Motion-induced heart rate variability prediction method based on random forest algorithm

Abstract

The invention discloses a motion-induced heart rate variability prediction method based on a random forest algorithm. The motion-induced heart rate variability prediction method comprises the following steps: S1, collecting multi-modal physiological data of a user during a motion period; s2, synchronously processing the data through a multi-channel signal fusion technology, and generating a time-aligned multi-dimensional data stream; s3, based on genome, epigenetic and physiological data, extracting heart rate variability related high-dimensional features by using holographic quantum calculation, and generating a biological feature library; s4, integrating the data sequence and the biological feature library, and performing time sequence feature extraction by using self-supervised learning; s5, in model training, optimizing a decision tree structure by utilizing reinforcement learning and distributed calculation; s6, adjusting the weight of the training sample in real time through an adaptive weighted resampling technology; s7, optimizing the random forest model by applying evolutionary learning; and S8, in combination with the biological characteristics of the user, a prediction result is visualized by using a quantum enhanced holographic projection technology. According to the invention, accurate prediction and dynamic management of motion-induced heart rate variability are realized.

Description

Technical Field

[0001] The present invention relates to the field of biomedical engineering technology, and in particular to a method for predicting exercise-induced heart rate variability based on a random forest algorithm. Background Art

[0002] With the increasing demand for health management and sports data monitoring in modern society, heart rate variability, as an important indicator for measuring heart health and autonomic nervous system function, has received widespread attention. Heart rate variability is of great significance in predicting physiological responses induced by exercise and evaluating the impact of exercise on the cardiovascular system. However, existing heart rate variability prediction methods often have many shortcomings when facing complex physiological changes induced by exercise, and it is difficult to meet the needs of high-precision, real-time and personalized prediction.

[0003] In the prior art, common heart rate variability prediction methods mainly rely on models based on traditional machine learning algorithms such as linear regression and support vector machines. These methods usually only consider single or limited physiological data inputs, ignoring the complex associations between multimodal physiological data. In addition, the existing methods mostly use static feature extraction and simple data fusion techniques, which cannot fully mine and utilize complex multidimensional physiological data, resulting in limited prediction accuracy and the adaptive ability of the model. Specifically, the prior art has obvious defects in the following aspects:

[0004] 1. Data uniformity and insufficient processing: Traditional heart rate variability prediction methods usually rely only on a single physiological signal, such as electrocardiogram or heart rate data, and ignore the fusion and processing of multimodal physiological data such as genomic data and epigenetic information. This method lacks effective feature extraction and data fusion technology when processing multi-dimensional physiological data, resulting in the inability to fully utilize the information in multi-source data, thereby limiting the accuracy and reliability of the prediction.

[0005] 2. Lack of dynamic adaptive capabilities: Most existing heart rate variability prediction methods are based on static models, which are difficult to cope with the rapid changes in physiological state during exercise. Traditional models have determined the model structure and parameters during the training phase, and it is difficult to dynamically adjust with the changes in physiological state in actual applications, resulting in insufficient prediction capabilities of the model for different individuals or the same user in different exercise states.

[0006] 3. Limitations of model optimization: In terms of model optimization, existing methods usually rely on a single optimization strategy, such as simple parameter adjustment or fixed feature selection methods, which are difficult to flexibly adjust according to different physiological states or exercise loads. Especially for complex multi-dimensional physiological data, traditional optimization methods often lack targeted strategies and cannot fully tap the potential of the model.

[0007] 4. Insufficient real-time and personalization: The current heart rate variability prediction system is insufficient in terms of real-time and personalization. Due to its reliance on predefined models and static data processing processes, existing methods often have delays in processing real-time data, making it difficult to meet the requirements of real-time monitoring and timely intervention. In addition, existing methods lack sufficient consideration of individual differences, making it difficult to provide personalized health management recommendations and reducing the applicability of prediction results.

[0008] 5. Limitations of visualization and user interaction: Existing heart rate variability prediction systems lack innovation in result display and user interaction. Traditional methods usually use simple data charts or text output methods, which cannot intuitively display complex physiological changes or exercise effects. In addition, these methods usually lack user interaction functions, making it difficult to dynamically adjust and optimize the model based on user feedback.

[0009] Therefore, how to provide a method for predicting exercise-induced heart rate variability based on a random forest algorithm is an urgent problem that technicians in this field need to solve. Summary of the invention

[0010] One purpose of the present invention is to propose a method for predicting exercise-induced heart rate variability based on a random forest algorithm. The present invention makes full use of holographic quantum computing, self-supervised learning and bio-inspired evolutionary learning algorithms, and describes in detail the collection and processing of multimodal physiological data, feature extraction and optimization, and visualization of real-time prediction results. It has the advantages of high precision, real-time and strong personalization.

[0011] According to an embodiment of the present invention, a method for predicting exercise-induced heart rate variability based on a random forest algorithm comprises the following steps:

[0012] S1. Collect multimodal physiological data of the user during exercise, synchronously process the physiological data through multi-channel signal fusion technology, and generate a time-aligned multi-dimensional data stream;

[0013] S2, perform data cleaning, denoising and standardization on multi-dimensional data streams, and use an adaptive spatiotemporal data enhancement algorithm to generate standardized input data sequences;

[0014] S3, based on the user's genomic data and epigenetic modification information, a multi-dimensional feature interaction network of holographic quantum computing is used to extract high-dimensional features related to heart rate variability from the input data sequence, and an adaptive gene regulation model is constructed through a multi-layer feature selection algorithm to generate a multi-dimensional biological feature library;

[0015] S4. Integrate the standardized input data sequence with the multidimensional biometric library to generate a high-dimensional input data set. Use the time series feature enhancement network driven by self-supervised learning to extract features from the time series data in the high-dimensional input data set to capture the complex dependencies and nonlinear dynamic behaviors in the data.

[0016] S5. During the training of the random forest algorithm model, feature selection is performed on high-dimensional input data sets, and the decision tree generation strategy is adjusted through the reinforcement learning algorithm. The structure and parameters of the decision tree are dynamically optimized in a distributed computing environment using the real-time collaborative optimization technology of distributed computing.

[0017] S6. Based on biofeedback data, the adaptive weighted sampling technology is used to adjust the weights of training samples in real time, and the model is updated through the incremental learning algorithm, so that the random forest algorithm can handle gradually changing physiological states and optimize in real time;

[0018] S7. Applying a biologically inspired evolutionary learning algorithm to simulate the biological evolution process, optimizing the structure and parameters of the random forest model through selection, crossover and mutation operations, so that the random forest model can adapt to the changes in physiological state caused by exercise;

[0019] S8. Analyze the real-time prediction results of the random forest model with the user's personalized biometric characteristics, use quantum enhanced holographic multi-dimensional projection technology to visualize the heart rate variability prediction results as multi-dimensional holographic images, and display and interact through augmented reality or virtual reality technology to provide users with health status and exercise suggestions.

[0020] Optionally, S3 includes the following steps:

[0021] S31, obtain the generated time-aligned multi-dimensional data stream, including the user's genome data G i , epigenetic modification information E j , and physiological data processed by multi-channel signal fusion technology P k , where i, j, and k represent different dimensions of genomic, epigenetic, and physiological data, respectively;

[0022] S32. Use holographic quantum computing technology to process multi-dimensional feature interactions on data streams, and combine quantum Fourier transform and nonlinear multi-dimensional feature mapping to generate a high-dimensional quantum state feature matrix after encoding:

[0023]

[0024] Among them, ∑ i，j，k represents the sum of all genomic data i, epigenetic modification information j, and physiological data k, QFT(ψ(G i , Ej , P k )) Quantum Fourier transform applied to complex amplitude ψ(G i , E j , P k ) after ψ(G i , E j , P k ) is the genome data G i , epigenetic modification information E j and physiological data P k The complex amplitude of is the dynamic phase factor associated with each data point, M(λ) is the multidimensional feature interaction matrix, and λ is the parameter that controls the multidimensional feature interaction;

[0025] S33. The high-dimensional quantum state feature matrix is ​​processed through a multi-dimensional feature interaction network, and a multi-layer hybrid quantum-classical feature extraction algorithm is used to identify key features related to heart rate variability:

[0026]

[0027]

[0028] Among them, F represents the key feature set related to heart rate variability that is finally extracted, ClassicalAnalysis(·) is used to extract features from the classical data obtained after quantum state measurement, and Measure(·) is the quantum state measurement operation. represents the sum of N independent modes in the quantum state space, m represents different modes or quantum states, represents the wave vector, is the position vector of the data in the feature space, V is the integral volume, α n is the feature selection weight, QuantumSelector is a quantum algorithm used to extract key features from high-dimensional feature space;

[0029] S34. Based on the selected feature set F, an adaptive gene regulation model is constructed, and the prediction parameters of heart rate variability are dynamically adjusted by combining machine learning technology and quantum computing technology;

[0030] S35. Integrate the output of the model and other data features that are not directly used in the model to generate a multidimensional biometric library to provide comprehensive data support for the random forest algorithm.

[0031] Optionally, S4 includes the following steps:

[0032] S41. Obtain the generated high-dimensional quantum state feature matrix H(G, E, P) and the selected key feature set F, perform multi-level fusion processing on H(G, E, P) and F as well as the user's personalized biometric feature set B to generate a high-dimensional fusion feature matrix Φ(H, F, B):

[0033]

[0034] Among them, H ijk (G, E, P) represents the i, j, and k elements in the high-dimensional quantum state characteristic matrix, F f represents the fth feature in the feature set F, B b represents the bth feature in the biometric feature set B, w ijkfb is the fusion weight coefficient, α m is the multidimensional interaction weight, tanh(·) is the nonlinear activation function, σ m (H ijk， F f , B b ) is the multi-layer feature interaction function, T(λ) is the feature space transformation matrix;

[0035] S42, input the high-dimensional fusion feature matrix Φ(H, F, B) into the time series feature enhancement network driven by self-supervised learning, extract features from the time series data in the matrix, and capture the complex dependencies and nonlinear dynamic behaviors in the time series data through the self-supervised mechanism, using time series convolution and dynamic feature selection, to generate an optimized time series feature matrix Ψ(Φ);

[0036] S43, using quantum state driven adaptive feature mapping technology, dynamically mapping the optimized time series feature matrix Ψ(Φ) to generate a quantum state enhanced feature matrix Θ(Ψ):

[0037]

[0038] Among them, Entangle(·) represents the quantum entanglement operation, is a high-order nonlinear feature transformation function, is the phase factor, U q is the quantum state mapping matrix, γ q is the adaptive weight coefficient, is the multi-dimensional feature interaction matrix;

[0039] S44, performing feature selection and extraction on the quantum state enhanced feature matrix to generate a feature subset related to heart rate variability prediction;

[0040] S45. Based on the generated feature subset, the adaptive feature reconstruction technology is applied, and PCA and quantum state reconstruction methods are used to reconstruct and optimize the features to generate more expressive feature representations;

[0041] S46. Input the reconstructed feature subset into the random forest algorithm model, perform real-time heart rate variability prediction based on the feature subset, and capture the complex dependencies and nonlinear dynamic behaviors in the data.

[0042] Optionally, S5 includes the following steps:

[0043] S51. Based on the high-dimensional quantum state feature matrix and the quantum state enhanced feature matrix, the trust value of each data source is dynamically evaluated through the enhanced fuzzy trust model. The trust value is used as a weight to fuse the feature matrix, and a weighted fusion feature matrix Φ is generated through nonlinear transformation and multidimensional interaction mechanism. weighted :

[0044]

[0045] Among them, T i represents the trust value of data source i, H ijk (G, E, P) represents the i, j, and k elements in the high-dimensional quantum state characteristic matrix H(G, E, P), Θ ijk (Ψ) represents the corresponding element in the quantum state enhancement characteristic matrix Θ(Ψ), α ij and β ij is the interaction weight, σ(·) is the nonlinear activation function, is the feature transformation function, N and M are the number and dimension of features respectively;

[0046] S52, inputting the generated weighted fusion feature matrix into the distributed computing platform, and dynamically adjusting the allocation weight according to the processing capacity and current load of each computing node, and distributing and parallel processing the feature matrix;

[0047] S53, after completing the parallel processing on the distributed computing platform, the weighted fusion feature matrix output by each node is summarized, and the heart rate variability prediction feature subset F is generated through a multi-layer dynamic weighting mechanism. HRV :

[0048]

[0049] Among them, W j is the weight of node j, γ pq is the multi-layer weight coefficient, θ pq is the phase adjustment parameter, Softmax(·) is the multi-layer weighted activation function, T pq (μ) is the feature transformation matrix, P and Q are the number and dimensions of different layers;

[0050] S54. Input the heart rate variability prediction feature subset into the random forest algorithm model for real-time heart rate variability prediction, and dynamically optimize the structure and parameters of the decision tree.

[0051] Optionally, the S7 includes the following steps:

[0052] S71, integrating the high-dimensional quantum state feature matrix, the extracted feature subset, and the generated weighted fusion feature matrix to generate an initial chromosome population for the evolutionary learning algorithm;

[0053] S72, through the biologically inspired evolutionary learning algorithm, the initial chromosome population is iteratively optimized. In each iteration, based on the fitness function F(C i )Evaluate the chromosomes:

[0054]

[0055] Among them, H ijk (G, E, P) represents the elements in the high-dimensional quantum state characteristic matrix, Θ ijk (Ψ) represents the element in the optimized feature matrix, Φ weighted，ijk is the element in the weighted fusion feature matrix, w k is the fusion weight, σ(·) is the nonlinear activation function, Loss(C i ) indicates chromosome C i The loss function value of the corresponding model;

[0056] S73, after fitness evaluation, select chromosomes with higher fitness for crossover operation, fuse feature combinations of different chromosomes to generate a new random forest model configuration, and mix the selected chromosome feature sets through crossover operation to form a new chromosome;

[0057] S74, performing mutation operation on the newly generated chromosome, changing the model configuration by perturbing the feature combination and decision tree structure parameters in the chromosome, and introducing a nonlinear adjustment mechanism to enable the model to have adaptability in dealing with changes in physiological state caused by exercise;

[0058] S75. After multiple iterations, the chromosome population with the highest fitness is selected as the final random forest model structure. The final optimized model structure is used for real-time heart rate variability prediction and adjusted according to feedback from actual applications to adapt to dynamically changing physiological states.

[0059] Optionally, the S8 includes the following steps:

[0060] S81, receiving the real-time input physiological data from the final optimized random forest model structure, and calculating the heart rate variability prediction value through the decision tree structure and weights within the model;

[0061] S82, combining the generated heart rate variability prediction value with the user's personalized biometric database, and applying quantum enhanced holographic multi-dimensional projection technology to process it to generate a multi-dimensional holographic feature image;

[0062] S83. Using augmented reality or virtual reality technology, the generated multi-dimensional holographic feature images are displayed to the user, and the user can view and analyze these holographic images from different angles in a virtual environment to understand the prediction results of heart rate variability and health status in real time;

[0063] S84. Dynamically update model parameters and personalized biometric library based on user interaction feedback, and generate personalized exercise suggestions.

[0064] The beneficial effects of the present invention are:

[0065] (1) The present invention combines holographic quantum computing technology with a multidimensional feature interaction network to extract high-dimensional features from genomic data, epigenetic information, and physiological data, significantly improving the accuracy of prediction of exercise-induced heart rate variability. In particular, in terms of multimodal data fusion and feature extraction, the present invention overcomes the limitations of traditional methods, can comprehensively and accurately process multi-source data, and generate a unified feature representation.

[0066] (2) The present invention optimizes the decision tree structure of the random forest model by introducing a self-supervised learning driven time series feature enhancement network and reinforcement learning algorithm, enhances the model's ability to adapt to dynamic changes in physiological state during exercise, and improves the real-time performance and accuracy of prediction. At the same time, a biologically inspired evolutionary learning algorithm is used to iteratively optimize the model structure and parameters, effectively improving the adaptability and stability of the model.

[0067] (3) The present invention provides an efficient and accurate solution for predicting exercise-induced heart rate variability by comprehensively using multimodal data processing and quantum computing technology. The prediction results are intuitively visualized through quantum-enhanced holographic projection technology, and personalized health status display and exercise suggestions are provided in combination with the user's personalized biological characteristics, thereby achieving comprehensive management and optimization of heart rate variability. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0069] Figure 1A flowchart of a method for predicting exercise-induced heart rate variability based on a random forest algorithm proposed by the present invention;

[0070] Figure 2 This is a flowchart of the network application based on self-supervised learning-driven temporal feature enhancement proposed by the present invention;

[0071] Figure 3 A schematic diagram of a random forest model optimized based on a biologically inspired evolutionary learning algorithm proposed in the present invention; DETAILED DESCRIPTION

[0072] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0073] refer to Figure 1-3 , a method for predicting exercise-induced heart rate variability based on random forest algorithm, comprising the following steps:

[0074] S1. Collect multimodal physiological data of the user during exercise, synchronously process the physiological data through multi-channel signal fusion technology, and generate a time-aligned multi-dimensional data stream;

[0075] S2, perform data cleaning, denoising and standardization on multi-dimensional data streams, and use an adaptive spatiotemporal data enhancement algorithm to generate a standardized input data sequence;

[0076] S3, based on the user's genomic data and epigenetic modification information, a multi-dimensional feature interaction network of holographic quantum computing is used to extract high-dimensional features related to heart rate variability from the input data sequence, and an adaptive gene regulation model is constructed through a multi-layer feature selection algorithm to generate a multi-dimensional biological feature library;

[0077] S4. Integrate the standardized input data sequence with the multidimensional biometric library to generate a high-dimensional input data set. Use the self-supervised learning driven temporal feature enhancement network to extract features from the time series data in the high-dimensional input data set to capture the complex dependencies and nonlinear dynamic behaviors in the data.

[0078] S5. During the training of the random forest algorithm model, feature selection is performed on high-dimensional input data sets, and the decision tree generation strategy is adjusted through the reinforcement learning algorithm. The structure and parameters of the decision tree are dynamically optimized in a distributed computing environment using the real-time collaborative optimization technology of distributed computing.

[0079] S6. Based on biofeedback data, the adaptive weighted sampling technology is used to adjust the weights of training samples in real time, and the model is updated through the incremental learning algorithm, so that the random forest algorithm can handle gradually changing physiological states and optimize in real time;

[0080] S7. Applying a biologically inspired evolutionary learning algorithm to simulate the biological evolution process, optimizing the structure and parameters of the random forest model through selection, crossover and mutation operations, so that the random forest model can adapt to the changes in physiological state caused by exercise;

[0081] S8. Analyze the real-time prediction results of the random forest model with the user's personalized biometric characteristics, use quantum enhanced holographic multi-dimensional projection technology to visualize the heart rate variability prediction results as multi-dimensional holographic images, and display and interact through augmented reality or virtual reality technology to provide users with health status and exercise suggestions.

[0082] In this implementation, S3 includes the following steps:

[0083] S31, obtain the generated time-aligned multi-dimensional data stream, including the user's genome data G i , epigenetic modification information E j , and physiological data processed by multi-channel signal fusion technology P k , where i, j, and k represent different dimensions of genomic, epigenetic, and physiological data, respectively;

[0084] S32. Use holographic quantum computing technology to process multi-dimensional feature interactions on data streams, and combine quantum Fourier transform and nonlinear multi-dimensional feature mapping to generate a high-dimensional quantum state feature matrix after encoding:

[0085]

[0086] Among them, ∑i ，j，k represents the sum of all genomic data i, epigenetic modification information j, and physiological data k, QFT(ψ(G i , E j , P k )) Quantum Fourier transform applied to complex amplitude ψ(G i , E j , P k ) after ψ(G i , E j , P k ) is the genome data G i , epigenetic modification information E j and physiological data P k The complex amplitude of is the dynamic phase factor associated with each data point, M(λ) is the multidimensional feature interaction matrix, and λ is the parameter that controls the multidimensional feature interaction;

[0087] S33. The high-dimensional quantum state feature matrix is ​​processed through a multi-dimensional feature interaction network, and a multi-layer hybrid quantum-classical feature extraction algorithm is used to identify key features related to heart rate variability:

[0088]

[0089] Among them, F represents the key feature set related to heart rate variability that is finally extracted, ClassicalAnalysis(·) is used to extract features from the classical data obtained after quantum state measurement, and Measure(·) is the quantum state measurement operation. represents the sum of N independent modes in the quantum state space, m represents different modes or quantum states, represents the wave vector, is the position vector of the data in the feature space, V is the integral volume, α n is the feature selection weight, QuantumSelector is a quantum algorithm used to extract key features from high-dimensional feature space;

[0090] S34. Based on the selected feature set F, an adaptive gene regulation model is constructed, and the prediction parameters of heart rate variability are dynamically adjusted by combining machine learning technology and quantum computing technology;

[0091] S35. Integrate the output of the model and other data features that are not directly used in the model to generate a multidimensional biometric library to provide comprehensive data support for the random forest algorithm.

[0092] In this implementation manner, the S4 includes the following steps:

[0093] S41. Obtain the generated high-dimensional quantum state feature matrix H(G, E, P) and the selected key feature set F, perform multi-level fusion processing on H(G, E, P) and F as well as the user's personalized biometric feature set B to generate a high-dimensional fusion feature matrix Φ(H, F, B):

[0094]

[0095] Among them, H ijk (G, E, P) represents the i, j, and k elements in the high-dimensional quantum state characteristic matrix, F f represents the fth feature in the feature set F, B b represents the bth feature in the biometric feature set B, w ijkfb is the fusion weight coefficient, α mis the multidimensional interaction weight, tanh(·) is the nonlinear activation function, σ m (H ijk , F f , B b ) is the multi-layer feature interaction function, T(λ) is the feature space transformation matrix;

[0096] S42, input the high-dimensional fusion feature matrix Φ(H, F, B) into the time series feature enhancement network driven by self-supervised learning, extract features from the time series data in the matrix, and capture the complex dependencies and nonlinear dynamic behaviors in the time series data through the self-supervised mechanism, using time series convolution and dynamic feature selection, to generate an optimized time series feature matrix Ψ(Φ);

[0097] S43, using quantum state driven adaptive feature mapping technology, dynamically mapping the optimized time series feature matrix Ψ(Φ) to generate a quantum state enhanced feature matrix Θ(Ψ):

[0098]

[0099] Among them, Entangle(·) represents the quantum entanglement operation, is a high-order nonlinear feature transformation function, is the phase factor, U q is the quantum state mapping matrix, γ q is the adaptive weight coefficient, is the multi-dimensional feature interaction matrix;

[0100] S44, performing feature selection and extraction on the quantum state enhanced feature matrix to generate a feature subset related to heart rate variability prediction;

[0101] S45. Based on the generated feature subset, the adaptive feature reconstruction technology is applied, and PCA and quantum state reconstruction methods are used to reconstruct and optimize the features to generate more expressive feature representations;

[0102] S46. Input the reconstructed feature subset into the random forest algorithm model, perform real-time heart rate variability prediction based on the feature subset, and capture the complex dependencies and nonlinear dynamic behaviors in the data.

[0103] In this implementation manner, S5 includes the following steps:

[0104] S51. Based on the high-dimensional quantum state feature matrix and the quantum state enhanced feature matrix, the trust value of each data source is dynamically evaluated through the enhanced fuzzy trust model. The trust value is used as a weight to fuse the feature matrix, and a weighted fusion feature matrix Φ is generated through nonlinear transformation and multidimensional interaction mechanism. weighted :

[0105]

[0106] Among them, T i represents the trust value of data source i, H ijk (G, E, P) represents the i, j, and k elements in the high-dimensional quantum state characteristic matrix H(G, E, P), Θ ijk (Ψ) represents the corresponding element in the quantum state enhancement characteristic matrix Θ(Ψ), α ij and β ij is the interaction weight, σ(·) is the nonlinear activation function, is the feature transformation function, N and M are the number and dimension of features respectively;

[0107] S52, inputting the generated weighted fusion feature matrix into the distributed computing platform, and dynamically adjusting the allocation weight according to the processing capacity and current load of each computing node, and distributing and parallel processing the feature matrix;

[0108] S53, after completing the parallel processing on the distributed computing platform, the weighted fusion feature matrix output by each node is summarized, and the heart rate variability prediction feature subset F is generated through a multi-layer dynamic weighting mechanism. HRV :

[0109]

[0110] Among them, W j is the weight of node j, γ pq is the multi-layer weight coefficient, θ pq is the phase adjustment parameter, Softmax(·) is the multi-layer weighted activation function, T pq (μ) is the feature transformation matrix, P and Q are the number and dimensions of different layers;

[0111] S54. Input the heart rate variability prediction feature subset into the random forest algorithm model for real-time heart rate variability prediction, and dynamically optimize the structure and parameters of the decision tree.

[0112] In this implementation manner, the S7 includes the following steps:

[0113] S71, integrating the high-dimensional quantum state feature matrix, the extracted feature subset, and the generated weighted fusion feature matrix to generate an initial chromosome population for the evolutionary learning algorithm;

[0114] S72, through the biologically inspired evolutionary learning algorithm, the initial chromosome population is iteratively optimized. In each iteration, based on the fitness function F(C i )Evaluate the chromosomes:

[0115]

[0116] Among them, H ijk (G, E, P) represents the elements in the high-dimensional quantum state characteristic matrix, Θ ijk (Ψ) represents the element in the optimized feature matrix, Φ weighted，ijk is the element in the weighted fusion feature matrix, w k is the fusion weight, σ(·) is the nonlinear activation function, Loss(C i ) indicates chromosome C i The loss function value of the corresponding model;

[0117] S73, after fitness evaluation, select chromosomes with higher fitness for crossover operation, fuse feature combinations of different chromosomes to generate a new random forest model configuration, and mix the selected chromosome feature sets through crossover operation to form a new chromosome;

[0118] S74, performing mutation operation on the newly generated chromosome, changing the model configuration by perturbing the feature combination and decision tree structure parameters in the chromosome, and introducing a nonlinear adjustment mechanism to enable the model to have adaptability in dealing with changes in physiological state caused by exercise;

[0119] S75. After multiple iterations, the chromosome population with the highest fitness is selected as the final random forest model structure. The final optimized model structure is used for real-time heart rate variability prediction and adjusted according to feedback from actual applications to adapt to dynamically changing physiological states.

[0120] In this implementation manner, the S8 includes the following steps:

[0121] S81, receiving the real-time input physiological data from the final optimized random forest model structure, and calculating the heart rate variability prediction value through the decision tree structure and weights within the model;

[0122] S82, combining the generated heart rate variability prediction value with the user's personalized biometric database, and applying quantum enhanced holographic multi-dimensional projection technology to process it to generate a multi-dimensional holographic feature image;

[0123] S83. Using augmented reality or virtual reality technology, the generated multi-dimensional holographic feature images are displayed to the user, and the user can view and analyze these holographic images from different angles in a virtual environment to understand the prediction results of heart rate variability and health status in real time;

[0124] S84. Dynamically update model parameters and personalized biometric library based on user interaction feedback, and generate personalized exercise suggestions.

[0125] Embodiment 1:

[0126] In order to verify the feasibility of the present invention in implementation, the present invention is applied to a large fitness center located in New York, USA, which has hundreds of professional athletes. Their training load is high, and their physical condition changes significantly during training. Especially after long-term high-intensity training, some athletes have irregular heart rate and excessive fatigue. Faced with this situation, the management of the fitness center urgently needs a technology that can accurately predict heart rate variability and monitor the physiological state of athletes in real time during training. In this embodiment, we selected a motion-induced heart rate variability prediction method based on a random forest algorithm of the present invention to perform non-invasive, real-time dynamic monitoring and analysis of the heart rate variability of athletes.

[0127] In actual application, the physiological data of 100 athletes participating in the experiment were first collected, including multimodal data such as heart rate, blood oxygen saturation, respiratory rate, body temperature, and blood pressure. These data were recorded at different stages of the athletes' regular training, covering the entire process from warm-up to high-intensity training to cooling recovery. The data was synchronously collected through multi-sensor devices worn by the athletes, and time alignment and data stream generation were performed through multi-channel signal fusion technology.

[0128] Next, the system uses holographic quantum computing technology to extract multidimensional features from the collected data, and combines the athlete's genomic information and epigenetic data to generate a high-dimensional biometric library. This feature library is used to further analyze physiological parameters related to heart rate variability, and extract features from time series data through a time series feature enhancement network driven by self-supervised learning. This process can capture complex dependencies and nonlinear dynamic behaviors in physiological data during training.

[0129] In order to further verify the accuracy of the prediction, the system optimized the random forest algorithm model during the training phase, and dynamically adjusted the decision tree structure and parameters of the model through reinforcement learning algorithms and distributed computing technology. Through the adaptive weighted sampling technology of biofeedback data, the weight of the training samples was also adjusted in real time to adapt to the dynamic changes in the athlete's physiological state.

[0130] Throughout the experiment, the system made real-time predictions of each athlete's heart rate variability, combined the predictions with the athlete's personalized biometrics, and visualized the results as multi-dimensional holographic images through quantum-enhanced holographic projection technology. Athletes and coaches can intuitively view these holographic images through augmented reality devices and understand the athlete's health status and training load in real time.

[0131] During the experimental data aggregation and analysis phase, the system optimized the model through multiple iterations and finally successfully predicted the heart rate variability of athletes at different training stages. During the high-intensity training phase, the system predicted the risk of arrhythmia for many athletes and issued a timely warning. Compared with traditional monitoring methods, the present invention has improved the prediction accuracy of athletes' heart rate variability by 15%, and has significant advantages in physiological status monitoring and real-time feedback during training.

[0132] Table 1: Summary of athlete heart rate variability prediction test data

[0133]

[0134]

[0135] Through the comparative analysis of the above data tables, it can be seen that the application of the present invention in the prediction of exercise-induced heart rate variability significantly improves the prediction accuracy, the real-time performance of the system and the user satisfaction. In particular, in key links such as model optimization and iteration, real-time monitoring of heart rate variability, data processing efficiency and holographic projection visualization, the method of the present invention has shown obvious advantages over traditional methods, providing more accurate and efficient technical support for the health management of athletes.

[0136] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for predicting exercise-induced heart rate variability based on random forest algorithm, characterized in that: The steps include: S1. Collect multimodal physiological data of the user during exercise, synchronously process the physiological data through multi-channel signal fusion technology, and generate a time-aligned multi-dimensional data stream; S2, perform data cleaning, denoising and standardization on multi-dimensional data streams, and use an adaptive spatiotemporal data enhancement algorithm to generate standardized input data sequences; S3, based on the user's genomic data and epigenetic modification information, a multi-dimensional feature interaction network of holographic quantum computing is used to extract high-dimensional features related to heart rate variability from the input data sequence, and an adaptive gene regulation model is constructed through a multi-layer feature selection algorithm to generate a multi-dimensional biological feature library; S4. Integrate the standardized input data sequence with the multidimensional biometric library to generate a high-dimensional input data set. Use the self-supervised learning driven temporal feature enhancement network to extract features from the time series data in the high-dimensional input data set to capture the complex dependencies and nonlinear dynamic behaviors in the data. S5. During the training of the random forest algorithm model, feature selection is performed on high-dimensional input data sets, and the decision tree generation strategy is adjusted through the reinforcement learning algorithm. The structure and parameters of the decision tree are dynamically optimized in a distributed computing environment using the real-time collaborative optimization technology of distributed computing. S6. Based on biofeedback data, the adaptive weighted sampling technology is used to adjust the weights of training samples in real time, and the model is updated through the incremental learning algorithm, so that the random forest algorithm can handle gradually changing physiological states and optimize in real time; S7. Applying a biologically inspired evolutionary learning algorithm to simulate the biological evolution process, optimizing the structure and parameters of the random forest model through selection, crossover and mutation operations, so that the random forest model can adapt to the changes in physiological state caused by exercise; S8. Analyze the real-time prediction results of the random forest model with the user's personalized biometric characteristics, use quantum enhanced holographic multi-dimensional projection technology to visualize the heart rate variability prediction results as multi-dimensional holographic images, and display and interact through augmented reality or virtual reality technology to provide users with health status and exercise recommendations.

2. The method for predicting exercise-induced heart rate variability based on random forest algorithm according to claim 1, characterized in that: The S3 specifically includes: S31, obtain the generated time-aligned multi-dimensional data stream, including the user's genome data G i , epigenetic modification information E j , and physiological data processed by multi-channel signal fusion technology P k , where i, j, and k represent different dimensions of genomic, epigenetic, and physiological data, respectively; S32. Use holographic quantum computing technology to process multi-dimensional feature interactions on data streams, and combine quantum Fourier transform and nonlinear multi-dimensional feature mapping to generate a high-dimensional quantum state feature matrix after encoding: Among them, ∑ i,j,k represents the sum of all genomic data i, epigenetic modification information j, and physiological data k, QFT(ψ(G i ,E j ,P k )) Quantum Fourier transform applied to complex amplitude ψ(G i ,E j ,P k ) after ψ(G i ,E j ,P k ) is the genome data G i , epigenetic modification information E j and physiological data P k The complex amplitude of is the dynamic phase factor associated with each data point, M(λ) is the multidimensional feature interaction matrix, and λ is the parameter that controls the multidimensional feature interaction; S33. The high-dimensional quantum state feature matrix is ​​processed through a multi-dimensional feature interaction network, and a multi-layer hybrid quantum-classical feature extraction algorithm is used to identify key features related to heart rate variability: Among them, F represents the key feature set related to heart rate variability that is finally extracted, ClassicalAnalysis(·) is used to extract features from the classical data obtained after quantum state measurement, and Measure(·) is the quantum state measurement operation. represents the sum of N independent modes in the quantum state space, m represents different modes or quantum states, represents the wave vector, is the position vector of the data in the feature space, V is the integral volume, α n is the feature selection weight, QuantumSelector is a quantum algorithm used to extract key features from high-dimensional feature space; S34. Based on the selected feature set F, an adaptive gene regulation model is constructed, and the prediction parameters of heart rate variability are dynamically adjusted by combining machine learning technology and quantum computing technology; S35. Integrate the output of the model and other data features that are not directly used in the model to generate a multidimensional biometric library to provide comprehensive data support for the random forest algorithm.

3. The method for predicting exercise-induced heart rate variability based on random forest algorithm according to claim 1, characterized in that: The S4 specifically includes: S41. Obtain the generated high-dimensional quantum state feature matrix H(G,E,P) and the selected key feature set F, perform multi-level fusion processing on H(G,E,P) and F as well as the user's personalized biometric feature set B to generate a high-dimensional fusion feature matrix Φ(H,F,B): Among them, H ijk (G, E, P) represents the i, j, and k elements in the high-dimensional quantum state characteristic matrix, F f represents the fth feature in the feature set F, B b represents the bth feature in the biometric feature set B, w ijkfb is the fusion weight coefficient, α m is the multidimensional interaction weight, tanh(·) is the nonlinear activation function, σ m (H ijk ,F f ,B b ) is the multi-layer feature interaction function, T(λ) is the feature space transformation matrix; S42, input the high-dimensional fusion feature matrix Φ(H, F, B) into the time series feature enhancement network driven by self-supervised learning, extract features from the time series data in the matrix, and capture the complex dependencies and nonlinear dynamic behaviors in the time series data through the self-supervised mechanism, using time series convolution and dynamic feature selection, to generate the optimized time series feature matrix Ψ(Φ); S43, using quantum state driven adaptive feature mapping technology, dynamically mapping the optimized time series feature matrix Ψ(Φ) to generate a quantum state enhanced feature matrix Θ(Ψ): Among them, Entangle(·) represents the quantum entanglement operation, is a high-order nonlinear feature transformation function, is the phase factor, U q is the quantum state mapping matrix, γ q is the adaptive weight coefficient, is the multi-dimensional feature interaction matrix; S44, performing feature selection and extraction on the quantum state enhanced feature matrix to generate a feature subset related to heart rate variability prediction; S45. Based on the generated feature subset, the adaptive feature reconstruction technology is applied, and PCA and quantum state reconstruction methods are used to reconstruct and optimize the features to generate more expressive feature representations; S46. Input the reconstructed feature subset into the random forest algorithm model, perform real-time heart rate variability prediction based on the feature subset, and capture the complex dependencies and nonlinear dynamic behaviors in the data.

4. The method for predicting exercise-induced heart rate variability based on random forest algorithm according to claim 1, characterized in that: The S5 specifically includes: S51. Based on the high-dimensional quantum state feature matrix and the quantum state enhanced feature matrix, the trust value of each data source is dynamically evaluated through the enhanced fuzzy trust model. The trust value is used as a weight to fuse the feature matrix, and a weighted fusion feature matrix Φ is generated through nonlinear transformation and multidimensional interaction mechanism. weighted : Among them, T i represents the trust value of data source i, H ijk (G, E, P) represents the i, j, and k elements in the high-dimensional quantum state characteristic matrix H(G, E, P), Θ ijk (Ψ) represents the corresponding element in the quantum state enhancement characteristic matrix Θ(Ψ), α ij and β ij is the interaction weight, σ(·) is the nonlinear activation function, is the feature transformation function, N and M are the number and dimension of features respectively; S52, inputting the generated weighted fusion feature matrix into the distributed computing platform, and dynamically adjusting the allocation weight according to the processing capacity and current load of each computing node, and distributing and parallel processing the feature matrix; S53, after completing the parallel processing on the distributed computing platform, the weighted fusion feature matrix output by each node is summarized, and the heart rate variability prediction feature subset F is generated through a multi-layer dynamic weighting mechanism. HRV : Among them, W j is the weight of node j, γ pq is the multi-layer weight coefficient, θ pq is the phase adjustment parameter, Softmax(·) is the multi-layer weighted activation function, T pq (μ) is the feature transformation matrix, P and Q are the number and dimensions of different layers; S54. Input the heart rate variability prediction feature subset into the random forest algorithm model for real-time heart rate variability prediction, and dynamically optimize the structure and parameters of the decision tree.

5. The method for predicting exercise-induced heart rate variability based on random forest algorithm according to claim 1, characterized in that: The S7 specifically includes: S71, integrating the high-dimensional quantum state feature matrix, the extracted feature subset, and the generated weighted fusion feature matrix to generate an initial chromosome population for the evolutionary learning algorithm; S72, through the biologically inspired evolutionary learning algorithm, the initial chromosome population is iteratively optimized. In each iteration, based on the fitness function F(C i )Evaluate the chromosomes: Among them, H ijk (G, E, P) represents the elements in the high-dimensional quantum state characteristic matrix, Θ ijk (Ψ) represents the element in the optimized feature matrix, Φ weighted,ijk is the element in the weighted fusion feature matrix, w k is the fusion weight, σ(·) is the nonlinear activation function, Loss(C i ) indicates chromosome C i The loss function value of the corresponding model; S73, after fitness evaluation, select chromosomes with higher fitness for crossover operation, fuse feature combinations of different chromosomes to generate a new random forest model configuration, and mix the selected chromosome feature sets through crossover operation to form a new chromosome; S74, performing mutation operation on the newly generated chromosome, changing the model configuration by perturbing the feature combination and decision tree structure parameters in the chromosome, and introducing a nonlinear adjustment mechanism to enable the model to have adaptability in dealing with changes in physiological state caused by exercise; S75. After multiple iterations, the chromosome population with the highest fitness is selected as the final random forest model structure. The final optimized model structure is used for real-time heart rate variability prediction and adjusted according to feedback from actual applications to adapt to dynamically changing physiological states.

6. The method for predicting exercise-induced heart rate variability based on random forest algorithm according to claim 1, characterized in that: The S8 specifically includes: S81, receiving the real-time input physiological data from the final optimized random forest model structure, and calculating the heart rate variability prediction value through the decision tree structure and weights within the model; S82, combining the generated heart rate variability prediction value with the user's personalized biometric database, and applying quantum enhanced holographic multi-dimensional projection technology to process it to generate a multi-dimensional holographic feature image; S83. Using augmented reality or virtual reality technology, the generated multi-dimensional holographic feature images are displayed to the user, and the user can view and analyze these holographic images from different angles in a virtual environment to understand the prediction results of heart rate variability and health status in real time; S84. Dynamically update model parameters and personalized biometric library based on user interaction feedback, and generate personalized exercise suggestions.

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