CN113076474A - Driving behavior recommendation method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application provides a driving behavior recommendation method, a driving behavior recommendation device, an electronic device and a storage medium, wherein the driving behavior recommendation method comprises the following steps: acquiring current time, position information of a vehicle driven by a user and scene information of the vehicle driven by the user; acquiring a user portrait of a target user which is constructed in advance; obtaining recommended driving behaviors according to the current time, the position information, the scene information, the user portrait of the target user and a preset behavior recommendation model; and sending a corresponding output instruction to the user-driven vehicle according to the recommended driving behavior so as to enable output equipment of the user-driven vehicle to output corresponding recommended information. The driving behavior recommendation method, the driving behavior recommendation device, the electronic equipment and the storage medium can recommend driving behaviors of the user deeply, so that a good recommendation effect can be obtained for the driving behavior recommendation of the user.
Description
Technical Field
The application relates to the technical field of intelligent driving, in particular to a driving behavior recommendation method and device, electronic equipment and a storage medium.
Background
With the continuous development and progress of science and technology, intelligent networked automobiles become more and more popular. At present, an intelligent internet automobile can recommend driving behaviors (such as closing an air conditioner, opening a window, reducing the temperature of the air conditioner, stopping the automobile and the like) of a user when the user drives, but the development of the intelligent internet automobile is not mature enough, most of intelligent internet automobiles can only recommend the driving behaviors superficially and simply when the driving behaviors are recommended to the user, and the driving behaviors are not deep enough, so that the better recommendation effect is difficult to obtain for the driving behavior recommendation of the user.
Disclosure of Invention
An object of the embodiments of the present application is to provide a driving behavior recommendation method, an apparatus, an electronic device, and a storage medium, which can recommend driving behaviors of a user more deeply, so that a better recommendation effect can be obtained for the driving behavior recommendation of the user.
In a first aspect, an embodiment of the present application provides a driving behavior recommendation method, including:
acquiring current time, position information of a vehicle driven by a user and scene information of the vehicle driven by the user;
acquiring a user portrait of a target user which is constructed in advance;
obtaining recommended driving behaviors according to the current time, the position information, the scene information, the user portrait of the target user and a preset behavior recommendation model;
and sending a corresponding output instruction to the user-driven vehicle according to the recommended driving behavior so as to enable output equipment of the user-driven vehicle to output corresponding recommended information.
In the implementation process, the driving behavior recommendation method of the embodiment of the application obtains the recommended driving behavior by the obtained current time, the obtained position information, the obtained scene information, the obtained user portrait of the target user and the preset behavior recommendation model, considers the possible driving behaviors of different users under different conditions, and can deeply sense the requirements of the users; and then, according to the recommended driving behavior, sending a corresponding output instruction to the user to drive the vehicle so as to enable the output equipment of the user to drive the vehicle to output corresponding recommendation information, thereby deeply recommending the driving behavior of the user and obtaining a better recommendation effect for the driving behavior recommendation of the user.
Further, the user representation of the target user includes one or more of a driving representation, a cockpit representation, an environment representation, and a travel intention representation of the target user.
In the implementation process, the contents included in the user portrait of the target user in the method can enable the driving behavior recommendation method of the embodiment of the application to more deeply sense the needs of the user and to more deeply recommend the driving behaviors of the user, so that the recommendation effect obtained by recommending the driving behaviors of the user is better.
Further, obtaining recommended driving behaviors according to the current time, the position information, the scene information, the user portrait of the target user and a preset behavior recommendation model, including:
acquiring the driving behavior of the user in a preset time before the current time;
forming a corresponding information sequence based on the position information, the scene information and the user driving behavior;
and obtaining the recommended driving behavior according to the information sequence, the user portrait of the target user and a preset behavior recommendation model.
In the implementation process, the method forms a corresponding information sequence through the acquired position information, the scene information and the driving behaviors of the user, obtains the recommended driving behaviors according to the information sequence, the user portrait of the target user and a preset behavior recommendation model, can obtain the recommended driving behaviors more accurately and effectively, and further recommends the driving behaviors of the user better, so that the recommendation effect obtained by recommending the driving behaviors of the user is better.
Further, the preset behavior recommendation model comprises a preset deep learning model and a preset machine learning model;
the obtaining of the recommended driving behavior according to the information sequence, the user portrait of the target user and a preset behavior recommendation model comprises:
recalling to obtain at least one predicted driving behavior according to the information sequence and the preset deep learning model;
and obtaining the recommended driving behaviors according to all the predicted driving behaviors, the user portrait of the target user and the preset machine learning model.
In the implementation process, the method recalls at least one predicted driving behavior through the information sequence and the preset deep learning model, and can accurately obtain the recommended driving behavior according to all the predicted driving behaviors, the user portrait of the target user and the preset machine learning model.
Further, the obtaining the recommended driving behavior according to all the predicted driving behaviors, the user portrait of the target user and the preset machine learning model comprises:
obtaining corresponding behavior acceptance scores according to all the predicted driving behaviors;
obtaining a recommended sequence of all the predicted driving behaviors according to all the predicted driving behaviors, all the behavior acceptance scores, the user portrait of the target user and the preset machine learning model;
and obtaining the recommended driving behaviors according to the recommended sequence of all the predicted driving behaviors.
In the implementation process, the method obtains the recommended sequence of all the predicted driving behaviors through all the predicted driving behaviors, all the behavior acceptance scores, the user portrait of the target user and a preset machine learning model, and can more accurately obtain the recommended driving behaviors according to the recommended sequence of all the predicted driving behaviors.
Further, after the sending a corresponding output instruction to the user-driven vehicle according to the recommended driving behavior so that an output device of the user-driven vehicle outputs corresponding recommended information, the method further includes:
acquiring feedback information of the target user on the recommended driving behavior;
and adjusting the behavior acceptance score corresponding to the recommended driving behavior according to the feedback information.
In the implementation process, the method can also adjust the behavior acceptance score of the corresponding recommended driving behavior according to the acquired feedback information of the target user on the recommended driving behavior, so that the recommended driving behavior can be more accurately obtained when the driving behavior is recommended next time, the driving behavior of the user can be better recommended, and the recommendation effect obtained by recommending the driving behavior of the user is better.
In a second aspect, an embodiment of the present application provides a driving behavior recommendation device, including:
the first acquisition module is used for acquiring the current time, the position information of the vehicle driven by the user and the scene information of the vehicle driven by the user;
the second acquisition module is used for acquiring a user portrait of a target user which is constructed in advance;
the driving behavior recommendation module is used for obtaining recommended driving behaviors according to the current time, the position information, the scene information, the user portrait of the target user and a preset behavior recommendation model;
and the sending module is used for sending a corresponding output instruction to the user-driven vehicle according to the recommended driving behavior so as to enable the output equipment of the user-driven vehicle to output corresponding recommended information.
In the implementation process, the driving behavior recommendation device of the embodiment of the application obtains the recommended driving behavior through the obtained current time, the obtained position information, the obtained scene information, the obtained user portrait of the target user and the preset behavior recommendation model, considers the possible driving behaviors of different users under different conditions, and can deeply sense the requirements of the users; and then, according to the recommended driving behavior, sending a corresponding output instruction to the user to drive the vehicle so as to enable the output equipment of the user to drive the vehicle to output corresponding recommendation information, thereby deeply recommending the driving behavior of the user and obtaining a better recommendation effect for the driving behavior recommendation of the user.
Further, the driving behavior recommendation module is specifically configured to:
acquiring the driving behavior of the user in a preset time before the current time;
forming a corresponding information sequence based on the position information, the scene information and the user driving behavior;
and obtaining recommended driving behaviors according to the information sequence, the user portrait of the target user and a preset behavior recommendation model.
In the implementation process, the device forms a corresponding information sequence through the acquired position information, the scene information and the driving behaviors of the user, obtains the recommended driving behaviors according to the information sequence, the user portrait of the target user and a preset behavior recommendation model, can obtain the recommended driving behaviors more accurately and effectively, and further recommends the driving behaviors of the user better, so that the recommendation effect obtained by recommending the driving behaviors of the user is better.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory and a processor, where the memory is used for storing a computer program, and the processor runs the computer program to make the electronic device execute the driving behavior recommendation method described above.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the driving behavior recommendation method described above.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of a driving behavior recommendation method according to an embodiment of the present application;
fig. 2 is a schematic flowchart of step S130 according to a first embodiment of the present application;
fig. 3 is a first structural block diagram of a driving behavior recommendation device according to a second embodiment of the present application;
fig. 4 is a second structural block diagram of a driving behavior recommendation device according to a second embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
At present, an intelligent internet automobile can recommend driving behaviors (such as closing an air conditioner, opening a window, reducing the temperature of the air conditioner, stopping the automobile and the like) of a user when the user drives, but the development of the intelligent internet automobile is not mature enough, most of intelligent internet automobiles can only recommend the driving behaviors superficially and simply when the driving behaviors are recommended to the user, and the driving behaviors are not deep enough, so that the better recommendation effect is difficult to obtain for the driving behavior recommendation of the user.
In view of the above problems in the prior art, the present application provides a driving behavior recommendation method, device, electronic device, and storage medium, which can recommend driving behaviors of a user more deeply, so that a better recommendation effect can be obtained for the driving behavior recommendation of the user.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of a driving behavior recommendation method provided in an embodiment of the present application. The following driving behavior recommendation method in the embodiment of the application can be applied to a server.
The driving behavior recommendation method comprises the following steps:
step S110, obtaining the current time, the position information of the vehicle driven by the user and the scene information of the vehicle driven by the user.
In the present embodiment, the user drives the position information of the vehicle, i.e., the position where the user drives the vehicle, and the position information where the user drives the vehicle may be a gas station, a parking lot, a highway, or the like.
The scene information of the user driving the vehicle can be understood as information of an external environment where the user drives the vehicle, and the scene information of the user driving the vehicle can include weather, temperature, humidity, air quality, content of certain substances in the air, and the like.
Step S120, a user portrait of a target user which is constructed in advance is obtained.
In the present embodiment, the pre-constructed user image of the target user may include basic information of the target user, a driving figure and a cab figure of the target user, and the like.
The driving portrait of the target user may include travel habits of the target user (e.g., whether the target user frequently travels at a peak in the morning and at a peak in the evening, or whether the target user drives for a long distance at night, etc.), idle time of the day, driving level, driving civilization degree (which may be measured by whether the target user frequently lights up at night or presses a horn in a downtown area, etc.), and other contents; the cabin portrait can include the content of the air conditioner starting time, the preference temperature, whether to ventilate and the like of a user in different environments; the user starts the contents such as duration, type and the like under different environments; or other content.
And step S130, obtaining recommended driving behaviors according to the current time, the position information, the scene information, the user portrait of the target user and a preset behavior recommendation model.
In this embodiment, the preset behavior recommendation model is a pre-trained behavior recommendation model.
The obtained recommended driving behavior is a driving behavior that is executed by the recommended target user, and in this embodiment, the obtained recommended driving behavior is a single driving behavior.
And step S140, according to the recommended driving behavior, sending a corresponding output instruction to the user to drive the vehicle, so that the output equipment of the user to drive the vehicle outputs corresponding recommendation information.
In this embodiment, when the user drives the vehicle and receives the corresponding output instruction, the output device of the vehicle driven by the user outputs the corresponding recommendation information.
The output device of the vehicle driven by the user can be a display screen of the vehicle driven by the user and/or a loudspeaker of the vehicle driven by the user, and correspondingly, the recommendation information can be character information and/or voice information.
According to the driving behavior recommendation method, the recommended driving behaviors are obtained through the obtained current time, the obtained position information, the obtained scene information, the obtained user portrait of the target user and the preset behavior recommendation model, the possible driving behaviors of different users under different conditions are considered, and the requirements of the users can be deeply sensed; and then, according to the recommended driving behavior, sending a corresponding output instruction to the user to drive the vehicle so as to enable the output equipment of the user to drive the vehicle to output corresponding recommendation information, thereby deeply recommending the driving behavior of the user and obtaining a better recommendation effect for the driving behavior recommendation of the user.
As an alternative embodiment, the user representation of the target user may include a driving representation, a cockpit representation, an environment representation, and a travel intent representation of the target user.
The environment representation may include information of an external environment where the user drives the vehicle, a position where the user drives the vehicle, and the like, the information of the external environment where the user drives the vehicle may include weather, temperature, humidity, air quality, and the like, and the position where the user drives the vehicle may include a common parking place, a common refueling place, a common driving road section, and the like;
travel intent representations may include daily commutes (which may be regularly driven from one place to another over the same period of time) while a user drives a vehicle, entertainment, proclains, recipients, rentals, and so forth.
In the process, the contents included in the user portrait of the target user in the method can enable the driving behavior recommendation method of the embodiment of the application to more deeply sense the needs of the user and to more deeply recommend the driving behaviors of the user, so that the recommendation effect obtained by recommending the driving behaviors of the user is better.
In order to obtain the recommended driving behavior more accurately and effectively, an embodiment of the present application provides a possible implementation manner, referring to fig. 2, where fig. 2 is a schematic flow diagram of step S130 provided in the embodiment of the present application, and a driving behavior recommendation method according to the embodiment of the present application, where step S130 obtains the recommended driving behavior according to the current time, the location information, the scene information, the user portrait of the target user, and a preset behavior recommendation model, and may include the following steps:
step S131, obtaining the driving behavior of the user in a preset time before the current time;
step S132, forming a corresponding information sequence based on the position information, the scene information and the driving behavior of the user;
and step S133, obtaining recommended driving behaviors according to the information sequence, the user portrait of the target user and a preset behavior recommendation model.
Specifically, the driving behavior of the user in the predetermined time before the current time may be a single driving behavior of the user, or may be multiple driving behaviors of the user.
In the process, the method forms a corresponding information sequence through the acquired position information, the scene information and the driving behaviors of the user, obtains the recommended driving behaviors according to the information sequence, the user portrait of the target user and a preset behavior recommendation model, can obtain the recommended driving behaviors more accurately and effectively, and further recommends the driving behaviors of the user better, so that the recommendation effect obtained by recommending the driving behaviors of the user is better.
Optionally, the preset behavior recommendation model includes a preset deep learning model and a preset machine learning model;
when the recommended driving behavior is obtained according to the information sequence, the user portrait of the target user and the preset behavior recommendation model, the following steps are performed:
recalling to obtain at least one predicted driving behavior according to the information sequence and a preset deep learning model;
and obtaining recommended driving behaviors according to all predicted driving behaviors, the user portrait of the target user and a preset machine learning model.
In the process, the method recalls at least one predicted driving behavior through the information sequence and the preset deep learning model, and can accurately obtain the recommended driving behavior according to all the predicted driving behaviors, the user portrait of the target user and the preset machine learning model.
Optionally, when the recommended driving behavior is obtained according to all the predicted driving behaviors, the user portrait of the target user and the preset machine learning model, the following steps may be performed:
obtaining corresponding behavior acceptance scores according to all predicted driving behaviors;
obtaining the recommended sequence of all predicted driving behaviors according to all predicted driving behaviors, all behavior acceptance scores, the user portrait of the target user and a preset machine learning model;
and obtaining the recommended driving behaviors according to the recommended sequence of all the predicted driving behaviors.
Specifically, the behavior acceptance scores of different driving behaviors can be obtained by pre-calculating the acceptance score of the overall audience group users, the acceptance score of each user, the manual intervention score, the time attenuation index and the rejection score of each user.
In the process, the method obtains the recommended sequence of all the predicted driving behaviors through all the predicted driving behaviors, all the behavior acceptance scores, the user portrait of the target user and a preset machine learning model, and can more accurately obtain the recommended driving behaviors according to the recommended sequence of all the predicted driving behaviors.
Optionally, in the driving behavior recommendation method according to the embodiment of the application, in step S140, after sending the corresponding output instruction to the user-driven vehicle according to the recommended driving behavior, so that the output device of the user-driven vehicle outputs the corresponding recommendation information, the method may further include the following steps:
obtaining feedback information of a target user on recommended driving behaviors;
and adjusting the behavior acceptance score corresponding to the recommended driving behavior according to the feedback information.
Specifically, the feedback information of the target user on the recommended driving behavior may be that the target user accepts the recommended driving behavior or that the target user refuses the recommended driving behavior.
And if the target user accepts the recommended driving behavior, the behavior acceptance score of the recommended driving behavior can be increased, and correspondingly, if the target user refuses to recommend the driving behavior, the behavior acceptance score of the recommended driving behavior can be decreased.
In the process, the method can also adjust the behavior acceptance score of the corresponding recommended driving behavior according to the acquired feedback information of the target user on the recommended driving behavior, so that the recommended driving behavior can be more accurately obtained when the driving behavior is recommended next time, the driving behavior of the user can be better recommended, and the recommendation effect obtained by recommending the driving behavior of the user is better.
Example two
In order to implement the corresponding method of the above embodiments to achieve the corresponding functions and technical effects, a driving behavior recommending device is provided below.
Referring to fig. 3, fig. 3 is a first structural block diagram of a driving behavior recommendation device provided in the embodiment of the present application.
The driving behavior recommendation device of the embodiment of the application comprises:
the first obtaining module 210 is configured to obtain current time, position information of a vehicle driven by a user, and scene information of the vehicle driven by the user;
a second obtaining module 220, configured to obtain a user portrait of a pre-constructed target user;
a driving behavior recommending module 230, configured to obtain recommended driving behaviors according to the current time, the location information, the scene information, the user figure of the target user, and a preset behavior recommending model;
the sending module 240 is configured to send a corresponding output instruction to the user-driven vehicle according to the recommended driving behavior, so that the user-driven vehicle output device outputs corresponding recommendation information.
The driving behavior recommending device obtains the recommended driving behavior through the obtained current time, the obtained position information, the obtained scene information, the obtained user portrait of the target user and the preset behavior recommending model, considers the possible driving behaviors of different users under different conditions, and can deeply sense the requirements of the users; and then, according to the recommended driving behavior, sending a corresponding output instruction to the user to drive the vehicle so as to enable the output equipment of the user to drive the vehicle to output corresponding recommendation information, thereby deeply recommending the driving behavior of the user and obtaining a better recommendation effect for the driving behavior recommendation of the user.
As an optional implementation manner, the driving behavior recommendation module 230 may be specifically configured to:
acquiring the driving behavior of the user in a preset time before the current time;
forming a corresponding information sequence based on the position information, the scene information and the driving behavior of the user;
and obtaining recommended driving behaviors according to the information sequence, the user portrait of the target user and a preset behavior recommendation model.
Optionally, when the driving behavior recommendation module 230 obtains the recommended driving behavior according to the information sequence, the user portrait of the target user, and the preset behavior recommendation model, it may:
recalling to obtain at least one predicted driving behavior according to the information sequence and a preset deep learning model;
and obtaining recommended driving behaviors according to all predicted driving behaviors, the user portrait of the target user and a preset machine learning model.
Optionally, when the driving behavior recommending module 230 obtains the recommended driving behavior according to all predicted driving behaviors, the user portrait of the target user, and the preset machine learning model, it may:
obtaining corresponding behavior acceptance scores according to all predicted driving behaviors;
obtaining the recommended sequence of all predicted driving behaviors according to all predicted driving behaviors, all behavior acceptance scores, the user portrait of the target user and a preset machine learning model;
and obtaining the recommended driving behaviors according to the recommended sequence of all the predicted driving behaviors.
Referring to fig. 4, fig. 4 is a second structural block diagram of the driving behavior recommendation device according to the embodiment of the present application.
As an optional implementation manner, the driving behavior recommendation device according to the embodiment of the present application further includes:
a third obtaining module 250, configured to obtain feedback information of the target user on the recommended driving behavior;
and the score adjusting module 260 is configured to adjust the behavior acceptance score corresponding to the recommended driving behavior according to the feedback information.
The driving behavior recommendation device can implement the driving behavior recommendation method of the first embodiment. The alternatives in the first embodiment are also applicable to the present embodiment, and are not described in detail here.
The rest of the embodiments of the present application may refer to the contents of the first embodiment, and in this embodiment, details are not repeated.
EXAMPLE III
The embodiment of the application provides an electronic device, which comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor runs the computer program to enable the electronic device to execute the driving behavior recommendation method.
Alternatively, the electronic device may be a server.
In addition, an embodiment of the present application further provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the driving behavior recommendation method described above.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A driving behavior recommendation method is characterized by comprising the following steps:
acquiring current time, position information of a vehicle driven by a user and scene information of the vehicle driven by the user;
acquiring a user portrait of a target user which is constructed in advance;
obtaining recommended driving behaviors according to the current time, the position information, the scene information, the user portrait of the target user and a preset behavior recommendation model;
and sending a corresponding output instruction to the user-driven vehicle according to the recommended driving behavior so as to enable output equipment of the user-driven vehicle to output corresponding recommended information.
2. The driving behavior recommendation method according to claim 1, wherein the user representation of the target user comprises one or more of a driving representation, a cockpit representation, an environment representation and a travel intention representation of the target user.
3. The driving behavior recommendation method according to claim 1, wherein obtaining the recommended driving behavior according to the current time, the location information, the scene information, the user portrait of the target user, and a preset behavior recommendation model comprises:
acquiring the driving behavior of the user in a preset time before the current time;
forming a corresponding information sequence based on the position information, the scene information and the user driving behavior;
and obtaining the recommended driving behavior according to the information sequence, the user portrait of the target user and a preset behavior recommendation model.
4. A driving behavior recommendation method according to claim 3, wherein the preset behavior recommendation model comprises a preset deep learning model and a preset machine learning model;
the obtaining of the recommended driving behavior according to the information sequence, the user portrait of the target user and a preset behavior recommendation model comprises:
recalling to obtain at least one predicted driving behavior according to the information sequence and the preset deep learning model;
and obtaining the recommended driving behaviors according to all the predicted driving behaviors, the user portrait of the target user and the preset machine learning model.
5. The driving behavior recommendation method according to claim 4, wherein the obtaining of the recommended driving behavior according to all the predicted driving behaviors, the user representation of the target user and the preset machine learning model comprises:
obtaining corresponding behavior acceptance scores according to all the predicted driving behaviors;
obtaining a recommended sequence of all the predicted driving behaviors according to all the predicted driving behaviors, all the behavior acceptance scores, the user portrait of the target user and the preset machine learning model;
and obtaining the recommended driving behaviors according to the recommended sequence of all the predicted driving behaviors.
6. The driving behavior recommendation method according to claim 5, wherein after the sending a corresponding output instruction to the user-driven vehicle according to the recommended driving behavior to enable an output device of the user-driven vehicle to output corresponding recommendation information, the method further comprises:
acquiring feedback information of the target user on the recommended driving behavior;
and adjusting the behavior acceptance score corresponding to the recommended driving behavior according to the feedback information.
7. A driving behavior recommendation device, comprising:
the first acquisition module is used for acquiring the current time, the position information of the vehicle driven by the user and the scene information of the vehicle driven by the user;
the second acquisition module is used for acquiring a user portrait of a target user which is constructed in advance;
the driving behavior recommendation module is used for obtaining recommended driving behaviors according to the current time, the position information, the scene information, the user portrait of the target user and a preset behavior recommendation model;
and the sending module is used for sending a corresponding output instruction to the user-driven vehicle according to the recommended driving behavior so as to enable the output equipment of the user-driven vehicle to output corresponding recommended information.
8. The driving behavior recommendation device according to claim 7, wherein the driving behavior recommendation module is specifically configured to:
acquiring the driving behavior of the user in a preset time before the current time;
forming a corresponding information sequence based on the position information, the scene information and the user driving behavior;
and obtaining recommended driving behaviors according to the information sequence, the user portrait of the target user and a preset behavior recommendation model.
9. An electronic device, comprising a memory for storing a computer program and a processor for executing the computer program to cause the electronic device to execute the driving behavior recommendation method according to any one of claims 1 to 6.
10. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements a driving behavior recommendation method according to any one of claims 1 to 6.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113673431A (en) * | 2021-08-20 | 2021-11-19 | 安徽江淮汽车集团股份有限公司 | Method for actively providing personalized scene service |
| CN114118582A (en) * | 2021-11-29 | 2022-03-01 | 中国第一汽车股份有限公司 | Destination prediction method, destination prediction device, electronic terminal and storage medium |
| CN114995658A (en) * | 2022-08-02 | 2022-09-02 | 联友智连科技有限公司 | Active interactive recommendation method applied to different emotions of driver |
| CN115482626A (en) * | 2022-08-17 | 2022-12-16 | 宁波美喵科技有限公司 | Method, device and equipment for voice broadcast of shared bicycle and storage medium |
| CN115883159A (en) * | 2022-11-25 | 2023-03-31 | 重庆长安新能源汽车科技有限公司 | Multi-account authority management control method, system and storage medium |
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- 2021-03-22 CN CN202110322787.3A patent/CN113076474A/en not_active Withdrawn
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113673431A (en) * | 2021-08-20 | 2021-11-19 | 安徽江淮汽车集团股份有限公司 | Method for actively providing personalized scene service |
| CN114118582A (en) * | 2021-11-29 | 2022-03-01 | 中国第一汽车股份有限公司 | Destination prediction method, destination prediction device, electronic terminal and storage medium |
| CN114995658A (en) * | 2022-08-02 | 2022-09-02 | 联友智连科技有限公司 | Active interactive recommendation method applied to different emotions of driver |
| CN115482626A (en) * | 2022-08-17 | 2022-12-16 | 宁波美喵科技有限公司 | Method, device and equipment for voice broadcast of shared bicycle and storage medium |
| CN115482626B (en) * | 2022-08-17 | 2023-08-29 | 宁波美喵科技有限公司 | Method, device, equipment and storage medium for voice broadcasting of shared bicycle |
| CN115883159A (en) * | 2022-11-25 | 2023-03-31 | 重庆长安新能源汽车科技有限公司 | Multi-account authority management control method, system and storage medium |
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