dimensional human body model in the second posture.
In another possible implementation form, the number of candidate vehicles is a plurality;
correspondingly, the recommendation module is specifically configured to:
displaying the simulated driving data in the plurality of candidate vehicles so that a user selects a vehicle according to the simulated driving data; determining the vehicle selected by the user as the vehicle to be recommended;
or,
and acquiring first simulated driving data meeting preset conditions in the simulated driving data in the plurality of candidate vehicles, and determining the vehicle corresponding to the first simulated driving data as the vehicle to be recommended.
In another possible implementation, the simulated driving data includes simulated driving videos;
The recommendation module is further used for:
and displaying the simulated driving video so that the user can select the vehicle according to the simulated driving video.
It should be noted that, for details not disclosed in the vehicle recommendation device according to the embodiment of the present invention, please refer to details disclosed in the vehicle recommendation method according to the above embodiment of the present invention, and details are not described here again.
In summary, the vehicle recommendation device of the embodiment of the invention firstly obtains attribute information and demand information of a user to be recommended, then queries a vehicle database according to the demand information, obtains vehicle data of a candidate vehicle matched with the demand information, and then generates simulated driving data of the user to be recommended in the candidate vehicle according to the attribute information and the vehicle data of the candidate vehicle, thereby determining the vehicle to be recommended according to the simulated driving data in the candidate vehicle and recommending the vehicle to be recommended to the user to be recommended. Therefore, the vehicle meeting the user requirements is intelligently recommended to the user, the time and energy of the user are saved, and the vehicle to be recommended is determined according to the simulated driving data of the user to be recommended in the candidate vehicle meeting the user requirements, so that the accuracy of the vehicle recommended to the user is improved, and the user experience is improved.
In order to implement the above embodiment, the present invention further proposes an electronic device 200, as shown in fig. 8, where the electronic device 200 includes a memory 21 and a processor 22. Wherein the processor 22 executes a program corresponding to the executable program code by reading the executable program code stored in the memory 21 for realizing the above-described vehicle recommendation method.
According to the electronic equipment provided by the embodiment of the invention, the computer program stored in the memory is executed by the processor, so that the vehicle meeting the user requirement can be intelligently recommended to the user, the time and energy of the user are saved, and the accuracy of the vehicle recommended to the user is improved.
In order to achieve the above-described embodiments, the present invention also proposes a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-described vehicle recommendation method.
The computer readable storage medium of the embodiment of the invention can intelligently recommend the vehicles meeting the user demands for the user by storing the computer program and executing the computer program by the processor, thereby saving the time and energy of the user and improving the accuracy of the vehicles recommended to the user.
In the description of the present invention, it should be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.

Claims (7)

1. A vehicle recommendation method, characterized by comprising:
Acquiring attribute information and demand information of a user to be recommended;
according to the demand information, inquiring a vehicle database, and acquiring vehicle data of a candidate vehicle matched with the demand information;
generating simulated driving data of the user to be recommended in the candidate vehicle according to the attribute information and the vehicle data of the candidate vehicle;
determining a vehicle to be recommended according to the simulated driving data in the candidate vehicle, and recommending the vehicle to the user to be recommended; the generating the simulated driving data of the user to be recommended in the candidate vehicle according to the attribute information and the vehicle data of the candidate vehicle comprises the following steps:
generating a three-dimensional human body model of the user to be recommended according to the attribute information of the user to be recommended;
generating simulated driving data of the user to be recommended in the candidate vehicle according to the three-dimensional human body model of the user to be recommended and the vehicle data of the candidate vehicle;
the three-dimensional human body model of the user to be recommended is a three-dimensional human body model in a first gesture;
the generating the simulated driving data of the user to be recommended in the candidate vehicle according to the three-dimensional human model of the user to be recommended and the vehicle data of the candidate vehicle further comprises:
Transforming the three-dimensional human body model in the first posture according to the posture mapping relation to obtain a three-dimensional human body model in a second posture, wherein the second posture is a sitting posture;
the method comprises the steps of transforming the three-dimensional human body model in the first gesture according to the gesture mapping relation, and before obtaining the three-dimensional human body model in the second gesture, further comprising:
acquiring a plurality of human body samples and characteristic point information of each human body sample under a plurality of postures;
aiming at each human body sample, determining an attitude mapping sub-relationship between any two attitudes according to characteristic point information of the human body sample under a plurality of attitudes;
for each two gestures, determining a gesture mapping relation between the two gestures according to gesture mapping sub-relations of the plurality of human body samples between the two gestures;
the transforming the three-dimensional human body model under the first gesture according to the gesture mapping relation to obtain the three-dimensional human body model under the second gesture comprises the following steps:
and transforming the three-dimensional human body model in the first posture according to the posture mapping relation between the first posture and the second posture to obtain the three-dimensional human body model in the second posture.
2. The method of claim 1, wherein the simulated driving data comprises at least one of the following information: the distance between the head of the user to be recommended and the roof of the candidate vehicle, the size of the activity space of the legs of the user to be recommended in the candidate vehicle, the seat adjusting position of the candidate vehicle, and the size of the rear evacuation space of the candidate vehicle when the user to be recommended is in the candidate vehicle.
3. The method according to claim 1 or 2, wherein the number of candidate vehicles is a plurality;
the determining the vehicle to be recommended according to the simulated driving data in the candidate vehicle comprises the following steps:
displaying the simulated driving data in the plurality of candidate vehicles so that a user selects a vehicle according to the simulated driving data; determining the vehicle selected by the user as the vehicle to be recommended;
or,
and acquiring first simulated driving data meeting preset conditions in the simulated driving data in the plurality of candidate vehicles, and determining the vehicle corresponding to the first simulated driving data as the vehicle to be recommended.
4. The method of claim 3, wherein the simulated driving data comprises a simulated driving video;
The presenting simulated driving data within the plurality of candidate vehicles includes:
and displaying the simulated driving video so that a user can select a vehicle according to the simulated driving video.
5. A vehicle recommendation device, characterized by comprising:
the first acquisition module is used for acquiring attribute information and demand information of the user to be recommended;
the second acquisition module is used for inquiring a vehicle database according to the demand information and acquiring vehicle data of the candidate vehicles matched with the demand information;
the generation module is used for generating simulated driving data of the user to be recommended in the candidate vehicle according to the attribute information and the vehicle data of the candidate vehicle;
the recommending module is used for determining a vehicle to be recommended according to the simulated driving data in the candidate vehicle and recommending the vehicle to the user to be recommended;
the generation module comprises:
the first generation unit is used for generating a three-dimensional human body model of the user to be recommended according to the attribute information of the user to be recommended, wherein the three-dimensional human body model of the user to be recommended is a three-dimensional human body model in a first gesture;
the second generation unit is used for generating simulated driving data of the user to be recommended in the candidate vehicle according to the three-dimensional human body model of the user to be recommended and the vehicle data of the candidate vehicle;
The generating module further comprises:
the transformation unit is used for transforming the three-dimensional human body model in the first posture according to the posture mapping relation to obtain a three-dimensional human body model in the second posture, wherein the second posture is a sitting posture;
the generating module further comprises:
the acquisition unit is used for acquiring a plurality of human body samples and characteristic point information of each human body sample under a plurality of postures;
the first determining unit is used for determining an attitude mapping sub-relationship between any two attitudes according to characteristic point information of each human body sample under a plurality of attitudes;
the second determining unit is used for determining an attitude mapping relation between the two attitudes according to the attitude mapping sub-relation of the plurality of human body samples between the two attitudes for every two attitudes;
the conversion unit is specifically used for:
and transforming the three-dimensional human body model in the first posture according to the posture mapping relation between the first posture and the second posture to obtain the three-dimensional human body model in the second posture.
6. An electronic device, comprising a memory and a processor;
wherein the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, for realizing the vehicle recommendation method according to any one of claims 1 to 4.
7. A computer-readable storage medium storing a computer program, characterized in that the program, when executed by a processor, implements the vehicle recommendation method according to any one of claims 1-4.
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Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008097548A (en) * 2006-10-16 2008-04-24 Toyota Motor Corp Vehicle upkeep simulation device and method
KR20100047674A (en) * 2008-10-29 2010-05-10 서강대학교산학협력단 Apparatus and method for driving assistance using feature of vehicle, and microprocessor and recording medium used thereto
CN104773178A (en) * 2015-03-26 2015-07-15 合肥工业大学 Driving gesture detecting system based on MEMS (Micro-electromechanical Systems) sensors
CN107564108A (en) * 2017-07-27 2018-01-09 湖北历拓网络科技有限公司 The vehicle method of commerce and device of a kind of virtual reality
CN107784562A (en) * 2017-11-10 2018-03-09 上海安吉星信息服务有限公司 A kind of information-pushing method and device of new car information
CN108932644A (en) * 2017-05-26 2018-12-04 车伯乐（北京）信息科技有限公司 A kind of user selects vehicle method, apparatus, equipment and computer-readable medium
CN110457589A (en) * 2019-08-19 2019-11-15 上海新共赢信息科技有限公司 A kind of vehicle recommended method, device, equipment and storage medium
CN110619094A (en) * 2019-09-09 2019-12-27 上海钧正网络科技有限公司 Riding vehicle recommendation method, device, system, computer equipment and storage medium
CN110807049A (en) * 2019-10-23 2020-02-18 上海能塔智能科技有限公司 Test driving vehicle type recommendation method and device and computer readable storage medium
CN111489233A (en) * 2020-03-23 2020-08-04 上海博泰悦臻电子设备制造有限公司 Vehicle recommendation method and system
CN111516622A (en) * 2019-09-23 2020-08-11 长城汽车股份有限公司 Vehicle function service recommendation method and device

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9644967B2 (en) * 2011-07-05 2017-05-09 Toyota Jidosha Kabushiki Kaisha Recommendation information provision system
JP5929702B2 (en) * 2012-10-22 2016-06-08 株式会社デンソー Automatic evaluation system and navigation device
KR20150071094A (en) * 2013-12-17 2015-06-26 현대자동차주식회사 Recommendation system of the type of a car based on a using information and status of the car, and Method thereof
US20160364783A1 (en) * 2014-06-13 2016-12-15 Truecar, Inc. Systems and methods for vehicle purchase recommendations
CN105808764A (en) * 2016-03-21 2016-07-27 百度在线网络技术（北京）有限公司 Recommended content display method and device
US11151461B2 (en) * 2017-07-05 2021-10-19 Ford Global Technologies Method and apparatus for behavior-based vehicle purchase recommendations
KR102485342B1 (en) * 2017-12-11 2023-01-05 현대자동차주식회사 Apparatus and method for determining recommendation reliability based on environment of vehicle
JP7139894B2 (en) * 2018-11-06 2022-09-21 トヨタ自動車株式会社 Information processing device, information processing system, information processing method and program

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008097548A (en) * 2006-10-16 2008-04-24 Toyota Motor Corp Vehicle upkeep simulation device and method
KR20100047674A (en) * 2008-10-29 2010-05-10 서강대학교산학협력단 Apparatus and method for driving assistance using feature of vehicle, and microprocessor and recording medium used thereto
CN104773178A (en) * 2015-03-26 2015-07-15 合肥工业大学 Driving gesture detecting system based on MEMS (Micro-electromechanical Systems) sensors
CN108932644A (en) * 2017-05-26 2018-12-04 车伯乐（北京）信息科技有限公司 A kind of user selects vehicle method, apparatus, equipment and computer-readable medium
CN107564108A (en) * 2017-07-27 2018-01-09 湖北历拓网络科技有限公司 The vehicle method of commerce and device of a kind of virtual reality
CN107784562A (en) * 2017-11-10 2018-03-09 上海安吉星信息服务有限公司 A kind of information-pushing method and device of new car information
CN110457589A (en) * 2019-08-19 2019-11-15 上海新共赢信息科技有限公司 A kind of vehicle recommended method, device, equipment and storage medium
CN110619094A (en) * 2019-09-09 2019-12-27 上海钧正网络科技有限公司 Riding vehicle recommendation method, device, system, computer equipment and storage medium
CN111516622A (en) * 2019-09-23 2020-08-11 长城汽车股份有限公司 Vehicle function service recommendation method and device
CN110807049A (en) * 2019-10-23 2020-02-18 上海能塔智能科技有限公司 Test driving vehicle type recommendation method and device and computer readable storage medium
CN111489233A (en) * 2020-03-23 2020-08-04 上海博泰悦臻电子设备制造有限公司 Vehicle recommendation method and system

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
The Design of Web Based Car Recommendation System using Hybrid Recommender Algorithm;Leyo Babu Thomas et al.;SPC;第3卷(第4期);第192-196页 *
基于上下文的VANET服务推荐中间件;杨倩等;计算机研究与发展(第9期);第131-139页 *
基于大数据挖掘技术的机动车驾驶人智能培训***的研究;石义红等;无线互联科技(第14期);第119-120页 *
装载机驾驶员的CATIA坐姿仿真及人机工程学分析;段世喜等;建筑机械(第17期);第67-70页 *

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