Calligraphy practice method and device based on augmented reality technology, equipment and medium

Abstract

The invention relates to a calligraphy practice method, device and equipment based on an augmented reality technology and a medium, and the calligraphy practice method based on the augmented reality technology comprises the steps: based on an optical character recognition library, extracting a corresponding practice text in paper character image information, and based on a natural language processing library, extracting keyword features of the practice text; based on an augmented reality library, determining a dynamic writing effect according to the keyword features, and creating a dynamic writing model according to the dynamic writing effect; and determining an associated memory library, and matching a corresponding associated memory fragment according to the dynamic writing model. And creating a dynamic writing effect according to the characteristics by utilizing an augmented reality library, wherein the dynamic writing effect comprises writing sequence, speed, font style and dynamic characteristics. According to the method, the memory fragments are matched through the associated memory library, and the dynamic writing models and the memory fragments are finally pushed to the user, so that personalized calligraphy learning experience with high interactivity and instant feedback is provided, and the efficiency and quality of calligraphy practice are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of calligraphy practice based on augmented reality technology, and in particular to a calligraphy practice method, device, equipment and medium based on augmented reality technology. Background Art

[0002] In traditional calligraphy practice methods, learners usually rely on paper teaching materials and personal experience to imitate and practice calligraphy works. This method has some obvious disadvantages, such as the lack of real-time feedback and personalized guidance, which makes it difficult for learners to correct mistakes and optimize writing skills in time. In addition, traditional methods often ignore the dynamics and rhythm of calligraphy art, and it is difficult for learners to intuitively understand and master the subtle changes in the writing process. Moreover, due to the lack of interactivity and fun, learners may feel boring, which affects their learning motivation and efficiency. Summary of the invention

[0003] In order to solve the problem of lack of interactivity, personalized guidance and dynamic visual effects in traditional calligraphy learning, which leads to reduced learning motivation and efficiency, the present application provides a calligraphy practice method, device, equipment and medium based on augmented reality technology.

[0004] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A calligraphy practice method based on augmented reality technology, the calligraphy practice method based on augmented reality technology comprising: Determine an application platform, and determine a corresponding scanning mode according to the application platform, wherein the scanning mode is used to select and integrate a corresponding optical character recognition library, a speech recognition library, a natural language processing library, and an augmented reality library; Acquire paper text image information and / or voice recognition information, extract corresponding practice text from the paper text image information based on the optical character recognition library, and extract corresponding practice text from the voice recognition information based on the voice recognition library; Based on the natural language processing library, extracting keyword features of the practice text; Based on the augmented reality library, according to the keyword features, determining a dynamic writing effect, wherein the dynamic writing effect includes a writing order, a writing speed, a writing font style, and a writing dynamic feature; Calling a three-dimensional modeling system to create a dynamic writing model according to the dynamic writing effect; Determine an associative memory library, and match corresponding associative memory segments according to the dynamic writing model; The dynamic writing model and the associated memory fragment are pushed.

[0005] By adopting the above technical solution, the calligraphy practice method based on augmented reality technology described in the above paragraph realizes the intelligent recognition and analysis of paper text images and voice information by integrating optical character recognition, speech recognition, natural language processing and augmented reality technology. The method first extracts the keyword features of the practice text, and then uses the augmented reality library to create dynamic writing effects based on these features, including writing order, speed, font style and dynamic features. Next, the three-dimensional modeling system is called to create a dynamic writing model, and the memory fragments are matched through the associated memory library. Finally, these dynamic writing models and memory fragments are pushed to the user, thereby providing a highly interactive, immediate feedback and personalized calligraphy learning experience, which effectively improves the efficiency and quality of calligraphy practice.

[0006] In a preferred example, the present application may be further configured as follows: after the step of calling the three-dimensional modeling system to create a dynamic writing model according to the dynamic writing effect, the step includes: Extracting key features according to the dynamic writing model; Determine a coding classification library according to the keyword features of the current dynamic writing model; Based on the coding classification library, the key features are encoded in vector order to generate corresponding feature vectors; After storing the feature vectors in the coding classification library, the vector sequence is updated.

[0007] By adopting the above technical solution, the steps extract the key features of the dynamic writing model, and use the coding classification library to orderly encode these features to generate feature vectors, and finally achieve accurate digital representation and storage of writing movements, thereby providing calligraphy practitioners with a structured and quantifiable calligraphy movement analysis and optimization tool, enhancing the personalization and systematic nature of calligraphy practice.

[0008] In a preferred example, the present application may be further configured as follows: the step of determining the associated memory library and matching the corresponding associated memory segment according to the dynamic writing model includes: Determining a feature vector of the dynamic writing model; Determine whether the dynamic writing model is generated for the first time, and if so, push the cloud template and related information retrieved by the artificial intelligence search tool based on the feature vector; Acquire a first interaction coordinate of the user, and determine a corresponding target cloud template and / or target association information according to the first interaction coordinate; Generate an associated memory segment according to the target cloud template and / or target associated information, establish a mapping relationship between the associated memory segment and the feature vector, and store the associated memory segment in the associated memory library; If not, then the corresponding associative memory segment is matched in the associative memory library based on the mapping relationship according to the feature vector.

[0009] By adopting the above-mentioned technical solution, the steps realize the intelligent retrieval and push of personalized cloud templates and related information through feature vector recognition and mapping of the dynamic writing model, and at the same time establish a mapping relationship between memory fragments and feature vectors, providing users with a dynamic and interactive calligraphy learning experience, and promoting the deepening of user memory and the personalized development of calligraphy skills.

[0010] In a preferred example, the present application may be further configured as follows: after the step of pushing the dynamic writing model and the associated memory segment, the method includes: Acquire user writing dynamic features, wherein the user writing dynamic features at least include a writing trajectory and a stroke sequence; Comparing the user's writing dynamic features with the dynamic writing model to generate a corresponding comparison result; According to the comparison result, the adjustment parameters of the dynamic writing model are determined, and a custom memory interaction window is pushed.

[0011] By adopting the above-mentioned technical solution, the steps capture and analyze the user's writing trajectory and stroke sequence in real time, compare them with the dynamic writing model, intelligently generate personalized writing improvement plans, and provide a customized memory interaction window, thereby providing users with dynamic, timely and personalized calligraphy practice feedback, effectively improving the effect of calligraphy learning and personalized experience.

[0012] In a preferred example, the present application may be further configured as follows: the step of determining the adjustment parameters of the dynamic writing model according to the comparison result, and pushing the custom memory interaction window includes: According to the comparison result, determining the error point and the evaluation value of the error point; Determining a key frame of the dynamic writing model according to the error point and the evaluation value; Determining adjustment parameters of the dynamic writing model according to the key frame, wherein the adjustment parameters are used to adjust the playback speed and playback times of the key frame; Determining an evaluation threshold, and if it is determined that the evaluation value is less than the evaluation threshold, disassembling the dynamic writing model to generate and push a corresponding individual stroke writing model; According to the comparison result, a custom memory interaction window is pushed, a second interaction coordinate of the custom memory interaction window is obtained, an associated memory segment is generated according to the second interaction coordinate, and the associated memory segment is stored in the associated memory library.

[0013] By adopting the above technical solution, this step analyzes the comparison results of the user's writing dynamic characteristics and the dynamic writing model in detail, accurately determines the error points and their evaluation values, intelligently adjusts the key frame parameters of the model, and disassembles the model when necessary to provide targeted stroke exercises. At the same time, it collects personalized feedback from users through a customized memory interaction window, further enriches the associative memory library, and realizes deep personalized customization and real-time optimization of the calligraphy practice process, which significantly improves the accuracy and efficiency of calligraphy learning.

[0014] In a preferred example, the present application may be further configured as follows: after the step of determining the evaluation threshold, if it is determined that the evaluation value is less than the evaluation threshold, disassembling the dynamic writing model to generate and push the corresponding individual stroke writing model, the step includes: Determining weight values ​​of each evaluation value according to the comparison result; Determining improvement suggestion information according to the weight value; Generate an exercise report based on the evaluation value, weight value and improvement suggestion information; The practice report is compared with the practice report data in the cloud, a corresponding practice comparison result is generated, and the practice comparison result is filled into the practice report.

[0015] By adopting the above technical solution, this step generates a personalized practice report by comprehensively evaluating the user's writing error points, weight values ​​and improvement suggestions, and compares it with the cloud data, providing users with a detailed writing performance analysis and progress trajectory, further enhancing the targeted and systematic nature of calligraphy practice, and promoting the continuous improvement of user skills and the optimization of personalized learning paths.

[0016] The second object of the invention is achieved by the following technical solutions: A calligraphy practice device based on augmented reality technology, the calligraphy practice device based on augmented reality technology comprising: A first determination module is used to determine an application platform, and according to the application platform, determine a corresponding scanning mode, wherein the scanning mode is used to select and integrate a corresponding optical character recognition library, a speech recognition library, a natural language processing library, and an augmented reality library; A first acquisition module is used to acquire the paper text image information and / or the voice recognition information, extract the corresponding practice text in the paper text image information based on the optical character recognition library, and extract the corresponding practice text in the voice recognition information based on the voice recognition library; A first extraction module, used for extracting keyword features of the practice text based on the natural language processing library; A second determination module is used to determine a dynamic writing effect based on the augmented reality library and the keyword features, wherein the dynamic writing effect includes a writing order, a writing speed, a writing font style, and a writing dynamic feature; A calling module, used for calling a three-dimensional modeling system to create a dynamic writing model according to the dynamic writing effect; A third determination module is used to determine an associative memory library and match corresponding associative memory segments according to the dynamic writing model; A push module is used to push the dynamic writing model and the associated memory fragment.

[0017] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the calligraphy practice method based on augmented reality technology are implemented.

[0018] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the calligraphy practice method based on augmented reality technology.

[0019] In summary, the present application includes at least one of the following beneficial technical effects: 1. The calligraphy practice method based on augmented reality technology described in the above paragraph realizes the intelligent recognition and analysis of paper text images and voice information by integrating optical character recognition, speech recognition, natural language processing and augmented reality technology. The method first extracts the keyword features of the practice text, and then uses the augmented reality library to create dynamic writing effects based on these features, including writing order, speed, font style and dynamic features. Next, the three-dimensional modeling system is called to create a dynamic writing model, and the memory fragments are matched through the associated memory library. Finally, these dynamic writing models and memory fragments are pushed to the user, thereby providing a highly interactive, immediate feedback and personalized calligraphy learning experience, which effectively improves the efficiency and quality of calligraphy practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of a calligraphy practice method based on augmented reality technology in one embodiment of the present application.

[0021] Figure 2 is another implementation flow chart of a calligraphy practice method based on augmented reality technology in one embodiment of the present application; Figure 3is a flowchart for implementing step S60 in the calligraphy practice method based on augmented reality technology in one embodiment of the present application; Figure 4 It is a flowchart of the implementation after step S70 in the calligraphy practice method based on augmented reality technology in one embodiment of the present application; Figure 5 This is a flowchart for implementing step S83 in the calligraphy practice method based on augmented reality technology in one embodiment of the present application; Figure 6 This is another implementation flow chart of step S83 in the calligraphy practice method based on augmented reality technology in one embodiment of the present application; Figure 7 This is a principle block diagram of a calligraphy practice device based on augmented reality technology in one embodiment of the present application; Figure 8 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The present application is further described in detail below in conjunction with the accompanying drawings.

[0023] In one embodiment, if Figure 1 As shown, the present application discloses a calligraphy practice method based on augmented reality technology, which specifically includes the following steps: The calligraphy practice method based on augmented reality technology includes: S10, determining an application platform, and determining a corresponding scanning mode according to the application platform, where the scanning mode is used to select and integrate a corresponding optical character recognition library, a speech recognition library, a natural language processing library, and an augmented reality library; In this embodiment, the application platform is the basic environment for users to interact with the technology, such as a smartphone, tablet computer, or augmented reality glasses; the scanning mode is the image or voice data collection method selected for different platform characteristics; the optical character recognition library is used to convert the text in the image into an editable text format; the voice recognition library converts the user's voice input into text information; the natural language processing library analyzes and understands the text content and extracts key information; the augmented reality library overlays virtual information or effects in the user's field of view to enhance the reality experience. For example, when using this technology on a smartphone, the application platform is the mobile phone operating system, the scanning mode may be to capture an image of a paper calligraphy work through a camera, the optical character recognition library will recognize and extract the text in the work, the voice recognition library allows the user to operate through voice commands, the natural language processing library analyzes the structure and style of the calligraphy work, and the augmented reality library displays the dynamic effect of writing or provides virtual calligraphy teaching guidance when the user views the work through the mobile phone screen.

[0024] S20, obtaining paper text image information and / or voice recognition information, extracting corresponding practice text from the paper text image information based on an optical character recognition library, and extracting corresponding practice text from the voice recognition information based on a voice recognition library; In this embodiment, paper text image information refers to image data obtained by scanning or photographing physical objects such as paper calligraphy works; voice recognition information is text data converted from the user's spoken instructions or readings through voice recognition technology; and practice text is extracted from these images or voice data and is used for specific text content in calligraphy practice. Continuing with the previous example, if a user uses a smartphone to practice calligraphy, they can take a picture of a calligraphy work, and the system extracts the practice text from the picture through the optical character recognition library; at the same time, if the user wants to obtain the practice text through voice, such as reading a poem, the voice recognition library will convert the voice information into corresponding text, so that whether through images or voice, users can obtain text content for practice, and then learn and create calligraphy.

[0025] S30, extracting keyword features of the practice text based on a natural language processing library; In this embodiment, keyword features refer to important words or phrases identified from the practice text, which can represent the core meaning or theme of the text; using the previous example, when a user practices calligraphy through a smartphone application, the natural language processing library will analyze the text content of the user's practice, such as an ancient poem or a famous saying, and extract keyword features such as "moon", "river", "homesickness", etc. These keywords not only help users grasp the main purpose of the text, but also emphasize these core elements in calligraphy creation, thereby enhancing the expressiveness and artistry of the work.

[0026] S40, based on the augmented reality library, determining a dynamic writing effect according to the keyword features, the dynamic writing effect including writing order, writing speed, writing font style and writing dynamic features; In this embodiment, the dynamic writing effect refers to the visual effect of simulating writing actions in an augmented reality environment, including writing order (i.e., the correct order of strokes), writing speed (referring to the speed change during the writing process), writing font style (different font styles, such as regular script, running script, etc.), and writing dynamic features (stroke changes and rhythm during the writing process); using the previous example, when a user practices calligraphy through a smartphone application, the augmented reality library will design corresponding dynamic writing effects based on extracted keyword features, such as "moon", "river", etc., such as simulating the order and speed of a calligrapher writing the word "moon", selecting a suitable font style, such as running script, and showing the pen strokes and ink thickness during writing, thereby creating an immersive experience in the user's field of view as if someone is writing in real time.

[0027] S50. Invoke the 3D modeling system to create a dynamic writing model based on the dynamic writing effect. In this embodiment, the 3D modeling system is a technology that uses computer software to create and manipulate 3D graphics. It can simulate objects and environments in the real world; the dynamic writing model is a virtual writing process model constructed according to the writing sequence, speed, font style, and dynamic features through 3D modeling technology. Continuing with the previous example, when the user practices calligraphy through a smartphone application, the 3D modeling system will create a virtual writing model based on the dynamic writing effect of the calligraphy work or practice text selected by the user. This model can display every detail of the writing, such as the depth of the brushstroke, the speed, and the stroke order, thus forming a three-dimensional and interactive calligraphy practice tool on the user's device, enabling the user to learn and imitate the brushwork and style of calligraphy masters in a virtual environment.

[0028] S60. Determine the associated memory library and match the corresponding associated memory segments according to the dynamic writing model. In this embodiment, the associated memory library is a database that stores memory information associated with specific writing actions or text content. It can retrieve relevant learning materials or historical practice data according to the dynamic writing model; the associated memory segments refer to the specific memory information closely related to the current practice text or writing action extracted from the associated memory library, such as historical writing samples, fragments of famous masters' works, or personalized practice suggestions. Continuing with the previous example, when the user practices calligraphy using a smartphone application, the system will match segments of the same character written by famous calligraphers in history or the user's previous records of practicing this character from the associated memory library according to the user's current dynamic writing model, such as the writing sequence and style of the character "moon" being practiced. These associated memory segments can provide reference and comparison for the user, helping the user understand different writing styles and improve calligraphy skills.

[0029] S70. Push the dynamic writing model and the associated memory segments.

[0030] In this embodiment, the technical effect of pushing dynamic writing models and associated memory fragments is to provide users with a highly interactive and personalized learning experience. The working principle is that by analyzing the user's writing movements and practice texts in real time, the system first creates a dynamic writing model that captures the order, speed, font style, and dynamic characteristics of writing. Subsequently, the system accesses the associated memory library and matches relevant learning materials, historical practice data, or fragments of famous works according to the feature vectors of this model to form associated memory fragments. These fragments not only provide users with rich reference and comparison materials, but also help users identify and improve their writing skills. Finally, the system pushes these dynamic writing models and associated memory fragments to users, enabling users to obtain instant feedback during imitation and practice, deepen memory impressions, and gradually improve calligraphy skills.

[0031] In summary, the calligraphy practice method based on augmented reality technology described in the above paragraph realizes the intelligent recognition and analysis of paper text images and voice information by integrating optical character recognition, speech recognition, natural language processing and augmented reality technology. The method first extracts the keyword features of the practice text, and then uses the augmented reality library to create dynamic writing effects based on these features, including writing order, speed, font style and dynamic features. Next, the three-dimensional modeling system is called to create a dynamic writing model, and the memory fragments are matched through the associated memory library. Finally, these dynamic writing models and memory fragments are pushed to the user, thereby providing a highly interactive, immediate feedback and personalized calligraphy learning experience, which effectively improves the efficiency and quality of calligraphy practice.

[0032] In one embodiment, if Figure 2 As shown, after step S50, that is, after the step of calling the three-dimensional modeling system to create a dynamic writing model according to the dynamic writing effect, the following steps are included: S51, extracting key features according to the dynamic writing model; In this embodiment, the key features refer to representative and distinctive writing attributes identified from the dynamic writing model, such as the starting point, turning point and ending point of the strokes, as well as the rhythm and force changes of the writing; using the previous example, when the user practices calligraphy through a smartphone application, the system will analyze the word "moon" written by the user and extract key features such as the starting position of the strokes, the roundness of the turning points, and the force changes of the pen tip. These features together define the core elements of the writing action, helping users to accurately understand and imitate the precise writing method of each stroke, thereby achieving a higher level of calligraphy skills in practice.

[0033] S52, determining a coding classification library according to keyword features of the current dynamic writing model; In this embodiment, the keyword features are representative characters or stroke attributes extracted from the dynamic writing model, such as specific stroke orders or writing styles; the encoding classification library is a predefined system for encoding and classifying these keyword features according to certain rules and categories. Continuing with the previous example, if the user is practicing writing the character "月" (moon), the keyword features may include the specific strokes and structure of the character "月". The encoding classification library then decomposes the writing actions of the character "月" into encodable elements based on these features, such as the numbers, directions, and lengths of the strokes, and then encodes them according to specific orders and rules, so that each stroke and action can be uniquely identified and classified in the system, facilitating subsequent analysis, comparison, and teaching applications.

[0034] S53. Based on the encoding classification library, encode the key features in vector order to generate corresponding feature vectors. In this embodiment, the vector order refers to the way in which the key features are arranged in a certain logical or chronological order during the encoding process; the feature vector is a numerical set composed of these sequentially encoded key features, which represents a quantitative representation of the writing action. Continuing with the previous example, when the user practices writing the character "月", the system will encode each key stroke feature of the character "月", such as the starting point, turning point, ending point, and the thickness change of the stroke, according to the natural order of writing to form a feature vector, such as [starting point coordinates, turning point angle, ending point coordinates, stroke thickness change]. This vector captures the precise dynamics of writing the character "月" and can be used for subsequent analysis, comparison, or as a basis for teaching feedback.

[0035] S54. After storing the feature vectors in the encoding classification library, update the vector order.

[0036] In this embodiment, the technical effect of storing the feature vectors in the encoding classification library is to establish a queryable and comparable digital archive for each unique writing action or text. The working principle is that through the encoding process, the system converts the key features of the user's writing into numerical data to form feature vectors, and then stores these vectors in the encoding classification library. In this way, whether it is for analyzing writing actions, formulating personalized learning paths, or comparing with other writing samples, the system can quickly retrieve and call the corresponding feature vectors. In addition, storing these data also helps machine learning algorithms to deeply learn writing patterns, continuously optimize the dynamic writing model, and provide more accurate and personalized calligraphy practice suggestions and feedback for users.

[0037] In summary, the steps are to extract the key features of the dynamic writing model, and use the coding classification library to encode these features in an orderly manner to generate feature vectors, and finally achieve accurate digital representation and storage of writing movements, thereby providing calligraphy practitioners with a structured and quantifiable calligraphy movement analysis and optimization tool, enhancing the personalization and systematicness of calligraphy practice.

[0038] In one embodiment, if Figure 3 As shown, in step S60, that is, determining the associated memory library, the step of matching the corresponding associated memory segment according to the dynamic writing model includes: S601, determining a feature vector of a dynamic writing model; In this embodiment, S602: Determine whether the dynamic writing model is generated for the first time. If so, push the cloud template and related information retrieved by the artificial intelligence search tool based on the feature vector; In this embodiment, the technical effect of determining the characteristic vector of the dynamic writing model is to achieve accurate capture and digital expression of the writing action, so that the writing process can be quantified and analyzed, and can be used for further learning and improvement. The working principle is to analyze the key elements in the dynamic writing model, such as the starting and ending positions of the strokes, the turning angle, the writing speed, the change in force, etc., convert these elements into numerical feature points, and then arrange them in a certain logical order to form a characteristic vector. This vector can not only reflect the microscopic details of writing, but also capture the overall style and rhythm of writing, providing a scientific basis for calligraphy teaching, evaluation and personalized practice.

[0039] S603, obtaining the first interaction coordinates of the user, and determining the corresponding target cloud template and / or target association information according to the first interaction coordinates; In this embodiment, the first interaction coordinate refers to the initial position or selection point where the user interacts with the system when using the application, such as the coordinates of clicking or touching on the interface; the target cloud template refers to the writing sample or teaching template that matches the user's needs retrieved from the cloud database based on the user's interaction coordinates; the target-related information refers to other useful information related to the user's interaction coordinates, such as writing skill instructions, historical practice records, or personalized suggestions. Continuing with the previous example, if the user selects a specific stroke of the word "moon" for in-depth study in the smartphone calligraphy application, the first interaction coordinate is the exact location where the user selects the stroke on the screen. Based on this coordinate, the system retrieves a famous calligraphy template related to the stroke from the cloud database as the target cloud template, and provides writing skills and user historical practice data as target-related information to help users understand and practice this stroke more deeply and improve their calligraphy skills.

[0040] S604, generating an associated memory segment according to the target cloud template and / or the target associated information, establishing a mapping relationship between the associated memory segment and the feature vector, and storing the associated memory segment in an associated memory library; In this embodiment, the associated memory fragment is the specific memory information related to the user's writing action extracted based on the target cloud template and the target associated information, such as a specific writing sample or personalized writing suggestion; the mapping relationship is the corresponding relationship established between these memory fragments and the feature vectors of the user's writing, ensuring that the relevant information can be accurately called every time the user practices. Continuing with the previous example, when the user chooses to learn a stroke of the word "moon" in the smartphone calligraphy application, the system will generate a famous calligrapher's writing fragment containing the stroke from the cloud template as an associated memory fragment based on the stroke selected by the user, and establish a mapping relationship between these fragments and the user's writing feature vector, so as to provide instant reference and guidance when the user practices. Subsequently, the system stores these associated memory fragments in the associated memory library, which facilitates the user's future practice and review, and also enriches the user's personalized learning archive.

[0041] S605: If not, then match the corresponding associative memory segment in the associative memory library based on the feature vector and the mapping relationship.

[0042] In this embodiment, the effect of this technology is that it can provide users with personalized and accurate learning resources. The working principle is to quickly retrieve the memory fragments that match the user's current writing characteristics by matching the feature vector with the mapping relationship in the associated memory library. When the user's writing feature vector matches a previously stored memory fragment, the system will extract the corresponding memory fragment from the associated memory library. These fragments may include historical practice data, similar strokes of famous works, or personalized writing suggestions. This process not only helps users review and consolidate what they have learned before, but also discovers and improves the shortcomings in the current writing through comparative analysis, thereby achieving continuous skill improvement and optimization of personalized learning paths.

[0043] To sum up, the steps realize the intelligent retrieval and push of personalized cloud templates and related information through feature vector recognition and mapping of the dynamic writing model, and at the same time establishes a mapping relationship between memory fragments and feature vectors, providing users with a dynamic and interactive calligraphy learning experience, and promoting the deepening of user memory and the personalized development of calligraphy skills.

[0044] In one embodiment, if Figure 4 As shown, after step S70, that is, after the step of pushing the dynamic writing model and the associated memory fragment, the following steps are included: S81, obtaining a user's writing dynamic features, where the user's writing dynamic features at least include a writing trajectory and a stroke sequence; In this embodiment, the dynamic characteristics of user writing refer to the collection of all actions and changes generated by the user during the writing process, including the writing trajectory (i.e., the continuous line path formed by the pen tip during the writing process) and the stroke sequence (the sequence of strokes performed according to specific rules); using the previous example, when the user uses a smartphone application to practice writing the word "moon", the system will capture every detail of the movement of the user's pen tip on the screen to form a writing trajectory, and at the same time record the sequence of strokes performed by the user according to calligraphy rules. These information together constitute the dynamic characteristics of the user's writing, providing the system with basic data for evaluation and feedback.

[0045] S82, comparing the user's writing dynamic features with the dynamic writing model, and generating corresponding comparison results; In this embodiment, the comparison result is evaluation data obtained by analyzing the difference between the dynamic characteristics of the user's writing and the preset dynamic writing model. It points out in detail the accuracy, fluency and difference from the standard in the user's writing; using the previous example, when the user practices writing the word "moon" on a smartphone application, the system compares the user's actual writing trajectory and stroke order with the standard trajectory and order in the dynamic writing model to generate a comparison result, such as pointing out which stroke has a deviation in the starting position, which stroke is not smooth enough, or the difference between the overall writing speed and the recommended speed. These comparison results help users understand the difference between their writing habits and calligraphy standards, and guide users to make corresponding adjustments and improvements.

[0046] S83. Determine adjustment parameters of the dynamic writing model according to the comparison result, and push a custom memory interaction window.

[0047] In this embodiment, adjusting parameters refers to determining the model parameters that need to be optimized or changed, such as the playback speed, number of repetitions, or thickness of the strokes, based on the comparison results between the user's writing dynamic characteristics and the dynamic writing model; the custom memory interaction window is an interface provided to the user, allowing the user to enter personalized writing memories or notes according to their needs and preferences. Continuing with the previous example, after analyzing the writing trajectory and stroke sequence of the user practicing the word "moon", the system will automatically adjust the parameters of the dynamic writing model according to the comparison results. For example, if the user's writing is not smooth enough at the turning point of a certain stroke, the system may increase the playback speed or number of repetitions of this stroke to help the user strengthen the practice. At the same time, the system will push a custom memory interaction window, allowing the user to record his or her understanding and feelings of this stroke, or save the content that the user wants to review in detail, thereby providing users with a more personalized and targeted calligraphy learning experience.

[0048] In summary, the steps capture and analyze the user's writing trajectory and stroke order in real time, compare them with the dynamic writing model, intelligently generate personalized writing improvement plans, and provide a custom memory interaction window, thereby providing the user with dynamic, timely, and personalized calligraphy practice feedback, effectively improving the effect and personalized experience of calligraphy learning.

[0049] In one embodiment, as Figure 5 shown, in step S803, that is, in the step of determining the adjustment parameters of the dynamic writing model and pushing the custom memory interaction window according to the comparison result, it includes: S8311. According to the comparison result, determine the error point and the evaluation value of the error point; In this embodiment, the error point refers to the specific stroke or position that does not match the standard dynamic writing model during the user's writing process, which identifies the deviation or error in writing; the evaluation value is a quantitative evaluation of the severity of these error points, usually represented by a numerical value, and the larger the value, the more serious the error. Continuing with the previous example, when the user practices calligraphy using a smartphone application and there is a deviation in a certain stroke when writing the character "月" (moon), the system will identify this specific error point, such as the incorrect starting position of the stroke or the angle at the turning point not meeting the standard. At the same time, the system will assign an evaluation value to this error point, such as determining it based on the size of the deviation or the similarity to the standard stroke, and then determine subsequent practice suggestions or adjustment plans based on this evaluation value.

[0050] S8312. According to the error point and the evaluation value, determine the key frame of the dynamic writing model; In this embodiment, the key frame is the frame of the specific stroke or action in the dynamic writing model where the user makes a writing error or needs special attention, which represents the key action or turning point in the writing process; continuing with the previous example, when the system analyzes the user's practice of writing the character "月", it will identify the stroke that needs to be focused on practicing or correcting based on the error point and the evaluation value, such as the turning point of the "横折钩" (horizontal fold hook) in the character "月". This turning point becomes the key frame. The system will specifically mark this key frame to allow the user to focus more on the accuracy and fluency of this action in subsequent practice to improve the overall quality of writing.

[0051] S8313. According to the key frame, determine the adjustment parameters of the dynamic writing model, and the adjustment parameters are used to adjust the playback speed and playback times of the key frame; In this embodiment, adjusting parameters refers to specific settings determined by the system according to the user's performance on key frames for optimizing the learning experience, such as the speed of playback and the number of repeated playbacks. Continuing with the previous example, if the user makes a mistake at the turning point of the "horizontal hook" in writing the character "month", the system will adjust the playback speed to be slower based on the performance of this key frame so that the user can more clearly observe and imitate the correct stroke movements. At the same time, the number of playbacks will be increased to give the user more opportunities to practice and master this movement until they can write it accurately without errors.

[0052] S8314. Determine an evaluation threshold. If it is determined that the evaluation value is less than the evaluation threshold, disassemble the dynamic writing model to generate and push the corresponding individual stroke writing model. In this embodiment, the evaluation threshold is a preset standard for judging whether the accuracy of the user's writing has reached an acceptable level. The individual stroke writing model is disassembled from the complete dynamic writing model for specific strokes with poor performance in the user's writing and is used for special practice and improvement. Continuing with the previous example, when the user is practicing writing the character "month", the system sets an evaluation threshold, such as 80 points, to measure the accuracy of the user's writing. When the user's evaluation value is lower than this threshold, the system will identify the strokes that need improvement, such as the "horizontal hook" in the character "month", and then disassemble this stroke from the dynamic writing model to generate an individual stroke writing model, which is specifically pushed to the user for targeted practice so that the user can concentrate on improving the writing quality of this stroke.

[0053] S832. According to the comparison result, push a custom memory interaction window, obtain the second interaction coordinate of the custom memory interaction window, generate an associated memory segment according to the second interaction coordinate, and store the associated memory segment in the associated memory library.

[0054] In this embodiment, the custom memory interaction window is a user interface that allows the user to add, edit, or select personalized memories or notes related to writing practice according to their own needs. The second interaction coordinate refers to the position information of the user's second selection or input in this interaction window. Continuing with the previous example, after the system generates a comparison result when the user is practicing writing the character "month", it will push a custom memory interaction window. The user can record their personal understanding and practice experience of a certain stroke in it, or select the strokes that need to be reviewed重点. When the user operates in this window, the system will record the position information of this operation as the second interaction coordinate and generate an associated memory segment according to this coordinate, such as the writing sample or personalized suggestions of the strokes that the user selects to focus on reviewing, and then save these segments to the associated memory library for the user to review and refer to in the future.

[0055] In summary, this step precisely determines the error points and their evaluation values by analyzing in detail the comparison results between the user's writing dynamic features and the dynamic writing model, intelligently adjusts the key frame parameters of the model, disassembles the model when necessary to provide targeted stroke exercises, and collects the user's personalized feedback through a custom memory interaction window to further enrich the associated memory library, achieving deep personalized customization and real-time optimization of the calligraphy practice process, and significantly improving the accuracy and efficiency of calligraphy learning.

[0056] In one embodiment, as Figure 6 shown, after step S8314, that is, after determining the evaluation threshold and determining that the evaluation value is less than the evaluation threshold and disassembling the dynamic writing model to generate and push the corresponding single-stroke writing model, the following steps are included: S8315. Determine the weight values of each evaluation value according to the comparison result; In this embodiment, the weight value refers to the relative importance value assigned to different evaluation items when evaluating the user's writing performance, which determines the influence degree of each evaluation result on the final evaluation result; continuing with the previous example, when the user practices writing the character "月" (moon), the system generates evaluation values based on multiple aspects of the writing (such as stroke accuracy, fluency, structural balance, etc.), and then assigns a weight value to each aspect. For example, stroke accuracy may have a higher weight than fluency because accuracy is more crucial for calligraphy. The system calculates a comprehensive evaluation result based on these weight values to help the user understand which aspects need more attention and improvement.

[0057] S8316. Determine the improvement suggestion information according to the weight value; In this embodiment, the improvement suggestion information is the targeted guidance and suggestions generated by the system based on the evaluation results of the user's writing and the weight values of each evaluation value, aiming to help the user identify the deficiencies in writing and provide directions for improvement; continuing with the previous example, after analyzing the dynamic features of the user's writing of the character "月", the system calculates the overall writing performance according to the weight values of each evaluation item, and then generates improvement suggestion information based on these data. For example, if the weight of stroke accuracy is relatively high and the user scores low in this aspect, the system may suggest that the user focus on practicing the starting and ending positions of the strokes, or adjust the strength and speed of the writing brush to improve the overall quality of the writing.

[0058] S8317. Generate a practice report according to the evaluation value, weight value, and improvement suggestion information; In this embodiment, the practice report is a comprehensive document that details the user's performance, strengths, weaknesses, and specific improvement suggestions in calligraphy practice based on the user's evaluation values, weight values, and improvement suggestion information. Continuing with the previous example, the system generates a practice report according to the various evaluation values and corresponding weights when the user writes the character "month", combined with the improvement suggestions provided by the system. This report will list in detail the user's performance in aspects such as stroke accuracy and fluency, point out what is done well and what needs improvement, and provide specific practice methods and strategies to help the user targetedly improve their calligraphy skills.

[0059] S8318. Compare the data of the practice report and the cloud practice report to generate the corresponding practice comparison result, and fill the practice comparison result into the practice report.

[0060] In this embodiment, the practice comparison result is a summary of the differences and development trends obtained by comparing and analyzing the user's practice report with the similar practice report data stored in the cloud. Continuing with the previous example, the system compares the user's current practice report with the practice report data of other users or the user's own past in the cloud, finds out the progress points and areas where there is still room for improvement in the user's calligraphy practice, such as whether the stroke accuracy has improved and whether the writing speed is more reasonable, and then fills these comparison results into the user's practice report to provide the user with a clear overview of their personal calligraphy learning progress.

[0061] In summary, this step generates a personalized practice report by comprehensively evaluating the user's writing error points, weight values, and improvement suggestions, and compares it with the cloud data, providing the user with a detailed analysis of their writing performance and progress trajectory, further enhancing the pertinence and systematicness of calligraphy practice, and promoting the continuous improvement of the user's skills and the optimization of the personalized learning path.

[0062] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution is prior or posterior. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0063] In one embodiment, a calligraphy practice device based on augmented reality technology is provided. The calligraphy practice device based on augmented reality technology corresponds one-to-one with the calligraphy practice method based on augmented reality technology in the above embodiment. As Figure 7 shown, the calligraphy practice device based on augmented reality technology includes a first determination module, a first acquisition module, a first extraction module, a second determination module, a call module, a third determination module, and a push module. The detailed description of each functional module is as follows: A first determination module is used to determine an application platform, and according to the application platform, determine a corresponding scanning mode, wherein the scanning mode is used to select and integrate a corresponding optical character recognition library, a speech recognition library, a natural language processing library, and an augmented reality library; A first acquisition module is used to acquire the paper text image information and / or the voice recognition information, extract the corresponding practice text in the paper text image information based on the optical character recognition library, and extract the corresponding practice text in the voice recognition information based on the voice recognition library; A first extraction module, used for extracting keyword features of the practice text based on the natural language processing library; A second determination module is used to determine a dynamic writing effect based on the augmented reality library and the keyword features, wherein the dynamic writing effect includes a writing order, a writing speed, a writing font style, and a writing dynamic feature; A calling module, used for calling a three-dimensional modeling system to create a dynamic writing model according to the dynamic writing effect; A third determination module is used to determine an associative memory library and match corresponding associative memory segments according to the dynamic writing model; A push module is used to push the dynamic writing model and the associated memory fragment.

[0064] Optionally, the calligraphy practice device based on augmented reality technology further includes: A second extraction module, used for extracting key features according to the dynamic writing model; A fourth determination module, used to determine a coding classification library according to the keyword features of the current dynamic writing model; A generation module, used to encode the key features in vector order based on the coding classification library to generate corresponding feature vectors; An updating module, used for updating the vector sequence after storing the feature vector in the coding classification library; Optionally, the third determining module includes: A first determining unit, used to determine a feature vector of the dynamic writing model; A judging unit, used to judge whether the dynamic writing model is generated for the first time, and if so, push a cloud template and related information retrieved by an artificial intelligence retrieval tool based on the feature vector; An acquisition unit, configured to acquire a first interaction coordinate of a user, and determine a corresponding target cloud template and / or target association information according to the first interaction coordinate; A first generating unit, configured to generate an associated memory segment according to the target cloud template and / or target associated information, establish a mapping relationship between the associated memory segment and the feature vector, and store the associated memory segment in the associated memory library; a matching unit, configured to, if not, match the corresponding associative memory segment in the associative memory base based on the mapping relationship according to the feature vector; Optionally, the calligraphy practice device based on augmented reality technology further includes: A second acquisition module is used to acquire the user's writing dynamic features, wherein the writing dynamic features at least include a writing trajectory and a stroke sequence; A comparison module, used for comparing the user's writing dynamic features with the dynamic writing model, and generating a corresponding comparison result; A fifth determination module, configured to determine adjustment parameters of the dynamic writing model according to the comparison result, and push a custom memory interaction window; Optionally, the fifth determining module includes: A second determining unit, configured to determine an error point and an evaluation value of the error point according to the comparison result; A third determining unit, configured to determine a key frame of the dynamic writing model according to the error point and the evaluation value; A fourth determining unit, configured to determine an adjustment parameter of the dynamic writing model according to the key frame, wherein the adjustment parameter is used to adjust a playback speed and a playback number of the key frame; a fifth determining unit, configured to determine an evaluation threshold, and if it is determined that the evaluation value is less than the evaluation threshold, disassemble the dynamic writing model to generate and push a corresponding individual stroke writing model; a pushing unit, configured to push a custom memory interaction window according to the comparison result, obtain a second interaction coordinate of the custom memory interaction window, generate an associated memory segment according to the second interaction coordinate, and store the associated memory segment in the associated memory library; Optionally, the fifth determining module also includes A sixth determining unit, configured to determine a weight value of each of the evaluation values ​​according to the comparison result; a seventh determining unit, configured to determine improvement suggestion information according to the weight value; A second generating unit, configured to generate a practice report according to the evaluation value, the weight value and the improvement suggestion information; The comparison unit is used to compare the practice report with the cloud practice report data, generate corresponding practice comparison results, and fill the practice comparison results into the practice report.

[0065] For the specific definition of the calligraphy practice device based on augmented reality technology, please refer to the definition of the calligraphy practice method based on augmented reality technology above, which will not be repeated here. Each module in the above-mentioned calligraphy practice device based on augmented reality technology can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0066] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a calligraphy practice method based on augmented reality technology is implemented.

[0067] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: S10, determining an application platform, and determining a corresponding scanning mode according to the application platform, where the scanning mode is used to select and integrate a corresponding optical character recognition library, a speech recognition library, a natural language processing library, and an augmented reality library; S20, obtaining paper text image information and / or voice recognition information, extracting corresponding practice text from the paper text image information based on an optical character recognition library, and extracting corresponding practice text from the voice recognition information based on a voice recognition library; S30, extracting keyword features of the practice text based on a natural language processing library; S40, based on the augmented reality library, determining a dynamic writing effect according to the keyword features, the dynamic writing effect including writing order, writing speed, writing font style and writing dynamic features; S50, calling a three-dimensional modeling system to create a dynamic writing model according to the dynamic writing effect; S60, determining an associated memory library, and matching corresponding associated memory segments according to the dynamic writing model; S70: Push the dynamic writing model and associated memory fragments.

[0068] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: S10, determining an application platform, and determining a corresponding scanning mode according to the application platform, where the scanning mode is used to select and integrate a corresponding optical character recognition library, a speech recognition library, a natural language processing library, and an augmented reality library; S20, obtaining paper text image information and / or voice recognition information, extracting corresponding practice text from the paper text image information based on an optical character recognition library, and extracting corresponding practice text from the voice recognition information based on a voice recognition library; S30, extracting keyword features of the practice text based on a natural language processing library; S40, based on the augmented reality library, determining a dynamic writing effect according to the keyword features, the dynamic writing effect including writing order, writing speed, writing font style and writing dynamic features; S50, calling a three-dimensional modeling system to create a dynamic writing model according to the dynamic writing effect; S60, determining an associated memory library, and matching corresponding associated memory segments according to the dynamic writing model; S70: Push the dynamic writing model and associated memory fragments.

[0069] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0070] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0071] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A calligraphy practice method based on augmented reality technology, characterized in that: The calligraphy practice method based on augmented reality technology includes: Determine an application platform, and determine a corresponding scanning mode based on the application platform, wherein the scanning mode is used to select and integrate a corresponding optical character recognition library, a speech recognition library, a natural language processing library, and an augmented reality library; Obtaining paper text image information and / or voice recognition information, extracting corresponding practice text from the paper text image information based on the optical character recognition library, and extracting corresponding practice text from the voice recognition information based on the voice recognition library; Extracting keyword features of the practice text based on the natural language processing library; Determining a dynamic writing effect based on the augmented reality library and the keyword features, wherein the dynamic writing effect includes a writing order, a writing speed, a writing font style, and a writing dynamic feature; Invoking a three-dimensional modeling system to create a dynamic writing model according to the dynamic writing effect; Determine an associative memory library, and match corresponding associative memory segments according to the dynamic writing model; Push the dynamic writing model and the associated memory fragment.

2. The calligraphy practice method based on augmented reality technology according to claim 1, characterized in that: After the step of calling the three-dimensional modeling system and creating a dynamic writing model according to the dynamic writing effect, the method includes: extracting key features according to the dynamic writing model; Determining a coding classification library based on keyword features of the current dynamic writing model; Based on the coding classification library, the key features are encoded in vector order to generate corresponding feature vectors; After storing the feature vectors in the coding classification library, the vector sequence is updated.

3. The calligraphy practice method based on augmented reality technology according to claim 2, characterized in that: The step of determining the associated memory library and matching the corresponding associated memory segments according to the dynamic writing model includes: determining a characteristic vector of the dynamic writing model; Determine whether the dynamic writing model is generated for the first time. If so, push the cloud template and related information retrieved by the artificial intelligence search tool based on the feature vector; Obtaining a first interaction coordinate of the user, and determining a corresponding target cloud template and / or target association information according to the first interaction coordinate; Generate an associated memory segment according to the target cloud template and / or target associated information, establish a mapping relationship between the associated memory segment and the feature vector, and store the associated memory segment in the associated memory library; If not, then the corresponding associative memory segment is matched in the associative memory library based on the mapping relationship according to the feature vector.

4. The calligraphy practice method based on augmented reality technology according to claim 1, characterized in that: After the step of pushing the dynamic writing model and the associated memory fragment, the method further includes: Acquiring user writing dynamic features, wherein the user writing dynamic features include at least a writing trajectory and a stroke order; Comparing the user's writing dynamic features with the dynamic writing model to generate a corresponding comparison result; According to the comparison result, adjustment parameters of the dynamic writing model are determined, and a custom memory interaction window is pushed.

5. The calligraphy practice method based on augmented reality technology according to claim 4, characterized in that: The step of determining the adjustment parameters of the dynamic writing model according to the comparison result and pushing the customized memory interaction window includes: Determining error points and evaluation values ​​of the error points based on the comparison results; Determining a key frame of the dynamic writing model according to the error point and the evaluation value; Determining adjustment parameters of the dynamic writing model according to the key frame, wherein the adjustment parameters are used to adjust the playback speed and playback times of the key frame; determining an evaluation threshold, and if it is determined that the evaluation value is less than the evaluation threshold, disassembling the dynamic writing model to generate and push a corresponding individual stroke writing model; According to the comparison result, a custom memory interaction window is pushed, a second interaction coordinate of the custom memory interaction window is obtained, an associated memory segment is generated according to the second interaction coordinate, and the associated memory segment is stored in the associated memory library.

6. The calligraphy practice method based on augmented reality technology according to claim 5, characterized in that: After determining the evaluation threshold and, if it is determined that the evaluation value is less than the evaluation threshold, disassembling the dynamic writing model to generate and push a corresponding individual stroke writing model, the method further includes: Determining weight values ​​of the respective evaluation values ​​according to the comparison results; Determining improvement suggestion information according to the weight value; Generate an exercise report based on the evaluation value, weight value and improvement suggestion information; Compare the practice report with the cloud practice report data, generate corresponding practice comparison results, and fill the practice comparison results into the practice report.

7. A calligraphy practice device based on augmented reality technology, characterized in that: The calligraphy practice device based on augmented reality technology includes: A first determination module is configured to determine an application platform and, based on the application platform, determine a corresponding scanning mode, wherein the scanning mode is configured to select and integrate a corresponding optical character recognition library, a speech recognition library, a natural language processing library, and an augmented reality library; a first acquisition module, configured to acquire the paper text image information and / or the voice recognition information, extract the corresponding practice text from the paper text image information based on the optical character recognition library, and extract the corresponding practice text from the voice recognition information based on the voice recognition library; A first extraction module, configured to extract keyword features of the practice text based on the natural language processing library; A second determining module is configured to determine a dynamic writing effect based on the augmented reality library and the keyword features, wherein the dynamic writing effect includes a writing order, a writing speed, a writing font style, and a writing dynamic feature; A calling module, configured to call a three-dimensional modeling system and create a dynamic writing model according to the dynamic writing effect; A third determination module is used to determine an associative memory library and match corresponding associative memory segments according to the dynamic writing model; A push module is used to push the dynamic writing model and the associated memory segment.

8. The calligraphy practice device based on augmented reality technology according to claim 7, characterized in that: The calligraphy practice device based on augmented reality technology also includes: A second extraction module is used to extract key features based on the dynamic writing model; a fourth determining module, configured to determine a coding classification library based on keyword features of the current dynamic writing model; A generation module is used to encode the key features in vector order based on the coding classification library to generate corresponding feature vectors; An updating module, configured to update the vector sequence after storing the feature vector in the coding classification library; The third determining module includes: a first determining unit, configured to determine a feature vector of the dynamic writing model; a judgment unit, configured to judge whether the dynamic writing model is generated for the first time, and if so, to push a cloud-based template and related information retrieved by an artificial intelligence search tool based on the feature vector; an acquiring unit, configured to acquire a first interaction coordinate of the user, and determine a corresponding target cloud template and / or target association information according to the first interaction coordinate; A first generating unit is configured to generate an associated memory segment according to the target cloud template and / or target associated information, establish a mapping relationship between the associated memory segment and the feature vector, and store the associated memory segment in the associated memory library; a matching unit, configured to, if not, match a corresponding associative memory segment in an associative memory database based on the mapping relationship according to the feature vector; The calligraphy practice device based on augmented reality technology also includes: A second acquisition module is used to acquire dynamic features of user writing, wherein the dynamic features of writing include at least a writing trajectory and a stroke sequence; A comparison module, configured to compare the user's writing dynamic features with the dynamic writing model and generate a corresponding comparison result; a fifth determining module, configured to determine adjustment parameters of the dynamic writing model according to the comparison result, and push a custom memory interaction window; The fifth determining module includes: A second determining unit is configured to determine an error point and an evaluation value of the error point according to the comparison result; a third determining unit, configured to determine a key frame of the dynamic writing model according to the error point and the evaluation value; a fourth determining unit, configured to determine adjustment parameters of the dynamic writing model according to the key frame, wherein the adjustment parameters are used to adjust a playback speed and a playback number of the key frame; a fifth determining unit, configured to determine an evaluation threshold, and if it is determined that the evaluation value is less than the evaluation threshold, disassemble the dynamic writing model to generate and push a corresponding individual stroke writing model; a pushing unit, configured to push a custom memory interaction window according to the comparison result, obtain a second interaction coordinate of the custom memory interaction window, generate an associated memory segment according to the second interaction coordinate, and store the associated memory segment in the associated memory library; The fifth determination module also includes a sixth determining unit, configured to determine a weight value of each of the evaluation values ​​according to the comparison result; a seventh determining unit, configured to determine improvement suggestion information according to the weight value; A second generating unit, configured to generate a practice report based on the evaluation value, weight value, and improvement suggestion information; The comparison unit is used to compare the practice report with the cloud practice report data, generate a corresponding practice comparison result, and fill the practice comparison result into the practice report.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the calligraphy practice method based on augmented reality technology as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the calligraphy practice method based on augmented reality technology as claimed in any one of claims 1 to 6 are implemented.

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