CN114357233A - Vehicle-mounted music recommendation method, system and storage medium - Google Patents
Vehicle-mounted music recommendation method, system and storage medium Download PDFInfo
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- CN114357233A CN114357233A CN202011089574.2A CN202011089574A CN114357233A CN 114357233 A CN114357233 A CN 114357233A CN 202011089574 A CN202011089574 A CN 202011089574A CN 114357233 A CN114357233 A CN 114357233A
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Abstract
The invention relates to a vehicle-mounted music recommendation method and system. The vehicle-mounted music recommendation system comprises: a data acquisition module configured to acquire vehicle-mounted environment data and user behavior data; a data analysis module configured to generate user preference information based on the in-vehicle environment data and the user behavior data; and a music recommendation module configured to recommend music to a user based on the user preference information. The vehicle-mounted music recommendation method is characterized by comprising the following steps: a data acquisition step: acquiring vehicle-mounted environment data and user behavior data; and (3) data analysis step: generating user preference information based on the in-vehicle environment data and the user behavior data; and a music recommendation step: recommending music to the user based on the user preference information.
Description
Technical Field
The invention relates to the field of automobile control. Specifically, the invention relates to a vehicle-mounted music recommendation method, a system and a storage medium.
Background
The traditional vehicle-mounted music playing system plays music according to a playing mode set by a user, and different playing modes can realize different music playing modes, music playing sequences and the like. In this way, the user listens to songs stored in advance on a storage device (such as a CD, on-board memory, flash memory, etc.) of the vehicle.
Some music applications on conventional mobile terminals have music recommendation functionality. In particular, internet-based music recommendation methods generally determine user preferences according to user behaviors, thereby recommending music that may be suitable for the user preferences to the corresponding users.
Disclosure of Invention
The traditional vehicle-mounted music playing system cannot recommend proper music to a user according to the behavior habit of the user on the vehicle, and can only play the music stored on the vehicle in different ways.
The invention provides a vehicle-mounted music recommendation method and a vehicle-mounted music recommendation system, which are used for recommending proper music for a user according to one or more of vehicle-mounted environment, driving habits or riding habits of the user and mood of the user.
According to a first aspect of the present invention, there is provided an in-vehicle music recommendation system, comprising: a data acquisition module configured to acquire vehicle-mounted environment data and user behavior data; a data analysis module configured to generate user preference information based on the in-vehicle environment data and the user behavior data; and a music recommendation module configured to recommend music to the user based on the user preference information.
According to the system of an embodiment of the present invention, the data acquisition module is further configured to acquire data in a data-embedded manner; and/or the data analysis module is further configured to perform data analysis in a machine learning manner.
A system according to another embodiment of the invention or any of the embodiments above, wherein the in-vehicle environment data comprises at least one of: vehicle-mounted scene, vehicle state, external environment, time data.
A system according to another embodiment of the invention or any of the embodiments above, wherein the user behavior data comprises at least one of: user mood, music selection, playing operation and stopping operation.
A system according to another embodiment of the invention or any of the embodiments above, wherein the data acquisition module is further configured to: in-vehicle environmental data and user behavior data are acquired from a plurality of vehicles.
A system according to another embodiment of the invention or any of the embodiments above, wherein the data analysis module is further configured to: user preference information is generated based on in-vehicle environment data and user behavior data from a plurality of vehicles.
The system according to another embodiment of the invention or any of the embodiments above, wherein the music recommendation module is further configured to: in the process of recommending music, the music obtained through the network is stored and/or played.
According to a second aspect of the present invention, there is provided a vehicle-mounted music recommendation method, including the steps of: a data acquisition step, which comprises acquiring vehicle-mounted environment data and user behavior data; a data analysis step including generating user preference information based on the in-vehicle environment data and the user behavior data; and a music recommendation step including recommending music to the user based on the user preference information.
The method according to an embodiment of the present invention further includes: acquiring data by adopting a data embedding mode; and/or performing data analysis by adopting a machine learning mode.
A method according to another embodiment of the invention or any of the embodiments above wherein the in-vehicle environment data comprises at least one of: vehicle-mounted scene, vehicle state, external environment, time data.
A method according to another embodiment of the invention or any of the embodiments above wherein the user behavior data comprises at least one of: user mood, music selection, playing operation and stopping operation.
A method according to another embodiment of the invention or any of the embodiments above, wherein the data obtaining step further comprises: in-vehicle environmental data and user behavior data are acquired from a plurality of vehicles.
A method according to another embodiment of the invention or any of the embodiments above, wherein the data analyzing step further comprises: user preference information is generated based on in-vehicle environment data and user behavior data from a plurality of vehicles.
A method according to another embodiment of the invention or any of the embodiments above, wherein the music recommendation step further comprises: and storing and/or playing the music acquired through the network.
According to a third aspect of the present invention, there is provided a computer storage medium having stored thereon program code executable by a processor, the program code implementing one or more of the steps of the method according to the second aspect of the invention when executed.
Drawings
The above and/or other aspects and advantages of the present invention will become more apparent and more readily appreciated from the following description of the various aspects taken in conjunction with the accompanying drawings, in which like or similar elements are designated with like reference numerals. The drawings comprise:
FIG. 1 is a schematic block diagram of an in-vehicle music recommendation system 100 according to an embodiment of the present invention; and
fig. 2 is a schematic flow chart of an in-vehicle music recommendation method 200 according to an embodiment of the invention.
Detailed Description
In this specification, the invention is described more fully with reference to the accompanying drawings, in which exemplary embodiments of the invention are shown. This invention may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. The embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Words such as "comprising" and "comprises" mean that, in addition to having elements or steps which are directly and unequivocally stated in the description and the claims, the solution of the invention does not exclude other elements or steps which are not directly or unequivocally stated. Terms such as "first" and "second" do not denote an order of the elements in time, space, size, etc., but rather are used to distinguish one element from another.
Referring now to fig. 1, according to a first aspect of the present invention, an in-vehicle music recommendation system 100 is provided, and the system 100 may include a data acquisition module 110, a data analysis module 120, and a music recommendation module 130.
The data acquisition module 110 may be configured to acquire vehicle environment data and user behavior data, among other things. Generally, the data acquisition module 110 and its corresponding devices may be located on a vehicle.
The in-vehicle environment data may include, at least, such as, for example, in-vehicle scenes (e.g., city driving, off-road driving, parking breaks, traffic jams), vehicle status (e.g., vehicle speed, length of driving, vehicle type data), ambient environment (e.g., weather, temperature, humidity), time data (e.g., a certain time or period of day, a certain month of the year). Such data can typically be acquired by sensors on the vehicle as needed; can be obtained through a vehicle-mounted communication bus; and may be acquired in real time or in advance over a network.
The user behavior data may include at least, for example, user mood, music selection, play operation, stop operation, and the like. Such data can typically be acquired by sensors on the vehicle as needed; can be obtained through intelligent interaction with a user; and may be acquired through the in-vehicle communication bus. For example, force and frequency of the user operating each control can be sensed through a force sensor mounted on the vehicle, so that whether the user is in a violent state, a melancholy state, an urgent state and the like can be sensed. In addition, through the intelligent interaction system, the vehicle-mounted system can inquire the mood of the user through voice interaction with the user, so that the mood of the user at the moment is judged.
In particular, the data acquisition module 110 may also acquire the user behavior data in a data-buried manner. In one embodiment, by way of an in-vehicle terminal (e.g., an in-vehicle touchscreen, an in-vehicle terminal controller, etc.), a buried point of user behavior data may be gathered. For example, a preliminary fixed point operation may implant a statistical code at a user operation key point, ensuring, by its independent ID, that data collection is not repeated (e.g., how often a user plays a certain type of music); the middle-level embedded point operation can be embedded with a plurality of sections of codes, the series of behaviors of the user in the driving process are tracked, and events are independent from each other (for example, the user starts playing music after starting a vehicle, generally starts playing music a, and stops playing music when driving to a place X); the high-level embedded point operation can be combined with company engineering and ETL to collect and analyze the user full behavior, establish user portrait and restore a user behavior model to be used as the basis of system analysis and optimization. In the vehicle-mounted music recommendation system according to one or more embodiments of the present invention, data tags may be added in different operation links, and then tag data corresponding to the operation may be transmitted to the data analysis module 120. The data analysis module 120 determines behavioral actions of the user by analyzing the collected tag data, as described below.
The data analysis module 120 may be configured to generate user preference information based on the in-vehicle environment data and user behavior data acquired or otherwise obtained by the data acquisition module 110. Optionally, the data analysis module 120 may be located on the vehicle, in a remote server, database, cloud, and communicate with other modules via wireless communication.
The user preference information generated by the data analysis module 120 may be information that associates in-vehicle environment data, user behavior data, with music that the user selects to play. The data analysis module 120 may associate one or more of the in-vehicle environment data, the user behavior data, with one or more pieces of music selected for play by the user. In one embodiment, the user preference information is: user a likes listening to a happy type of music in rainy days. In another embodiment, the user preference information is: user B likes in the morning 8: 30 listen to music a and music b when traffic congestion occurs. In yet another embodiment, the user preference information is: user C likes to stop playing music while parking for a rest.
The data analysis module 120 may also be configured to perform data analysis in a machine learning manner to obtain user preference information. In one or more embodiments according to the present invention, the vehicle-mounted environment data and the user behavior data may be analyzed and learned (e.g., in the cloud) using an intelligent learning manner (e.g., decision tree, random forest, artificial neural network, bayesian learning, etc.). In one embodiment, real-time analysis may be used to learn user preference labels for timely recommendation of music that the user is most likely to like at the time based on the current environment. For example, the data analysis module 120 may analyze and classify the user's preference according to the user behavior data collected and stored in different vehicle-mounted environments, and then the music recommendation module 130 recommends a corresponding song. After recommending the song, the data analysis module 120 further performs analysis, classification, learning, optimization, classification again, and the like according to the feedback user operation. Through repeated and cyclic learning, the preference of the user is basically matched finally, and accurate recommendation is achieved.
Compared to a conventional recommendation system for internet music applications that does not refer to surrounding environment data, the in-vehicle music recommendation system 100 according to one or more embodiments of the present invention can more accurately select music that meets the user's preference at that time in conjunction with the current scene, improving the comfort of the user's ride.
The music recommendation module 130 may be configured to recommend music to the user based on the user preference information from the data analysis module 120. In recommending music, the music recommendation module 130 may store and/or play music obtained via a network. For example, after filtering out a particular piece or pieces of music based on the user preference information, the music recommendation module 130 may make the music into a playlist and send the playlist to the player of the user's vehicle; or the screened music can be stored in a memory of a corresponding user vehicle for playing when the user selects to play the recommended music.
The vehicle-mounted music recommendation system 100 according to one or more embodiments of the present invention has great advantages in both quantity and real-time update compared to a conventional vehicle-mounted music system because music in a music library from the internet cloud can be utilized. The music that the user listens to may not be limited to music stored in advance in the vehicle storage device or in the mobile storage device, and more new music may be found that meets his preferences.
In embodiments involving multiple vehicles, the in-vehicle music recommendation system 100 makes music recommendations based on data from multiple vehicles. For example, the data acquisition module 110 may be configured to acquire in-vehicle environmental data as well as user behavior data from a plurality of vehicles. The data analysis module 120 may be configured to: user preference information is generated based on in-vehicle environment data and user behavior data from a plurality of vehicles.
In one embodiment, the first to tenth data acquisition modules 110 of the first to tenth vehicles acquire the on-board environment data of the vehicles one to ten and the user behavior data of the users one to ten corresponding thereto, and transmit to the data analysis module 120. The data analysis module 120 may generate the first to tenth user preference information, respectively, and then perform further analysis. Further analysis may include categorizing user preference information, for example, grouping users who like listening to a fast-style music in rainy weather, grouping users who like listening to a fast-style music in the morning 8: 30 the users listening to music a and music b in the event of traffic congestion are grouped into one group. Next, the music recommendation module 130 may be configured to attempt to recommend music in a situation that one user in a group likes to another user in the group in the same situation.
Further, the user's actions on the music recommended based on the manner (e.g., listening to the entire piece of music, switching the music in the first 30 seconds, stopping playing the music) may be fed back to the data analysis module 120, and the data analysis module 120 adjusts the preference correlation between the respective users accordingly. In the subsequent recommendation process, the music recommendation module 130 preferentially recommends the music of the preference of the two or more users with the stronger association to each other.
Referring now to fig. 2, according to a second aspect of the present invention, there is provided an in-vehicle music recommendation method 200, which includes a data acquisition step S210, a data analysis step S220 and a music recommendation step S230. The data acquisition step S210 comprises acquiring vehicle-mounted environment data and user behavior data; the data analysis step S220 includes generating user preference information based on the in-vehicle environment data and the user behavior data; and the music recommending step S230 includes recommending music to the user based on the user preference information. Wherein the in-vehicle environment data comprises at least one of: vehicle-mounted scene, vehicle state, external environment, time data. Wherein the user behavior data comprises at least one of: user mood, music selection, playing operation and stopping operation. The vehicle-mounted music recommendation method 200 further includes: acquiring data by adopting a data embedding mode; and/or performing data analysis by adopting a machine learning mode. The music recommending step S230 may further include: and storing and/or playing the music acquired through the network.
The data acquisition step S210 may further include: in-vehicle environmental data and user behavior data are acquired from a plurality of vehicles. Accordingly, the data analyzing step S220 may further include: user preference information is generated based on in-vehicle environment data and user behavior data from a plurality of vehicles.
The vehicle-mounted music recommendation method 200 according to the present invention further includes the operations performed by the modules of the vehicle-mounted music recommendation system 100 according to the present invention, which are not described herein again.
According to a third aspect of the present invention, there is provided a computer storage medium having stored thereon program code executable by a processor, the program code, when executed, implementing the steps of the in-vehicle music recommendation method 200 according to the second aspect of the present invention.
The embodiments and examples set forth herein are presented to best explain the embodiments in accordance with the present technology and its particular application and to thereby enable those skilled in the art to make and utilize the invention. However, those skilled in the art will recognize that the foregoing description and examples have been presented for the purpose of illustration and example only. The description as set forth is not intended to cover all aspects of the invention or to limit the invention to the precise form disclosed.
Claims (15)
1. An in-vehicle music recommendation system, comprising:
a data acquisition module configured to acquire vehicle-mounted environment data and user behavior data;
a data analysis module configured to generate user preference information based on the in-vehicle environment data and the user behavior data; and
a music recommendation module configured to recommend music to a user based on the user preference information.
2. The system of claim 1, wherein:
the data acquisition module is also configured to acquire data in a data embedding mode; and/or
The data analysis module is further configured to perform data analysis in a machine learning manner.
3. The system of claim 1, wherein the in-vehicle environment data comprises at least one of:
vehicle-mounted scene, vehicle state, external environment, time data.
4. The system of claim 1, wherein the user behavior data comprises at least one of:
user mood, music selection, playing operation and stopping operation.
5. The system of claim 1, wherein the data acquisition module is further configured to:
the in-vehicle environment data and the user behavior data are acquired from a plurality of vehicles.
6. The system of claim 5, wherein the data analysis module is further configured to:
generating the user preference information based on the in-vehicle environment data and the user behavior data from the plurality of vehicles.
7. The system of claim 1, wherein the music recommendation module is further configured to:
in the process of recommending music, the music obtained through the network is stored and/or played.
8. A vehicle-mounted music recommendation method is characterized by comprising the following steps:
a data acquisition step, which comprises acquiring vehicle-mounted environment data and user behavior data;
a data analysis step including generating user preference information based on the in-vehicle environment data and the user behavior data; and
a music recommendation step including recommending music to a user based on the user preference information.
9. The method of claim 8, further comprising:
acquiring data by adopting a data embedding mode; and/or
And performing data analysis in a machine learning mode.
10. The method of claim 8, wherein the in-vehicle environment data comprises at least one of:
vehicle-mounted scene, vehicle state, external environment, time data.
11. The method of claim 8, wherein the user behavior data comprises at least one of:
user mood, music selection, playing operation and stopping operation.
12. The method of claim 8, the data acquisition step further comprising:
the in-vehicle environment data and the user behavior data are acquired from a plurality of vehicles.
13. The method of claim 12, the data analysis step further comprising:
generating the user preference information based on the in-vehicle environment data and the user behavior data from the plurality of vehicles.
14. The method of claim 8, the music recommendation step further comprising:
and storing and/or playing the music acquired through the network.
15. A computer storage medium having stored thereon program code executable by a processor, the program code implementing one or more of the steps of the method according to claims 8-14 when executed.
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| CN202011089574.2A CN114357233A (en) | 2020-10-13 | 2020-10-13 | Vehicle-mounted music recommendation method, system and storage medium |
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| CN202011089574.2A CN114357233A (en) | 2020-10-13 | 2020-10-13 | Vehicle-mounted music recommendation method, system and storage medium |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117370599A (en) * | 2023-10-31 | 2024-01-09 | 鱼快创领智能科技(南京)有限公司 | System and method for recommending vehicle-mounted audio in real time based on user habit |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117370599A (en) * | 2023-10-31 | 2024-01-09 | 鱼快创领智能科技(南京)有限公司 | System and method for recommending vehicle-mounted audio in real time based on user habit |
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