Non-abandoned technology simulation learning method and system based on augmented reality

Abstract

The invention relates to the field of virtual reality, in particular to a non-missing technology simulation learning method and system based on augmented reality, and the method comprises the steps: recognizing a non-missing learning instruction when the non-missing learning instruction of a user side is received, and determining a target non-missing parameter according to a recognition result; environment parameters and user parameters of the user side are obtained, and an initial simulation environment is constructed according to the environment parameters, the user parameters and the target non-perpetual parameters; acquiring simulated behavior data of the user side in real time, and performing comparative analysis on the simulated behavior data and the target non-perpetual parameters; judging whether the simulation behavior data meets the simulation condition of the next stage or not according to the analysis result; when the simulation behavior data does not meet the simulation condition of the next stage, generating a guide parameter and sending the guide parameter to the user side; and when the simulation behavior data meets the simulation condition of the next stage, acquiring simulation behavior data of the next stage of the user side based on a judgment result. The user can obtain real-time feedback in the simulation learning process, and the user is helped to correct errors in time.

Description

Technical Field

[0001] The present application relates to the field of virtual reality technology, and in particular to an augmented reality-based intangible cultural heritage craft simulation learning method and system. Background Art

[0002] Intangible cultural heritage craft simulation learning is a way of digitally simulating and interactively learning traditional crafts in intangible cultural heritage (abbreviated as "intangible cultural heritage") by combining modern scientific and technological means. Its core purpose is to enable learners to experience, learn and pass on these precious traditional skills in a virtual environment through simulation technology. For example, learners learn folk dances through AR glasses, which capture the learners' movements, synchronize dances with virtual folk dancers, and the system analyzes the accuracy of the movements and provides feedback.

[0003] Traditional methods of learning intangible cultural heritage crafts rely on static tutorials or video demonstrations, lacking timely feedback, which makes it difficult for learners to correct operational errors in a timely manner during the learning process. Secondly, in the existing intangible cultural heritage craft simulation learning technology, feedback on whether the user's actions are standardized usually uses key point comparison. The system presets a series of key points, which represent standard actions or positions in intangible cultural heritage crafts. Due to the single standard, regardless of the user's height and body shape, the system uses a unified standard key point for evaluation, that is, all users learn and evaluate according to the same key point standard. However, due to the height difference of users, tall users are more likely to be detected by the system when performing actions, while short users may need a larger movement amplitude to reach the same key point position, which leads to inaccurate evaluation results. When users of different body shapes perform the same action, the trajectory and amplitude of their limb movements may be different. The existing technology fails to take this difference into account, resulting in the evaluation results not being consistent with the actual learning effect. Intangible cultural heritage crafts often focus on the coherence and rhythm of the action. The existing technology only evaluates the key points, and it is difficult to effectively judge whether the coherence and rhythm of the user's actions are in line with the current craft type.

[0004] Therefore, the prior art has defects and needs to be improved. Summary of the invention

[0005] In order to solve one or several problems in the prior art, the main purpose of this application is to provide an augmented reality-based intangible cultural heritage craft simulation learning method and system.

[0006] In order to achieve the above-mentioned invention object, the present application proposes a method for simulating learning of intangible cultural heritage crafts based on augmented reality, the method comprising: When receiving the intangible cultural heritage learning instruction from the user end, identifying the intangible cultural heritage learning instruction, and determining the target intangible cultural heritage parameters according to the identification result; Obtaining environmental parameters and user parameters of the user end, and constructing an initial simulation environment according to the environmental parameters, user parameters and target intangible cultural heritage parameters; Acquire the simulated behavior data of the user end in real time, and compare and analyze the simulated behavior data with the target intangible cultural heritage parameters; Determine whether the simulated behavior data meets the conditions for the next stage of simulation based on the analysis results; When the simulation behavior data does not meet the conditions for the next stage of simulation, a guidance parameter is generated and sent to the user end; When the simulated behavior data meets the conditions for the next stage of simulation, the next stage of simulated behavior data of the user terminal is obtained based on the result of the judgment.

[0007] The embodiment of the present application also provides an augmented reality-based intangible cultural heritage craft simulation learning system, including: An identification module is used to identify the intangible cultural heritage learning instruction when receiving the intangible cultural heritage learning instruction from the user end, and determine the target intangible cultural heritage parameters according to the identification result; The first acquisition module is used to acquire the environment parameters and user parameters of the user terminal, and construct an initial simulation environment according to the environment parameters, user parameters and target intangible cultural heritage parameters; An analysis module is used to obtain the simulated behavior data of the user end in real time, and compare and analyze the simulated behavior data with the target intangible cultural heritage parameters; A judgment module, used to judge whether the simulation behavior data meets the conditions for the next stage of simulation according to the analysis results; A generating module, used for generating guiding parameters and sending them to the user end when the simulation behavior data does not meet the conditions for the next stage of simulation; The second acquisition module is used to acquire the next stage simulation behavior data of the user terminal based on the judgment result when the simulation behavior data meets the conditions of the next stage simulation.

[0008] The present application also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0009] The present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0010] The intangible cultural heritage craft simulation learning method and system based on augmented reality of the embodiment of the present application, by identifying the intangible cultural heritage learning instructions of the user end and obtaining environmental parameters and user parameters, the system can build an initial simulation environment that meets the actual situation of the individual, provide a personalized learning experience, and adapt to the learning needs and environmental conditions of different users. The simulated behavior data of the user end is obtained in real time, and compared and analyzed with the target intangible cultural heritage parameters to ensure that the user can obtain immediate feedback during the simulation learning process, help the user to correct errors in time, and improve learning efficiency. According to the analysis results, it is judged whether the simulated behavior data meets the conditions of the next stage of simulation, and phased learning is realized, and the user is gradually guided to master the intangible cultural heritage skills to ensure the systematicness and coherence of learning. When the simulated behavior data does not meet the conditions, the system generates a guidance parameter and sends it to the user end, providing targeted improvement suggestions and operation demonstrations, effectively improving the user's learning effect and skill level. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A flowchart of an augmented reality-based intangible cultural heritage craft simulation learning method according to an embodiment of the present application; Figure 2 A flowchart of an augmented reality-based intangible cultural heritage craft simulation learning method according to an embodiment of the present application; Figure 3 This is a schematic structural block diagram of an intangible cultural heritage craft simulation learning system based on augmented reality according to an embodiment of the present application; Figure 4 A schematic block diagram of the structure of a computer device according to an embodiment of the present application.

[0012] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] Reference Figure 1 In an embodiment of the present application, a method for simulating learning of intangible cultural heritage crafts based on augmented reality is provided, and the method comprises: S1. When receiving the intangible cultural heritage learning instruction from the user terminal, identifying the intangible cultural heritage learning instruction, and determining the target intangible cultural heritage parameters according to the identification result; S2, obtaining the environment parameters and user parameters of the user terminal, and constructing an initial simulation environment according to the environment parameters, user parameters and target intangible cultural heritage parameters; S3, acquiring the simulated behavior data of the user end in real time, and comparing and analyzing the simulated behavior data with the target intangible cultural heritage parameters; S4, judging whether the simulation behavior data meets the conditions for the next stage of simulation according to the analysis results; S5. When the simulation behavior data does not meet the conditions for the next stage of simulation, a guidance parameter is generated and sent to the user end; S6. When the simulation behavior data meets the conditions for the next stage of simulation, the simulation behavior data for the next stage of the user terminal is obtained based on the judgment result.

[0015] As described in the above steps S1-S3, the user sends a learning instruction to the system through a device (such as a mobile phone, tablet or augmented reality glasses), indicating that he wants to start or continue the learning process of a certain intangible cultural heritage craft. The purpose of this instruction is to activate the entire learning process and ensure that the system starts the corresponding simulation environment according to the user's needs. By receiving the user's instructions, the system can clarify the learning goals and start related functions. This step is the starting point of the entire learning process and provides a personalized start signal for the subsequent intangible cultural heritage craft learning. The system uses natural language processing (NLP) or image recognition technology to identify the intangible cultural heritage craft content in the learning instruction, thereby determining the specific learning goals and process requirements. For example, the user may choose to learn "Cloisonné Craft", and the system determines the target intangible cultural heritage parameters after identification. Through accurate identification of instructions, the system can accurately lock the learning goals and specify specific learning standards for each craft (such as process steps, operating skills, etc.). This helps to provide clear directions and requirements for subsequent learning. Environmental parameters refer to the actual physical environment information of the user (such as lighting, spatial layout, etc.), and user parameters refer to the user's personal data (such as height, body shape, hand movement ability, etc.). Based on these parameters, the system generates a virtual learning environment that matches the user's actual situation. This step uses augmented reality technology to map the virtual process environment to the user's space. By combining the user's actual environment and personal abilities for customized design, the system can create a learning environment that suits the user. This ensures the smooth progress of the learning process, matching the virtual process with the user's actual conditions, and thus enhancing the immersion and practicality of learning.

[0016] As described in the above steps S4-S6, the system monitors the user's operation behavior (such as gestures, movements, etc.) in real time through devices such as sensors or cameras, and compares these simulated behavior data with the preset process parameters. Based on the analysis results, the system determines whether the user has completed the learning goals or operation steps of the current stage. This real-time comparative analysis can provide dynamic feedback to help users clearly understand whether they have completed the current learning goals. If there are errors or deviations, the system can provide adjustment suggestions in time. This method improves the efficiency and accuracy of learning and avoids users from continuing to learn the next step due to unknowing mistakes. When the system recognizes that the user's simulated behavior does not meet the requirements of the target process, the system automatically generates guidance parameters (such as prompt voice, text instructions or video tutorials) through algorithms to help users correct errors or improve operations. These guidance contents are generated based on system comparative analysis and help users understand the essentials of operation. The generation of guidance parameters and real-time feedback mechanism can help users correct errors and avoid delays in learning progress due to improper operations. Through guidance and adjustment, learners can accurately master each process step, thereby accelerating the progress of learning and reducing the occurrence of errors. When the system detects that the user has completed the current learning stage and meets the conditions for entering the next stage, the system automatically switches to the next stage. This includes acquiring new simulated behavior data and adjusting the virtual environment to adapt to higher-level learning content (such as more complex process steps, more skill requirements). The dynamic adjustment of this stage ensures that the learning content can be continuously improved as the user progresses, avoids repetition of low-level stages, and provides a progressive learning process to adapt to the user's learning progress. This step-by-step approach can enhance the user's confidence and gradually increase the difficulty of learning, ensuring the systematic and hierarchical nature of learning.

[0017] As mentioned above, by identifying the intangible cultural heritage learning instructions on the user side and obtaining environmental parameters and user parameters, the system can build an initial simulation environment that meets the individual's actual situation, provide a personalized learning experience, and adapt to the learning needs and environmental conditions of different users. The simulated behavior data on the user side is obtained in real time, and compared and analyzed with the target intangible cultural heritage parameters to ensure that users can obtain immediate feedback during the simulation learning process, help users correct errors in a timely manner, and improve learning efficiency. According to the analysis results, it is judged whether the simulated behavior data meets the conditions for the next stage of simulation, and phased learning is realized to gradually guide users to master intangible cultural heritage skills, ensuring the systematicness and coherence of learning. When the simulated behavior data does not meet the conditions, the system generates guidance parameters and sends them to the user side, providing targeted improvement suggestions and operation demonstrations, effectively improving the user's learning effect and skill level.

[0018] Reference Figure 2 In one embodiment, the method of judging whether the simulated behavior data meets the conditions for the next stage of simulation based on the analysis results includes: S41, parsing the simulated behavior data, extracting action features of the simulated behavior data, wherein the action features include action trajectory, angle, and continuity; S42, performing comparative analysis on the action feature and the target intangible cultural heritage parameter, quantifying the result of the comparative analysis, and calculating the matching coefficient between the action feature and the target intangible cultural heritage parameter; S43, when the matching coefficient is greater than a preset coefficient threshold, extracting emotional features of the simulated behavior data; S44, inputting the emotion feature and the action feature into a preset scene analysis model, analyzing the fit of the emotion feature and the action feature with the target intangible cultural heritage parameter through the scene analysis model, and outputting a fit score result; S45. When the fit score is greater than a preset score threshold, it is determined that the simulation behavior data meets the conditions for the next stage of simulation.

[0019] As described in the above steps, the simulated behavior data is obtained through the user's action input and is expressed as a series of time series data. In order to effectively evaluate these data, it is necessary to extract key action features, including: Action trajectory: refers to the movement path of the object or the user's hand during the execution of the action, reflecting the spatial change of the action. Angle: refers to the angle change of each joint during the execution of the action, which can evaluate the accuracy and technical details of the action. Coherence: indicates the degree of smooth connection between actions, which can reflect whether the user has mastered the rhythm and fluency of the action. Extracting these action features can effectively quantify and describe the structure of the action. The target intangible cultural heritage craft parameters refer to the ideal form of the craft under traditional cultural and technical standards. By comparing the user's action characteristics with these standards, a matching coefficient can be calculated to quantify the degree of compliance of the user's action with the target intangible cultural heritage craft standards. The characteristics of the simulated behavior data are compared with the standard parameters of the target process by mathematical and algorithmic methods. The matching coefficient is a numerical value calculated by a series of mathematical models and data processing methods, reflecting the similarity between the action characteristics and the target process requirements. The larger the matching coefficient, the closer the user's action is to the target process requirements, which can improve the effectiveness of learning. Through this quantitative analysis, the system can evaluate the user's learning progress and skill level in real time, helping users correct mistakes and improve their skills during practice. Emotional features refer to the impact of the user's emotional state on the performance of actions during skill training or performance. These emotional features may involve emotional expression, strength or lightness of actions, etc. The setting of this step assumes that the user's emotional changes will affect the performance of actions, especially in the learning of intangible cultural heritage crafts, where emotional involvement is often closely related to the degree of mastery of skills. Emotional features can be extracted through expression recognition, voice analysis, body movement patterns, etc. After extracting emotional features, the system can more comprehensively evaluate non-technical factors in the learning process, such as learners' enthusiasm and involvement. This is crucial for the optimization of personalized learning processes and helps the system further customize training recommendations that match the user's status. The scenario analysis model refers to a system based on a data model that analyzes and predicts how users' behavior and emotions affect the learning process in a specific environment. By inputting action features and emotional features, the model can analyze the fit between the two and the target intangible cultural heritage parameters. The fit measures the degree of match between the user's learning behavior, emotional state and the requirements of intangible cultural heritage crafts, and is usually calculated by weighted average, deep learning or other machine learning methods. Through this analysis, the system can comprehensively consider the user's skills and emotional state, more comprehensively evaluate the learning effect, and then provide accurate guidance for the next step of learning, ensuring that the user is always on the most appropriate learning path. A score threshold is set in the system. When the fit score reaches or exceeds the threshold, the system will determine that the user has the conditions to enter the next stage of simulation.This stage is usually a key node or challenge in the learning process, which means that the user's skills have reached a certain standard. The setting of the threshold needs to be dynamically adjusted according to the standards of the target intangible cultural heritage craft and the actual performance of the user to ensure that the learning process is both challenging and can effectively promote skill improvement.

[0020] In one embodiment, after the step of calculating the matching coefficient between the action feature and the target intangible cultural heritage parameter, the method further comprises: When the matching coefficient is less than a preset coefficient threshold, obtaining the user parameters, including height, weight and limb size parameters; Combining the user parameters and the target intangible cultural heritage parameters, analyzing the reasons why the simulated behavior data does not meet the conditions of the next stage; When the reason for not meeting the next stage condition is that the detection point of the action position is limited due to the user parameters, adjusting the action feature according to the user parameters; Recalculate the matching coefficient between the action feature and the target intangible cultural heritage parameter according to the adjusted action feature; When the matching coefficient between the adjusted action feature and the target intangible cultural heritage parameter is greater than a preset coefficient threshold, the emotional feature of the simulated behavior data is extracted.

[0021] As mentioned above, user parameters include height, weight, and limb size, because these factors may directly affect the execution of actions when simulating and actually operating intangible cultural heritage crafts. For example, height and limb length will affect the coordination and posture of the user when performing certain actions. For example, people with shorter height may need to adjust the amplitude of the action, while people with heavier weight may have different strengths and angles of the action. By obtaining the user's physiological characteristics, it is possible to accurately analyze the user's possible physical limitations when the matching coefficient requirements are not met, provide a basis for subsequent adjustment of the action characteristics, and thus provide an optimization solution in the personalized learning process. The user parameters are combined with the target intangible cultural heritage parameters for analysis in order to find out the root cause of why the current action characteristics do not meet the requirements of the target process. The user's physiological characteristics (such as height, weight, and limb size) may differ from the standard action characteristics of the target process, so the system needs to analyze whether these factors cause problems in the execution of the action. For example, the target process may require a larger range of action trajectories, and shorter individuals may not be able to fully reach their destination due to limb limitations. At this point, the analysis not only considers the accuracy of the action, but also the user's physiological characteristics. This combined analysis helps ensure that the system can fully identify problems in the learning process, not just from a skill perspective. It can delve into the differences between physical conditions and technical requirements to better adjust the training plan. When the analysis finds that user parameters (such as height, weight, limb size, etc.) have resulted in limited detection points for the action position, the action characteristics need to be adjusted. For example, a certain action requires the arm to be extended to a certain position, but for a shorter user, the arm extension range may be insufficient. At this time, the system will appropriately adjust the detection standard method of the action based on the user's height, limb length and other parameters, and ensure that the action can be completed within its physical limitations by changing the amplitude or angle of the action to effectively detect whether it is standard. In addition, adjusting the action characteristics can enable the user to complete the target action to the greatest extent under physiological conditions by appropriately reducing the range of motion of certain joints or by redesigning the action trajectory. By adjusting the action characteristics to adapt to the user's physical conditions, the user can effectively perform the action under realistic conditions. This personalized adjustment makes the learning process more in line with the user's ability. The matching coefficient is recalculated because after adjusting the action characteristics, the fit between the user's action and the target process standard needs to be re-evaluated. Through the adjusted action characteristics, the system will recalculate the matching coefficient according to the new action performance to see if it meets the requirements of the target intangible cultural heritage parameters. This step is to ensure that the adjusted action characteristics can still achieve a reasonable match under the modified standards and ensure that the learning process does not deviate from the correct direction. After recalculating the matching coefficient, you can confirm whether the adjustment is effective and ensure that the user's actions meet the requirements of the target intangible cultural heritage craftsmanship within the newly adjusted range. If the adjustment is effective, the system will use the new matching coefficient to determine whether it can enter the next stage.By adjusting the evaluation criteria for standard movements or positions based on differences in user height, body shape, etc., the evaluation bias caused by height differences in traditional technologies is avoided, allowing each user to perform accurate movement evaluation based on their own physiological characteristics, providing a fairer and more personalized learning experience.

[0022] In one embodiment, after the step of recalculating the matching coefficient between the action feature and the target intangible cultural heritage parameter according to the adjusted action feature, the method further comprises: When the matching coefficient between the adjusted action feature and the target intangible cultural heritage parameter is less than a preset coefficient threshold, it is determined that the simulated behavior data does not meet the conditions for the next stage of simulation; Based on the judgment result, a guidance parameter is generated and sent to the user end.

[0023] As mentioned above, in the learning process of simulated behavior data, the accuracy of the action features is closely related to the requirements of the target process. The adjusted action features will be compared with the standards of the target intangible cultural heritage process to calculate a matching coefficient. This coefficient measures the degree of fit between the current action features and the target process parameters. When the matching coefficient between the adjusted action features and the target intangible cultural heritage parameters is less than the preset coefficient threshold, it means that the current simulated behavior data cannot meet the standards of the target process, and the system will judge that the simulated behavior data does not meet the conditions for entering the next stage. The purpose of this step is to ensure that each simulated behavior meets the target standard and sets clear evaluation criteria for each learning stage. If the matching coefficient does not meet the requirements, the system will prevent the learning process from continuing to advance and prevent the user from entering an unprepared stage. In this way, the system can effectively control the learning progress and will not let the user easily skip the parts that have not been mastered. For complex skills such as intangible cultural heritage crafts, it is very important to gradually meet the required standards, which helps to ensure the solidity and stability of the learning results. If the system determines that the user's simulated behavior data does not meet the simulation conditions for the next stage, the system will generate guidance parameters. These guidance parameters may include suggestions on how users can adjust movement characteristics, improve movement execution skills, pay attention to posture or other feedback, with the goal of helping users improve their current learning status. The content of guidance parameters may include: Movement adjustment suggestions: such as changing the movement angle, amplitude, speed, etc. Posture adjustment suggestions: such as increasing symmetry, optimizing body coordination, etc. Physiological adaptation suggestions: provide personalized adjustment suggestions based on the user's height, weight and other physiological characteristics. The generated guidance parameters will help users improve movement characteristics more specifically and avoid repeating wrong movements aimlessly. Personalized feedback enables users to quickly find solutions when faced with challenges and improve learning outcomes.

[0024] In one embodiment, the method of generating a guidance parameter based on the result of the judgment and sending it to the user terminal includes: Calculating the key point positions in the target intangible cultural heritage parameters according to the adjusted action features; Based on the calculation result, marking a virtual track between the action feature and the key point; The virtual trajectory generation guidance parameter is sent to the user end.

[0025] As described above, by calculating the relationship between the user's action features and the target key points, the system can mark a "virtual trajectory". The virtual trajectory represents the path that the user takes from an action feature to the target key point. This trajectory can be an approximation of the physical path or a mathematical model of the ideal action trajectory. The purpose of marking the virtual trajectory is to help users better understand how to transition from the current action features to the correct key points. Through the visual trajectory, users can intuitively see the evolution path of their actions and adjust the direction in time to avoid deviation from the target. This trajectory can be presented to users through a graphical interface, animation or augmented reality, depending on the system design. Marking the virtual trajectory can clearly distinguish the wrong action path from the correct path, so that users can see their progress at a glance. Through the marking of the virtual trajectory, users can intuitively see the gap between their current action and the ideal goal. Such feedback is easier to understand and grasp than text descriptions or digital data. After seeing the virtual trajectory, users can clearly know where their actions have deviated, adjust their action paths in time, and reduce unnecessary errors. Through the visual feedback of the trajectory, users are not only repeatedly performing operations, but can also control the accuracy of their actions by adjusting the virtual trajectory, thereby enhancing the sense of participation and interactivity in learning. Based on the marked virtual track, the system generates guidance parameters and provides improvement suggestions to the user. These guidance parameters may include specific action instructions such as adjusting posture, path changes, or execution speed. The visualization of the virtual track alone may not be enough for the user to understand how to make adjustments. Therefore, the system further provides specific operation suggestions by generating guidance parameters. The guidance parameters will provide actionable adjustment solutions based on the differences between the virtual track and key points. The guidance parameters will be fed back to the user through visual, auditory or other appropriate means. For example, the system may guide the user through a new adjustment route marked on the screen or through voice prompts.

[0026] In one embodiment, the initial simulation environment is constructed according to the environment parameters, user parameters and target intangible cultural heritage parameters, and the method includes: Acquire the environmental parameters, and determine the brightness parameters, noise parameters and operation space parameters of the current environment according to the environmental parameters; Determine the interaction parameters corresponding to the intangible cultural heritage craft type according to the target intangible cultural heritage parameters; The brightness parameter, noise parameter and operation space parameter of the current environment are subjected to scene fusion with the interaction parameter, and the user parameter is filled into the scene fusion to obtain an initial simulation environment.

[0027] As mentioned above, when learning or practicing intangible cultural heritage crafts, environmental factors have an important impact on the user's learning experience and action performance. Environmental parameters usually include various physical characteristics related to the space where the user is located, such as lighting, noise, and the size of the space available for operation. Brightness refers to the light intensity in the environment. Too dark or too bright environments may affect the user's performance, especially in intangible cultural heritage crafts that require precise visual feedback. For example, in delicate work such as carving, painting, and hand-made, differences in light intensity may cause users to be unable to accurately see the details of the operation. Noise is the sound interference in the environment. Excessive noise will affect the user's concentration and judgment, and even affect the meticulousness of the operation. Especially in intangible cultural heritage crafts that require high concentration (such as weaving, carving, etc.), noise control is very important. This refers to the size of the physical space available to users when performing tasks. Narrow spaces may restrict users' activities, while overly spacious spaces may make users feel a lack of positioning. Ensuring appropriate operating space allows users to perform actions more flexibly. These environmental factors directly affect the user's operation accuracy and overall learning effect. For example, in an inappropriate lighting or noisy environment, it is difficult for users to focus on complex manual operations. By collecting and adjusting these environmental parameters, an ideal learning or operating environment can be provided to ensure the efficiency of the learning process. Obtaining these environmental parameters helps to evaluate whether the current environment is suitable for learning the target intangible cultural heritage craft. By monitoring and optimizing environmental parameters, an optimal learning condition can be provided for users. For example, adjusting lighting and noise conditions can help reduce external interference and improve the user's operation accuracy and concentration. A suitable operation space allows users to perform actions more freely, thereby reducing errors caused by space limitations. Each intangible cultural heritage craft has its own specific requirements and standards. The target intangible cultural heritage parameters can include the type of craft, required tools, action requirements, etc. For example, the tools, actions, and operation methods of crafts such as painting, pottery, and weaving are different. According to the requirements of different intangible cultural heritage crafts, the interaction parameters determine how users interact with the virtual environment or learning system. For example, painting may require fine hand operation input, and pottery may require tactile feedback to adjust the strength and direction. The interaction parameters may include the flexibility of the action, the accuracy requirements, the use of tools, the feedback frequency, etc. The learning methods and operation requirements of different intangible cultural heritage crafts vary greatly, and the required interaction methods will also be different. For example, weaving and pottery have completely different interaction requirements. Weaving may require delicate hand movements, while pottery may focus more on tactile feedback. Determining the interaction parameters based on the target process parameters can ensure that the system provides users with operation guidance that is consistent with the specific process. By setting different interaction parameters according to the requirements of the target intangible cultural heritage craft, users can get a more suitable operation interface and learning method based on the type of craft they are learning. This can greatly improve the learning experience and efficiency.The adjustment of interaction parameters for different processes will directly affect the user's skill mastery. Correctly setting the interaction parameters can ensure that users can operate accurately under appropriate feedback and gradually improve their technical level. The fusion of environmental parameters and interaction parameters is to create a comprehensive learning or operating environment based on the needs of the real environment and the target process. The process of scene fusion involves integrating parameters such as brightness and noise of the physical environment with interactive feedback in virtual or augmented reality. Specifically, by dynamically adjusting these parameters, the user's real operating experience under specific process conditions is simulated. The fusion of environmental and interactive parameters can ensure that users learn in an environment that matches the actual operating situation. When environmental factors and interactive requirements are highly consistent, users can learn in a more realistic scene, which not only helps to master skills, but also reduces interference in the learning process and improves the effect. By integrating environmental and interactive parameters, users can experience a more realistic operating environment. This sense of reality can make it easier for users to enter the learning state and reduce the psychological burden caused by environmental mismatch. The fusion of scenes enables users to get interactive feedback under actual environmental conditions. This feedback is not limited to the operation of the tool, but also the adaptation to the entire environment. For example, under different brightness or noise levels, the system will make appropriate adjustments based on the interaction needs to ensure that users can get effective learning support under any conditions. Filling in user parameters: User parameters refer to characteristics related to the user itself, such as personal body size, operating habits, skill level, etc. Filling these parameters into scene fusion can personalize the learning environment so that the system can adjust the operation mode and feedback according to the actual situation of the user. For example, the system can adjust the operating space according to the user's height, or adjust the difficulty of feedback according to the skill level. Different users have diverse operating habits, skill levels, physical characteristics, etc., and a single scene setting cannot meet the needs of all users. By personalizing the simulation environment according to user parameters, the adaptability and effectiveness of learning can be improved, allowing each user to train in the environment that best suits them.

[0028] In one embodiment, performing arts include dance, music, drama and other forms, emphasizing the expressiveness of movements, sense of rhythm, sound control and the like. When learning these techniques, users have high requirements for movement and reaction speed and accuracy, and also need to have a good perception of rhythm, emotional expression and other aspects. The performance space of performing arts usually requires appropriate light adjustment to highlight the atmosphere of the performance. The brightness may vary with different stages of the performance to match the performance mood and rhythm. In music or drama performances, the clarity of the sound and the control of ambient noise are crucial. When learning music, the user's perception of notes, rhythm and melody requires a high-fidelity sound environment. The intangible cultural heritage crafts of dance and drama require a larger space for movement practice, and the space should be able to meet the user's stretch and freedom during the performance. Depending on the type and style of the performance, the interactive parameters may include dynamic movement recognition and feedback. Through virtual reality or augmented reality technology, the user's performance movements can be captured and analyzed in real time, giving real-time feedback to help them adjust their rhythm, posture or expression. For example, through motion sensors and audio feedback technology, different stage environments can be simulated for immersive performance training. The craft production category includes traditional handicrafts such as carving, weaving, pottery, papermaking, metal processing, etc., which emphasizes the precision, patience and skills in the hand-made process. When learning these crafts, users need to master the skills through repeated practice, and they need to be extremely focused during the operation. In the craft production process, the light needs to be sufficient and soft to ensure delicate operation, such as carving, embroidery, etc. These processes require meticulous visual observation. For example, the learning system of pottery can provide feedback on force control to help users accurately perceive the force and skills of manual operation. By simulating the use of tools and hand movements, the system can provide real-time guidance and adjustments based on the user's skill level and operation progress to ensure that users can gradually improve during the practice process. The visual arts category includes traditional art forms such as painting, sculpture, calligraphy, embroidery, etc., emphasizing visual expression, artistic creation techniques, and the details and aesthetics of the work. When learning these crafts, users not only need technical training, but also need to have a certain artistic sense and creativity. Brightness parameters: Visual arts crafts have high requirements for light, especially in the fields of painting and sculpture. Good lighting can help users see the details of the work, especially in the creation of fine works, and changes in light can also affect the effect of artistic creation. Noise parameters: The creation process of visual arts requires a quiet environment to avoid interference from external noise, especially when doing fine creation and painting. A quiet environment helps to concentrate. Operating space parameters: Art forms such as painting and sculpture usually require a larger creative space to ensure that users can play freely, especially in the creation of sculptures and large paintings.Interaction parameters: In the visual arts category, interaction parameters may include simulating the use of different art tools, such as brushes, knives, carving tools, etc., through gestures or touch screen operations, and providing feedback based on the user's operating accuracy and skill level. Augmented reality technology can be combined to combine the user's creative process with virtual tools and materials to simulate different art creation environments, allowing users to practice in different art creation scenarios.

[0029] In one embodiment, the step of determining the interaction parameters corresponding to the intangible cultural heritage craft type according to the target intangible cultural heritage parameters comprises: When the type of the intangible cultural heritage craft is handmade, the type of the hand-held tool on the user end is detected by an image recognition module; According to the tool type, obtaining the tool operation parameters, including a grip contact point number threshold, a tool weight simulation value, and an action surface shape parameter; Collecting elastic deformation data of the currently operated material through a pressure sensor, and matching a deformation coefficient threshold of a material property database according to the elastic deformation data; Based on the tool operation parameters and the deformation coefficient threshold, setting the vibration intensity and frequency parameters of the tactile feedback device; Identify the process complexity level in the target intangible cultural heritage parameters, and adjust the three-dimensional modeling accuracy parameters according to the process complexity level; Based on the adjusted results, the handcrafted interaction parameters are determined.

[0030] As mentioned above, the image recognition module identifies the tool held by the user by processing the image information on the user side. This can be achieved through computer vision technology, such as convolutional neural networks (CNNs) in deep learning. Based on different tool shapes, colors, features and other information, the system can determine the type of tool. The purpose of this feature is to enable the system to intelligently identify the tool the user is using, so as to provide the correct operating parameters for subsequent steps. If the user is using different types of tools, the system can adjust the feedback parameters according to the physical characteristics of different tools (such as weight, shape, etc.) to improve the interactive experience. Different types of tools have different operating methods and contact surfaces when operating. The number of grip contact points, tool weight, and the shape of the action surface are all key factors that affect the use effect of the tool. These parameters affect the force, pressure, and operating stability applied by the user during operation. Obtaining these operating parameters can help the system accurately simulate and understand the user's operating method. For example, heavy tools may require greater force and control, while small tools may require higher precision. Based on this data, the system can adjust the tactile feedback intensity, vibration frequency, etc., to make the user's operation perception more natural and realistic. Materials undergo elastic deformation when subjected to force, and pressure sensors can monitor the deformation of materials in real time. By analyzing the deformation data, the physical properties of the material, such as stiffness and softness, can be inferred. Different materials have different elastic moduli (deformation coefficients), which is crucial in intangible cultural heritage crafts because different processes require different material processing methods. By collecting and analyzing the elastic deformation data of the material in real time, the system can determine the current state of the material and adjust subsequent operations according to the physical properties of the material. This enables the system to provide personalized feedback for each material and improve the accuracy and flexibility of the process operation. According to the operation mode of the tool and the deformation characteristics of the material, the system can provide real-time feedback to the user through tactile feedback devices (such as vibration motors). The setting of vibration intensity and frequency needs to be adjusted according to the difficulty of the operation, the nature of the material, and the use of the tool. For example, when using harder materials, stronger vibration is required to provide feedback; when using soft materials, lighter vibration is more appropriate. By reasonably adjusting the vibration intensity and frequency, the system can enhance the user's operating perception and make the feedback during the operation more intuitive and real. This tactile feedback helps users better control the operation process and improve the accuracy and effect of the process. The complexity of the process usually affects the fineness of the production process. High-complexity processes may require more precise operations and modeling, while low-complexity processes may require lower precision. By identifying the complexity level of the process, the system can automatically adjust the accuracy of 3D modeling to ensure that the presentation effect of the process meets expectations. Specifically, high-complexity processes require higher 3D modeling accuracy to accurately reflect their details, while low-complexity processes may allow for lower accuracy.Adjusting the accuracy of 3D modeling can make the system better adapt to different types of intangible cultural heritage crafts, ensuring that the display or operation of each craft can meet its actual requirements. At the same time, this can also improve the efficiency of the system and avoid unnecessary waste of computing resources. Based on all the information obtained in the previous steps, including tool type, operating parameters, material properties, process complexity, etc., the system can comprehensively evaluate the requirements of the current process and determine the final interaction parameters. These interaction parameters will affect the way users interact with the system, including the strength and accuracy of tactile feedback, and the response to user operations.

[0031] Reference Figure 3 In an embodiment of the present application, there is also provided an augmented reality-based intangible cultural heritage craft simulation learning system, comprising: Identification module 1, used for identifying the intangible cultural heritage learning instruction when receiving the intangible cultural heritage learning instruction from the user end, and determining the target intangible cultural heritage parameter according to the identification result; The first acquisition module 2 is used to acquire the environment parameters and user parameters of the user terminal, and construct an initial simulation environment according to the environment parameters, user parameters and target intangible cultural heritage parameters; Analysis module 3, used to obtain the simulated behavior data of the user terminal in real time, and compare and analyze the simulated behavior data with the target intangible cultural heritage parameters; A judgment module 4 is used to judge whether the simulation behavior data meets the conditions for the next stage of simulation according to the analysis results; A generating module 5, for generating guiding parameters and sending them to the user end when the simulation behavior data does not meet the conditions for the next stage of simulation; The second acquisition module 6 is used to acquire the next stage simulation behavior data of the user terminal based on the judgment result when the simulation behavior data meets the conditions of the next stage simulation.

[0032] As described above, it can be understood that the various components of the intangible cultural heritage craft simulation learning system based on augmented reality proposed in this application can realize the functions of any of the intangible cultural heritage craft simulation learning methods based on augmented reality described above, and the specific structure will not be repeated here.

[0033] Reference Figure 4 In an embodiment of the present application, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 4As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor designed by the computer 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 memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as monitoring data. 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 method for learning intangible cultural heritage craft simulation based on augmented reality is implemented.

[0034] The above-mentioned processor executes the above-mentioned intangible cultural heritage craft simulation learning method based on augmented reality, including: when receiving the intangible cultural heritage learning instruction of the user end, identifying the intangible cultural heritage learning instruction, and determining the target intangible cultural heritage parameters according to the identification result; obtaining the environmental parameters and user parameters of the user end, and building an initial simulation environment according to the environmental parameters, user parameters and target intangible cultural heritage parameters; obtaining the simulation behavior data of the user end in real time, and comparing and analyzing the simulation behavior data with the target intangible cultural heritage parameters; judging whether the simulation behavior data meets the conditions for the next stage of simulation according to the analysis results; when the simulation behavior data does not meet the conditions for the next stage of simulation, generating guidance parameters and sending them to the user end; when the simulation behavior data meets the conditions for the next stage of simulation, obtaining the simulation behavior data of the user end for the next stage based on the judgment result.

[0035] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, an augmented reality-based intangible cultural heritage craft simulation learning method is implemented, comprising the following steps: when an intangible cultural heritage learning instruction from a user terminal is received, the intangible cultural heritage learning instruction is identified, and target intangible cultural heritage parameters are determined according to the identification result; environmental parameters and user parameters from the user terminal are acquired, and an initial simulation environment is constructed according to the environmental parameters, user parameters and target intangible cultural heritage parameters; simulation behavior data from the user terminal is acquired in real time, and the simulation behavior data is compared and analyzed with the target intangible cultural heritage parameters; whether the simulation behavior data meets the conditions for the next stage of simulation is determined according to the analysis results; when the simulation behavior data does not meet the conditions for the next stage of simulation, a guidance parameter is generated and sent to the user terminal; when the simulation behavior data meets the conditions for the next stage of simulation, the simulation behavior data for the next stage of the user terminal is acquired based on the judgment result.

[0036] 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 provided in this application and used in the embodiments 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 (SSRSDRAM), 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.

[0037] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, device, article or method including the element.

[0038] The above description is only a preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for simulating learning of intangible cultural heritage crafts based on augmented reality, characterized in that: The method comprises: When receiving the intangible cultural heritage learning instruction from the user end, identifying the intangible cultural heritage learning instruction, and determining the target intangible cultural heritage parameters according to the identification result; Obtaining environmental parameters and user parameters of the user end, and constructing an initial simulation environment according to the environmental parameters, user parameters and target intangible cultural heritage parameters; Acquire the simulated behavior data of the user end in real time, and compare and analyze the simulated behavior data with the target intangible cultural heritage parameters; Determine whether the simulated behavior data meets the conditions for the next stage of simulation based on the analysis results; When the simulation behavior data does not meet the conditions for the next stage of simulation, a guidance parameter is generated and sent to the user end; When the simulated behavior data meets the conditions for the next stage of simulation, the next stage of simulated behavior data of the user terminal is obtained based on the result of the judgment.

2. The method for learning intangible cultural heritage craft simulation based on augmented reality according to claim 1, characterized in that: The method of judging whether the simulated behavior data meets the conditions for the next stage of simulation according to the analysis results includes: parsing the simulated behavior data to extract action features of the simulated behavior data, wherein the action features include action trajectory, angle, and continuity; Comparing and analyzing the action features with the target intangible cultural heritage parameters, quantifying the results of the comparative analysis, and calculating the matching coefficient between the action features and the target intangible cultural heritage parameters; When the matching coefficient is greater than a preset coefficient threshold, extracting the emotional features of the simulated behavior data; Inputting the emotion features and action features into a preset scene analysis model, analyzing the fit between the emotion features and action features and the target intangible cultural heritage parameters through the scene analysis model, and outputting a fit score result; When the fit score is greater than a preset score threshold, it is determined that the simulated behavior data meets the conditions for the next stage of simulation.

3. The method for learning intangible cultural heritage craft simulation based on augmented reality according to claim 2, characterized in that: After the step of calculating the matching coefficient between the action feature and the target intangible cultural heritage parameter, the method further comprises: When the matching coefficient is less than a preset coefficient threshold, obtaining the user parameters, including height, weight and limb size parameters; Combining the user parameters and the target intangible cultural heritage parameters, analyzing the reasons why the simulated behavior data does not meet the conditions of the next stage; When the reason for not meeting the next stage condition is that the detection point of the action position is limited due to the user parameters, adjusting the action feature according to the user parameters; Recalculate the matching coefficient between the action feature and the target intangible cultural heritage parameter according to the adjusted action feature; When the matching coefficient between the adjusted action feature and the target intangible cultural heritage parameter is greater than a preset coefficient threshold, the emotional feature of the simulated behavior data is extracted.

4. The method for learning intangible cultural heritage craft simulation based on augmented reality according to claim 3 is characterized in that: After the step of recalculating the matching coefficient between the action feature and the target non-legacy parameter according to the adjusted action feature, the method further comprises: When the matching coefficient between the adjusted action feature and the target intangible cultural heritage parameter is less than a preset coefficient threshold, it is determined that the simulated behavior data does not meet the conditions for the next stage of simulation; Based on the judgment result, a guidance parameter is generated and sent to the user end.

5. The method for learning intangible cultural heritage craft simulation based on augmented reality according to claim 4 is characterized in that: Based on the result of the judgment, the guiding parameter is generated and sent to the user terminal, and the method includes: Calculating the key point positions in the target intangible cultural heritage parameters according to the adjusted action features; Based on the calculation result, marking a virtual track between the action feature and the key point; The virtual trajectory generation guidance parameter is sent to the user end.

6. The method for learning intangible cultural heritage craft simulation based on augmented reality according to claim 1, characterized in that: The method of constructing an initial simulation environment according to the environmental parameters, user parameters and target intangible cultural heritage parameters comprises: Acquire the environmental parameters, and determine the brightness parameters, noise parameters and operation space parameters of the current environment according to the environmental parameters; Determine the interaction parameters corresponding to the intangible cultural heritage craft type according to the target intangible cultural heritage parameters; The brightness parameter, noise parameter and operation space parameter of the current environment are subjected to scene fusion with the interaction parameter, and the user parameter is filled into the scene fusion to obtain an initial simulation environment.

7. The method for learning intangible cultural heritage craft simulation based on augmented reality according to claim 6 is characterized in that: The step of determining the interaction parameters corresponding to the intangible cultural heritage craft type according to the target intangible cultural heritage parameters comprises: When the type of the intangible cultural heritage craft is handmade, the type of the hand-held tool on the user end is detected by an image recognition module; According to the tool type, obtaining the tool operation parameters, including a grip contact point number threshold, a tool weight simulation value, and an action surface shape parameter; Collecting elastic deformation data of the currently operated material through a pressure sensor, and matching a deformation coefficient threshold of a material property database according to the elastic deformation data; Based on the tool operation parameters and the deformation coefficient threshold, setting the vibration intensity and frequency parameters of the tactile feedback device; Identify the process complexity level in the target intangible cultural heritage parameters, and adjust the three-dimensional modeling accuracy parameters according to the process complexity level; Based on the adjusted results, the handcrafted interaction parameters are determined.

8. An augmented reality-based intangible cultural heritage craft simulation learning system, characterized in that: include: An identification module is used to identify the intangible cultural heritage learning instruction when receiving the intangible cultural heritage learning instruction from the user end, and determine the target intangible cultural heritage parameters according to the identification result; The first acquisition module is used to acquire the environment parameters and user parameters of the user terminal, and construct an initial simulation environment according to the environment parameters, user parameters and target intangible cultural heritage parameters; An analysis module is used to obtain the simulated behavior data of the user end in real time, and compare and analyze the simulated behavior data with the target intangible cultural heritage parameters; A judgment module, used to judge whether the simulation behavior data meets the conditions for the next stage of simulation according to the analysis results; A generating module, used for generating guiding parameters and sending them to the user end when the simulation behavior data does not meet the conditions for the next stage of simulation; The second acquisition module is used to acquire the next stage simulation behavior data of the user terminal based on the judgment result when the simulation behavior data meets the conditions of the next stage simulation.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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