A method and apparatus for intelligently recommending music

Abstract

The application provides a method and device for intelligently recommending music, the method comprising collecting voice information and user data of a user, screening music-related information from the voice information to generate text data, analyzing the text data and the user data based on an intelligent model to obtain an analysis result, querying music information corresponding to the analysis result, and displaying the music information to the user. Through the collection of voice information and data of the user and the screening of music-related information from the voice information, the demand and preference of the user for music can be more effectively obtained, the user can be more accurately recommended suitable music, thereby providing personalized music recommendation, helping the user to better explore and discover new music, meeting the diversified demand of the user for music, and improving the use experience of the user.

Description

Technical Field

[0001] This invention belongs to the field of intelligent interactive technology, specifically relating to a method and apparatus for intelligent music recommendation. Background Technology

[0002] Personalized music recommendations are an important requirement for improving user experience. However, traditional music recommendation systems rely solely on users' historical playback records or simple tag matching, resulting in low accuracy and a lack of personalization.

[0003] With the continuous advancement of intelligent technology, many solutions for personalized music recommendations have emerged. However, many intelligent technologies require human interaction, and current technologies still suffer from some human-computer interaction problems, such as low accuracy in speech recognition and inaccurate emotion recognition. These issues may affect the practicality of the technology and the user experience.

[0004] In addition, many intelligent technologies require a large amount of data to train algorithms, but this data is often affected by factors such as collection, sample selection, and labeling, which can lead to data bias. This data bias may affect the accuracy and generalization ability of the model, reducing the reliability and practicality of the technology. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, the present invention proposes a method for intelligent music recommendation, characterized in that the method includes:

[0006] Collect user voice information and user data, and filter music-related information from the voice information to generate text data;

[0007] The intelligent model parses the text data and user data to obtain the parsing results;

[0008] The system queries the music information corresponding to the parsing result and displays the music information to the user.

[0009] Preferably, the "collecting user's voice information" includes:

[0010] Acquire the user's voice data and generate feedback data based on the user's voice data;

[0011] The feedback data is conveyed to the user via voice to prompt the user to respond via voice based on the feedback data;

[0012] Repeat the above steps until the number of times voice data is acquired reaches a first preset number, or if the user's voice data is still not acquired after a first preset time, then all the acquired voice data shall be used as the voice information.

[0013] Furthermore, the "parse based on the text data using an intelligent model" includes:

[0014] After generating the text data, the text data is corrected based on all the acquired voice data, and the processed text is displayed to the user, prompting the user to confirm the processed text.

[0015] After the user confirms, the intelligent model parses the processed text.

[0016] Specifically, the user data includes the user's speaking volume, speaking tone, speaking speed, and the user's usage record data.

[0017] Preferably, the step of "parsening based on the text data and the user data using an intelligent model" can be executed by multiple intelligent models, and the method further includes:

[0018] After displaying the music information to the user, the user is prompted to provide voice feedback on their satisfaction with the displayed music information.

[0019] If the voice feedback result is unsatisfactory, the intelligent model used is changed, and the step of "parse based on the text data and the user data by intelligent model" is executed again.

[0020] Furthermore, the method also includes:

[0021] Record the voice feedback results, and when the number of times the feedback results based on the same intelligent model are recorded reaches a second preset number, generate a satisfaction value corresponding to the intelligent model based on all the previous voice feedback results;

[0022] Based on the satisfaction scores of each intelligent model, a priority order is determined for each intelligent model from high to low.

[0023] When performing the step of "parse the voice data using intelligent models", each of the intelligent models parses the voice data in sequence according to the priority order.

[0024] Preferably, the music information includes several songs and / or several playlists, and "displaying the music information to the user" includes:

[0025] The system reads the name of the song or the name of the playlist to the user via voice, and prompts the user to provide voice feedback on whether they are satisfied with the song or the playlist.

[0026] If the voice feedback result is satisfactory, or if the user's voice data is not obtained after a second preset time, the song or playlist will be played for the user.

[0027] The present invention also proposes a device for intelligent music recommendation, the device comprising:

[0028] The data acquisition module is used to collect users' voice information and user data;

[0029] A filtering module is used to filter music-related information from the voice information to generate text data;

[0030] The parsing module is used to parse the text data and user data using an intelligent model to obtain the parsing results;

[0031] The query module is used to query music information corresponding to the parsing results;

[0032] The display module is used to display the music information to the user.

[0033] The present invention also proposes an electronic device equipped with a device for intelligent music recommendation as described above.

[0034] The present invention also proposes a transportation vehicle equipped with a device for intelligent music recommendation as described above.

[0035] The present invention has at least the following beneficial effects:

[0036] The proposed solution combines language models with music recommendation services, enabling a deeper understanding of user intent and needs. By understanding user semantics, it achieves more accurate and personalized music recommendations, allowing users to intuitively understand the music content they are interested in. This provides accurate music recommendations and displays, and the method of use is relatively simple and easy to understand, meeting users' needs for product simplicity and ease of use.

[0037] Furthermore, this solution can leverage the powerful semantic understanding capabilities of language models to engage in dialogue with users, thereby further uncovering user interests and preferences, conducting accurate analysis based on user context, and dynamically adjusting recommendation results based on user feedback and behavior, thus improving recommendation accuracy and user experience.

[0038] Therefore, the present invention provides a method and apparatus for intelligent music recommendation. This solution collects users' voice information and data and filters music-related information from the voice information, which can more effectively obtain users' music needs and preferences, and more accurately recommend suitable music to users, thereby providing personalized music recommendations. At the same time, it helps users better explore and discover new music, meets users' diverse music needs, and improves the user experience. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a schematic diagram of the overall process of the intelligent music recommendation method provided in Example 1;

[0041] Figure 2 A flowchart illustrating the process of collecting users' voice information;

[0042] Figure 3 This is a flowchart illustrating the process of analysis using an intelligent model.

[0043] Figure 4 A flowchart illustrating the process of displaying music information to users;

[0044] Figure 5 This is a schematic diagram of the module structure of the intelligent music recommendation device provided in Example 2.

[0045] Figure label:

[0046] 21-Acquisition Module; 22-Filtering Module; 23-Parsing Module; 24-Query Module; 25-Display Module; 27-Replace Module; 28-Recording Module; 29-Sorting Module; 211-Acquisition Unit; 212-Generation Unit; 213-Feedback Unit; 214-Summary Unit; 231-Processing Unit; 232-Confirmation Unit; 233-Parsing Unit; 251-Broadcasting Unit; 252-Prompt Unit; 253-Playback Unit. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0048] Various embodiments of the invention will be described more fully below. The invention may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of the invention to the specific embodiments disclosed herein, but rather the invention should be understood to cover all modifications, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of the invention.

[0049] In the following, the terms “comprising” or “may include” as used in various embodiments of the invention indicate the presence of the disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. Furthermore, as used in various embodiments of the invention, the terms “comprising,” “having,” and their cognates are intended only to indicate a specific feature, number, step, operation, element, component, or combination of the foregoing, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing, or the possibility of adding one or more combinations of the foregoing.

[0050] In various embodiments of the invention, the expression "or" or "at least one of A and / or B" includes any combination or all combinations of the words listed simultaneously. For example, the expression "A or B" or "at least one of A and / or B" may include A, may include B, or may include both A and B.

[0051] The expressions used in the various embodiments of the present invention (such as "first," "second," etc.) may modify various constituent elements in the various embodiments, but do not limit the corresponding constituent elements. For example, the above expressions do not limit the order and / or importance of the elements. The above expressions are only used for the purpose of distinguishing one element from other elements. For example, a first user device and a second user device refer to different user devices, although both are user devices. For example, a first element may be referred to as a second element without departing from the scope of the various embodiments of the present invention, and similarly, a second element may also be referred to as a first element.

[0052] It should be noted that, in this invention, unless otherwise explicitly specified and defined, terms such as "installation," "connection," and "fixation" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0053] In this invention, those skilled in the art should understand that the terms indicating orientation or positional relationship in the text are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the purpose of facilitating the description of this invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0054] The terminology used in the various embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to limit the various embodiments of the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of the invention pertain. The terms (such as those defined in a generally used dictionary) are to be interpreted as having the same meaning as in the context of the relevant technical field and are not to be interpreted as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of the invention.

[0055] Example 1

[0056] This embodiment proposes a method for intelligent music recommendation; see [link to documentation]. Figure 1 The method includes:

[0057] S100: Collects user voice information and user data, and filters music-related information from the voice information to generate text data.

[0058] In another embodiment, step S100 may provide the user with a method for collecting input text, or generate text data by combining collected voice data with the user's text input.

[0059] In this embodiment, user data includes user behavior data, which may specifically include the user's speaking volume, speaking tone, speaking speed, and user usage record data.

[0060] This solution identifies and judges the emotions or emotional states expressed in text through emotion judgment. Emotion judgment is a research direction in the fields of affective computing and natural language processing. It can determine the emotional category expressed in text, such as joy, sadness, anger, fear, etc., by analyzing information such as words, tone, and context in the text.

[0061] In addition, this solution can analyze user habits and preferences through user usage data to make the generated results more in line with the user's personal situation and achieve personalized recommendations.

[0062] S200: The intelligent model parses text data and user data to obtain the parsing results.

[0063] It should be noted that semantic parsing is the process of converting natural language text into a structured semantic representation. It is an important task in natural language processing, aiming to understand the meaning and semantic information of text. The goal of semantic parsing is to map natural language text to a formal semantic representation, such as a logical form, query language, or semantic graph.

[0064] S300: Query and parse the music information corresponding to the results, and display the music information to the user.

[0065] It should be noted that in step S300, displaying music information to the user can be done by displaying text on an electronic device with display function, or by broadcasting voice information on an electronic device with audio function.

[0066] Music recommendation services are technologies based on personalized algorithms and music data analysis, designed to recommend suitable music content to users according to their preferences and interests. These services generate personalized music recommendation lists by analyzing users' music preferences, listening history, social network behavior, and other relevant information.

[0067] The goal of music recommendation services is to provide users with a music experience that matches their tastes and interests, while helping them discover new music and artists. Through the use of algorithms, music recommendation services can understand users' musical preferences and match and recommend music based on these preferences. Recommendation algorithms typically employ methods such as collaborative filtering, content filtering, and tag-based recommendation, combining users' historical data and social network information to create personalized music recommendations.

[0068] Therefore, this solution's music recommendation service not only allows users to easily discover new music and artists, but also provides a personalized music experience, enabling users to better enjoy music and broaden their musical horizons.

[0069] Preferably, to prevent excessive user input from causing parsing failures and to handle situations where the user does not make further requests, an input waiting time can be preset, see [link to relevant documentation]. Figure 2 The "collecting user's voice information" mentioned in step S100 includes:

[0070] S110: Acquire user's voice data and generate feedback data based on the user's voice data.

[0071] In this embodiment, a language model is used to execute step S110. The language model generates an appropriate dialogue as feedback data based on the user's voice input.

[0072] It's important to note that a language model is a statistical or neural network model, generally used to understand and generate human language. It learns the patterns and probability distributions of language by training on large amounts of text data, enabling it to predict the likelihood of the next word or a passage of text given a context. Language models can be used for various natural language processing tasks, such as machine translation, speech recognition, text generation, and automatic summarization. They can predict the probability of the next word based on existing text context and can also generate new text similar to the training data.

[0073] The basic principle of language models is to establish connections and grammatical rules between words by learning the word order and probability distribution in a text. Simpler language models can be based on n-gram statistical models, where n represents the n-1 words that the prediction of the next word depends on. More complex language models can be based on deep learning models such as recurrent neural networks (RNNs), long short-term memory networks (LSTMs), or transformers.

[0074] Training a language model requires a large amount of text data, which can be refined and customized as needed. A well-trained language model can be used to generate text, complete sentences, correct grammatical errors, and play an important role in the field of natural language processing.

[0075] S120: The feedback data is conveyed to the user via voice to prompt the user to respond via voice based on the feedback data.

[0076] Repeat the above steps until the number of times voice data is acquired reaches the first preset number, or if the user's voice data is still not acquired after the first preset time, then execute step S130.

[0077] S130: Treat all acquired voice data as voice information.

[0078] In one specific embodiment, the first preset number of times is 10 times and the first preset time is 20 seconds. Then, when the number of times voice data is acquired reaches 10 times, or when no user voice data is acquired after 20 seconds, all acquired voice data is used as voice information.

[0079] See Figure 3 Furthermore, the "parse based on text data using an intelligent model" mentioned in step S200 includes:

[0080] S210: After generating text data, perform error correction processing on the text data based on all acquired speech data.

[0081] In this embodiment, step S210 is also executed by the server. The server will continuously update the information based on the user's voice input, and further complete the error correction through the context correction algorithm based on all the acquired voice data to ensure the accuracy of the text data.

[0082] S220: Display the processed text to the user and prompt the user to confirm the processed text.

[0083] S230: After user confirmation, enable the intelligent model to parse the processed text.

[0084] Preferably, the music information includes several songs and / or several playlists, see [link / reference]. Figure 4 Step S300, "displaying music information to the user," includes:

[0085] S310: Prompts users to hear the names of songs or playlists via voice, and provides voice feedback on whether the user is satisfied with the songs or playlists.

[0086] If the voice feedback result is satisfactory, or if no user voice data is collected after the second preset time, proceed to step S320.

[0087] S320: Plays songs or playlists for users.

[0088] Preferably, the step of "parse based on text data and user data using intelligent models" in step S200 can be executed by multiple intelligent models. The intelligent music recommendation method proposed in this embodiment further includes:

[0089] S400: After displaying music information to the user, prompt the user to provide voice feedback on their satisfaction with the displayed music information.

[0090] It should be noted that, in one specific embodiment, step S310 and step S400 can be regarded as the same step.

[0091] If the user's voice feedback is unsatisfactory, proceed to step S500.

[0092] S500: Replace the smart model in use and repeat step S200.

[0093] Furthermore, the intelligent music recommendation method proposed in this embodiment also includes:

[0094] S600: Records voice feedback results, and when the number of times the feedback results based on the same intelligent model are recorded reaches a second preset number, it generates the corresponding intelligent model's satisfaction value based on all prior voice feedback results.

[0095] It should be noted that in order to make the satisfaction value more accurate, a large sample size is required. In a specific embodiment, the second preset number of times is 10 times, and the satisfaction value is expressed as a percentage. If the number of times the feedback results based on a certain intelligent model are recorded reaches 10 times, and if the user's voice feedback results are unsatisfactory 4 times out of 10 times, then the satisfaction value of the intelligent model is 60%.

[0096] S700: Based on the satisfaction scores of each intelligent model, the priority order of each intelligent model is determined from high to low.

[0097] Therefore, when performing the "parse speech data through intelligent models" step S200, each intelligent model can parse the speech data in turn according to the priority order of the intelligent models determined in step S700. That is, the intelligent model with the highest priority order is used first for parsing, and when the user's speech feedback result in step S400 is unsatisfactory, the next intelligent model is replaced for parsing according to the priority order.

[0098] In one specific embodiment, the parsing operation is performed through two intelligent models. The number of times the feedback results based on the first intelligent model are recorded is 15, and the satisfaction value of the first intelligent model is 66.7% and the satisfaction value of the second intelligent model is 60%. Therefore, the first intelligent model is used first for parsing.

[0099] If a user is dissatisfied with the voice feedback result of the recommended music generated based on the first intelligent model, the satisfaction value of the first intelligent model will be reset to 62.5%, and the second intelligent model will be used to re-analyze the music.

[0100] The method proposed in this embodiment has wide applicability, and its application scenarios include, but are not limited to, daily music recommendation, mood-assisted therapy, automatic DJing, etc. In each application scenario, the user's voice input content and emotions may be different, but the basic technical implementation process remains consistent. Furthermore, this method can also adjust the recommendation strategy and dialogue content according to different application scenarios in the music recommendation and dialogue generation stages.

[0101] The semantic analysis based on context and user emotions proposed in this solution brings new possibilities to the music industry. It not only allows users to interact with music through voice, but also enables a deeper understanding and satisfaction of users' emotional needs. This innovation not only provides users with a richer and more personalized music experience, but also brings broader development space and closer user relationships to the music industry.

[0102] Therefore, this solution can not only recommend music based on user preferences, but also sense user emotions and emotional states, and provide music recommendations that are more in line with user moods. Whether users want to relax, have fun, or need encouragement and motivation, the assistant can intelligently generate corresponding music works based on context and emotion analysis, providing users with a personalized and emotionally resonant music experience.

[0103] Example 2

[0104] This embodiment proposes an intelligent music recommendation device to implement the intelligent music recommendation method proposed in Embodiment 1. (See also...) Figure 5 The device includes:

[0105] Acquisition module 21 is used to collect users' voice information and user data;

[0106] Filtering module 22 is used to filter music-related information from voice information to generate text data;

[0107] Parsing module 23 is used to parse based on text data and user data using an intelligent model to obtain parsing results;

[0108] Query module 24 is used to query music information corresponding to the parsing results;

[0109] Display module 25 is used to display music information to users.

[0110] Preferably, the acquisition module 21 includes:

[0111] Acquisition unit 211 is used to acquire the user's voice data;

[0112] Generation unit 212 generates feedback data based on the user's voice data;

[0113] Feedback unit 213 is used to convey feedback data to the user via voice, so as to prompt the user to respond with voice based on the feedback data;

[0114] The summarization unit 214 is used to treat all acquired voice data as voice information when the number of times the acquisition unit 211 acquires voice data reaches a first preset number, or when the acquisition unit 211 fails to acquire the user's voice data after a first preset time.

[0115] In this embodiment, the acquisition unit 211, the generation unit 212 and the feedback unit 213 repeatedly execute preset functions until the conditions for the summarization unit 214 to execute preset functions are met. The generation unit 212 includes a language model, which generates an appropriate dialogue content as feedback data based on the user's voice input.

[0116] Furthermore, the parsing module 23 includes:

[0117] The processing unit 231 is used to perform error correction processing on the text data based on all the acquired speech data after the text data is generated.

[0118] The confirmation unit 232 is used to display the processed text to the user and prompt the user to confirm the processed text.

[0119] The parsing unit 233 is used to enable the intelligent model to parse the processed text after user confirmation.

[0120] Preferably, the music information includes several songs and / or several playlists, and the display module 25 includes:

[0121] The broadcasting unit 251 is used to announce the name of a song or the name of a playlist to the user via voice.

[0122] The prompting unit 252 is used to provide voice feedback to the user regarding their satisfaction with a song or playlist.

[0123] The playback unit 253 is used to play songs or playlists for the user when the voice feedback result is satisfactory, or when the user's voice data has not been obtained after a second preset time.

[0124] Preferably, the parsing module 23 includes multiple intelligent models, and the device for intelligently recommending music further includes:

[0125] The prompting module is used to prompt the user to provide voice feedback on whether they are satisfied with the displayed music information. In this embodiment, the prompting module includes a prompting unit 252.

[0126] Replacement module 27 is used to replace the intelligent model used when the voice feedback result is unsatisfactory, and to make parsing module 23 re-execute the preset function.

[0127] Furthermore, the intelligent music recommendation device proposed in this embodiment also includes:

[0128] The recording module 28 is used to record the voice feedback results, and when the number of times the feedback results based on the same intelligent model are recorded reaches a second preset number, it generates the satisfaction value of the corresponding intelligent model based on all the previous voice feedback results.

[0129] The sorting module 29 is used to determine the priority order of each intelligent model from high to low based on the satisfaction value of each intelligent model.

[0130] Therefore, when the parsing module 23 performs the preset function, it can parse the voice data in turn according to the priority order of the intelligent models determined by the sorting module 29. That is, the intelligent model with the highest priority order is used first for parsing, and when the user's voice feedback result is unsatisfactory, the next intelligent model is replaced for parsing according to the priority order.

[0131] Example 3

[0132] An electronic device characterized in that it is equipped with a device for intelligent music recommendation as described in Example 2.

[0133] This embodiment introduces voice input functionality into electronic devices to provide a more convenient user experience. Through advanced semantic analysis technology, it can accurately understand the user's voice commands and intentions. Based on the analysis of the user, the electronic device can not only answer the user's questions and provide relevant information, but also intelligently recommend suitable music according to the user's preferences and mood.

[0134] Therefore, the innovative method of integrating chat and music functions in electronic devices proposed in this embodiment allows users to enjoy interactive dialogue with intelligent systems through simple voice interaction, while enjoying music that suits their tastes anytime and anywhere. This greatly enhances the human-computer interaction experience of users using electronic devices, enabling users to explore, discover and enjoy music more conveniently.

[0135] Example 4

[0136] A transportation vehicle is characterized in that it is equipped with a device for intelligent music recommendation as described in Example 2.

[0137] The proposed solution in this embodiment is a vehicle equipped with a device for intelligent music recommendation. This solution focuses on providing a safe and convenient user experience, so only the voice function is retained, which aims to help the driver stay focused on driving and avoid distraction.

[0138] Therefore, users do not need to look away or touch the screen. Through highly accurate voice recognition technology and intelligent semantic analysis, the system can quickly respond and provide drivers with an intelligent and safe driving experience, making the driving process more convenient, comfortable and worry-free.

[0139] In summary, this invention provides a method and apparatus for intelligent music recommendation. By collecting users' voice information and data and filtering music-related information from the voice information, this solution can more effectively obtain users' music needs and preferences, and more accurately recommend suitable music to users, thereby providing personalized music recommendations. At the same time, it helps users better explore and discover new music, meets users' diverse music needs, and improves the user experience.

[0140] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method of intelligently recommending music, characterized by, The method comprises: collecting voice information and user data of a user, and screening music-related information from the voice information to generate text data; analyzing based on the text data and the user data through an intelligent model to obtain an analysis result; the step of analyzing based on the text data and the user data through an intelligent model can be performed through multiple intelligent models; querying music information corresponding to the analysis result and displaying the music information to the user; after displaying the music information to the user, prompting the user to provide voice feedback on whether the displayed music information is satisfactory; if the voice feedback result is unsatisfactory, replacing the used intelligent model and re-executing the step of analyzing based on the text data and the user data through an intelligent model; recording the voice feedback result, and when the number of feedback results recorded based on the same intelligent model reaches a second preset number, generating a satisfaction value corresponding to the intelligent model based on all previous voice feedback results; based on the satisfaction values of the intelligent models, determining a priority order for each intelligent model from high to low; when executing the step of analyzing based on the text data and the user data through an intelligent model, sequentially analyzing voice data as the source of the voice information through each intelligent model in the priority order.

2. The method of intelligently recommending music of claim 1, wherein, The "collecting voice information of a user" comprises: obtaining voice data of the user, and generating feedback data based on the voice data of the user; communicating the feedback data to the user in a voice manner to prompt the user to provide voice feedback according to the feedback data; repeating the above steps until the number of times of obtaining voice data reaches a first preset number, or when the voice data of the user is still not obtained after a first preset time, using all obtained voice data as the voice information.

3. The method of intelligently recommending music of claim 2, wherein, The "analyzing based on the text data through an intelligent model" comprises: after generating the text data, performing error correction processing on the text data according to all obtained voice data, displaying the processed text to the user, and prompting the user to confirm the processed text; after the user confirms, making the intelligent model analyze based on the processed text.

4. The method for intelligently recommending music of claim 1, wherein, The user data includes the user's speaking volume, speaking tone, speaking speed, and usage record data of the user.

5. The method for intelligently recommending music of claim 1, wherein, The music information includes a plurality of music songs and / or a plurality of music playlists, and the "displaying the music information to the user" comprises: reporting the names of the songs or the names of the playlists to the user in a voice manner, and prompting the user to provide voice feedback on whether the songs or the playlists are satisfactory; if the voice feedback result is satisfactory, or when the voice data of the user is still not collected after a second preset time, playing the songs or the playlists for the user.

6. An apparatus for intelligently recommending music, the apparatus comprising: The device comprises: a collection module for collecting voice information and user data of a user; The screening module is configured to screen music-related information from the voice information to generate text data; The analysis module includes a plurality of intelligent models and is configured to analyze the text data and the user data based on the intelligent models to obtain an analysis result; The query module is configured to query music information corresponding to the analysis result; The display module is configured to display the music information to the user; The prompting module is configured to prompt the user to provide voice feedback on whether the displayed music information is satisfactory; The replacement module is configured to replace the intelligent model used and cause the analysis module to re-execute a preset function when the voice feedback is unsatisfactory; The recording module is configured to record the voice feedback result and generate a satisfaction value corresponding to the intelligent model based on all previous voice feedback results when the number of times of recording the feedback result based on the same intelligent model reaches a second preset number of times; The sorting module is configured to determine a priority order for each intelligent model from high to low based on the satisfaction value of each intelligent model, and cause each intelligent model to sequentially analyze voice data as the source of the voice information when the analysis module executes a preset function according to the priority order.

7. An electronic device, comprising: The electronic device is loaded with the intelligent music recommendation device of claim 6.

8. A transportation vehicle, characterized by The traffic carrier is loaded with the intelligent music recommendation device of claim 6.

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