Music education method and system based on artificial intelligence

Abstract

The invention discloses a music education method and system based on artificial intelligence, and relates to the technical field of music education, and the music education method based on artificial intelligence comprises the following steps: obtaining music learning data of a user; based on a preset classification mode, performing cloud service matching on the music learning data of the user to obtain a plurality of original storage positions; artificial intelligence is applied to the field of music education, learning data of a user is analyzed through the deep learning model, making of a learning scheme and resource recommendation are achieved, and the learning efficiency and effect are improved. According to the method, historical music learning data is utilized to construct a deep learning model for feature analysis and pattern recognition, so that the learning demand and the learning scheme of a user are predicted, and more accurate learning guidance is provided for the user.

Description

Technical Field

[0001] The present invention relates to the technical field of music education, and in particular to an artificial intelligence-based music education method and system. Background Art

[0002] Music education is the process of cultivating musical literacy, aesthetic appreciation, and creativity through the acquisition of musical knowledge, skills, and appreciation. It goes beyond the study of musical techniques and focuses on cultivating musical perception, understanding, and expression, as well as the inheritance and development of musical culture.

[0003] Existing music learning resources are often limited to traditional formats like textbooks and videos. These resources lack interactivity and interest, making it difficult to stimulate students' learning interest. Furthermore, educational methods often lack targeted recommendations and fail to tailor instruction to students' learning foundations, learning styles, and learning goals, resulting in poor learning outcomes. Therefore, we propose an AI-based music education method and system to address these issues. Summary of the Invention

[0004] The purpose of the present invention is to provide a music education method and system based on artificial intelligence to solve the problems raised by the above background technology, such as the single learning resources in the current market and the lack of guidance in the learning process.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A music education method based on artificial intelligence, comprising the following steps:

[0007] Obtain user music learning data;

[0008] Based on a preset classification method, the user's music learning data is matched with a cloud service to obtain multiple original storage locations;

[0009] Based on the data storage order list, the layout of the plurality of original storage locations is adjusted to obtain data storage layout information, and the user music learning data is stored based on the data storage layout information;

[0010] The data storage process is monitored, the data storage sequence list is adjusted according to the storage effect and resource utilization, and the user music learning data is re-stored according to the adjusted data storage sequence list.

[0011] As a further optimization solution of the present invention, the step of obtaining the user's music learning data includes:

[0012] Obtain the user's basic information, music learning records, and music learning behavior data;

[0013] The method for obtaining the data storage order list includes:

[0014] Acquire historical music learning data and perform feature extraction on the historical music learning data to obtain music learning feature data; the music learning feature data is used to characterize the music learning pattern and learning needs corresponding to the historical music learning data;

[0015] A target cloud resource allocation plan is established based on the music learning feature data features, and the data storage sequence list is generated according to the target cloud resource allocation plan.

[0016] As a further optimization solution of the present invention, establishing a target cloud resource allocation plan based on the music learning data characteristics and generating the data storage sequence list according to the target cloud resource allocation plan include:

[0017] Performing music learning pattern recognition on the music learning feature data to obtain a target music learning pattern;

[0018] Predicting music learning needs for the target music learning model to obtain music learning need data volume;

[0019] Classifying and identifying the amount of music learning demand data to obtain music learning data with different learning needs;

[0020] According to the music learning data with different learning needs, cloud service nodes are configured in the cloud computing center to obtain multiple cloud service nodes;

[0021] Establishing a mapping relationship between the music learning data with different learning needs and the corresponding cloud computing center according to the multiple cloud service nodes;

[0022] Creating a cloud resource allocation plan between the music learning data with different learning needs and the cloud computing center according to the mapping relationship;

[0023] Performing plan fusion on the cloud resource allocation plan to obtain a corresponding target cloud resource allocation plan;

[0024] A data storage sequence list is generated according to the target cloud resource allocation plan.

[0025] As a further optimization solution of the present invention, the storage process of the user's music learning data further includes:

[0026] Calculating the user's music learning data using a hash algorithm to obtain a first hash value of the user's music learning data, and storing the first hash value on a blockchain;

[0027] When the user music learning data is updated, the second hash value of the user music learning data is recalculated, and the second hash value is synchronously updated to the blockchain.

[0028] An artificial intelligence-based music education system, comprising: a terminal, a cloud server, a cloud service node, and a blockchain network;

[0029] Among them, the terminal is used to obtain the user's music learning data and send it to the cloud server;

[0030] The cloud server is used to receive users' music learning data and perform feature extraction, pattern recognition, demand forecasting, resource allocation, learning plan formulation, data storage and monitoring functions;

[0031] Cloud service nodes are used to store and calculate users’ music learning data;

[0032] The blockchain network is used to store the hash value of the user's music learning data.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] This invention applies artificial intelligence to music education. By analyzing user learning data through deep learning models, it enables the development of learning plans and resource recommendations, improving learning efficiency and effectiveness. By leveraging historical music learning data, a deep learning model is constructed to perform feature analysis and pattern recognition, predicting users' learning needs and plans, and providing more accurate learning guidance.

[0035] This invention dynamically adjusts learning plans and resource allocation based on the user's learning progress and results, adapting to changes in learning and providing a more flexible learning experience. It also uses blockchain technology to store and encrypt user music learning data, protecting user privacy. Based on the user's learning needs and music learning patterns, learning data is categorized and accurately matched to corresponding cloud service nodes, improving resource utilization efficiency and learning outcomes.

[0036] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A flowchart of the music education system based on artificial intelligence of the present invention;

[0038] Figure 2 This is a flowchart of the music education method based on artificial intelligence of the present invention. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0040] Example 1

[0041] See also Figure 1 , an artificial intelligence-based music education method, including:

[0042] Obtain user music learning data;

[0043] Based on the preset classification method, the user's music learning data is matched with cloud services to obtain multiple original storage locations;

[0044] Based on the data storage sequence list, a plurality of original storage locations are arranged to obtain data storage layout information, and the user's music learning data is stored based on the data storage layout information;

[0045] Monitor the data storage process, adjust the data storage sequence list according to the storage effect and resource utilization, and re-store the user's music learning data based on the adjusted data storage sequence list.

[0046] Obtain user music learning data, including:

[0047] Obtain the user's basic information, music learning records, and music learning behavior data;

[0048] Monitor the data storage process, including:

[0049] Based on the preset monitoring mechanism, obtain the cloud resource utilization parameters during the user's music learning data storage process;

[0050] When the cloud resource utilization parameters meet the preset conditions, the data storage order list is adjusted; the preset conditions include at least one of the following: data reading response time is greater than the time threshold, data is lost, data errors occur, and storage capacity is less than the capacity threshold.

[0051] The data storage order list is obtained by the following methods, including:

[0052] Acquire historical music learning data and perform feature extraction on the historical music learning data to obtain music learning feature data; the music learning feature data is used to characterize the music learning pattern and learning needs corresponding to the historical music learning data;

[0053] A target cloud resource allocation plan is established through music learning feature data characteristics, and a data storage order list is generated according to the target cloud resource allocation plan.

[0054] Among them, basic information includes name, age, gender, music foundation, educational background, learning goals, learning time, and learning style;

[0055] Music learning records include practice time, practice repertoire, practice progress, practice quality, practice records, practice notes, and practice goals;

[0056] Distribution of practice time, choice of learning methods, type of learning content, use of learning tools, use of learning platforms, learning communication partners, learning interaction participation, and acceptance of learning feedback;

[0057] Establish a target cloud resource allocation plan based on the characteristics of music learning data, and generate a data storage order list based on the target cloud resource allocation plan, including:

[0058] Performing music learning pattern recognition on the music learning feature data to obtain a target music learning pattern;

[0059] Predicting music learning needs for the target music learning model to obtain the amount of music learning demand data;

[0060] Classify and identify the amount of music learning demand data to obtain music learning data with different learning needs;

[0061] According to the music learning data with different learning needs, cloud service nodes are configured in the cloud computing center to obtain multiple cloud service nodes;

[0062] Based on multiple cloud service nodes, a mapping relationship between music learning data with different learning needs and cloud computing centers is established;

[0063] Create a cloud resource allocation plan between music learning data with different learning needs and the cloud computing center based on the mapping relationship;

[0064] Perform plan integration on the cloud resource allocation plan to obtain the corresponding target cloud resource allocation plan;

[0065] Generate a data storage order list based on the target cloud resource allocation plan.

[0066] Adjust the data storage order list, including:

[0067] Re-identify the music learning pattern of the music learning feature data and update the target music learning pattern;

[0068] Re-predict the music learning needs of the target music learning model and update the amount of music learning needs data;

[0069] Reclassify and identify the amount of music learning demand data, and update music learning data for different learning needs;

[0070] Reconfigure cloud service nodes based on the reclassification results;

[0071] A new mapping relationship and a new target cloud resource allocation plan are established, and a new data storage sequence list is generated according to the new target cloud resource allocation plan.

[0072] The storage process of the user's music learning data also includes:

[0073] A hash algorithm is used to calculate the user's music learning data to obtain a first hash value of the user's music learning data, and the first hash value is stored on the blockchain;

[0074] When the user's music learning data is updated, the second hash value of the user's music learning data is recalculated and the second hash value is synchronously updated to the blockchain.

[0075] Acquire historical music learning data and perform feature extraction on the historical music learning data to obtain music learning feature data, including:

[0076] Acquire historical music learning data, classify the historical music learning data, and obtain music learning patterns, learning repertoires, and learning progress information;

[0077] Vector encoding of music learning mode, learning repertoire, and learning progress information to obtain music learning feature vectors;

[0078] The music learning feature vector is input into a pre-set deep learning model for feature analysis to obtain music learning feature data.

[0079] Example 2

[0080] See also Figure 2 , an artificial intelligence-based music education system, comprising:

[0081] The terminal is used to obtain the user's music learning data and send it to the cloud server;

[0082] Cloud server, used to receive users' music learning data and perform feature extraction, pattern recognition, demand forecasting, resource allocation, learning plan formulation, data storage and monitoring;

[0083] Cloud service node, used to store and calculate users' music learning data;

[0084] The blockchain network is used to store the hash value of the user's music learning data to ensure the security and transparency of the data.

[0085] The cloud server includes: data acquisition module, feature extraction module, pattern recognition module, demand forecasting module, resource allocation module, learning plan formulation module, data storage module, monitoring module, adjustment module, and blockchain module;

[0086] The data acquisition module is used to receive the user's music learning data sent by the terminal;

[0087] The feature extraction module is used to extract features from the user's music learning data;

[0088] The pattern recognition module is used to identify the user's music learning pattern;

[0089] The demand prediction module is used to predict users' learning needs;

[0090] The resource allocation module is used to allocate learning data to different cloud service nodes according to the user's learning needs and music learning mode;

[0091] The learning plan formulation module is used to formulate personalized learning plans based on the user's learning needs and music learning mode;

[0092] Data storage module, used to store user music learning data to cloud service node;

[0093] Monitoring module, used to monitor data storage process and resource utilization;

[0094] An adjustment module, used to adjust the data storage sequence list and learning plan according to the monitoring results;

[0095] The blockchain module is used to store the hash value of the user's music learning data in the blockchain network.

[0096] Cloud service nodes can be servers or storage devices in different geographical locations, which can provide different types of cloud services.

[0097] Blockchain networks are either public or private.

[0098] The system may also include an artificial intelligence assistant module for interacting with users and providing personalized learning guidance and suggestions.

[0099] The terminal can be various devices, such as computers, mobile phones, and tablets, which are used to obtain users' music learning data, including basic information, learning records, and learning behaviors.

[0100] Cloud server: Receives user data sent by the terminal and performs feature extraction, pattern recognition, demand forecasting, resource allocation, learning plan formulation, data storage and monitoring.

[0101] Cloud service nodes: store and calculate users' music learning data, which can be servers or storage devices in different geographical locations.

[0102] Blockchain network: stores the hash value of the user's music learning data, which can be a public chain or a private chain.

[0103] The specific implementation steps are:

[0104] The terminal collects user music learning data, including:

[0105] Basic information: name, age, gender, musical background, educational background, learning goals, learning time, and learning style.

[0106] Music learning records: practice time, practice repertoire, practice progress, practice quality, practice records, practice notes, and practice goals.

[0107] Music learning behavior data: practice time distribution, learning method selection, learning content type, learning tool usage, learning platform usage, learning communication objects, learning interaction participation, and learning feedback acceptance.

[0108] For data storage and cloud service matching: Based on a pre-defined classification method, user data is matched to cloud services to obtain multiple original storage locations. Feature extraction is performed using historical music learning data to establish a target cloud resource allocation plan and generate a data storage sequence list. Based on the data storage sequence list, the original storage locations are reconfigured to obtain data storage layout information. Based on this data storage layout information, user data is stored in the corresponding cloud service node.

[0109] Based on a pre-set monitoring mechanism, cloud resource utilization parameters are obtained. The data storage order list is adjusted based on storage performance and resource utilization. The user's music learning data is then restored based on the adjusted data storage order list. A hash algorithm is used to calculate the user's data, generating a hash value that is stored on the blockchain. Whenever the user's data is updated, the hash value is recalculated and synchronized to the blockchain. A learning plan is developed based on the user's learning needs and music learning patterns. The AI ​​assistant interacts with the user, providing learning guidance and suggestions.

[0110] Example 3

[0111] Users use a mobile app to record their practice time and repertoire. The cloud server analyzes the user's learning patterns and needs based on these learning records and behavior data, and develops a learning plan. The cloud server stores user data across multiple cloud service nodes and adjusts storage locations based on monitoring results to ensure data security and storage efficiency. The hash value of user data is stored on the blockchain, ensuring that the data cannot be tampered with and is traceable.

[0112] Example 4

[0113] Users learn music online via their computers, and the cloud server analyzes their learning behavior and history to recommend appropriate learning content and practice tracks. The cloud server stores user data on distributed cloud service nodes and dynamically adjusts storage locations based on user data volume and access frequency, improving resource utilization. User data hash values ​​are stored on a public blockchain, ensuring data transparency and security.

[0114] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0115] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0116] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0117] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A music education method based on artificial intelligence, characterized in that: The following steps are involved: Obtain user music learning data; Based on a preset classification method, the user's music learning data is matched with a cloud service to obtain multiple original storage locations; Based on the data storage order list, the layout of the plurality of original storage locations is adjusted to obtain data storage layout information, and the user music learning data is stored based on the data storage layout information; The data storage process is monitored, the data storage sequence list is adjusted according to the storage effect and resource utilization, and the user music learning data is re-stored according to the adjusted data storage sequence list.

2. The music education method based on artificial intelligence according to claim 1, characterized in that The obtaining of user music learning data includes: Obtain the user's basic information, music learning records, and music learning behavior data; The method for obtaining the data storage order list includes: Acquire historical music learning data and perform feature extraction on the historical music learning data to obtain music learning feature data; the music learning feature data is used to characterize the music learning pattern and learning needs corresponding to the historical music learning data; A target cloud resource allocation plan is established based on the music learning feature data features, and the data storage sequence list is generated according to the target cloud resource allocation plan.

3. The music education method based on artificial intelligence according to claim 2, characterized in that The step of establishing a target cloud resource allocation plan based on the music learning data features and generating the data storage sequence list according to the target cloud resource allocation plan includes: Performing music learning pattern recognition on the music learning feature data to obtain a target music learning pattern; Predicting music learning needs for the target music learning model to obtain music learning need data volume; Classifying and identifying the amount of music learning demand data to obtain music learning data with different learning needs; According to the music learning data with different learning needs, cloud service nodes are configured in the cloud computing center to obtain multiple cloud service nodes; Establishing a mapping relationship between the music learning data with different learning needs and the corresponding cloud computing center according to the multiple cloud service nodes; Creating a cloud resource allocation plan between the music learning data with different learning needs and the cloud computing center according to the mapping relationship; Performing plan fusion on the cloud resource allocation plan to obtain a corresponding target cloud resource allocation plan; A data storage sequence list is generated according to the target cloud resource allocation plan.

4. The music education method based on artificial intelligence according to claim 1, characterized in that The storage process of the user's music learning data also includes: Calculating the user's music learning data using a hash algorithm to obtain a first hash value of the user's music learning data, and storing the first hash value on a blockchain; When the user music learning data is updated, the second hash value of the user music learning data is recalculated, and the second hash value is synchronously updated to the blockchain.

5. A music education system based on artificial intelligence, characterized in that: include: Terminals, cloud servers, cloud service nodes, blockchain networks; Among them, the terminal is used to obtain the user's music learning data and send it to the cloud server; The cloud server is used to receive users' music learning data and perform feature extraction, pattern recognition, demand forecasting, resource allocation, learning plan formulation, data storage and monitoring functions; Cloud service nodes are used to store and calculate users’ music learning data; The blockchain network is used to store the hash value of the user's music learning data.

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