Intelligent teenager body posture evaluation method based on 3D modeling

Abstract

The invention relates to the technical field of 3D modeling, and discloses a 3D modeling-based teenager body posture intelligent evaluation method, which comprises the following steps of: 1, performing spatial modeling on teenager body postures by using a topology method, and representing the teenager body postures as a high-dimensional manifold space, the topological relation between postures in the posture space is captured by conducting homology group analysis on the high-dimensional manifold space; and 2, after the topological structure of the body posture of the teenager is obtained, carrying out dimension reduction on high-dimensional posture data by applying a manifold learning technology, and optimizing the expression of the data by using a local linear embedding method. Through combination of a topology method and homology group analysis, modeling of teenager body postures in a high-dimensional manifold space is realized, topological relations among postures in a posture space can be effectively captured, a 3D posture model is obtained, and the problem that complex posture changes cannot be accurately described in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of 3D modeling, and in particular to an intelligent evaluation method for adolescent body posture based on 3D modeling. Background Art

[0002] With the changes in lifestyle in modern society, the physical health of teenagers has increasingly attracted widespread attention. Long-term bad sitting and standing postures and unhealthy postures without proper exercise have become one of the main causes of spinal diseases, muscle fatigue, joint problems and poor posture in teenagers. The above problems will affect the physical health of teenagers, have a negative impact on their mental health, and even have a long-term impact on their future quality of life. Therefore, early detection and correction of bad postures in teenagers has become an important task in the field of modern health management and education.

[0003] In order to effectively evaluate and correct the body posture of adolescents, most studies have begun to use 3D modeling technology. Compared with the traditional two-dimensional image analysis method, the posture assessment method based on 3D modeling can provide three-dimensional spatial posture data and can capture the changes in the body posture of adolescents in different environments and activities in real time and dynamically. Therefore, the application prospects of 3D modeling methods in posture assessment, health management and posture correction are broad.

[0004] However, existing posture assessment methods based on 3D modeling still face challenges, mainly in the following aspects:

[0005] Existing 3D modeling technology usually uses simplified geometric models to describe human posture. It lacks an in-depth understanding of posture space and cannot accurately reflect the complex changes of adolescents between different postures. As a result, it is impossible to obtain accurate measurement results in dynamic environments, especially when adolescents move quickly and make small posture changes.

[0006] Traditional posture assessment methods mostly rely on static images or simple sensor data, which cannot accurately reflect the change process of posture in real time. Especially when teenagers are doing various activities, the rapid changes in posture cannot be effectively captured by traditional methods, resulting in delayed and inaccurate posture assessment.

[0007] Most current posture correction technologies use a one-size-fits-all standard solution that ignores the differences between individual adolescents. This approach fails to fully consider the specific circumstances of each adolescent and, therefore, is unable to provide personalized posture correction guidance for each adolescent, resulting in limited correction effects and poor sustainability.

[0008] In view of the problems raised, how to combine the latest scientific and technological means to provide accurate, real-time and personalized posture assessment and correction solutions has become an important topic in posture research. Therefore, those skilled in the art provide a method for intelligent assessment of adolescent body posture based on 3D modeling to solve the above problems. Summary of the invention

[0009] In view of the deficiencies in the prior art, the present invention provides an intelligent assessment method for adolescent body posture based on 3D modeling to solve the problems raised in the above-mentioned background technology.

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for intelligent evaluation of adolescent body posture based on 3D modeling, comprising:

[0011] In the first step, the body posture of adolescents is spatially modeled by using topological methods, and the body posture of adolescents is represented as a high-dimensional manifold space. The topological relationship between the postures in the posture space is captured by performing homology group analysis on the high-dimensional manifold space.

[0012] The second step is to obtain the topological structure of the teenagers' body postures, apply manifold learning technology to reduce the dimensionality of the high-dimensional posture data, and use the local linear embedding method to optimize the data expression, so that the different changes of the teenagers' body postures can be expressed in a lower-dimensional space and retain the local structural information;

[0013] The third step is to describe the temporal evolution of adolescents’ body postures through the wave function model of quantum mechanics based on the low-dimensional representation of posture data. By constructing wave functions to capture the probability fluctuations of adolescents’ body postures, the trajectory of posture changes and the transition states between different postures can be reflected in real time during dynamic posture assessment.

[0014] The fourth step is to generate 3D posture data using a generative adversarial network based on the description of the quantum wave function. The generator generates a new posture model by learning from the posture data of teenagers, and the discriminator evaluates the difference between the generated posture and the real data to optimize the output of the generator.

[0015] The fifth step is to optimize the 3D posture model generated by the generative adversarial network, and then optimize the posture by combining the elastic mechanics model in physics, and modeling the elastic deformation of various parts of the adolescent's body;

[0016] The sixth step is to use the muscle dynamics model and neural feedback mechanism to simulate the muscle activity and neural reflexes of various parts of the adolescent body based on the optimized physical model;

[0017] Step 7: By integrating the results of the physics model and neural feedback, and combining the 3D posture model optimized by real-time feedback of holographic technology, the real-time posture assessment results are projected onto the holographic display device, so that teenagers can observe their current body posture through intuitive 3D images.

[0018] The eighth step is to provide adjustment guidance tailored to the specific circumstances of adolescents during dynamic posture changes by updating the holographic feedback results in real time and combining them with personalized correction suggestions.

[0019] Preferably, the topological method includes performing topological transformation on each posture state in the high-dimensional manifold space through homology group analysis, and using the following formula to calculate the topological invariant in the posture space:

[0020]

[0021] Among them, H k( M ) is the kth order homology group, representing the topological invariance in the attitude space;

[0022] Ker ( d k) is the kernel of the k-th order derivative, representing the part not affected by the posture change;

[0023] Im ( d k+1) is the image of the k+1th derivative, representing the part where the posture changes;

[0024] k represents the order of the homology group.

[0025] Preferably, the manifold learning method reduces the dimension of high-dimensional posture data by using a local linear embedding technique, using an optimization objective formula:

[0026]

[0027] Among them, f is the function that maps high-dimensional posture data to low-dimensional space, x i is the ith original data point, x j is the jth original data point, N ( i ) It is point x i Neighborhood, w ij Represents point x i With x j is the strength of the relationship between them, and n is the total number of data points.

[0028] Preferably, the wave function model describes the evolution of the posture based on the Schrödinger equation in quantum mechanics, and the Schrödinger equation is as follows:

[0029]

[0030] Among them, i is the imaginary unit; h is Planck's constant, which represents the unit of quantum scale;

[0031] &ψ ( x,t ) is the wave function ψ ( x,t ) The partial derivative with respect to time t;

[0032] is the Hamiltonian operator;

[0033] ψ ( x,t ) is a wave function that describes the probability distribution of the teenager’s posture at time t and position x.

[0034] Preferably, the quantum wave function model determines the expected value of each posture state through the measurement operator of quantum mechanics, and the calculation formula is:

[0035]

[0036] in, is an operator The expected value of represents the average result of the posture measurement of adolescents; is an operator, which represents the observed value of the adolescent posture measurement;

[0037] ψ(x,t) is the wave function that describes the probability distribution of the adolescent’s posture at time t and position x; ψ * (x, t) is the complex conjugate of the wave function; dx represents the integration over attitude space.

[0038] Preferably, during the training process of the generative adversarial network, the generator outputs a new 3D pose model, and the generator and the discriminator are optimized by the following optimization objective formula:

[0039]

[0040] Among them, G is the generator; D is the discriminator, D(x) is the output probability of the discriminator for the real data x; G(z) is the fake data generated by the generator;

[0041] logD(x) is the logarithmic probability that the discriminator determines that the input x is real data.

[0042] log(1-D(G(z))) is the logarithmic probability that the discriminator determines that the generated false data G(z) is false data;

[0043] E x～p(x) is the expected value of the real data x; E z～p(z) is the expected value of the noise z.

[0044] Preferably, the generative adversarial network is combined with a variational autoencoder, which uses the variational autoencoder to generate a posture representation of a low-dimensional latent space, and then the generated low-dimensional latent space representation is optimized by the generative adversarial network to generate a 3D posture model.

[0045] Preferably, the optimized 3D posture model is optimized by elastic mechanics in physics, and the optimization formula is:

[0046]

[0047] Where E is the total energy of the elastic body, which represents the energy generated during the posture adjustment process;

[0048] σ is the stress tensor, which represents the internal forces acting on various parts of the body;

[0049] ε is the strain tensor, which indicates the degree of deformation of each part of the body;

[0050] V is the volume element; dV is the differential of the volume element.

[0051] Preferably, the physical model optimization combines the muscle dynamics model and the neural feedback mechanism to simulate the muscle activities and neural reflexes of various parts of the adolescent body, so that during the posture adjustment process, the muscle and neural reactions can receive appropriate feedback and control.

[0052] Preferably, the optimized 3D posture model provides real-time feedback of the adolescent's current posture through holographic technology, updates the posture assessment results in real time and combines personalized correction suggestions to provide dynamic posture adjustment guidance, ensuring that the adolescent can correct bad posture in real-time feedback.

[0053] The present invention provides a method for intelligent assessment of adolescent body posture based on 3D modeling.

[0054] Beneficial effects:

[0055] 1. The present invention realizes the modeling of adolescent body posture in high-dimensional manifold space by combining topological methods and homology group analysis, which can effectively capture the topological relationship between postures in the posture space and obtain a 3D posture model, thus solving the problem that the existing technology cannot accurately describe complex posture changes.

[0056] 2. The present invention introduces a quantum mechanical wave function model to describe the time evolution of posture, reflecting in real time the changing trajectory of adolescent body posture and the transition state between different postures, obtaining a real-time evaluation of dynamic posture changes, and effectively solving the problem of insufficient accuracy of traditional methods when dealing with rapid and subtle posture changes of adolescents.

[0057] 3. The present invention provides personalized posture feedback and correction suggestions by combining the muscle dynamics model and the neural feedback mechanism. It can make real-time adjustments based on the specific posture needs of adolescents to obtain scientific and personalized posture correction solutions, thus solving the problem of lack of personalized posture correction in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0059] In order to make the technical personnel in the technical field understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in combination with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a partial embodiment of the present invention, not a complete embodiment. Based on the embodiment of the present invention, other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0060] The present invention is described in detail below in conjunction with the accompanying drawings:

[0061] Example:

[0062] Please refer to the attached Figure 1 The embodiment of the present invention provides a method for intelligent evaluation of adolescent body posture based on 3D modeling, comprising:

[0063] In the first step, the body posture of adolescents is spatially modeled by using topological methods, and the body posture of adolescents is represented as a high-dimensional manifold space. The topological relationship between the postures in the posture space is captured by performing homology group analysis on the high-dimensional manifold space.

[0064] The second step is to obtain the topological structure of the teenagers' body postures, apply manifold learning technology to reduce the dimensionality of the high-dimensional posture data, and use the local linear embedding method to optimize the data expression, so that the different changes of the teenagers' body postures can be expressed in a lower-dimensional space and retain the local structural information;

[0065] The third step is to describe the temporal evolution of adolescents’ body postures through the wave function model of quantum mechanics based on the low-dimensional representation of posture data. By constructing wave functions to capture the probability fluctuations of adolescents’ body postures, the trajectory of posture changes and the transition states between different postures can be reflected in real time during dynamic posture assessment.

[0066] The fourth step is to generate 3D posture data using a generative adversarial network based on the description of the quantum wave function. The generator generates a new posture model by learning from the posture data of teenagers, and the discriminator evaluates the difference between the generated posture and the real data to optimize the output of the generator.

[0067] The fifth step is to optimize the 3D posture model generated by the generative adversarial network, and then optimize the posture by combining the elastic mechanics model in physics, and modeling the elastic deformation of various parts of the adolescent's body;

[0068] The sixth step is to use the muscle dynamics model and neural feedback mechanism to simulate the muscle activity and neural reflexes of various parts of the adolescent body based on the optimized physical model;

[0069] Step 7: By integrating the results of the physics model and neural feedback, and combining the 3D posture model optimized by real-time feedback of holographic technology, the real-time posture assessment results are projected onto the holographic display device, so that teenagers can observe their current body posture through intuitive 3D images.

[0070] The eighth step is to provide adjustment guidance tailored to the specific circumstances of adolescents during dynamic posture changes by updating the holographic feedback results in real time and combining them with personalized correction suggestions.

[0071] The benefit of the first step is that it can model the complex changes of body posture and capture the topological features of posture changes. It helps solve the problem that traditional modeling methods cannot effectively represent the complex relationships in posture space and provides a theoretical basis for subsequent posture modeling and evaluation.

[0072] The benefit of the second step is to reduce the complexity of high-dimensional data, so that the posture data can be effectively represented in low-dimensional space, retain important local structural information, and avoid information loss.

[0073] The benefit of the third step is that the wave function can capture small changes in posture in a probabilistic way, enhancing the accurate description of dynamic posture changes. It can achieve accurate evaluation of fast and small posture changes and solve the shortcomings of traditional methods in dynamic evaluation.

[0074] The benefit of the fourth step is that the generative adversarial network can improve the accuracy and authenticity of the 3D posture data by continuously optimizing the generated model. The adversarial training avoids the gap between the generated posture data and the actual data, making the generated posture model consistent with the real body posture of teenagers.

[0075] The benefit of the fifth step is to optimize the posture through physical constraints, ensuring that the posture adjustment process complies with the principles of biomechanics. By simulating the elastic response of body parts, a scientific and actionable posture correction solution can be provided.

[0076] The benefit of the sixth step is that it can simulate the real-time feedback of muscles and nerves during the actual posture adjustment process, ensuring the personalization and scientific nature of the posture correction program. It provides real-time feedback and adjustment to enhance the effectiveness and operability of posture correction.

[0077] The benefit of the seventh step is that it provides intuitive holographic feedback, allowing teenagers to observe and correct bad postures in real time, enhancing interactivity and participation. Real-time updating of feedback information helps teenagers to continuously adjust their postures during activities.

[0078] The benefit of the eighth step is that it provides a personalized posture correction solution to solve the "one-size-fits-all" problem in traditional technology. Through continuous feedback and personalized suggestions, it helps teenagers gradually develop correct body posture, with significant long-term effects.

[0079] The topological method involves topological transformation of each posture state in the high-dimensional manifold space through homology group analysis, and uses the following formula to calculate the topological invariant in the posture space:

[0080]

[0081] Among them, H k( M ) is the kth order homology group, representing the topological invariance in the attitude space;

[0082] Ker ( d k) is the kernel of the k-th order derivative, representing the part not affected by the posture change;

[0083] Im ( d k+1) is the image of the k+1th derivative, representing the part where the posture changes;

[0084] k represents the order of the homology group.

[0085] Through homology group analysis, the topological relationship between postures in the posture space can be effectively captured. Unlike traditional methods, topological methods can handle complex posture transformations and identify topological invariants that are not easily perceived in postures, thereby accurately modeling complex body posture changes.

[0086] Using homology group analysis, the topological invariance in the posture space can be preserved by calculating the k-th order homology group. Topological invariance represents the inherent characteristics between postures, which is particularly important for processing complex posture changes. It can ensure that key biological features and movement patterns are not lost during the transformation of postures.

[0087] High-dimensional posture data contains a lot of redundant information, and homology group analysis can minimize the complexity of posture data by extracting topological invariance.

[0088] The manifold learning method uses local linear embedding technology to reduce the dimension of high-dimensional posture data, using the optimization objective formula:

[0089]

[0090] Among them, f is the function that maps high-dimensional posture data to low-dimensional space, x i is the ith original data point, x j is the jth original data point, N ( i ) It is point x i Neighborhood, w ij Represents point x i With x j is the strength of the relationship between them, and n is the total number of data points.

[0091] High-dimensional posture data usually contains a lot of redundant information, making data processing and calculation very complicated. By reducing the data to a low-dimensional space, the complexity of calculation can be effectively reduced, making subsequent posture analysis and modeling more efficient.

[0092] The local linear embedding technique can preserve the local structure of the original data during the dimensionality reduction process. Specifically, the local linear embedding method can preserve the neighborhood relationship between data points, so that the low-dimensional space after dimensionality reduction can accurately represent the structure of similar data points in the high-dimensional space.

[0093] The dimensionality reduction operation performed by optimizing the target formula can ensure that the high-dimensional data in the low-dimensional space can still reflect the real body posture characteristics. The local linear embedding method preserves the relationship between data points, making the reduced-dimensional data consistent with the actual body posture changes and improving the accuracy of the model.

[0094] High-dimensional data contains redundant information, which will affect the performance of the model. Local linear embedding technology reduces the dimension of data, retains important feature information, removes unnecessary noise and redundancy, and ensures the simplicity and interpretability of the model.

[0095] The wave function model describes the evolution of posture based on the Schrödinger equation in quantum mechanics. The Schrödinger equation is as follows:

[0096]

[0097] Among them, i is the imaginary unit; h is Planck's constant, which represents the unit of quantum scale;

[0098] &ψ ( x,t ) is the wave function ψ ( x,t ) The partial derivative with respect to time t;

[0099] is the Hamiltonian operator;

[0100] ψ ( x,t )is a wave function that describes the probability distribution of the teenager’s posture at time t and position x.

[0101] The Schrödinger equation can accurately describe the changes in posture over time, especially small dynamic posture changes, through the evolution of quantum wave functions. Compared with traditional physical modeling methods, wave function models can capture extremely subtle posture fluctuations, which is particularly important for real-time assessment of changes in adolescent posture, especially in motion or rapidly changing postures.

[0102] The Schrödinger equation describes a wave function that changes with time. Therefore, this model can be used to track the changes in the body posture of teenagers in real time in a dynamic environment. This method can accurately simulate the transition state of posture, reflect the smooth changes between different postures, and improve the accuracy and real-time performance of dynamic posture assessment.

[0103] Through the wave function model, the Schrödinger equation can capture the current posture state and predict the future trajectory of posture changes. Its predictive ability is particularly suitable for the posture changes of teenagers in daily activities and sports, and can provide predictive guidance for posture correction.

[0104] Posture changes are highly complex dynamic systems. The wave function model can process system changes from the perspective of quantum mechanics, providing more detailed posture analysis and evaluation than traditional methods. It can effectively deal with high-dimensional, nonlinear posture changes and is not limited by the linear assumptions in traditional methods.

[0105] The quantum wave function model determines the expected value of each posture state through the measurement operator of quantum mechanics. The calculation formula is:

[0106]

[0107] in, is an operator The expected value of represents the average result of the posture measurement of adolescents; is an operator, which represents the observed value of the adolescent posture measurement;

[0108] ψ ( x,t ) is the wave function, describing the probability distribution of the adolescent’s posture at time t and position x; ψ * (x, t) is the complex conjugate of the wave function; dx represents the integration over attitude space.

[0109] Quantum measurement operators can calculate the expected value of the posture state and provide numerical values ​​in complex posture evaluation. Compared with traditional posture measurement methods, quantum measurement theory can use the probability distribution of wave functions to optimize measurement results, reduce measurement errors, and improve measurement stability.

[0110] By calculating the expected value of the wave function, the posture state of the teenager at different time points and different positions can be obtained. This method can integrate multiple measurement data to ensure that the dynamic changes of posture are fully captured, so that the system can reflect the overall trend of the teenager's body posture.

[0111] Since the quantum measurement operator can calculate the expected value of posture in real time, this method can dynamically adjust the posture evaluation model according to the latest measurement data, so that the system can respond to posture changes in real time and provide posture correction guidance for teenagers.

[0112] Traditional posture assessment methods are susceptible to noise interference, while quantum measurement methods can effectively reduce the impact of external interference on measurement results through the mathematical description of wave functions, improve the stability of posture assessment, and ensure that the system can still provide assessment data in complex environments.

[0113] During the training process of the generative adversarial network, the generator outputs a new 3D pose model, and the generator and discriminator are optimized through the following optimization objective formula:

[0114]

[0115] Among them, G is the generator; D is the discriminator, D(x) is the output probability of the discriminator for the real data x; G(z) is the fake data generated by the generator;

[0116] logD(x) is the logarithmic probability that the discriminator determines that the input x is real data.

[0117] log(1-D(G(z))) is the logarithmic probability that the discriminator determines that the generated false data G(z) is false data;

[0118] E x～p(x) is the expected value of the real data x; E z～p(z) is the expected value of the noise z.

[0119] The generative adversarial network is combined with the variational autoencoder, which uses the variational autoencoder to generate a posture representation in a low-dimensional latent space. The generated low-dimensional latent space representation is then optimized by the generative adversarial network to generate a 3D posture model.

[0120] Generative adversarial networks use adversarial training between the generator and the discriminator to continuously optimize the generator's output and generate a 3D pose model that is close to reality. The discriminator optimizes the generator's output based on its judgment of real data and generated data, ensuring that the final generated 3D pose model has a high degree of authenticity and accuracy.

[0121] By introducing a variational autoencoder, the present invention maps posture data to a low-dimensional latent space through a variational autoencoder, making the representation of posture data compact and easy to handle. The post-generative adversarial network generates a diverse and reasonable 3D posture model by optimizing the low-dimensional latent space. This method can avoid the problem of insufficient posture diversity in traditional methods, so that the generated posture model can cover a variety of different posture changes and improve the flexibility of the system.

[0122] The optimized 3D posture model is optimized through elastic mechanics in physics, and the optimization formula is:

[0123]

[0124] Where E is the total energy of the elastic body, which represents the energy generated during the posture adjustment process;

[0125] σ is the stress tensor, which represents the internal forces acting on various parts of the body;

[0126] ε is the strain tensor, which indicates the degree of deformation of each part of the body;

[0127] V is the volume element; dV is the differential of the volume element.

[0128] The elastic mechanics model ensures that the posture optimization process complies with the basic principles of biomechanics by describing stress and strain. In the actual posture adjustment process, various parts of the body will deform when external forces are applied. The elastic mechanics model can describe the deformation, ensuring that the optimized posture will not cause unreasonable biomechanical reactions and avoid muscle and bone injuries caused by posture adjustment.

[0129] This method uses an energy minimization optimization strategy to keep the body posture adjustment process within the physically feasible range, ensuring the smoothness and naturalness of posture changes.

[0130] Through the description of stress tensors and strain tensors, elastic mechanics can analyze the deformation of body parts during posture adjustment. This is particularly important for posture assessment, because the deformation degree and internal force distribution of each body part will have different effects on the overall posture.

[0131] The physical model optimization combines the muscle dynamics model and the neural feedback mechanism to simulate the muscle activities and neural reflexes of various parts of the adolescent body, so that the muscle and neural reactions can receive proper feedback and control during the posture adjustment process.

[0132] The optimized 3D posture model uses holographic technology to provide real-time feedback on the teenager’s current posture, update the posture assessment results in real time and combine it with personalized correction suggestions to provide dynamic posture adjustment guidance, ensuring that teenagers can correct bad posture through real-time feedback.

[0133] The present invention combines the muscle dynamics model and the neural feedback mechanism, and can provide adolescents with personalized, real-time and dynamic posture correction solutions through real-time feedback holographic technology. The system simulates muscle activity and neural reflexes to ensure that the body's intrinsic response is scientifically controlled during the posture adjustment process. At the same time, through holographic feedback, adolescents can observe and adjust their postures in real time, effectively improving the accuracy, comfort and sustainability of posture correction. This method can enhance the personalization and accuracy of posture correction, and can provide support for long-term health management of adolescents, help prevent and correct adolescent posture problems, and promote the healthy growth of adolescents.

[0134] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent assessment of adolescent body posture based on 3D modeling, characterized in that: include: In the first step, the body posture of adolescents is spatially modeled by using topological methods, and the body posture of adolescents is represented as a high-dimensional manifold space. The topological relationship between the postures in the posture space is captured by performing homology group analysis on the high-dimensional manifold space. The second step is to obtain the topological structure of the teenagers' body postures, apply manifold learning technology to reduce the dimensionality of the high-dimensional posture data, and use the local linear embedding method to optimize the data expression, so that the different changes of the teenagers' body postures can be expressed in a lower-dimensional space and retain the local structural information; The third step is to describe the temporal evolution of adolescents’ body postures through the wave function model of quantum mechanics based on the low-dimensional representation of posture data. By constructing wave functions to capture the probability fluctuations of adolescents’ body postures, the trajectory of posture changes and the transition states between different postures can be reflected in real time during dynamic posture assessment. The fourth step is to generate 3D posture data using a generative adversarial network based on the description of the quantum wave function. The generator generates a new posture model by learning from the posture data of teenagers, and the discriminator evaluates the difference between the generated posture and the real data to optimize the output of the generator. The fifth step is to optimize the 3D posture model generated by the generative adversarial network, and then optimize the posture by combining the elastic mechanics model in physics, and modeling the elastic deformation of various parts of the adolescent's body; The sixth step is to use the muscle dynamics model and neural feedback mechanism to simulate the muscle activity and neural reflexes of various parts of the adolescent body based on the optimized physical model; Step 7: By integrating the results of the physics model and neural feedback, and combining the 3D posture model optimized by real-time feedback of holographic technology, the real-time posture assessment results are projected onto the holographic display device, so that teenagers can observe their current body posture through intuitive 3D images. The eighth step is to provide adjustment guidance tailored to the specific circumstances of adolescents during dynamic posture changes by updating the holographic feedback results in real time and combining them with personalized correction suggestions.

2. The method for intelligent evaluation of adolescent body posture based on 3D modeling according to claim 1, characterized in that: The topological method includes performing topological transformation on each posture state in the high-dimensional manifold space through homology group analysis, and using the following formula to calculate the topological invariant in the posture space: Among them, H k (M) is the kth order homology group, representing the topological invariance in the attitude space; Ker(d k ) is the kernel of the k-th order derivative, representing the part not affected by the posture change; Im(d k+1 ) is the image of the k+1th order derivative, representing the part of the posture change; k represents the order of the homology group.

3. The method for intelligent evaluation of adolescent body posture based on 3D modeling according to claim 1, characterized in that: The manifold learning method reduces the dimension of high-dimensional posture data through local linear embedding technology, using the optimization objective formula: Among them, f is the function that maps high-dimensional posture data to low-dimensional space, x i is the ith original data point, x j is the jth original data point, N(i) is the point x i Neighborhood, w ij Represents point x i With x j is the strength of the relationship between them, and n is the total number of data points.

4. The method for intelligent evaluation of adolescent body posture based on 3D modeling according to claim 1, characterized in that: The wave function model describes the evolution of the posture based on the Schrödinger equation in quantum mechanics. The Schrödinger equation is as follows: Among them, i is the imaginary unit; h is Planck's constant, which represents the unit of quantum scale; &ψ(x,t) is the partial derivative of the wave function ψ(x,t) with respect to time t; is the Hamiltonian operator; ψ(x,t) is a wave function that describes the probability distribution of the adolescent’s posture at time t and location x.

5. The method for intelligent evaluation of adolescent body posture based on 3D modeling according to claim 1, characterized in that: The quantum wave function model determines the expected value of each posture state through the measurement operator of quantum mechanics, and the calculation formula is: in, is an operator The expected value of represents the average result of the posture measurement of adolescents; is an operator, which represents the observed value of the adolescent posture measurement; ψ(x,t) is the wave function that describes the probability distribution of the adolescent’s posture at time t and position x; ψ * (x, t) is the complex conjugate of the wave function; dx represents the integration over attitude space.

6. The method for intelligent evaluation of adolescent body posture based on 3D modeling according to claim 1, characterized in that: The generator of the generative adversarial network outputs a new 3D pose model during the training process, and optimizes the generator and discriminator through the following optimization objective formula: Among them, G is the generator; D is the discriminator, D(x) is the output probability of the discriminator for the real data x; G(z) is the fake data generated by the generator; logD(x) is the logarithmic probability that the discriminator determines that the input x is real data. log(1-D(G(z))) is the logarithmic probability that the discriminator determines that the generated false data G(z) is false data; E x～p(x) is the expected value of the real data x; E z～p(z) is the expected value of the noise z.

7. The method for intelligent evaluation of adolescent body posture based on 3D modeling according to claim 6, characterized in that: The generative adversarial network is combined with a variational autoencoder, which uses the variational autoencoder to generate a posture representation of a low-dimensional latent space, and then the generated low-dimensional latent space representation is optimized by the generative adversarial network to generate a 3D posture model.

8. The method for intelligent evaluation of adolescent body posture based on 3D modeling according to claim 1, characterized in that: The optimized 3D posture model is optimized by elastic mechanics in physics, and the optimization formula is: Where E is the total energy of the elastic body, which represents the energy generated during the posture adjustment process; σ is the stress tensor, which represents the internal forces acting on various parts of the body; ε is the strain tensor, which indicates the degree of deformation of each part of the body; V is the volume element; dV is the differential of the volume element.

9. The method for intelligent evaluation of adolescent body posture based on 3D modeling according to claim 1, characterized in that: The physical model optimization combines the muscle dynamics model and the neural feedback mechanism to simulate the muscle activities and neural reflexes of various parts of the adolescent body, so that during the posture adjustment process, the muscle and neural reactions can receive appropriate feedback and control.

10. The method for intelligent evaluation of adolescent body posture based on 3D modeling according to claim 1, characterized in that: The optimized 3D posture model uses holographic technology to provide real-time feedback on the teenager's current posture, updates the posture assessment results in real time and combines personalized correction suggestions to provide dynamic posture adjustment guidance, ensuring that the teenager can correct bad postures in real-time feedback.

Images