Intelligent generation method and device for music learning strategy

Abstract

The present invention relates to the field of data processing technology, and discloses a method and device for intelligently generating a music learning strategy, the method comprising: performing a music ability test on a target user according to user information, obtaining a music ability parameter of the target user, generating a music learning strategy for the target user according to the user information and the music ability parameter, obtaining a node learning result of each music learning node of the target user in the music learning strategy, and determining a music learning score of the target user according to each node learning result, optimizing and updating the music learning strategy according to the music learning score, and obtaining a target music learning strategy corresponding to the target user. It can be seen that the implementation of the present invention can formulate a corresponding music learning strategy for a user based on user information, can reduce the user's music learning cost, reduce the difficulty of music learning, and thus improve the user's efficiency in music learning and enhance the user's experience.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent generation method and device for music learning strategies. Background Art

[0002] As people's living standards continue to improve, they gradually pay attention to the enrichment and satisfaction of their spiritual world. Music, as an art form and cultural activity that can convey and express emotions, can have a positive impact on children. Learning music can enrich children's emotional experience, subtly infect and edify their emotional world, and gradually establish a love for humanity, nature, and all beautiful things. Therefore, more and more children are beginning to come into contact with and learn music.

[0003] However, learning and mastering music requires more professional music theory knowledge, which is difficult to learn, takes a long time to master, and has low learning efficiency. Therefore, it is particularly important to propose a technical solution that can improve the efficiency of music learning. Summary of the invention

[0004] The present invention provides a method and device for intelligently generating a music learning strategy, which can be helpful in improving the efficiency of music learning.

[0005] In order to solve the above technical problems, the first aspect of the present invention discloses an intelligent generation method of music learning strategy, the method comprising:

[0006] Acquire user information of a target user, and perform a music ability test on the target user according to the user information to obtain a music ability parameter of the target user;

[0007] Generate a music learning strategy for the target user according to the user information and the music ability parameter, and guide the target user to perform music learning according to the music learning strategy, wherein the music learning strategy includes at least one music learning node;

[0008] Obtaining the node learning result of the target user at each of the music learning nodes, and determining the music learning score of the target user according to each of the node learning results;

[0009] The music learning strategy is optimized and updated according to the music learning score to obtain a target music learning strategy corresponding to the target user.

[0010] As an optional implementation, in the first aspect of the present invention, the user information includes the target user's age information, music type interest information, and music learning time information;

[0011] The performing a music ability test on the target user according to the user information to obtain the music ability parameter of the target user includes:

[0012] Determine at least one music tag corresponding to the target user according to the user information, and select a preset number of music knowledge points matching each of the music tags in a pre-established music test database;

[0013] Generating a music ability test question for the target user according to each of the music knowledge points, wherein the music ability test question includes at least one of a lyrics test question, a melody test question, and a music theory test question;

[0014] The target user is tested for music ability according to the music ability test questions to obtain music ability parameters of the target user, wherein the music ability parameters include at least one of lyrics ability parameters, melody ability parameters and music theory ability parameters.

[0015] As an optional implementation, in the first aspect of the present invention, the music learning node includes at least one of a lyrics learning node, a melody learning node, and a performance learning node, and the node learning result includes at least one of a lyrics learning result, a melody learning result, and a performance learning result;

[0016] Determining the music learning score of the target user according to the learning result of each node includes:

[0017] Input each of the node learning results into a pre-trained target model for analysis to obtain a node learning score corresponding to each of the node learning results;

[0018] Determine, according to the music ability parameter of the target user, the node weight corresponding to each of the music learning nodes of the target user;

[0019] The music learning score of the target user is calculated according to the node learning score corresponding to each of the node learning results and the node weight corresponding to each of the music learning nodes.

[0020] As an optional implementation, in the first aspect of the present invention, the music learning strategy is optimized and updated according to the music learning score to obtain the target music learning strategy corresponding to the target user, including:

[0021] Determining a node score threshold of the target user at each music learning node according to the user information and the music ability parameter of the target user;

[0022] For each of the music learning nodes, determining whether a node learning score corresponding to the music learning node is greater than a node score threshold of the music learning node;

[0023] For each of the music learning nodes, when the node learning score corresponding to the music learning node is less than the node score threshold of the music learning node, a node score difference between the node learning score corresponding to the music learning node and the node score threshold of the music learning node is calculated, and the node learning weakness information of the target user at the music learning node is determined according to the node score difference;

[0024] The music learning strategy is optimized and updated according to the node learning weakness information of each of the music learning nodes to obtain the target music learning strategy corresponding to the target user.

[0025] As an optional embodiment, in the first aspect of the present invention, the method further comprises:

[0026] In the process of guiding the target user to learn music according to the music learning strategy, obtaining a music composition input by the target user, wherein the music composition includes a lyrics composition and / or a melody composition;

[0027] Analyze the musical creation to obtain work information corresponding to the musical creation;

[0028] The work information is input into a pre-trained target model to generate optimization suggestions for the music creation work, and the target user is prompted to optimize the music creation work according to the optimization suggestions.

[0029] As an optional implementation, in the first aspect of the present invention, the work information includes lyrics information corresponding to the lyrics creation work and / or melody information corresponding to the melody creation work, the lyrics information includes main idea information and lyrics vocabulary information, and the melody information includes tune information and note information;

[0030] The step of inputting the work information into a pre-trained target model to generate optimization suggestions for the music creation work includes:

[0031] When the music composition includes the lyrics composition, the lyrics information is input into the target model to generate lyrics optimization suggestions for the lyrics composition, wherein the lyrics optimization suggestions include main idea optimization suggestions and lyrics vocabulary optimization suggestions;

[0032] When the musical composition includes the melody composition, the melody information is input into the target model to generate a melody optimization suggestion for the melody composition, wherein the melody optimization suggestion includes a tune optimization suggestion and a note optimization suggestion.

[0033] As an optional implementation manner, in the first aspect of the present invention, the lyrics information further includes lyrics structure information, and the method further includes:

[0034] When the musical composition includes the lyrics composition and the melody composition, the lyrics composition is divided into at least one lyrics group matching the melody information according to the melody information and the lyrics structure information, each of the lyrics group includes at least one lyrics unit;

[0035] Determine the correspondence between each of the lyrics units and the notes according to the note information, each of the lyrics units corresponds to at least one of the notes;

[0036] synthesizing the lyrics creation work and the melody creation work into a musical work according to the corresponding relationship, and determining a musical work score of the musical work;

[0037] The target music learning strategy is optimized and updated according to the score of the music work.

[0038] A second aspect of the present invention discloses an intelligent generation device for music learning strategies, the device comprising:

[0039] An acquisition module, used to acquire user information of a target user, and perform a music ability test on the target user according to the user information to obtain a music ability parameter of the target user;

[0040] A generation module, configured to generate a music learning strategy for the target user according to the user information and the music ability parameter, and guide the target user to perform music learning according to the music learning strategy, wherein the music learning strategy includes at least one music learning node;

[0041] The acquisition module is further used to acquire the node learning result of the target user at each of the music learning nodes, and determine the music learning score of the target user according to each of the node learning results;

[0042] An optimization module is used to optimize and update the music learning strategy according to the music learning score to obtain a target music learning strategy corresponding to the target user.

[0043] As an optional implementation, in the second aspect of the present invention, the user information includes the target user's age information, music type interest information, and music learning time information;

[0044] The acquisition module performs a music ability test on the target user according to the user information, and obtains the music ability parameter of the target user in a manner that specifically includes:

[0045] Determine at least one music tag corresponding to the target user according to the user information, and select a preset number of music knowledge points matching each of the music tags in a pre-established music test database;

[0046] Generating a music ability test question for the target user according to each of the music knowledge points, wherein the music ability test question includes at least one of a lyrics test question, a melody test question, and a music theory test question;

[0047] The target user is tested for music ability according to the music ability test questions to obtain music ability parameters of the target user, wherein the music ability parameters include at least one of lyrics ability parameters, melody ability parameters and music theory ability parameters.

[0048] As an optional implementation, in the second aspect of the present invention, the music learning node includes at least one of a lyrics learning node, a melody learning node, and a performance learning node, and the node learning result includes at least one of a lyrics learning result, a melody learning result, and a performance learning result;

[0049] The acquisition module determines the music learning score of the target user according to each of the node learning results in the following manner:

[0050] Input each of the node learning results into a pre-trained target model for analysis to obtain a node learning score corresponding to each of the node learning results;

[0051] Determine, according to the music ability parameter of the target user, the node weight corresponding to each of the music learning nodes of the target user;

[0052] The music learning score of the target user is calculated according to the node learning score corresponding to each of the node learning results and the node weight corresponding to each of the music learning nodes.

[0053] As an optional implementation, in the second aspect of the present invention, the optimization module optimizes and updates the music learning strategy according to the music learning score, and the method of obtaining the target music learning strategy corresponding to the target user specifically includes:

[0054] Determining a node score threshold of the target user at each music learning node according to the user information and the music ability parameter of the target user;

[0055] For each of the music learning nodes, determining whether a node learning score corresponding to the music learning node is greater than a node score threshold of the music learning node;

[0056] For each of the music learning nodes, when the node learning score corresponding to the music learning node is less than the node score threshold of the music learning node, a node score difference between the node learning score corresponding to the music learning node and the node score threshold of the music learning node is calculated, and the node learning weakness information of the target user at the music learning node is determined according to the node score difference;

[0057] The music learning strategy is optimized and updated according to the node learning weakness information of each of the music learning nodes to obtain the target music learning strategy corresponding to the target user.

[0058] As an optional implementation, in the second aspect of the present invention, the acquisition module is further used to acquire the music creation works input by the target user in the process of guiding the target user to learn music according to the music learning strategy, and the music creation works include lyrics creation works and / or melody creation works;

[0059] The device also includes:

[0060] An analysis module, used to analyze the musical creation work to obtain work information corresponding to the musical creation work;

[0061] The generation module is also used to input the work information into a pre-trained target model, generate optimization suggestions for the music creation work, and prompt the target user to optimize the music creation work according to the optimization suggestions.

[0062] As an optional implementation, in the second aspect of the present invention, the work information includes lyrics information corresponding to the lyrics creation work and / or melody information corresponding to the melody creation work, the lyrics information includes main idea information and lyrics vocabulary information, and the melody information includes tune information and note information;

[0063] The generation module inputs the work information into a pre-trained target model, and generates optimization suggestions for the music creation work in the following manner:

[0064] When the music composition includes the lyrics composition, the lyrics information is input into the target model to generate lyrics optimization suggestions for the lyrics composition, wherein the lyrics optimization suggestions include main idea optimization suggestions and lyrics vocabulary optimization suggestions;

[0065] When the musical composition includes the melody composition, the melody information is input into the target model to generate a melody optimization suggestion for the melody composition, wherein the melody optimization suggestion includes a tune optimization suggestion and a note optimization suggestion.

[0066] As an optional implementation manner, in the second aspect of the present invention, the lyrics information further includes lyrics structure information, and the device further includes:

[0067] a splitting module, for splitting the lyrics creation work into at least one lyrics group matching the melody information according to the melody information and the lyrics structure information when the music creation work includes the lyrics creation work and the melody creation work, each of the lyrics group including at least one lyrics unit;

[0068] A synthesis module, used for synthesizing the lyrics creation work and the melody creation work into a music work according to the corresponding relationship, and determining a music work score of the music work;

[0069] The optimization module is also used to optimize and update the target music learning strategy according to the score of the music work.

[0070] The third aspect of the present invention discloses another intelligent generation device for music learning strategy, the device comprising:

[0071] A memory storing executable program code;

[0072] a processor coupled to the memory;

[0073] The processor calls the executable program code stored in the memory to execute the intelligent generation method of music learning strategies disclosed in the first aspect of the present invention.

[0074] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the intelligent generation method of music learning strategies disclosed in the first aspect of the present invention.

[0075] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0076] In an embodiment of the present invention, the user information of the target user is obtained, and the target user is tested for music ability according to the user information to obtain the music ability parameters of the target user, a music learning strategy for the target user is generated according to the user information and the music ability parameters, and the target user is guided to learn music according to the music learning strategy, the node learning results of the target user at each music learning node are obtained, and the music learning score of the target user is determined according to each node learning result, and the music learning strategy is optimized and updated according to the music learning score to obtain the target music learning strategy corresponding to the target user. It can be seen that the implementation of the present invention can formulate corresponding music learning strategies for users based on user information, can reduce the music learning costs of users, reduce the difficulty of music learning, and thus improve the efficiency of music learning for users and enhance the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0078] Figure 1 It is a flowchart of an intelligent generation method of a music learning strategy disclosed in an embodiment of the present invention;

[0079] Figure 2 It is a flow chart of another intelligent generation method of music learning strategy disclosed in an embodiment of the present invention;

[0080] Figure 3 It is a structural schematic diagram of an intelligent generation device for music learning strategy disclosed in an embodiment of the present invention;

[0081] Figure 4 It is a structural schematic diagram of another intelligent generation device for music learning strategy disclosed in an embodiment of the present invention;

[0082] Figure 5 It is a structural schematic diagram of another intelligent generation device for music learning strategy disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0083] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0084] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

[0085] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0086] The present invention discloses an intelligent generation method and device of a music learning strategy, which can formulate a corresponding music learning strategy for a user based on user information, reduce the user's music learning cost, reduce the difficulty of music learning, and thus improve the user's music learning efficiency and enhance the user's experience. The following are detailed descriptions.

[0087] Embodiment 1

[0088] See also Figure 1 , Figure 1 is a flow chart of an intelligent generation method of a music learning strategy disclosed in an embodiment of the present invention. Figure 1 The described intelligent generation method of music learning strategy can be applied to an intelligent generation device of music learning strategy, wherein the intelligent generation device of music learning strategy may include an intelligent device for users to learn music, and the intelligent device may include any one of a display screen, a button, an intelligent server or an intelligent platform for generating music learning strategy, and the intelligent server includes a local server or a cloud server. The intelligent device can be connected to an external music device, which is not limited in the embodiments of the present invention. Figure 1 As shown, the intelligent generation method of the music learning strategy may include the following operations:

[0089] 101. Obtain user information of a target user, and perform a music ability test on the target user based on the user information to obtain music ability parameters of the target user.

[0090] In an embodiment of the present invention, optionally, the user information of the target user may include at least one of age information, music type interest information, and music learning time information, and the target user may be divided into infants, children, teenagers, youth, middle-aged people, and the elderly according to the age information of the target user. The music type interest information of the target user may include the music style type and / or instrument type that the target user is interested in. The music style type may include one or more of classical music, folk music, rock music, metal music, modern pop music, etc. The instrument type may include one or more of bowed string music, brass music, woodwind music, plucked instrument, keyboard music, and percussion music, etc., wherein the bowed string music may include one or more of violin, viola, cello, double bass, etc., the brass music may include one or more of trumpet, trombone, tuba, French horn, etc., the woodwind music may include one or more of flute, piccolo, clarinet, oboe, etc., the plucked instrument may include one or more of guitar, bass, pipa, soft, guzheng, etc., the keyboard music may include one or more of piano, keyboard, etc., and the percussion music may include one or more of drum kit, marimba, African drum, cajon box, etc., and the present invention is not limited thereto.

[0091] In the embodiment of the present invention, optionally, the music ability parameter of the target user may include at least one of a lyrics ability parameter, a melody ability parameter and a music theory ability parameter, which is not limited in the present invention.

[0092] 102. Generate a music learning strategy for a target user according to the user information and the music ability parameter, and guide the target user to perform music learning according to the music learning strategy, wherein the music learning strategy includes at least one music learning node.

[0093] In the embodiment of the present invention, optionally, the music learning strategy may include at least one music learning node, and the music learning node may include at least one of a lyrics learning node, a melody learning node, and a performance learning node, that is, the target user may perform lyrics learning, melody learning, and performance learning according to the music learning strategy. Taking lyrics learning as an example, lyrics learning may include one or more of lyrics appreciation, lyrics structure analysis, lyrics theme analysis, lyrics creation learning, etc., and the present invention does not limit

[0094] 103. Obtain the node learning result of the target user at each music learning node, and determine the music learning score of the target user according to each node learning result.

[0095] In the embodiment of the present invention, optionally, the node learning result of the target user at each music learning node may include at least one of a lyrics learning result, a melody learning result, and a performance learning result, which is not limited by the present invention.

[0096] 104. Optimize and update the music learning strategy according to the music learning score to obtain the target music learning strategy corresponding to the target user.

[0097] In an embodiment of the present invention, optionally, the music learning score can be used to reflect the target user's music learning weaknesses in one or several aspects, and the music learning strategy can be optimized and updated based on these music learning weaknesses to strengthen the target user's learning of these music learning weaknesses, which is not limited by the present invention.

[0098] It can be seen that implementation Figure 1 The described intelligent generation method of music learning strategy can obtain user information of target users, and perform music ability test on target users based on the user information to obtain music ability parameters of target users, generate music learning strategies for target users based on user information and music ability parameters, and guide target users to perform music learning based on the music learning strategies, obtain node learning results of target users at each music learning node, and determine the music learning score of the target user based on the learning results of each node, optimize and update the music learning strategy based on the music learning score, and obtain the target music learning strategy corresponding to the target user, and can formulate corresponding music learning strategies for users based on user information, which can reduce the user's music learning cost and difficulty, thereby improving the user's music learning efficiency and enhancing the user's usage experience.

[0099] In an optional embodiment, the intelligent generation method of the music learning strategy may further include the following operations:

[0100] In the process of guiding the target user to learn music according to the music learning strategy, obtaining the music creation works input by the target user, the music creation works including lyrics creation works and / or melody creation works;

[0101] Analyze the musical creation works to obtain the work information corresponding to the musical creation works;

[0102] The work information is input into a pre-trained target model to generate optimization suggestions for the music creation works, and the target users are prompted to optimize the music creation works based on the optimization suggestions.

[0103] In this optional embodiment, optionally, the music works input by the target user may include lyrics works and / or melody works, wherein the target user may directly input the lyrics works on the smart device used for the user to learn music, or may input the lyrics works through an external device connected to the smart device, the smart device and the external device may be connected by wire or by wirelessly, the external device may include one or more of a computer, a smart phone (Android phone, iOS phone, etc.) and a smart tablet, etc., the lyrics works may be input by text or by voice, which is not limited in this embodiment.

[0104] In this optional embodiment, optionally, the target user can input the melody creation work through the smart device and / or the music device connected to the smart device, the smart device and the music device can be connected by wire or wirelessly, and the music device can include one or more of bowed string instruments, brass instruments, woodwind instruments, plucked instruments, keyboard instruments and percussion instruments, among which bowed string instruments can include one or more of violin, viola, cello, double bass, etc., brass instruments can include one or more of trumpet, trombone, tuba, French horn, etc., woodwind instruments can include flute, piccolo, clarinet, double reed, etc. The target user may input the melody creation work through the smart device connected to the smart device, the melody creation work may include the sheet music, the humming melody of the target user, etc.; when the target user inputs the melody creation work through the music device connected to the smart device, the melody creation work may include the played melody of the music device, which is not limited in this embodiment.

[0105] In this optional embodiment, optionally, the work information corresponding to the musical creation work may include lyrics information corresponding to the lyrics creation work and / or melody information corresponding to the melody creation work. The pre-trained target model can be used to analyze the node learning results of the target user to obtain the node learning score corresponding to each node learning result. It can also be used to analyze the input work information to generate optimization suggestions for the musical creation work, which is not limited in this embodiment.

[0106] It can be seen that the implementation of this optional embodiment can obtain the music works input by the target user, analyze the music works, obtain the work information corresponding to the music works, input the work information into the pre-trained target model, generate optimization suggestions for the music works, and prompt the target user to optimize the music works according to the optimization suggestions. It can analyze the music works created by the user and give optimization suggestions, thereby improving the user's music creation ability and thus improving the user's music learning efficiency.

[0107] In another optional embodiment, the work information includes lyrics information corresponding to the lyrics creation work and / or melody information corresponding to the melody creation work, the lyrics information includes main idea information and lyrics vocabulary information, and the melody information includes tune information and note information;

[0108] Inputting the work information into the pre-trained target model to generate optimization suggestions for the music creation work may include the following operations:

[0109] When the music creation work includes lyrics creation work, the lyrics information is input into the target model to generate lyrics optimization suggestions for the lyrics creation work, the lyrics optimization suggestions including main idea optimization suggestions and lyrics vocabulary optimization suggestions;

[0110] When the musical composition includes a melody composition, the melody information is input into the target model to generate melody optimization suggestions for the melody composition, and the melody optimization suggestions include tune optimization suggestions and note optimization suggestions.

[0111] In this optional embodiment, optionally, the work information may include lyrics information corresponding to the lyrics creation work and / or melody information corresponding to the melody creation work, the lyrics information may include main idea information and lyrics vocabulary information, the melody information may include melody information and note information, when the music creation work includes a lyrics creation work, the lyrics optimization suggestions may include main idea optimization suggestions and lyrics vocabulary optimization suggestions, when the music creation work includes a melody creation work, the melody optimization suggestions may include melody optimization suggestions and note optimization suggestions, and this embodiment does not limit this.

[0112] It can be seen that the implementation of this optional embodiment can, when the music creation work includes lyrics creation works, input the lyrics information into the target model to generate lyrics optimization suggestions for the lyrics creation works, the lyrics optimization suggestions include main idea optimization suggestions and lyrics vocabulary optimization suggestions; when the music creation work includes melody creation works, input the melody information into the target model to generate melody optimization suggestions for the melody creation works, the melody optimization suggestions include tune optimization suggestions and note optimization suggestions, and different targeted optimization suggestions can be given according to the user's lyrics creation works and melody creation works, thereby improving the user's creative ability in different aspects, and thereby improving the user's music learning efficiency.

[0113] In yet another optional embodiment, the lyrics information further includes lyrics structure information, and the intelligent generation method of the music learning strategy may further include the following operations:

[0114] When the musical composition includes lyrics composition and melody composition, the lyrics composition is divided into at least one lyrics group matching the melody information according to the melody information and the lyrics structure information, each lyrics group including at least one lyrics unit;

[0115] Determine the correspondence between each lyric unit and the note according to the note information, each lyric unit corresponds to at least one note;

[0116] synthesizing the lyrics creation work and the melody creation work into a musical work according to the corresponding relationship, and determining a musical work score of the musical work;

[0117] Optimize and update the target music learning strategy based on the music work ratings.

[0118] In this optional embodiment, optionally, when the musical composition includes a lyric composition and a melody composition, the lyric composition can be split into at least one lyric group matching the melody information according to the melody information and the lyric structure information, wherein the melody information can include melody segmentation information, each lyric group includes at least one lyric unit, a single lyric unit can include a single word, or a single character, the correspondence between each lyric unit and the note is determined according to the note information, and each lyric unit can correspond to at least one note, which is not limited in this embodiment.

[0119] In this optional embodiment, optionally, the lyrics creation works and the melody creation works can be synthesized into a music work according to the corresponding relationship, and the music work score of the music work can be determined through the target model, and the target music learning strategy can be optimized and updated according to the music work score to enhance the music creation ability of the target user, which is not limited in this embodiment.

[0120] It can be seen that the implementation of this optional embodiment can, when the music creation work includes a lyric creation work and a melody creation work, split the lyric creation work into at least one lyric group matching the melody information according to the melody information and the lyric structure information, determine the correspondence between each lyric unit and the note according to the note information, synthesize the lyric creation work and the melody creation work into a music work according to the correspondence, determine the music work score of the music work, optimize and update the target music learning strategy according to the music work score, integrate the lyrics and melody created by the user into a complete song, and score the song to optimize the music learning strategy for the user, improve the user's lyric and melody creation ability, reduce the difficulty of music learning, and thereby improve the user's music learning efficiency and enhance the user experience.

[0121] Embodiment 2

[0122] See also Figure 2 , Figure 2 is a flow chart of an intelligent generation method of a music learning strategy disclosed in an embodiment of the present invention. Figure 2 The described intelligent generation method of music learning strategy can be applied to an intelligent generation device of music learning strategy, wherein the intelligent generation device of music learning strategy may include an intelligent device for users to learn music, and the intelligent device may include any one of a display screen, a button, an intelligent server or an intelligent platform for generating music learning strategy, and the intelligent server includes a local server or a cloud server. The intelligent device can be connected to an external music device, which is not limited in the embodiments of the present invention. Figure 2 As shown, the intelligent generation method of the music learning strategy may include the following operations:

[0123] 201. Obtain user information of a target user, determine at least one music tag corresponding to the target user based on the user information, and screen a preset number of music knowledge points matching each music tag in a pre-established music test database.

[0124] In an embodiment of the present invention, optionally, the user information of the target user may include at least one of age information, music type interest information, and music learning time information, and the at least one music tag corresponding to the target user may include at least one of an age tag, a music type interest tag, and a music learning time tag, and a pre-established music test database may include a set of music knowledge points corresponding to each music tag, and each music knowledge point set includes at least one music knowledge point. A preset number of music knowledge points matching each music tag is screened in the pre-established music test database, and the preset number may be set by backend personnel or determined based on the user information of the target user, and the present invention is not limited thereto.

[0125] 202. Generate music ability test questions for target users based on each music knowledge point.

[0126] In the embodiment of the present invention, optionally, the music ability test questions may include at least one of lyrics test questions, melody test questions and music theory test questions, which is not limited by the present invention.

[0127] 203. Perform a music ability test on the target user according to the music ability test questions to obtain the music ability parameters of the target user.

[0128] In the embodiment of the present invention, optionally, the music ability parameter includes at least one of a lyrics ability parameter, a melody ability parameter and a music theory ability parameter, which is not limited in the present invention.

[0129] 204. Generate a music learning strategy for the target user according to the user information and the music ability parameter, and guide the target user to perform music learning according to the music learning strategy, wherein the music learning strategy includes at least one music learning node.

[0130] 205. Obtain the node learning result of the target user at each music learning node, and determine the music learning score of the target user according to each node learning result.

[0131] 206. Optimize and update the music learning strategy according to the music learning score to obtain a target music learning strategy corresponding to the target user.

[0132] In the embodiment of the present invention, for other descriptions of step 204 to step 206, please refer to the detailed description of step 102 to step 104 in the first embodiment, and the embodiment of the present invention will not be repeated here.

[0133] It can be seen that implementation Figure 2The described intelligent generation method of the music learning strategy can obtain user information of the target user, determine at least one music tag corresponding to the target user based on the user information, and screen a preset number of music knowledge points matching each music tag in a pre-established music test database, generate a music ability test question for the target user based on each music knowledge point, conduct a music ability test on the target user based on the music ability test question, and obtain the music ability parameter of the target user, and can screen the music knowledge points corresponding to the user in the database based on the user's music tag, and then test and obtain the user's music ability parameter, which can improve the matching degree and representation ability of the determined music ability parameter with the user's actual music ability. , thereby improving the accuracy, rationality and reliability of the determined music learning strategy, generating a music learning strategy for the target user according to the user information and music ability parameters, and guiding the target user to learn music according to the music learning strategy, obtaining the node learning results of the target user at each music learning node, and determining the music learning score of the target user according to the learning result of each node, optimizing and updating the music learning strategy according to the music learning score, and obtaining the target music learning strategy corresponding to the target user, which can formulate a corresponding music learning strategy for the user based on the user information, can reduce the user's music learning cost, reduce the difficulty of music learning, and thereby improve the user's music learning efficiency and enhance the user experience.

[0134] In an optional embodiment, the music learning node includes at least one of a lyrics learning node, a melody learning node, and a performance learning node, and the node learning result includes at least one of a lyrics learning result, a melody learning result, and a performance learning result;

[0135] According to the learning results of each node, determining the music learning score of the target user may include the following operations:

[0136] Input each node learning result into the pre-trained target model for analysis to obtain the node learning score corresponding to each node learning result;

[0137] Determine the node weight corresponding to each music learning node of the target user according to the music ability parameters of the target user;

[0138] The music learning score of the target user is calculated based on the node learning score corresponding to each node learning result and the node weight corresponding to each music learning node.

[0139] In this optional embodiment, optionally, the music learning node may include at least one of a lyrics learning node, a melody learning node, and a performance learning node, that is, the target user may perform lyrics learning, melody learning, and performance learning according to the music learning strategy. Taking lyrics learning as an example, lyrics learning may include one or more of lyrics appreciation, lyrics structure analysis, lyrics theme analysis, lyrics creation learning, etc. The node learning result may include at least one of a lyrics and music learning result, a melody learning result, and a performance learning result, which is not limited in this embodiment.

[0140] In this optional embodiment, optionally, a pre-trained target model can be used to analyze the node learning results of the target user to obtain a node learning score corresponding to each node learning result, and determine the node weight of the target user corresponding to each music learning node based on the music ability parameters of the target user. The node weight can represent the target user's music foundation for the music learning node. Taking the music learning node as a lyrics learning node as an example, the larger the node weight of the lyrics learning node, the weaker the target user's lyrics foundation, and the smaller the node weight of the lyrics learning node, the stronger the target user's lyrics foundation. The music learning score of the target user is calculated based on the node learning score corresponding to each node learning result and the node weight corresponding to each music learning node, which is not limited in this embodiment.

[0141] It can be seen that implementing this optional embodiment can input each node learning result into a pre-trained target model for analysis, obtain a node learning score corresponding to each node learning result, determine the node weight corresponding to each music learning node of the target user according to the music ability parameters of the target user, calculate the music learning score of the target user according to the node learning score corresponding to each node learning result and the node weight corresponding to each music learning node, and calculate the music learning score of the user according to the user's score and weight at each music learning node, which can reflect the user's music learning weaknesses at different nodes, and thus provide targeted music learning guidance to the user, thereby improving the user's music learning efficiency.

[0142] In another optional embodiment, optimizing and updating the music learning strategy according to the music learning score to obtain the target music learning strategy corresponding to the target user may include the following operations:

[0143] Determine the node score threshold of the target user at each music learning node according to the user information and music ability parameters of the target user;

[0144] For each music learning node, determine whether the node learning score corresponding to the music learning node is greater than the node score threshold of the music learning node;

[0145] For each music learning node, when the node learning score corresponding to the music learning node is less than the node score threshold of the music learning node, the node score difference between the node learning score corresponding to the music learning node and the node score threshold of the music learning node is calculated, and the node learning weakness information of the target user at the music learning node is determined according to the node score difference;

[0146] The music learning strategy is optimized and updated according to the node learning weakness information of each music learning node to obtain the target music learning strategy corresponding to the target user.

[0147] In this optional embodiment, optionally, based on the user information and music ability parameters of the target user, a node score threshold of the target user at each music learning node is determined. Taking the music learning node as a lyrics learning node as an example, based on the age group of the target user and the lyrics ability parameters, the node score threshold of the target user at the lyrics learning node is determined. The node score threshold may represent a qualified threshold of the target user at the lyrics learning node. Optionally, when the node learning score of the target user at the lyrics learning node exceeds the node score threshold, the target user may proceed to the learning of the next node. This embodiment does not limit this.

[0148] In this optional embodiment, optionally, for each music learning node, when the node learning score corresponding to the music learning node is less than the node score threshold of the music learning node, the node score difference between the node learning score corresponding to the music learning node and the node score threshold of the music learning node is calculated, and the node learning weakness information of the target user at the music learning node is determined based on the node score difference. The node learning weakness information can represent the node learning weakness of the target user at the music learning node. Specifically, taking the music learning node as a lyrics learning node as an example, the node learning weakness information of the lyrics learning node may include one or more of lyrics structure learning weaknesses, lyrics accumulation learning weaknesses, lyrics creation learning weaknesses, etc. The music learning strategy is optimized and updated based on the node learning weakness information of each music learning node to strengthen the target user's learning of the node learning weaknesses of each music learning node, and obtain the target music learning strategy corresponding to the target user. This embodiment is not limited.

[0149] It can be seen that the implementation of this optional embodiment can determine the node score threshold of the target user at each music learning node based on the user information and music ability parameters of the target user. For each music learning node, determine whether the node learning score corresponding to the music learning node is greater than the node score threshold of the music learning node. For each music learning node, when the node learning score corresponding to the music learning node is less than the node score threshold of the music learning node, calculate the node score difference between the node learning score corresponding to the music learning node and the node score threshold of the music learning node, and determine the node learning weakness information of the target user at the music learning node based on the node score difference. Optimize and update the music learning strategy based on the node learning weakness information of each music learning node to obtain the target music learning strategy corresponding to the target user. Based on the relationship between the user's score and the threshold at each music learning node, determine the node learning weakness information of the user at each music learning node, and then provide targeted music learning guidance to the user based on the node learning weakness information, thereby improving the user's music learning efficiency, reducing the music learning time, and enhancing the user's usage experience.

[0150] Embodiment 3

[0151] See also Figure 3 , Figure 3 : is a schematic diagram of the structure of an intelligent generation device for a music learning strategy disclosed in an embodiment of the present invention. Figure 3 The intelligent generation device of the music learning strategy described may include an intelligent device for users to learn music, and the intelligent device may include any one of a display screen, a button, an intelligent server or an intelligent platform for generating music learning strategies, and the intelligent server includes a local server or a cloud server. The intelligent device may be connected to an external music device, which is not limited in the embodiments of the present invention. Figure 3 As shown, the intelligent generation device of the music learning strategy may include:

[0152] The acquisition module 301 is used to acquire user information of a target user, and perform a music ability test on the target user according to the user information to obtain a music ability parameter of the target user;

[0153] A generating module 302, configured to generate a music learning strategy for a target user according to the user information and the music ability parameter, and guide the target user to perform music learning according to the music learning strategy, wherein the music learning strategy includes at least one music learning node;

[0154] The acquisition module 301 is further used to acquire the node learning result of the target user at each music learning node, and determine the music learning score of the target user according to each node learning result;

[0155] The optimization module 303 is used to optimize and update the music learning strategy according to the music learning score to obtain the target music learning strategy corresponding to the target user.

[0156] It can be seen that implementation Figure 3 The described intelligent generation device of music learning strategy can obtain user information of target users, and perform music ability test on target users according to the user information to obtain music ability parameters of target users, generate music learning strategy for target users according to user information and music ability parameters, and guide target users to perform music learning according to the music learning strategy, obtain node learning results of target users at each music learning node, and determine the music learning score of target users according to each node learning result, optimize and update the music learning strategy according to the music learning score, and obtain the target music learning strategy corresponding to the target user, and can formulate corresponding music learning strategy for users based on user information, which can reduce users' music learning cost and difficulty, thereby improving users' music learning efficiency and enhancing users' usage experience.

[0157] In an optional embodiment, if Figure 4 As shown, the user information includes the target user's age information, music type interest information, and music learning time information;

[0158] The acquisition module 301 performs a music ability test on the target user according to the user information, and the specific method of obtaining the music ability parameter of the target user includes:

[0159] Determine at least one music tag corresponding to the target user according to the user information, and select a preset number of music knowledge points matching each music tag in a pre-established music test database;

[0160] Generate music ability test questions for target users according to each music knowledge point, the music ability test questions including at least one of lyrics test questions, melody test questions and music theory test questions;

[0161] A music ability test is performed on the target user according to the music ability test questions to obtain the music ability parameters of the target user, wherein the music ability parameters include at least one of lyrics ability parameters, melody ability parameters and music theory ability parameters.

[0162] It can be seen that implementation Figure 4The intelligent generation device of the described music learning strategy can obtain user information of the target user, determine at least one music tag corresponding to the target user based on the user information, and screen a preset number of music knowledge points matching each music tag in a pre-established music test database, generate music ability test questions for the target user based on each music knowledge point, conduct a music ability test on the target user based on the music ability test questions, and obtain the music ability parameters of the target user, and can screen the music knowledge points corresponding to the user in the database based on the user's music tag, and then test and obtain the user's music ability parameters, which can improve the matching degree and representation ability of the determined music ability parameters with the user's actual music ability. , thereby improving the accuracy, rationality and reliability of the determined music learning strategy, generating a music learning strategy for the target user according to the user information and music ability parameters, and guiding the target user to learn music according to the music learning strategy, obtaining the node learning results of the target user at each music learning node, and determining the music learning score of the target user according to the learning result of each node, optimizing and updating the music learning strategy according to the music learning score, and obtaining the target music learning strategy corresponding to the target user, which can formulate a corresponding music learning strategy for the user based on the user information, can reduce the user's music learning cost, reduce the difficulty of music learning, and thereby improve the user's music learning efficiency and enhance the user experience.

[0163] In another optional embodiment, Figure 4 As shown, the music learning node includes at least one of a lyrics learning node, a melody learning node, and a performance learning node, and the node learning result includes at least one of a lyrics learning result, a melody learning result, and a performance learning result;

[0164] The specific method of obtaining module 301 to determine the music learning score of the target user according to the learning result of each node includes:

[0165] Input each node learning result into the pre-trained target model for analysis to obtain the node learning score corresponding to each node learning result;

[0166] Determine the node weight corresponding to each music learning node of the target user according to the music ability parameters of the target user;

[0167] The music learning score of the target user is calculated based on the node learning score corresponding to each node learning result and the node weight corresponding to each music learning node.

[0168] It can be seen that implementation Figure 4The intelligent generation device of the described music learning strategy can input each node learning result into a pre-trained target model for analysis, and obtain the node learning score corresponding to each node learning result, and determine the node weight corresponding to each music learning node of the target user according to the music ability parameters of the target user, and calculate the music learning score of the target user according to the node learning score corresponding to each node learning result and the node weight corresponding to each music learning node. The music learning score of the user can be calculated according to the score and weight of the user at each music learning node, which can reflect the user's music learning weaknesses at different nodes, and then provide targeted music learning guidance to the user, thereby improving the user's music learning efficiency.

[0169] In yet another optional embodiment, Figure 4 As shown, the optimization module 303 optimizes and updates the music learning strategy according to the music learning score, and the specific method of obtaining the target music learning strategy corresponding to the target user includes:

[0170] Determine the node score threshold of the target user at each music learning node according to the user information and music ability parameters of the target user;

[0171] For each music learning node, determine whether the node learning score corresponding to the music learning node is greater than the node score threshold of the music learning node;

[0172] For each music learning node, when the node learning score corresponding to the music learning node is less than the node score threshold of the music learning node, the node score difference between the node learning score corresponding to the music learning node and the node score threshold of the music learning node is calculated, and the node learning weakness information of the target user at the music learning node is determined according to the node score difference;

[0173] The music learning strategy is optimized and updated according to the node learning weakness information of each music learning node to obtain the target music learning strategy corresponding to the target user.

[0174] It can be seen that implementation Figure 4The described intelligent generation device of the music learning strategy can determine the node score threshold of the target user at each music learning node based on the user information and music ability parameters of the target user. For each music learning node, determine whether the node learning score corresponding to the music learning node is greater than the node score threshold of the music learning node. For each music learning node, when the node learning score corresponding to the music learning node is less than the node score threshold of the music learning node, calculate the node score difference between the node learning score corresponding to the music learning node and the node score threshold of the music learning node, and determine the node learning weakness information of the target user at the music learning node based on the node score difference. According to the node learning weakness information of each music learning node, the music learning strategy is optimized and updated to obtain the target music learning strategy corresponding to the target user. According to the relationship between the user's score and the threshold at each music learning node, the node learning weakness information of the user at each music learning node can be determined, and then targeted music learning guidance can be provided to the user based on the node learning weakness information, thereby improving the user's music learning efficiency, reducing the music learning time, and enhancing the user's usage experience.

[0175] In yet another optional embodiment, Figure 4 As shown, the acquisition module 301 is also used to acquire the music creation works input by the target user in the process of guiding the target user to learn music according to the music learning strategy, and the music creation works include lyrics creation works and / or melody creation works;

[0176] The intelligent generation device of the music learning strategy may also include:

[0177] The analysis module 304 is used to analyze the music creation work and obtain the work information corresponding to the music creation work;

[0178] The generation module 302 is also used to input the work information into a pre-trained target model, generate optimization suggestions for the music creation work, and prompt the target user to optimize the music creation work according to the optimization suggestions.

[0179] It can be seen that implementation Figure 4 The described intelligent generation device of the music learning strategy can obtain the music works input by the target user, analyze the music works, obtain the work information corresponding to the music works, input the work information into the pre-trained target model, generate optimization suggestions for the music works, and prompt the target user to optimize the music works according to the optimization suggestions. It can analyze the music works created by the user and give optimization suggestions to improve the user's music creation ability, thereby improving the user's music learning efficiency.

[0180] In yet another optional embodiment, Figure 4 As shown, the work information includes lyrics information corresponding to the lyrics creation work and / or melody information corresponding to the melody creation work, the lyrics information includes main idea information and lyrics vocabulary information, and the melody information includes tune information and note information;

[0181] The generation module 302 inputs the work information into the pre-trained target model, and generates optimization suggestions for the music creation work in the following specific ways:

[0182] When the music creation work includes lyrics creation work, the lyrics information is input into the target model to generate lyrics optimization suggestions for the lyrics creation work, the lyrics optimization suggestions including main idea optimization suggestions and lyrics vocabulary optimization suggestions;

[0183] When the musical composition includes a melody composition, the melody information is input into the target model to generate melody optimization suggestions for the melody composition, and the melody optimization suggestions include tune optimization suggestions and note optimization suggestions.

[0184] It can be seen that implementation Figure 4 The described intelligent generation device of the music learning strategy can, when the music creation work includes lyrics creation works, input the lyrics information into the target model, and generate lyrics optimization suggestions for the lyrics creation works, the lyrics optimization suggestions include main idea optimization suggestions and lyrics vocabulary optimization suggestions; when the music creation work includes melody creation works, input the melody information into the target model, and generate melody optimization suggestions for the melody creation works, the melody optimization suggestions include tune optimization suggestions and note optimization suggestions; different targeted optimization suggestions can be given according to the user's lyrics creation works and melody creation works, thereby improving the user's creative ability in different aspects, and thus improving the user's music learning efficiency.

[0185] In yet another optional embodiment, Figure 4 As shown, the lyrics information also includes lyrics structure information, and the intelligent generation device of the music learning strategy may also include:

[0186] A splitting module 305 is used for splitting the lyrics creation work into at least one lyrics group matching the melody information according to the melody information and the lyrics structure information when the music creation work includes lyrics creation work and melody creation work, each lyrics group including at least one lyrics unit;

[0187] A synthesis module 306 is used to synthesize the lyrics creation work and the melody creation work into a music work according to the corresponding relationship, and determine a music work score of the music work;

[0188] The optimization module 303 is also used to optimize and update the target music learning strategy according to the music work scores.

[0189] It can be seen that implementation Figure 4 The described intelligent generation device of the music learning strategy can, when the music creation work includes lyrics creation work and melody creation work, split the lyrics creation work into at least one lyrics group matching the melody information according to the melody information and the lyrics structure information, determine the correspondence between each lyrics unit and the note according to the note information, synthesize the lyrics creation work and the melody creation work into a music work according to the correspondence, and determine the music work score of the music work, optimize and update the target music learning strategy according to the music work score, integrate the lyrics and melody created by the user into a complete song, and score the song to optimize the music learning strategy for the user, improve the user's lyrics and melody creation ability, reduce the difficulty of music learning, and thereby improve the user's music learning efficiency and enhance the user experience.

[0190] Embodiment 4

[0191] See also Figure 5 , Figure 5 FIG. 1 is a schematic diagram of the structure of another intelligent generation device for music learning strategy disclosed in an embodiment of the present invention. Figure 5 As shown, the intelligent generation device of the music learning strategy may include:

[0192] A memory 401 storing executable program codes;

[0193] a processor 402 coupled to the memory 401;

[0194] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the intelligent generation method of the music learning strategy described in the first embodiment of the present invention or the second embodiment of the present invention.

[0195] Embodiment 5

[0196] An embodiment of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the steps in the intelligent generation method of music learning strategies described in Embodiment 1 or Embodiment 2 of the present invention.

[0197] Embodiment 6

[0198] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the intelligent generation method of music learning strategies described in Example 1 or Example 2.

[0199] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0200] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0201] Finally, it should be noted that the intelligent generation method and device of music learning strategies disclosed in the embodiments of the present invention only disclose the preferred embodiments of the present invention, which are only used to illustrate the technical scheme of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical schemes described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical schemes from the spirit and scope of the technical schemes of the embodiments of the present invention.

Claims

1. A method for intelligently generating music learning strategies, characterized in that: The method comprises: Acquire user information of a target user, and perform a music ability test on the target user according to the user information to obtain a music ability parameter of the target user; Generate a music learning strategy for the target user according to the user information and the music ability parameter, and guide the target user to perform music learning according to the music learning strategy, wherein the music learning strategy includes at least one music learning node; Obtaining the node learning result of the target user at each of the music learning nodes, and determining the music learning score of the target user according to each of the node learning results; The music learning strategy is optimized and updated according to the music learning score to obtain a target music learning strategy corresponding to the target user.

2. The intelligent generation method of music learning strategy according to claim 1 is characterized in that: The user information includes the target user's age information, music type interest information, and music learning time information; The performing a music ability test on the target user according to the user information to obtain the music ability parameter of the target user includes: Determine at least one music tag corresponding to the target user according to the user information, and select a preset number of music knowledge points matching each of the music tags in a pre-established music test database; Generating a music ability test question for the target user according to each of the music knowledge points, wherein the music ability test question includes at least one of a lyrics test question, a melody test question, and a music theory test question; The target user is tested for music ability according to the music ability test questions to obtain music ability parameters of the target user, wherein the music ability parameters include at least one of lyrics ability parameters, melody ability parameters and music theory ability parameters.

3. The intelligent generation method of music learning strategy according to claim 2 is characterized in that: The music learning node includes at least one of a lyrics learning node, a melody learning node, and a performance learning node, and the node learning result includes at least one of a lyrics learning result, a melody learning result, and a performance learning result; Determining the music learning score of the target user according to the learning result of each node includes: Input each of the node learning results into a pre-trained target model for analysis to obtain a node learning score corresponding to each of the node learning results; Determine, according to the music ability parameter of the target user, a node weight corresponding to each of the music learning nodes of the target user; The music learning score of the target user is calculated according to the node learning score corresponding to each of the node learning results and the node weight corresponding to each of the music learning nodes.

4. The intelligent generation method of music learning strategy according to claim 3 is characterized in that: The step of optimizing and updating the music learning strategy according to the music learning score to obtain a target music learning strategy corresponding to the target user includes: Determining a node score threshold of the target user at each music learning node according to the user information and the music ability parameter of the target user; For each of the music learning nodes, determining whether a node learning score corresponding to the music learning node is greater than a node score threshold of the music learning node; For each of the music learning nodes, when the node learning score corresponding to the music learning node is less than the node score threshold of the music learning node, a node score difference between the node learning score corresponding to the music learning node and the node score threshold of the music learning node is calculated, and the node learning weakness information of the target user at the music learning node is determined according to the node score difference; The music learning strategy is optimized and updated according to the node learning weakness information of each of the music learning nodes to obtain the target music learning strategy corresponding to the target user.

5. The intelligent generation method of music learning strategy according to any one of claims 1 to 4, characterized in that: The method further comprises: In the process of guiding the target user to learn music according to the music learning strategy, obtaining a music composition input by the target user, wherein the music composition includes a lyrics composition and / or a melody composition; Analyze the musical creation to obtain work information corresponding to the musical creation; The work information is input into a pre-trained target model to generate optimization suggestions for the music creation work, and the target user is prompted to optimize the music creation work according to the optimization suggestions.

6. The intelligent generation method of music learning strategy according to claim 5 is characterized in that: The work information includes lyrics information corresponding to the lyrics creation work and / or melody information corresponding to the melody creation work, the lyrics information includes main idea information and lyrics vocabulary information, and the melody information includes tune information and note information; The step of inputting the work information into a pre-trained target model to generate optimization suggestions for the music creation work includes: When the music composition includes the lyrics composition, the lyrics information is input into the target model to generate lyrics optimization suggestions for the lyrics composition, wherein the lyrics optimization suggestions include main idea optimization suggestions and lyrics vocabulary optimization suggestions; When the musical composition includes the melody composition, the melody information is input into the target model to generate a melody optimization suggestion for the melody composition, wherein the melody optimization suggestion includes a tune optimization suggestion and a note optimization suggestion.

7. The intelligent generation method of music learning strategy according to claim 6 is characterized in that: The lyrics information also includes lyrics structure information, and the method further includes: When the musical composition includes the lyrics composition and the melody composition, the lyrics composition is divided into at least one lyrics group matching the melody information according to the melody information and the lyrics structure information, each of the lyrics group includes at least one lyrics unit; Determine the correspondence between each of the lyrics units and the notes according to the note information, each of the lyrics units corresponds to at least one of the notes; synthesizing the lyrics creation work and the melody creation work into a musical work according to the corresponding relationship, and determining a musical work score of the musical work; The target music learning strategy is optimized and updated according to the score of the music work.

8. An intelligent generation device for music learning strategy, characterized in that: The device comprises: An acquisition module, used to acquire user information of a target user, and perform a music ability test on the target user according to the user information to obtain a music ability parameter of the target user; A generation module, configured to generate a music learning strategy for the target user according to the user information and the music ability parameter, and guide the target user to perform music learning according to the music learning strategy, wherein the music learning strategy includes at least one music learning node; The acquisition module is further used to acquire the node learning result of the target user at each of the music learning nodes, and determine the music learning score of the target user according to each of the node learning results; An optimization module is used to optimize and update the music learning strategy according to the music learning score to obtain a target music learning strategy corresponding to the target user.

9. An intelligent generation device for music learning strategy, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the intelligent generation method of music learning strategies as described in any one of claims 1-7.

10. A computer storage medium, characterized in that: The computer storage medium stores computer instructions, which, when called, are used to execute the intelligent generation method of music learning strategies as described in any one of claims 1-7.

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