Music playing method and device based on vehicle, equipment and medium

Abstract

The embodiment of the invention relates to a music playing method and device based on a vehicle, equipment and a medium, and the method comprises the steps: obtaining a plurality of driving scene data sets in one-to-one correspondence with a plurality of types of driving scene data, and determining a preset weight of each type of driving scene data, each driving scene data set comprises driving scene data of at least one category; obtaining candidate music according to the plurality of driving scene data sets and a plurality of corresponding preset weights; determining a target music playing element according to the plurality of driving scene data sets, and determining a music playing volume according to the plurality of driving scene data sets; and processing the candidate music according to the target music playing element to generate target music, and playing the target music according to the music playing volume. According to the technical scheme, the music is generated in combination with the multiple types of driving scene data, the playing volume of the music is determined, the matching degree of the played music and the multiple types of driving scene data is achieved, and the interaction experience is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicle control technology, and in particular to a vehicle-based music playing method, device, equipment and medium. Background Art

[0002] With the popularization of vehicles, the functions of vehicle entertainment systems are becoming more and more diversified. For example, the music playback function in the vehicle can play music in the car, and the music playback function is widely used in driving scenarios.

[0003] In the related art, the vehicle is connected to the Internet, and the user can manually select music to play, wherein the selected music can come from online resources on the Internet.

[0004] However, in the above music playback scenario, music is only selected from the online resource library based on the user's selection operation, and the selected music does not match the actual driving scenario, which affects the interactive experience. Summary of the invention

[0005] In order to solve the above technical problems or at least partially solve the above technical problems, the present disclosure provides a vehicle-based music playback method, device, equipment and medium, which generate music and determine the playback volume of the music in combination with multiple types of driving scene data, thereby achieving a matching degree between the played music and multiple types of driving scene data and improving the interactive experience.

[0006] An embodiment of the present disclosure provides a vehicle-based music playback method, the method comprising the following steps: acquiring multiple driving scene data sets corresponding one-to-one to multiple categories of driving scene data, and determining preset weights for each category of the driving scene data, wherein each of the driving scene data sets contains at least one driving scene data of a corresponding category; acquiring candidate music according to the multiple driving scene data sets and the corresponding multiple preset weights; determining target music playback elements according to the multiple driving scene data sets, and determining music playback volumes according to the multiple driving scene data sets; processing the candidate music according to the target music playback elements to generate target music, and playing the target music according to the music playback volume.

[0007] An embodiment of the present disclosure also provides a vehicle-based music playback device, the device comprising: a first determination module, used to obtain multiple driving scene data sets corresponding one-to-one to multiple categories of driving scene data, and determine preset weights for each category of the driving scene data, wherein each of the driving scene data sets contains at least one driving scene data of the corresponding category; an acquisition module, used to obtain candidate music according to the multiple driving scene data sets and the corresponding multiple preset weights; a second determination module, used to determine target music playback elements according to the multiple driving scene data sets, and determine music playback volumes according to the multiple driving scene data sets; a playback processing module, used to process the candidate music according to the target music playback elements to generate target music, and play the target music according to the music playback volume.

[0008] An embodiment of the present disclosure also provides an electronic device, which includes: a processor; a memory for storing executable instructions of the processor; the processor is used to read the executable instructions from the memory and execute the instructions to implement the vehicle-based music playback method provided in the embodiment of the present disclosure.

[0009] The embodiment of the present disclosure also provides a computer-readable storage medium, which stores a computer program, and the computer program is used to execute the vehicle-based music playing method provided by the embodiment of the present disclosure.

[0010] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages:

[0011] The vehicle-based music playback solution provided by the embodiment of the present disclosure obtains multiple driving scene data sets corresponding to multiple categories of driving scene data one by one, and determines the preset weights for each category of driving scene data, wherein each driving scene data set contains at least one driving scene data of the corresponding category, and obtains candidate music according to the multiple driving scene data sets and the corresponding multiple preset weights, and then determines the target music playback elements according to the multiple driving scene data sets, and determines the music playback volume according to the multiple driving scene data sets, processes the candidate music according to the target music playback elements to generate the target music, and plays the target music according to the music playback volume. In this technical solution, music is generated and the music playback volume is determined in combination with multiple categories of driving scene data, so that the matching degree between the played music and the multiple categories of driving scene data is achieved, and the interactive experience is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0013] Figure 1 A schematic diagram of a process flow of a vehicle-based music playing method provided by an embodiment of the present disclosure;

[0014] Figure 2 A structural schematic diagram of a vehicle-based music playing device provided by an embodiment of the present disclosure;

[0015] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0016] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0017] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0018] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0019] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0020] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0021] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0022] In order to solve the above problems, the embodiments of the present disclosure provide a vehicle-based music playing method, which is introduced below in conjunction with specific embodiments.

[0023] Figure 1 The present invention provides a flow chart of a vehicle-based music playing method provided by an embodiment of the present invention. The method can be performed by a vehicle-based music playing device, wherein the device can be implemented by software and / or hardware and can generally be integrated into an electronic device. Figure 1 As shown, the method includes:

[0024] Step 101, obtaining a plurality of driving scene data sets corresponding one-to-one to a plurality of categories of driving scene data, and determining a preset weight for each category of driving scene data, wherein each driving scene data set contains at least one driving scene data of a corresponding category.

[0025] Among them, the multiple types of driving scene data include but are not limited to at least multiple types of driving data, external environment data, and in-car personnel data, among which the driving data include but are not limited to at least one of the vehicle speed, vehicle acceleration, throttle opening, braking action, etc., the external environment data include but are not limited to the type of scenery outside the vehicle (desert, beach, city, countryside, etc.), the distance of obstacles outside the vehicle, the number of obstacles outside the vehicle within a preset range, etc., and the in-car personnel data include the type of in-car personnel (children, elderly), the driver's mood, the driver's concentration, the behavior of the in-car personnel (playing the phone, crying), etc. Among them, the above multiple types of driving scene data can be obtained by existing technologies and will not be elaborated here.

[0026] In one embodiment of the present disclosure, multiple driving scene data sets corresponding to multiple types of driving scene data are obtained, and preset weights for each type of driving scene data are determined, wherein each driving scene data set contains at least one type of driving scene data, and the preset weights can be pre-calibrated, and the preset weights of different types of driving scene data can be partially the same or completely different. For example, the preset weight of the external environment data is 30%, the preset weight of the driving data is 20%, and the preset weight of the in-vehicle personnel data is 50%.

[0027] In the actual implementation process, in order to enhance the personalization of the service, a corresponding preset weight can also be set for the user portrait information, that is, the corresponding relationship between the user portrait information and the corresponding preset weight is stored in the preset database, the driver portrait information of the driver of the current vehicle is identified, the driver portrait information is matched with the user portrait information in the above corresponding relationship, and the preset weight of the successful match is determined. The user portrait information includes relevant personal information authorized by the user, such as age, gender, expression, occupation, etc.

[0028] Step 102, obtaining candidate music according to multiple driving scene data sets and corresponding multiple preset weights.

[0029] In this embodiment, candidate music is obtained by combining multiple driving scene data sets and corresponding multiple preset weights, thereby ensuring the matching degree between the candidate music and the actual driving scene and improving the user's satisfaction with the candidate music.

[0030] It should be noted that in different application scenarios, the methods for obtaining candidate music according to multiple driving scenario data sets and corresponding multiple preset weights are different, as shown in the following examples:

[0031] In some possible examples, a first preset model is trained in advance based on a large amount of sample data, and multiple driving scene data sets and multiple preset weights are input into the first preset model to obtain candidate music output by the first preset model. In this example, the candidate music is created and generated by combining multiple types of driving scene data and corresponding weights, and the candidate music is more interesting. For example, when the driving data includes children, the generated candidate music includes music scores of the type of children's songs.

[0032] In some possible examples, multiple data features corresponding to multiple driving scene data sets are extracted, the product value of each data feature and the corresponding preset weight is calculated respectively, and all product values ​​are summed to obtain a fusion feature, and candidate music is obtained in the preset music library according to the fusion feature. In this example, the similarity between the fusion feature and the music feature of each preset music in the preset music library can be calculated, and at least one preset music with the highest similarity is determined as the candidate music, etc.

[0033] Step 103: determine a target music playing element according to the plurality of driving scene data sets, and determine a music playing volume according to the plurality of driving scene data sets.

[0034] Among them, the music playback elements may include at least one of music type elements and music playback effect elements, wherein the music type elements include at least one of singers, music styles, music eras, etc., and the music playback effect elements include at least one of at least one rhythm, at least one melody, at least one harmony, at least one timbre, at least one type of emotion, at least one type of intensity, at least one type of accent, etc.

[0035] In one embodiment of the present disclosure, a target music playback element is determined based on a plurality of driving scene data sets, so that the target music playback element is related to the driving scene data, further improving the matching degree between the finally determined music and the scene.

[0036] Among them, the method of determining the target music playback element based on multiple driving scene data sets is different in different application scenarios. In some possible implementation methods, a second preset model can be pre-trained based on a large amount of sample data, and then, multiple driving scene data sets are input into the second preset model, and the target music playback element output by the second preset model is obtained.

[0037] In some possible implementations, a preset driving scene data threshold value corresponding to each candidate music playback element in a preset candidate music playback element set is obtained, wherein the preset driving scene data threshold value corresponding to each candidate music playback element may correspond to one type of driving scene data, or may correspond to multiple types of scene driving data, wherein the preset driving scene data threshold value corresponds to the type of driving data one-to-one. For example, when the candidate music playback elements include harmony A and harmony B, the driving scene data category corresponding to harmony A is in-vehicle occupant data, and the corresponding preset driving scene data threshold value for in-vehicle occupant data is "elderly", the driving scene data category corresponding to harmony B is in-vehicle occupant data, and the corresponding preset driving scene data threshold value for in-vehicle occupant data is "children", etc. In this embodiment, multiple driving scene data sets are matched with all preset driving scene data threshold values ​​corresponding to the preset candidate music playback element sets, and the successfully matched candidate music playback element is determined as the target music playback element.

[0038] In one embodiment of the present disclosure, in order to further enhance the sense of interactivity, the music playback volume may also be determined based on multiple driving scene data sets.

[0039] Among them, in different application scenarios, the method of determining the music playback volume according to multiple driving scenario data sets is also different, and the examples are as follows:

[0040] In some possible examples, it is identified whether multiple driving scene data sets contain driving scene data that meets preset volume adjustment conditions, for example, driving scene data that affects the volume is preset, and it is identified whether multiple driving scene data sets contain the preset driving scene data that affects the volume. If it does, it is determined that the driving scene data that meets the preset volume adjustment conditions is included. For example, the driving scene data that affects the volume is preset as "elderly people", then when it is identified that the multiple driving scene data sets contain "elderly people", it is determined that "elderly people" are driving scene data that meets the preset volume adjustment conditions. For example, the driving scene data that affects the volume is preset as "making a phone call",

[0041] In this example, when the driving scene data that meets the preset volume adjustment condition is included, the preset data level of the driving scene data that meets the preset volume adjustment condition is identified, that is, the preset data level is set in advance for the driving scene data that meets the preset volume adjustment condition, for example, the preset data level corresponding to "elderly" is set to -1, etc., the volume adjustment amount is determined according to the preset data level, and the preset standard volume is adjusted according to the volume adjustment amount to determine the music playback volume. Among them, the preset data level can be a positive value or a negative value. When it is a positive value, the volume adjustment amount is a positive value, so that the volume can be increased based on the preset standard volume. When it is a negative value, the volume adjustment amount is a negative value, so that the volume can be reduced based on the preset standard volume.

[0042] For example, the product value of the preset data level and the preset unit volume adjustment amount may be calculated, wherein the preset unit volume may be calibrated according to the scene, and the product value may be determined as the volume adjustment amount.

[0043] For example, the correspondence between different preset data levels and volume adjustment amounts may be preset, and when the preset data level is determined, the volume adjustment amount may be obtained by querying the preset correspondence.

[0044] When there are multiple driving scene data satisfying the preset volume adjustment conditions, the highest preset data level of the corresponding multiple preset data levels may be determined, and the volume adjustment amount may be determined according to the highest preset data level.

[0045] In this example, if the multiple driving scene data sets do not include driving scene data that meets the preset volume adjustment conditions, the preset standard volume is directly determined as the music playback volume.

[0046] In some possible examples, a third preset model is pre-trained based on a large amount of sample data, and a plurality of driving scene data sets are input into the third preset model to obtain the music playback volume output by the third preset model.

[0047] Step 104, processing the candidate music according to the target music playing element to generate the target music, and playing the target music according to the music playing volume.

[0048] After obtaining the target music playback element, the candidate music is processed according to the target music playback element to generate the target music. Since the target music playback element is determined based on multiple driving scene data sets, the matching degree between the target music and the driving scene is further improved.

[0049] Among them, in different application scenarios, the method of processing candidate music according to the target music playing element to generate the target music is also different, and the examples are as follows:

[0050] In some possible embodiments, a fourth preset model may be pre-trained, and the target music playback element and the candidate music may be input into the fourth preset model to obtain the target music.

[0051] In some possible embodiments, when there are multiple candidate music, the candidate music can be screened and processed according to the target playback element. For example, when the target playback element includes "singer", the candidate music with the same style as the "singer" can be screened out, and then the candidate music can be further processed according to the target playback element. In this example, the music processing strategy corresponding to each playback element is pre-set. For example, when the playback element includes "harmony", the music processing strategy is to add the corresponding "harmony" to the corresponding music, etc. Therefore, in this example, the candidate music can be processed according to the music processing strategy corresponding to the target playback element to obtain the target music. Further, after obtaining the target music, the target music is played according to the music playback volume.

[0052] Thus, in the embodiments of the present disclosure, it is possible to deeply combine multiple types of driving scene data to generate target music, and determine the playback volume of the target music, which can accurately match the user's driving scene and personal characteristics, and provide a unique music experience. For example, a middle-aged woman is driving through a forest with her children and elderly parents in the car. The driving scene data acquired by the system includes: the vehicle speed is moderate, the scenery outside the car is a forest, and there are children and the elderly in the car. At this time, according to the above method, music with a soothing rhythm, beautiful melody, harmonious harmony, and soft timbre will be generated, and some cheerful notes that children like and soft melodies suitable for the elderly will be incorporated. Among them, due to the inclusion of the elderly, the playback volume is lower than the normal volume.

[0053] During the actual implementation process, the above preset weights can also be iteratively optimized to improve the matching degree between the generated candidate music and the user's preferences.

[0054] In this embodiment, the user's evaluation operation on the target music can be monitored, and the evaluation operation includes but is not limited to voice evaluation, text input evaluation, preset control trigger evaluation, etc. In one embodiment of the present disclosure, the weight correction value is determined according to the evaluation operation, for example, the evaluation level corresponding to the evaluation operation can be determined, wherein the acquisition of the evaluation level is related to the operation type of the evaluation operation, wherein, when the operation type of the evaluation operation is the trigger evaluation of the preset control, then the evaluation level corresponding to the preset control triggered by the user can be determined (for example, the preset control is a progress bar, and the evaluation level is determined according to the end position of the user dragging the progress bar, etc.), and then, the weight correction value is determined according to the evaluation level, wherein the higher the evaluation level, the higher the weight correction value, and the lower the evaluation level, the lower the corresponding weight correction value. Wherein, the weight correction value is less than the preset weight correction maximum value.

[0055] For another example, the evaluation type (such as satisfaction or dissatisfaction) corresponding to the evaluation operation may be determined, and a pre-set weight correction value corresponding to the evaluation type may be determined based on the evaluation type. For example, if the evaluation type is satisfaction, the weight correction value is 10%.

[0056] After determining the weight correction value, the preset weight of at least one driving scene data set is corrected according to the weight correction value, thereby achieving iterative preset weights according to user feedback.

[0057] Among them, in some possible examples, the highest weight value among multiple preset weight values ​​corresponding to multiple driving scene data sets can be determined, and the highest weight value can be corrected according to the weight correction value. For example, if the weight correction value is 10%, the highest weight value is increased by 10%.

[0058] In this embodiment, after the highest weight value is increased, when the weight correction value is a positive value, the minimum weight value among other weight values ​​can be determined, and the corresponding weight correction value can be subtracted from the minimum weight value; when the weight correction value is a negative value, the maximum weight value among other weight values ​​can be determined, and the absolute value of the weight correction value can be increased based on the maximum weight value.

[0059] In other possible embodiments, when processing the method for music playback according to the above-mentioned preset model, the parameters and algorithms of the model can also be optimized based on the user's evaluation operations to further improve the quality and accuracy of music generation.

[0060] In summary, the vehicle-based music playing method of the disclosed embodiment obtains multiple driving scene data sets corresponding to multiple categories of driving scene data, and determines the preset weights for each category of driving scene data, wherein each driving scene data set contains at least one driving scene data of the corresponding category, and obtains candidate music according to the multiple driving scene data sets and the corresponding multiple preset weights, and then determines the target music playing elements according to the multiple driving scene data sets, and determines the music playing volume according to the multiple driving scene data sets, processes the candidate music according to the target music playing elements to generate the target music, and plays the target music according to the music playing volume. In this technical solution, music is generated and the music playing volume is determined in combination with multiple categories of driving scene data, so that the matching degree between the played music and the multiple categories of driving scene data is achieved, and the interactive experience is improved.

[0061] In order to implement the above embodiments, the present disclosure also proposes a vehicle-based music playing device.

[0062] Figure 2 This is a schematic diagram of the structure of a vehicle-based music player proposed in the present disclosure, which can generally be integrated into an electronic device to play music based on the vehicle. Figure 2 As shown, the device includes: a first determination module 210, an acquisition module 220, a second determination module 230 and a playback processing module 240, wherein:

[0063] A first determination module 210 is used to obtain a plurality of driving scene data sets corresponding to the plurality of categories of driving scene data, and determine a preset weight for each category of driving scene data, wherein each driving scene data set contains at least one driving scene data of the corresponding category;

[0064] An acquisition module 220 is used to acquire candidate music according to a plurality of driving scene data sets and corresponding plurality of preset weights;

[0065] A second determination module 230, configured to determine a target music playback element according to the plurality of driving scene data sets, and determine a music playback volume according to the plurality of driving scene data sets;

[0066] The playing processing module 240 is used to process the candidate music according to the target music playing element to generate the target music, and play the target music according to the music playing volume.

[0067] In one embodiment of the present disclosure, the acquisition module 220 is specifically configured to:

[0068] Inputting a plurality of driving scene data sets and a plurality of preset weights into a first preset model to obtain candidate music output by the first preset model; or,

[0069] Extracting multiple data features corresponding to multiple driving scene data sets;

[0070] Calculate the product value of each data feature and the corresponding preset weight respectively, and sum all the product values ​​to obtain the fusion feature;

[0071] The candidate music is obtained from the preset music library according to the fusion features.

[0072] In one embodiment of the present disclosure, the second determining module 230 is specifically configured to:

[0073] Inputting a plurality of driving scene data sets into a second preset model, and obtaining a target music playing element output by the second preset model; or,

[0074] Obtaining a preset driving scene data threshold value corresponding to each candidate music playing element in the preset candidate music playing element set;

[0075] The plurality of driving scene data sets are matched with all preset driving scene data threshold values ​​corresponding to the preset candidate music playback element sets, and the successfully matched candidate music playback elements are determined as target music playback elements.

[0076] In one embodiment of the present disclosure, the second determining module 230 is specifically configured to:

[0077] Identifying whether a plurality of driving scene data sets include driving scene data that meets a preset volume adjustment condition;

[0078] When the driving scene data that satisfies the preset volume adjustment condition is included, identifying a preset data level of the driving scene data that satisfies the preset volume adjustment condition;

[0079] The volume adjustment amount is determined according to the preset data level, and the preset standard volume is adjusted according to the volume adjustment amount to determine the music playing volume.

[0080] In one embodiment of the present disclosure, the second determination module 230 is specifically configured to: calculate a product value of a preset data level and a preset unit volume adjustment amount;

[0081] Determine the product value as the volume adjustment amount.

[0082] In one embodiment of the present disclosure, it further includes: a feedback optimization module, which is used to:

[0083] Monitoring the user's evaluation operation on the target music;

[0084] Determine the weight correction value according to the evaluation operation;

[0085] The preset weight of at least one driving scene data set is corrected according to the weight correction value.

[0086] In one embodiment of the present disclosure, the feedback optimization module is further used to:

[0087] Determining a highest weight value among a plurality of preset weight values ​​corresponding to a plurality of driving scenario data sets;

[0088] Correct the highest weight value according to the weight correction value.

[0089] The vehicle-based music playback device provided in the embodiments of the present disclosure can execute the vehicle-based music playback method provided in any embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

[0090] In order to implement the above embodiments, the present disclosure also proposes a computer program product, including a computer program / instruction, which implements the vehicle-based music playing method in the above embodiments when executed by a processor.

[0091] Figure 3 The present invention provides a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, wherein the electronic device can be built into a vehicle or can be arranged outside the vehicle.

[0092] The following specific reference Figure 3 , which shows a schematic diagram of the structure of an electronic device 300 suitable for implementing the embodiment of the present disclosure. The electronic device 300 in the embodiment of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0093] like Figure 3 As shown, the electronic device 300 may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a memory 308 to a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processor 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0094] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0095] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 309, or installed from a memory 308, or installed from a ROM 302. When the computer program is executed by the processor 301, the above-mentioned functions defined in the vehicle-based music playback method of the embodiment of the present disclosure are executed.

[0096] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0097] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0098] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0099] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device:

[0100] Acquire multiple driving scene data sets corresponding to multiple categories of driving scene data, and determine preset weights for each category of driving scene data, wherein each driving scene data set contains at least one category of driving scene data, obtain candidate music according to multiple driving scene data sets and corresponding multiple preset weights, and then determine target music playback elements according to multiple driving scene data sets, and determine music playback volumes according to multiple driving scene data sets, process candidate music according to the target music playback elements to generate target music, and play the target music according to the music playback volume. In this technical solution, music is generated and the playback volume of music is determined in combination with multiple categories of driving scene data, so that the matching degree of the played music and multiple categories of driving scene data is achieved, and the interactive experience is improved.

[0101] The electronic device may be written in one or more programming languages ​​or a combination thereof to write computer program code for performing the operations of the present disclosure, including but not limited to object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0102] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0103] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, constitute a limitation on the unit itself.

[0104] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0105] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0106] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0107] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0108] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A vehicle-based music playing method, characterized in that: The following steps are involved: Acquire multiple driving scene data sets corresponding one-to-one to multiple categories of driving scene data, and determine a preset weight for each category of the driving scene data, wherein each of the driving scene data sets contains at least one driving scene data of the corresponding category; Acquire candidate music according to the plurality of driving scene data sets and the corresponding plurality of preset weights; determining a target music playback element according to the plurality of driving scene data sets, and determining a music playback volume according to the plurality of driving scene data sets; The candidate music is processed according to the target music playing element to generate target music, and the target music is played according to the music playing volume.

2. The method according to claim 1, characterized in that The acquiring candidate music according to the plurality of driving scene data sets and the corresponding plurality of preset weights comprises: Input the plurality of driving scene data sets and the plurality of preset weights into a first preset model, and obtain the candidate music output by the first preset model; or, Extracting a plurality of data features corresponding one-to-one to the plurality of driving scene data sets; Calculate the product value of each of the data features and the corresponding preset weight respectively, and sum all the product values ​​to obtain the fusion feature; The candidate music is obtained from a preset music library according to the fusion feature.

3. The method according to claim 1, characterized in that The determining a target music playback element according to the plurality of driving scene data sets comprises: Input the plurality of driving scene data sets into a second preset model, and obtain the target music playback element output by the second preset model; or, Obtaining a preset driving scene data threshold value corresponding to each candidate music playing element in the preset candidate music playing element set; The multiple driving scene data sets are matched with all preset driving scene data thresholds corresponding to the preset candidate music playback element set, and the successfully matched candidate music playback element is determined to be the target music playback element.

4. The method according to claim 1, characterized in that The determining the music playback volume according to the plurality of driving scene data sets comprises: Identifying whether the plurality of driving scene data sets include driving scene data that meets a preset volume adjustment condition; When the driving scene data satisfying the preset volume adjustment condition is included, identifying a preset data level of the driving scene data satisfying the preset volume adjustment condition; A volume adjustment amount is determined according to the preset data level, and a preset standard volume is adjusted according to the volume adjustment amount to determine the music playing volume.

5. The method according to claim 4, characterized in that The step of determining the volume adjustment amount according to the preset data level includes: Calculating a product value of the preset data level and a preset unit volume adjustment amount; The product value is determined as the volume adjustment amount.

6. The method according to any one of claims 1 to 5, characterized in that: Also includes: Monitoring the user's evaluation operation on the target music; Determining a weight correction value according to the evaluation operation; The preset weight of at least one of the driving scene data sets is corrected according to the weight correction value.

7. The method according to claim 6, characterized in that The step of correcting the preset weight of at least one of the driving scene data sets according to the weight correction value comprises: Determining a highest weight value among a plurality of preset weight values ​​corresponding to the plurality of driving scene data sets; The highest weight value is corrected according to the weight correction value.

8. A vehicle-based music playing device, characterized in that: include: A first determination module is used to obtain a plurality of driving scene data sets corresponding one-to-one to a plurality of categories of driving scene data, and determine a preset weight for each category of the driving scene data, wherein each of the driving scene data sets contains at least one driving scene data of the corresponding category; An acquisition module, configured to acquire candidate music according to the plurality of driving scene data sets and corresponding plurality of preset weights; A second determination module is used to determine a target music playback element according to the plurality of driving scene data sets, and to determine a music playback volume according to the plurality of driving scene data sets; The playing processing module is used to process the candidate music according to the target music playing element to generate the target music, and play the target music according to the music playing volume.

9. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the executable instructions to implement the vehicle-based music playing method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is used to execute the vehicle-based music playing method described in any one of claims 1-7.

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