Old people turning over and back patting auxiliary system based on artificial intelligence

Abstract

The invention discloses an old people turning over and back patting auxiliary system based on artificial intelligence. The old people turning over and back patting auxiliary system comprises a data acquisition module, a data inspection module, a turning over and back patting demand prediction model building module and a turning over and back patting auxiliary module. The invention relates to the technical field of medical assistance, in particular to an old people turning over and back patting assisting system based on artificial intelligence, according to the scheme, abnormal data are cleared through defining an enhanced weight, constructing an enhanced mixed feature model, calculating probability and updating parameters, the distribution characteristics of the data are described more accurately, the influence of noise data on the model is reduced, and the accuracy of turning over and back patting of old people is improved. The quality and the reliability of the data are improved; by introducing a difference entropy and an oscillation factor, designing an interleaving kernel, optimizing a target function and carrying out parameter search and optimization, a complex relationship among data is described more comprehensively, so that the model can capture distribution characteristics and rules of the data more accurately, the efficiency and accuracy of parameter search are improved, model parameters are set more properly, and the accuracy of the model is improved. And the precision of the model is improved.

Description

Technical Field

[0001] The present invention relates to the field of medical assistance technology, and in particular to an artificial intelligence-based elderly people's turning-over and back-patting assistance system. Background Art

[0002] The AI-based elderly turning over and back-patting assistance system uses AI technology and data processing technology to build a turning over and back-patting demand prediction model for the elderly, accurately predict the demand, and provide timely turning over and back-patting assistance services. However, the traditional data verification method has the problem of inaccurate verification of data related to the turning over and back-patting needs of the elderly, and it is difficult to effectively identify abnormal data; the traditional turning over and back-patting demand prediction model has the problem of low prediction accuracy, difficulty in effectively capturing the complex relationship and dynamic changes between data, and inappropriate model parameter settings. Summary of the invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an artificial intelligence-based elderly turning over and back-patting auxiliary system. In view of the problems that the traditional data verification method has inaccurate verification of data related to the elderly turning over and back-patting needs and is difficult to effectively identify abnormal data, this solution constructs feature vectors, defines enhanced weights, constructs enhanced mixed feature models, probability calculations and parameter updates to remove abnormal data, more accurately describes the distribution characteristics of the data, improves the model's fitting and generalization capabilities for the data, reduces the impact of noise data on the model, and improves the quality and reliability of the data; in view of the problems that the traditional turning over and back-patting demand prediction model has low prediction accuracy, is difficult to effectively capture the complex relationship and dynamic changes between data, and the model parameter settings are inappropriate, this solution introduces differential entropy and oscillation factors, designs interleaved kernels, optimizes objective functions, and performs parameter search and optimization to more comprehensively describe the complex relationship between data, so that the model can more accurately capture the distribution characteristics and laws of the data, improve the efficiency and accuracy of parameter search, make the model parameters more appropriately set, and improve the accuracy of the model.

[0004] The technical solution adopted by the present invention is as follows: the elderly turning over and patting the back auxiliary system based on artificial intelligence provided by the present invention comprises a data acquisition module, a data verification module, a turning over and patting the back demand prediction model building module and a turning over and patting the back auxiliary module;

[0005] The data acquisition module collects historical physiological data, environmental data and the level of demand for turning over and patting the back of the elderly; the physiological data includes heart rate, respiratory rate, body temperature, blood pressure and bed rest time; the environmental data refers to the temperature, humidity and noise intensity of the environment for turning over and patting the back; the level of demand for turning over and patting the back includes two levels: no demand and demand;

[0006] The data verification module removes abnormal data by constructing feature vectors, defining enhancement weights, constructing enhanced hybrid feature models, probability calculations, and parameter updates;

[0007] The module for constructing a prediction model for the demand for turning over and patting the back is constructed by setting label data, defining difference entropy, defining oscillation factors, designing interleaving kernels, determining targets, calculating optimal hyperplane weights, calculating optimal hyperplane biases, preparing parameter optimization, generating jump factors, generating initial parameter points, defining kinetic energy control factors, designing parameter search functions, introducing global reset mechanisms, and customizing start-stop strategies to construct a prediction model for the demand for turning over and patting the back;

[0008] The turning over and back-patting auxiliary module predicts the turning over and back-patting demand level of the elderly through the turning over and back-patting demand prediction model, and assists the elderly in turning over and back-patting.

[0009] Furthermore, the data verification module specifically includes the following contents:

[0010] Construct a feature vector by combining the feature data: heart rate, respiratory rate, body temperature, blood pressure, bed rest time, temperature, humidity, noise intensity, and the level of need for turning over and patting the back;

[0011] Define the enhancement weight as follows:

[0012] ;

[0013] in, represents the input feature vector, u and represents the index of the Gaussian component, Represents the input feature vector The enhancement weight, e represents the natural constant, U represents the total number of Gaussian components, represents the mean of the u-th Gaussian component, Indicates The mean of the Gaussian components, Indicates the modulus length;

[0014] Construct an enhanced mixed feature model, which is expressed as follows:

[0015] ;

[0016] in, Represents the parameter set of the enhanced hybrid feature model, including: hybrid weight , mean vector and the covariance matrix , The feature vector representing the input In the parameter The probability density of the enhanced mixed feature model under the condition of, The feature vector representing the input For a mean , the variance is The probability density of the Gaussian distribution of

[0017] The probability calculation is expressed as follows:

[0018] ;

[0019] Among them, g represents the index of the feature vector, represents the probability that the g-th eigenvector comes from the u-th Gaussian component, represents the g-th eigenvector;

[0020] Parameter update, update the mixing weights, mean vector and covariance matrix, expressed as follows:

[0021] ;

[0022] Where G represents the total number of eigenvectors, Represents the transpose operation;

[0023] Abnormal data is cleared, probability calculation and parameter update are repeated until the parameters converge, the probability density threshold is set, the probability density of the enhanced mixed feature model of each feature vector is calculated, and the feature vector with probability density lower than the probability density threshold is set as an abnormal vector and cleared.

[0024] Furthermore, the construction of the turning over and patting back demand prediction model module specifically includes the following contents:

[0025] Set label data, and set the turning over and patting back demand level as label data of the turning over and patting back demand prediction model;

[0026] The difference entropy is defined as follows:

[0027] ;

[0028] Among them, x1 and x2 represent the input feature vectors, represents the difference entropy between x1 and x2, i represents the dimension index of the feature vector, and They represent the probability of mapping the difference between the value of the feature vector x1 and x2 in the i-th dimension and the mean to the interval [0,1], represents the logarithmic function;

[0029] Define the oscillation factor as follows:

[0030] ;

[0031] in, represents the oscillation factor between eigenvectors x1 and x2, and Respectively represent the second-order derivatives of the eigenvectors x2 and x2 in the i-th dimension, and Represent the first-order derivatives of the eigenvectors x2 and x2 in the i-th dimension, respectively. Indicates taking the absolute value, represents the zero removal factor;

[0032] Design the interleaving kernel, expressed as follows:

[0033] ;

[0034] in, represents the interleaved kernel between feature vectors x1 and x2, D represents the maximum dimension of the feature vector, represents the eigenvalue of the eigenvector x1 in the i-th dimension, represents the eigenvalue of the eigenvector x2 in the i-th dimension, Represents the width parameter of the i-th dimension, and denote the cross-weight and oscillation weight respectively;

[0035] Determine the goal, expressed as follows:

[0036] ;

[0037] Where w is the hyperplane weight vector, represents the discrete weight vector of the i-th dimension, Indicates taking the maximum value, represents the square of the L2 norm, j and k represent the index of the feature vector, represents the adjustment weight, and denote the Lagrange multipliers of the j-th and k-th eigenvectors, respectively, and Represent the labels of the j-th and k-th feature vectors respectively;

[0038] Calculate the optimal hyperplane weight, expressed as follows:

[0039] ;

[0040] in, represents the weight of the optimal hyperplane, represents the optimal solution of the Lagrange multiplier of the jth eigenvector, represents the feature mapping of the interleaved kernel for the jth eigenvector;

[0041] Calculate the optimal hyperplane bias, expressed as follows:

[0042] ;

[0043] in, represents the bias of the optimal hyperplane, represents the total number of support vectors, denotes the eigenvector index belonging to the support vector, represents the optimal solution of the Lagrange multiplier of the kth eigenvector;

[0044] Parameter optimization preparation, setting the accuracy of the turning over and patting back demand prediction model as the performance value of the parameter individual, and determining the optimization parameters, including width parameter, cross weight, oscillation weight and adjustment weight;

[0045] Generate the jump factor, expressed as follows:

[0046] ;

[0047] Where q represents the number of times the initial parameter points are generated. Indicates the jump factor when generating the initial parameter point for the q+1th time, Indicates the jump factor when generating the initial parameter point for the qth time, where the initial jump factor is a random number ranging from 0 to 1;

[0048] Generate initial parameter points, expressed as follows:

[0049] ;

[0050] in, represents the position of the initial parameter point generated for the q+1th time, represents the upper bound of the parameter space, represents the lower bound of the parameter space;

[0051] The kinetic energy control factor is defined as follows:

[0052] ;

[0053] Among them, t represents the number of current parameter searches, represents the kinetic energy control factor during the t-th parameter search, Indicates the maximum number of parameter searches, Represents a random number between 0 and 1. represents a random number between 0 and 2. represents the symbolic function, represents the average position of the generated initial parameter search points;

[0054] Design parameter search function, expressed as follows:

[0055] ;

[0056] in, Indicates the parameter position obtained by the t+1th parameter search, represents the parameter position obtained by the tth parameter search, represents the position with the highest global parameter performance during the tth parameter search, r3 represents a random number ranging from 0 to 1, Indicates the position with the lowest global parameter performance during the t-th parameter search;

[0057] A global reset mechanism is introduced, which is expressed as follows:

[0058] ;

[0059] in, Indicates the parameter position during the parameter search process, Indicates the parameter position after reset, Indicates taking the parameter position with the closest distance;

[0060] Customize the start-stop strategy, set the parameter performance threshold and the maximum number of parameter searches, and use the parameter search function to perform parameter search on the initial parameter search point. During the search process, if there is a parameter position with parameter performance greater than the parameter performance threshold, stop the search and set the parameter at the position with the highest global parameter performance at this time as the model parameter; if the parameter search number reaches the maximum number of parameter searches, search again; otherwise, continue searching.

[0061] Furthermore, the turning over and back-patting assistance module collects the elderly's physiological data and environmental data in real time, and inputs the data into a turning over and back-patting demand prediction model. The model predicts the elderly's turning over and back-patting demand level, and assists the elderly in turning over and back-patting in real time.

[0062] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0063] (1) In view of the problems that traditional data verification methods have inaccurate verification of data related to the needs of the elderly to turn over and pat their backs, and it is difficult to effectively identify abnormal data, this solution removes abnormal data by constructing feature vectors, defining enhancement weights, constructing enhanced mixed feature models, probability calculation and parameter update, and more accurately describes the distribution characteristics of the data. It improves the model's ability to fit and generalize data, reduces the impact of noise data on the model, and improves the quality and reliability of the data.

[0064] (2) In view of the problems of low prediction accuracy, difficulty in effectively capturing the complex relationship and dynamic changes between data, and inappropriate model parameter settings in the traditional model for predicting the demand for turning over and patting the back, this scheme introduces differential entropy and oscillation factors, designs interleaved kernels, optimizes the objective function, and performs parameter search and optimization to more comprehensively describe the complex relationship between data, so that the model can more accurately capture the distribution characteristics and laws of the data, improve the efficiency and accuracy of parameter search, make the model parameters more appropriately set, and improve the accuracy of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A schematic diagram of the artificial intelligence-based elderly turning-over and back-patting auxiliary system provided by the present invention;

[0066] Figure 2 is a schematic diagram of a data verification module;

[0067] Figure 3 A schematic diagram of a module for forecasting the demand for turning over and patting the back;

[0068] Figure 4 Schematic diagram of customized start-stop strategy.

[0069] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0070] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0071] In the description of the present invention, it should be understood that terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “top”, “bottom”, “inside” and “outside” indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0072] Example 1, see Figure 1 The artificial intelligence-based elderly back-patting assistance system provided by the present invention includes a data acquisition module, a data verification module, a module for building a back-patting demand prediction model for turning over and patting the back, and a turning over and patting the back assistance module;

[0073] The data collection module collects historical physiological data, environmental data and the level of demand for turning over and patting the back of the elderly, and sends the data to the data verification module;

[0074] The data verification module removes abnormal data by constructing feature vectors, defining enhanced weights, constructing enhanced mixed feature models, probability calculations and parameter updates, and sends the data to the module for constructing a turning over and patting back demand prediction model;

[0075] The module for constructing a prediction model for the demand for turning over and patting the back constructs a prediction model for the demand for turning over and patting the back by setting label data, defining difference entropy, defining oscillation factors, designing interleaving kernels, determining targets, calculating optimal hyperplane weights, calculating optimal hyperplane biases, preparing parameter optimization, generating jump factors, generating initial parameter points, defining kinetic energy control factors, designing parameter search functions, introducing a global reset mechanism and customizing a start-stop strategy, and sends the data to the auxiliary module for turning over and patting the back;

[0076] The turning over and back-patting auxiliary module predicts the turning over and back-patting demand level of the elderly through the turning over and back-patting demand prediction model, and assists the elderly in turning over and back-patting.

[0077] Example 2, see Figure 1 and Figure 2 This embodiment is based on the above embodiment, and the data verification module specifically includes the following contents:

[0078] Construct a feature vector by combining the feature data: heart rate, respiratory rate, body temperature, blood pressure, bed rest time, temperature, humidity, noise intensity, and the level of need for turning over and patting the back;

[0079] Define the enhancement weight as follows:

[0080] ;

[0081] in, represents the input feature vector, u and represents the index of the Gaussian component, Represents the input feature vector The enhancement weight, e represents the natural constant, U represents the total number of Gaussian components, represents the mean of the u-th Gaussian component, Indicates The mean of the Gaussian components, Indicates the modulus length;

[0082] Construct an enhanced mixed feature model, which is expressed as follows:

[0083] ;

[0084] in, Represents the parameter set of the enhanced hybrid feature model, including: hybrid weight , mean vector and the covariance matrix , The feature vector representing the input In the parameter The probability density of the enhanced mixed feature model under the condition of, The feature vector representing the input For a mean , the variance is The probability density of the Gaussian distribution of

[0085] The probability calculation is expressed as follows:

[0086] ;

[0087] Among them, g represents the index of the feature vector, represents the probability that the g-th eigenvector comes from the u-th Gaussian component, represents the g-th eigenvector;

[0088] Parameter update, update the mixing weights, mean vector and covariance matrix, expressed as follows:

[0089] ;

[0090] Where G represents the total number of eigenvectors, Represents the transpose operation;

[0091] Abnormal data is cleared, probability calculation and parameter update are repeated until the parameters converge, the probability density threshold is set, the probability density of the enhanced mixed feature model of each feature vector is calculated, and the feature vector with probability density lower than the probability density threshold is set as an abnormal vector and cleared.

[0092] By performing the above operations, the traditional data verification method has the problem of inaccurate verification of data related to the needs of the elderly to turn over and pat their backs, and difficulty in effectively identifying abnormal data. This solution constructs feature vectors, defines enhancement weights, constructs enhanced mixed feature models, calculates probabilities, and updates parameters to remove abnormal data, more accurately describes the distribution characteristics of the data, improves the model's ability to fit and generalize data, reduces the impact of noise data on the model, and improves the quality and reliability of the data.

[0093] Example 3, see Figure 1 , Figure 3 and Figure 4 This embodiment is based on the above embodiment, and the construction of the turning over and back patting demand prediction model module specifically includes the following contents:

[0094] Set label data, and set the turning over and patting back demand level as label data of the turning over and patting back demand prediction model;

[0095] The difference entropy is defined as follows:

[0096] ;

[0097] Among them, x1 and x2 represent the input feature vectors, represents the difference entropy between x1 and x2, i represents the dimension index of the feature vector, and They represent the probability of mapping the difference between the value of the feature vector x1 and x2 in the i-th dimension and the mean to the interval [0,1], represents the logarithmic function;

[0098] Define the oscillation factor as follows:

[0099] ;

[0100] in, represents the oscillation factor between eigenvectors x1 and x2, and Respectively represent the second-order derivatives of the eigenvectors x2 and x2 in the i-th dimension, and Represent the first-order derivatives of the eigenvectors x2 and x2 in the i-th dimension, respectively. Indicates taking the absolute value, represents the zero removal factor;

[0101] Design the interleaving kernel, expressed as follows:

[0102] ;

[0103] in, represents the interleaved kernel between feature vectors x1 and x2, D represents the maximum dimension of the feature vector, represents the eigenvalue of the eigenvector x1 in the i-th dimension, represents the eigenvalue of the eigenvector x2 in the i-th dimension, Represents the width parameter of the i-th dimension, and denote the cross-weight and oscillation weight respectively;

[0104] Determine the goal, expressed as follows:

[0105] ;

[0106] Where w is the hyperplane weight vector, represents the discrete weight vector of the i-th dimension, Indicates taking the maximum value, represents the square of the L2 norm, j and k represent the index of the feature vector, represents the adjustment weight, and denote the Lagrange multipliers of the j-th and k-th eigenvectors, respectively, and Represent the labels of the j-th and k-th feature vectors respectively;

[0107] Calculate the optimal hyperplane weight, expressed as follows:

[0108] ;

[0109] in, represents the weight of the optimal hyperplane, represents the optimal solution of the Lagrange multiplier of the jth eigenvector, represents the feature mapping of the interleaved kernel for the jth eigenvector;

[0110] Calculate the optimal hyperplane bias, expressed as follows:

[0111] ;

[0112] in, represents the bias of the optimal hyperplane, represents the total number of support vectors, denotes the eigenvector index belonging to the support vector, represents the optimal solution of the Lagrange multiplier of the kth eigenvector;

[0113] Parameter optimization preparation, setting the accuracy of the turning over and patting back demand prediction model as the performance value of the parameter individual, and determining the optimization parameters, including width parameter, cross weight, oscillation weight and adjustment weight;

[0114] Generate the jump factor, expressed as follows:

[0115] ;

[0116] Where q represents the number of times the initial parameter points are generated. Indicates the jump factor when generating the initial parameter point for the q+1th time, Indicates the jump factor when generating the initial parameter point for the qth time, where the initial jump factor is a random number ranging from 0 to 1;

[0117] Generate initial parameter points, expressed as follows:

[0118] ;

[0119] in, represents the position of the initial parameter point generated for the q+1th time, represents the upper bound of the parameter space, represents the lower bound of the parameter space;

[0120] The kinetic energy control factor is defined as follows:

[0121] ;

[0122] Among them, t represents the number of current parameter searches, represents the kinetic energy control factor during the t-th parameter search, Indicates the maximum number of parameter searches, Represents a random number between 0 and 1. represents a random number between 0 and 2. represents the symbolic function, represents the average position of the generated initial parameter search points;

[0123] Design parameter search function, expressed as follows:

[0124] ;

[0125] in, Indicates the parameter position obtained by the t+1th parameter search, represents the parameter position obtained by the tth parameter search, represents the position with the highest global parameter performance during the tth parameter search, r3 represents a random number ranging from 0 to 1, Indicates the position with the lowest global parameter performance during the t-th parameter search;

[0126] A global reset mechanism is introduced, which is expressed as follows:

[0127] ;

[0128] in, Indicates the parameter position during the parameter search process, Indicates the parameter position after reset, Indicates taking the parameter position with the closest distance;

[0129] Customize the start-stop strategy, set the parameter performance threshold and the maximum number of parameter searches, and use the parameter search function to perform parameter search on the initial parameter search point. During the search process, if there is a parameter position with parameter performance greater than the parameter performance threshold, stop the search and set the parameter at the position with the highest global parameter performance at this time as the model parameter; if the parameter search number reaches the maximum number of parameter searches, search again; otherwise, continue searching.

[0130] By performing the above operations, the traditional turning over and patting back demand prediction model has the problems of low prediction accuracy, difficulty in effectively capturing the complex relationship and dynamic changes between data, and inappropriate model parameter settings. This scheme introduces differential entropy and oscillation factors, designs interleaved kernels, optimizes objective functions, and performs parameter search and optimization to more comprehensively describe the complex relationship between data, so that the model can more accurately capture the distribution characteristics and laws of data, improve the efficiency and accuracy of parameter search, make the model parameters more appropriately set, and improve the accuracy of the model.

[0131] Example 4, see Figure 1 This embodiment is based on the above embodiment. The turning over and back-patting auxiliary module collects the physiological data and environmental data of the elderly in real time, and inputs the data into the turning over and back-patting demand prediction model. The model predicts the turning over and back-patting demand level of the elderly, and assists the elderly in turning over and back-patting in real time.

[0132] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0133] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

[0134] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. An artificial intelligence-based elderly turning-over and back-patting assistance system, characterized by: It includes data collection module, data verification module, module for building prediction model of turning over and patting back demand and turning over and patting back auxiliary module; The data collection module collects historical physiological data, environmental data and the level of demand for turning over and patting the back of the elderly; The data verification module removes abnormal data by constructing feature vectors, defining enhancement weights, constructing enhanced hybrid feature models, probability calculations, and parameter updates; The module for constructing a prediction model for the demand for turning over and patting the back is constructed by setting label data, defining difference entropy, defining oscillation factors, designing interleaving kernels, determining targets, calculating optimal hyperplane weights, calculating optimal hyperplane biases, preparing parameter optimization, generating jump factors, generating initial parameter points, defining kinetic energy control factors, designing parameter search functions, introducing global reset mechanisms, and customizing start-stop strategies to construct a prediction model for the demand for turning over and patting the back; The definition of difference entropy is expressed as follows: ； Among them, x1 and x2 represent the input feature vectors, represents the difference entropy between x1 and x2, i represents the dimension index of the feature vector, and They represent the probability of mapping the difference between the value of the feature vector x1 and x2 in the i-th dimension and the mean to the interval [0,1], represents the logarithmic function; The turning over and back-patting auxiliary module predicts the turning over and back-patting demand level of the elderly through the turning over and back-patting demand prediction model, and assists the elderly in turning over and back-patting.

2. The artificial intelligence-based elderly back-patting assistance system according to claim 1 is characterized by: The module for predicting the demand for turning over and patting the back includes the following contents: Set label data, and set the turning over and patting back demand level as label data of the turning over and patting back demand prediction model; Define differential entropy; Define the oscillation factor, expressed as follows: ； in, represents the oscillation factor between eigenvectors x1 and x2, and Respectively represent the second-order derivatives of the eigenvectors x2 and x2 in the i-th dimension, and Represent the first-order derivatives of the eigenvectors x2 and x2 in the i-th dimension, respectively. Indicates taking the absolute value, represents the zero removal factor; Design the interleaving kernel, expressed as follows: ； in, represents the interleaved kernel between feature vectors x1 and x2, D represents the maximum dimension of the feature vector, represents the eigenvalue of the eigenvector x1 in the i-th dimension, represents the eigenvalue of the eigenvector x2 in the i-th dimension, Represents the width parameter of the i-th dimension, and denote the cross-weight and oscillation weight respectively; Determine the goal, expressed as follows: ； Where w is the hyperplane weight vector, represents the discrete weight vector of the i-th dimension, Indicates taking the maximum value, represents the square of the L2 norm, j and k represent the index of the feature vector, represents the adjustment weight, and denote the Lagrange multipliers of the j-th and k-th eigenvectors, respectively, and Represent the labels of the j-th and k-th feature vectors respectively; Calculate the optimal hyperplane weight, expressed as follows: ； in, represents the weight of the optimal hyperplane, represents the optimal solution of the Lagrange multiplier of the jth eigenvector, represents the feature mapping of the interleaved kernel for the jth eigenvector; Calculate the optimal hyperplane bias, expressed as follows: ； in, represents the bias of the optimal hyperplane, represents the total number of support vectors, denotes the eigenvector index belonging to the support vector, represents the optimal solution of the Lagrange multiplier of the kth eigenvector; Parameter optimization preparation, setting the accuracy of the turning over and patting back demand prediction model as the performance value of the parameter individual, and determining the optimization parameters, including width parameter, cross weight, oscillation weight and adjustment weight; Generate the jump factor, expressed as follows: ； Where q represents the number of times the initial parameter points are generated. Indicates the jump factor when generating the initial parameter point for the q+1th time, Indicates the jump factor when generating the initial parameter point for the qth time, where the initial jump factor is a random number ranging from 0 to 1; Generate initial parameter points, expressed as follows: ； in, represents the position of the initial parameter point generated for the q+1th time, represents the upper bound of the parameter space, represents the lower bound of the parameter space; The kinetic energy control factor is defined as follows: ； Among them, t represents the number of current parameter searches, represents the kinetic energy control factor during the t-th parameter search, Indicates the maximum number of parameter searches, Represents a random number between 0 and 1. represents a random number between 0 and 2. represents the symbolic function, represents the average position of the generated initial parameter search points; Design parameter search function, expressed as follows: ； in, Indicates the parameter position obtained by the t+1th parameter search, represents the parameter position obtained by the tth parameter search, represents the position with the highest global parameter performance during the tth parameter search, r3 represents a random number ranging from 0 to 1, Indicates the position with the lowest global parameter performance during the t-th parameter search; A global reset mechanism is introduced, which is expressed as follows: ； in, Indicates the parameter position during the parameter search process, Indicates the parameter position after reset, Indicates taking the parameter position with the closest distance; Customize the start-stop strategy, set the parameter performance threshold and the maximum number of parameter searches, and use the parameter search function to perform parameter search on the initial parameter search point. During the search process, if there is a parameter position with parameter performance greater than the parameter performance threshold, stop the search and set the parameter at the position with the highest global parameter performance at this time as the model parameter; if the parameter search number reaches the maximum number of parameter searches, search again; otherwise, continue searching.

3. The artificial intelligence-based elderly back-patting assistance system according to claim 1 is characterized by: The data verification module specifically includes the following contents: Construct a feature vector by combining the feature data: heart rate, respiratory rate, body temperature, blood pressure, bed rest time, temperature, humidity, noise intensity, and the level of need for turning over and patting the back; Define the enhancement weight as follows: ； in, represents the input feature vector, u and represents the index of the Gaussian component, Represents the input feature vector The enhancement weight, e represents the natural constant, U represents the total number of Gaussian components, represents the mean of the u-th Gaussian component, Indicates The mean of the Gaussian components, Indicates the modulus length; Construct an enhanced mixed feature model, which is expressed as follows: ； in, Represents the parameter set of the enhanced hybrid feature model, including: hybrid weight , mean vector and the covariance matrix , The feature vector representing the input In the parameter The probability density of the enhanced mixed feature model under the condition of, The feature vector representing the input For a mean , the variance is The probability density of the Gaussian distribution of The probability calculation is expressed as follows: ； Among them, g represents the index of the feature vector, represents the probability that the g-th eigenvector comes from the u-th Gaussian component, represents the g-th eigenvector; Parameter update, update the mixing weights, mean vector and covariance matrix, expressed as follows: ； Where G represents the total number of eigenvectors, Represents the transpose operation; Abnormal data is cleared, probability calculation and parameter update are repeated until the parameters converge, the probability density threshold is set, the probability density of the enhanced mixed feature model of each feature vector is calculated, and the feature vector with probability density lower than the probability density threshold is set as an abnormal vector and cleared.

4. The artificial intelligence-based elderly back-patting assistance system according to claim 1 is characterized by: The data acquisition module collects historical physiological data, environmental data and the level of demand for turning over and patting the back of the elderly; the physiological data includes heart rate, respiratory rate, body temperature, blood pressure and bed rest time; the environmental data refers to the temperature, humidity and noise intensity of the environment for turning over and patting the back; the level of demand for turning over and patting the back includes two levels: no demand and demand.

5. The artificial intelligence-based elderly back-patting assistance system according to claim 1 is characterized by: The turning over and back-patting auxiliary module collects the elderly's physiological data and environmental data in real time, and inputs the data into the turning over and back-patting demand prediction model. The model predicts the elderly's turning over and back-patting demand level, and assists the elderly in turning over and back-patting in real time.

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