Individual health assessment method based on geometric deep learning

Abstract

The invention discloses an individual health assessment method based on geometric deep learning. The method comprises the following steps: acquiring health parameter data of multiple dimensions of an object to be assessed; on the basis of a graph structure algorithm and a graph convolutional neural network, graph feature parameters corresponding to the health parameter data of the multiple dimensions are extracted; on the basis of a geometric mean algorithm, health scale parameters corresponding to the health parameter data of the multiple dimensions are calculated; and determining a health assessment result of the to-be-assessed object based on a dynamic time sequence analysis algorithm according to the graph feature parameters and the health scale parameters. Therefore, the complex relevance and the time sequence change of the health data can be accurately captured in combination with the multi-dimensional data, the graph convolution and the time sequence analysis, and the accuracy and the reliability of health assessment are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an individual health assessment method based on geometric deep learning. Background Art

[0002] With the rapid development of artificial intelligence, big data technology and medical monitoring equipment, personalized health management has gradually become a research hotspot in the medical field. By analyzing individual health parameter data (such as blood pressure, heart rate, blood sugar, etc.), it can provide important support for disease prediction, health management and personalized medicine. At present, more and more health monitoring devices (such as smart bracelets and medical-grade wearable devices) can collect multi-dimensional health parameters and record them in the form of time series. However, how to extract key information from complex and multi-dimensional health data and accurately classify and dynamically predict individual health status is still an urgent problem to be solved. Traditional health assessment methods are mostly based on mean analysis, linear regression or a single classification model. These methods are incapable of capturing complex relationships of nonlinear and multi-parameter associations. At the same time, many methods lack the ability to dynamically analyze time series data, resulting in limited performance in health trend prediction and early disease intervention. Especially in chronic disease management (such as Alzheimer's disease, diabetes, etc.), the health status of an individual depends not only on the value of a single parameter, but also needs to fully consider the complex relationship between multi-dimensional parameters and the trend of change over time. This complexity poses a huge challenge to existing technologies. It can be seen that existing technologies have defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an individual health assessment method and system based on geometric deep learning, which can combine multi-dimensional data with graph convolution and time series analysis to accurately capture the complex correlation and time series changes of health data, and improve the accuracy and reliability of health assessment.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses an individual health assessment method based on geometric deep learning, the method comprising: Obtain health parameter data of multiple dimensions of the object to be evaluated; Based on a graph structure algorithm and a graph convolutional neural network, graph feature parameters corresponding to the health parameter data of the multiple dimensions are extracted; Based on the geometric mean algorithm, calculate the health scale parameters corresponding to the health parameter data of the multiple dimensions; According to the graph feature parameters and the health scale parameters, based on a dynamic time series analysis algorithm, a health assessment result of the object to be assessed is determined.

[0005] As an optional embodiment, in the first aspect of the present invention, the data type of the health parameter data includes at least one of blood sugar, blood pressure, heart rate, body temperature, activity level, number of steps, exercise time, energy consumption, blood oxygen saturation, heart rate variability, electrocardiogram, MRI image features, cognitive test scores and brain metabolism data.

[0006] As an optional implementation, in the first aspect of the present invention, the graph feature parameters corresponding to the health parameter data of the multiple dimensions are extracted based on the graph structure algorithm and the graph convolutional neural network, including: Based on the graph structure algorithm, calculate the graph structure information corresponding to the health parameter data of the multiple dimensions; The health parameter data of the multiple dimensions and the graph structure information are input into a trained graph convolutional neural network to obtain output graph feature parameters.

[0007] As an optional implementation, in the first aspect of the present invention, the step of calculating the graph structure information corresponding to the health parameter data of the multiple dimensions based on the graph structure algorithm includes: Determine each health parameter data in the multiple dimensions of health parameter data as a parameter node in a preset graph structure; For any two of the parameter nodes, determining the type association between the data types of the health parameter data respectively corresponding to the two parameter nodes; the type association is determined by a preset association rule between different data types; Calculating the data similarity of the health parameter data respectively corresponding to the two parameter nodes; Determine the historical data sequences of multiple historical time points corresponding to the health parameter data respectively corresponding to the two parameter nodes; Calculating the change rate similarity between the data change rates of the historical data sequences respectively corresponding to the two parameter nodes; Calculating a weighted average of the type association degree, the data similarity and the change rate similarity to obtain an inter-node relationship value between the two parameter nodes; All the parameter nodes and the corresponding inter-node relationship values ​​are output as graph structure information corresponding to the health parameter data of the multiple dimensions.

[0008] As an optional implementation, in the first aspect of the present invention, the calculating the health scale parameters corresponding to the health parameter data of the multiple dimensions based on the geometric mean algorithm includes: For each data type, calculating the geometric mean of all the health parameter data corresponding to the data type; Obtaining the relationship weight corresponding to the data type according to the average value of all the inter-node relationship values ​​corresponding to the parameter nodes corresponding to all the health parameter data corresponding to the data type; Calculate a power with the geometric mean as the base and the relationship weight as the exponent to obtain a type scale parameter corresponding to the data type; The average value of the type scale parameters corresponding to all the data types is calculated to obtain the health scale parameter.

[0009] As an optional implementation, in the first aspect of the present invention, determining the health assessment result of the object to be assessed based on the graph feature parameters and the health scale parameters and a dynamic time series analysis algorithm includes: Sorting the health scale parameters corresponding to multiple time points of the subject to be evaluated from early to late according to time to obtain a time parameter sequence; Inputting the time parameter sequence into a trained LSTM neural network model to obtain predicted future health scale parameters corresponding to the object to be evaluated; the LSTM neural network is trained by a training data set including a plurality of training time parameter sequences; Determine the evaluation algorithm model corresponding to the current evaluation scenario; According to at least one of the graph feature parameter, the future health scale parameter and the time parameter sequence, based on the evaluation algorithm model, a health evaluation result of the object to be evaluated is determined.

[0010] As an optional embodiment, in the first aspect of the present invention, the current evaluation scenario is a chronic management scenario, a sports health evaluation scenario, a sudden disease warning scenario or an Alzheimer's disease cognitive evaluation scenario; when the current evaluation scenario is a chronic management scenario, the evaluation algorithm model is used to calculate the stability parameter, abnormality rate parameter and trend slope parameter corresponding to the time parameter sequence to determine the health evaluation result of the object to be evaluated; when the current evaluation scenario is a sports health evaluation scenario, the evaluation algorithm model is used to calculate the difference between the health scale parameter of the object to be evaluated in a motion state and the health scale parameter in a resting state to determine the health evaluation result of the object to be evaluated; when the current evaluation scenario is a sudden disease warning scenario, the evaluation algorithm model is used to calculate the change rate and parameter proportion of the health scale parameter exceeding the parameter threshold in the time parameter sequence to determine the health evaluation result of the object to be evaluated; when the current evaluation scenario is an Alzheimer's disease cognitive evaluation scenario, the evaluation algorithm model is used to calculate the change trend of the average value of the health scale parameter corresponding to multiple time periods in the time parameter sequence to determine the health evaluation result of the object to be evaluated.

[0011] As an optional implementation, in the first aspect of the present invention, determining the health assessment result of the object to be assessed based on the assessment algorithm model according to at least one of the graph feature parameter, the future health scale parameter and the time parameter sequence includes: Input the graph feature parameters and the health scale parameters at the most recent time point into the evaluation algorithm model to obtain the current evaluation result; Inputting the graph feature parameters and the future health scale parameters into the evaluation algorithm model to obtain future evaluation results; Calculate the weighted average of the current assessment result and the future assessment result to obtain the health assessment result of the object to be assessed; wherein the weighted calculation weight corresponding to the current assessment result includes a first weight and a second weight; the weighted calculation weight corresponding to the future assessment result includes a third weight and a fourth weight; the first weight and the third weight are both proportional to the difference between the corresponding assessment result value and the reference assessment result value; the second weight is inversely proportional to the time difference between the time point corresponding to the current assessment result and the current time point; the fourth weight is proportional to the number of parameters in the time parameter sequence corresponding to the future health scale parameter corresponding to the future assessment result; the weighted calculation weight corresponding to the current assessment result is greater than the weighted calculation weight corresponding to the future assessment result.

[0012] A second aspect of an embodiment of the present invention discloses an individual health assessment system based on geometric deep learning, the system comprising: A data acquisition module, used to acquire health parameter data of multiple dimensions of the object to be evaluated; A graph feature extraction module, used to extract graph feature parameters corresponding to the health parameter data of the multiple dimensions based on a graph structure algorithm and a graph convolutional neural network; A geometric calculation module, used for calculating the health scale parameters corresponding to the health parameter data of the multiple dimensions based on a geometric mean algorithm; The health assessment module is used to determine the health assessment result of the object to be assessed based on the graph feature parameters and the health scale parameters and on a dynamic time series analysis algorithm.

[0013] As an optional embodiment, in the second aspect of the present invention, the data type of the health parameter data includes at least one of blood sugar, blood pressure, heart rate, body temperature, activity level, number of steps, exercise time, energy consumption, blood oxygen saturation, heart rate variability, electrocardiogram, MRI image features, cognitive test scores and brain metabolism data.

[0014] As an optional implementation, in the second aspect of the present invention, the graph feature extraction module extracts the graph feature parameters corresponding to the health parameter data of the multiple dimensions based on the graph structure algorithm and the graph convolutional neural network, including: Based on the graph structure algorithm, calculate the graph structure information corresponding to the health parameter data of the multiple dimensions; The health parameter data of the multiple dimensions and the graph structure information are input into a trained graph convolutional neural network to obtain output graph feature parameters.

[0015] As an optional implementation, in the second aspect of the present invention, the specific manner in which the graph feature extraction module calculates the graph structure information corresponding to the health parameter data of the multiple dimensions based on the graph structure algorithm includes: Determine each health parameter data in the multiple dimensions of health parameter data as a parameter node in a preset graph structure; For any two of the parameter nodes, determining the type association between the data types of the health parameter data respectively corresponding to the two parameter nodes; the type association is determined by a preset association rule between different data types; Calculating the data similarity of the health parameter data respectively corresponding to the two parameter nodes; Determine the historical data sequences of multiple historical time points corresponding to the health parameter data respectively corresponding to the two parameter nodes; Calculating the change rate similarity between the data change rates of the historical data sequences respectively corresponding to the two parameter nodes; Calculating a weighted average of the type association degree, the data similarity and the change rate similarity to obtain an inter-node relationship value between the two parameter nodes; All the parameter nodes and the corresponding inter-node relationship values ​​are output as graph structure information corresponding to the health parameter data of the multiple dimensions.

[0016] As an optional implementation, in the second aspect of the present invention, the specific manner in which the geometric calculation module calculates the health scale parameters corresponding to the health parameter data of the multiple dimensions based on the geometric mean algorithm includes: For each data type, calculating the geometric mean of all the health parameter data corresponding to the data type; Obtaining the relationship weight corresponding to the data type according to the average value of all the inter-node relationship values ​​corresponding to the parameter nodes corresponding to all the health parameter data corresponding to the data type; Calculate a power with the geometric mean as the base and the relationship weight as the exponent to obtain a type scale parameter corresponding to the data type; The average value of the type scale parameters corresponding to all the data types is calculated to obtain the health scale parameter.

[0017] As an optional implementation, in the second aspect of the present invention, the health assessment module determines the specific manner of the health assessment result of the object to be assessed based on the graph feature parameters and the health scale parameters and based on a dynamic time series analysis algorithm, including: Sorting the health scale parameters corresponding to multiple time points of the subject to be evaluated from early to late according to time to obtain a time parameter sequence; Inputting the time parameter sequence into a trained LSTM neural network model to obtain predicted future health scale parameters corresponding to the object to be evaluated; the LSTM neural network is trained by a training data set including a plurality of training time parameter sequences; Determine the evaluation algorithm model corresponding to the current evaluation scenario; According to at least one of the graph feature parameter, the future health scale parameter and the time parameter sequence, based on the evaluation algorithm model, a health evaluation result of the object to be evaluated is determined.

[0018] As an optional embodiment, in the second aspect of the present invention, the current evaluation scenario is a chronic management scenario, a sports health evaluation scenario, a sudden disease warning scenario or an Alzheimer's disease cognitive evaluation scenario; when the current evaluation scenario is a chronic management scenario, the evaluation algorithm model is used to calculate the stability parameter, abnormality rate parameter and trend slope parameter corresponding to the time parameter sequence to determine the health evaluation result of the object to be evaluated; when the current evaluation scenario is a sports health evaluation scenario, the evaluation algorithm model is used to calculate the difference between the health scale parameter of the object to be evaluated in a motion state and the health scale parameter in a resting state to determine the health evaluation result of the object to be evaluated; when the current evaluation scenario is a sudden disease warning scenario, the evaluation algorithm model is used to calculate the change rate and parameter proportion of the health scale parameter exceeding the parameter threshold in the time parameter sequence to determine the health evaluation result of the object to be evaluated; when the current evaluation scenario is an Alzheimer's disease cognitive evaluation scenario, the evaluation algorithm model is used to calculate the change trend of the average value of the health scale parameter corresponding to multiple time periods in the time parameter sequence to determine the health evaluation result of the object to be evaluated.

[0019] As an optional implementation, in the second aspect of the present invention, the health assessment module determines the specific manner of the health assessment result of the object to be assessed based on the assessment algorithm model according to at least one of the graph feature parameters, the future health scale parameters and the time parameter sequence, including: Input the graph feature parameters and the health scale parameters at the most recent time point into the evaluation algorithm model to obtain the current evaluation result; Inputting the graph feature parameters and the future health scale parameters into the evaluation algorithm model to obtain future evaluation results; Calculate the weighted average of the current assessment result and the future assessment result to obtain the health assessment result of the object to be assessed; wherein the weighted calculation weight corresponding to the current assessment result includes a first weight and a second weight; the weighted calculation weight corresponding to the future assessment result includes a third weight and a fourth weight; the first weight and the third weight are both proportional to the difference between the corresponding assessment result value and the reference assessment result value; the second weight is inversely proportional to the time difference between the time point corresponding to the current assessment result and the current time point; the fourth weight is proportional to the number of parameters in the time parameter sequence corresponding to the future health scale parameter corresponding to the future assessment result; the weighted calculation weight corresponding to the current assessment result is greater than the weighted calculation weight corresponding to the future assessment result.

[0020] The third aspect of the present invention discloses another individual health assessment system based on geometric deep learning, the system comprising: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the individual health assessment method based on geometric deep learning disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute part or all of the steps in the individual health assessment method based on geometric deep learning disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention can obtain health parameter data of multiple dimensions of the object to be evaluated, and use graph structure algorithm and graph convolution neural network to extract corresponding graph feature parameters, calculate health scale parameters in combination with geometric mean algorithm, and then comprehensively analyze these data based on dynamic timing analysis algorithm. It can thus combine multi-dimensional data with graph convolution and timing analysis to accurately capture the complex correlation and timing changes of health data, thereby improving the accuracy and reliability of health assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a flowchart of an individual health assessment method based on geometric deep learning disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural schematic diagram of an individual health assessment system based on geometric deep learning disclosed in an embodiment of the present invention.

[0026] Figure 3 It is a structural schematic diagram of another individual health assessment system based on geometric deep learning disclosed in an embodiment of the present invention.

[0027] Figure 4 This is an example diagram of graph structure information disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme 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, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0030] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0031] The present invention discloses an individual health assessment method and system based on geometric deep learning, which can obtain health parameter data of multiple dimensions of the object to be assessed, extract corresponding graph feature parameters using graph structure algorithm and graph convolution neural network, calculate health scale parameters in combination with geometric mean algorithm, and then comprehensively analyze these data based on dynamic time series analysis algorithm, so as to accurately capture the complex correlation and time series changes of health data by combining multi-dimensional data, graph convolution and time series analysis, and improve the accuracy and reliability of health assessment. The following are detailed descriptions.

[0032] Embodiment 1 See also Figure 1 , Figure 1 : is a flowchart of an individual health assessment method based on geometric deep learning disclosed in an embodiment of the present invention. Figure 1 The individual health assessment method based on geometric deep learning described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the individual health assessment method based on geometric deep learning may include the following operations: 101. Obtain health parameter data of multiple dimensions of the object to be evaluated.

[0033] Optionally, health parameter data can come from different types of sensors or records, covering multiple modalities such as physiological, biochemical, behavioral, and cognitive.

[0034] 102. Based on graph structure algorithm and graph convolutional neural network, graph feature parameters corresponding to health parameter data of multiple dimensions are extracted. Optionally, the above health parameter data can be constructed into a graph structure to represent the relationship between the parameters, and the graph data can be input into a pre-trained graph neural network model to output a feature representation that reflects the complex relationship between the health parameters.

[0035] 103. Based on the geometric mean algorithm, calculate the health scale parameters corresponding to the health parameter data of multiple dimensions. Optionally, the geometric mean is calculated for each type of health parameter, and combined with the associated weights between the parameters, the scale values ​​of each type are obtained through power operation, and then summarized to obtain a comprehensive health scale parameter reflecting the overall health status.

[0036] 104. According to the graph feature parameters and the health scale parameters, based on the dynamic time series analysis algorithm, determine the health assessment result of the object to be assessed.

[0037] Optionally, the current and historical health scale parameters are analyzed in time series, and combined with graph features, a trained time series model (such as long short-term memory network LSTM) is used to predict health status trends, and then the preset evaluation model is used to give the evaluation results of the individual's current and future health status.

[0038] It can be seen that the above-mentioned embodiments of the invention can obtain health parameter data of multiple dimensions of the object to be evaluated, and use the graph structure algorithm and the graph convolution neural network to extract the corresponding graph feature parameters, and calculate the health scale parameters in combination with the geometric mean algorithm, and then comprehensively analyze these data based on the dynamic timing analysis algorithm, so as to combine multi-dimensional data and graph convolution and timing analysis to accurately capture the complex correlation and timing changes of health data, thereby improving the accuracy and reliability of health assessment.

[0039] As an optional embodiment, in the above steps, the data type of the health parameter data includes at least one of blood sugar, blood pressure, heart rate, body temperature, activity level, number of steps, exercise time, energy consumption, blood oxygen saturation, heart rate variability, electrocardiogram, MRI image features, cognitive test scores and brain metabolism data.

[0040] In a practical implementation, data preprocessing is also implemented to standardize health parameters and eliminate dimensional differences between different parameters, including data cleaning, data standardization and sliding window processing. Data cleaning eliminates outliers (such as values ​​beyond the physiological range) and defines abnormal intervals through the IQR method. Data standardization is used to standardize all health parameters. The formula is: in, is the standardized health parameter data P obtained at time t, is the mean, is the standard deviation. Specifically, the sliding window processing implements segmentation of time series data: , ensuring that the model captures short-term dynamic changes.

[0041] It can be seen that through the above optional embodiments, the content of the health parameter data is limited to comprehensively characterize the physiological-related characteristics of the object to be evaluated, so as to facilitate the subsequent accurate evaluation of the health status, and assist in combining multi-dimensional data and graph convolution and time series analysis to accurately capture the complex correlation and time series changes of health data, thereby improving the accuracy and reliability of health assessment.

[0042] As an optional embodiment, in the above steps, based on the graph structure algorithm and the graph convolutional neural network, the graph feature parameters corresponding to the health parameter data of multiple dimensions are extracted, including: Based on the graph structure algorithm, the graph structure information corresponding to the health parameter data of multiple dimensions is calculated; The health parameter data and graph structure information of multiple dimensions are input into the trained graph convolutional neural network to obtain the output graph feature parameters.

[0043] It can be seen that through the above optional embodiments, the graph structure information of health parameter data of multiple dimensions is calculated based on the graph structure algorithm, and the health parameter data and the graph structure information are input into the trained graph convolutional neural network to extract graph feature parameters, which can effectively capture the complex relationship between health parameters and improve the accuracy of feature extraction. It can also assist in combining multi-dimensional data with graph convolution and time series analysis to accurately capture the complex correlation and time series changes of health data, thereby improving the accuracy and reliability of health assessment.

[0044] As an optional embodiment, in the above steps, the graph structure information corresponding to the health parameter data of multiple dimensions is calculated based on the graph structure algorithm, including: Determine each health parameter data in the health parameter data of multiple dimensions as a parameter node in a preset graph structure; For any two parameter nodes, determine the type association between the data types of the health parameter data corresponding to the two parameter nodes respectively; optionally, the type association is determined by a preset association rule between different data types; Calculate the data similarity of the health parameter data corresponding to the two parameter nodes respectively; Determine the historical data sequences of multiple historical time points corresponding to the health parameter data respectively corresponding to the two parameter nodes; Calculate the change rate similarity between the data change rates of the historical data sequences respectively corresponding to the two parameter nodes; Calculate the weighted sum average of type association, data similarity and change rate similarity to obtain the inter-node relationship value between the two parameter nodes; All parameter nodes and the corresponding inter-node relationship values ​​are output as graph structure information corresponding to health parameter data in multiple dimensions.

[0045] For example, a weighted adjacency matrix can be constructed to represent the connection weights between nodes (health indicators) in the graph. This graph structure information will be used as one of the inputs of the graph neural network model.

[0046] In the above implementation scheme, a form of constructing graph structure information is implemented: taking health parameters as nodes, an adjacency matrix is ​​generated based on correlation, where the similarity between node i and node j, i.e., the adjacency matrix, is: ,in is the value of the standard deviation of the parameter. An example of graph structure information can be found in Figure 4 .

[0047] In a specific embodiment, a graph convolutional neural network is used to extract high-order nonlinear relationships of health parameter data: ,in: : adjacency matrix plus identity matrix; : node degree matrix; : No. Layer node features; : No. The weight matrix of the layer.

[0048] It can be seen that through the above optional embodiments, health parameter data of multiple dimensions are mapped into parameter nodes in a preset graph structure, and the relationship values ​​between nodes are calculated in combination with type association rules, data similarity and historical data change rate similarity to construct the graph structure information of health parameter data. This can accurately model the complex associations between different health parameters, and assist in combining multi-dimensional data with graph convolution and time series analysis to accurately capture the complex associations and time series changes of health data, thereby improving the accuracy and reliability of health assessments.

[0049] As an optional embodiment, in the above steps, the health scale parameters corresponding to the health parameter data of multiple dimensions are calculated based on the geometric mean algorithm, including: For each data type, the geometric mean of all health parameter data corresponding to the data type is calculated; specifically, the geometric mean can comprehensively reflect the product characteristics of the overall level of parameters of this type, is less affected by extreme values ​​than the simple arithmetic mean, and is suitable for measuring the overall status of physiological indicators; According to the average value of all inter-node relationship values ​​corresponding to the parameter nodes corresponding to all health parameter data corresponding to the data type, the relationship weight corresponding to the data type is obtained; specifically, the relationship weight represents the strength of the overall association between the type parameter and other parameters; Calculate the power with the geometric mean as the base and the relationship weight as the exponent to obtain the type scale parameter corresponding to the data type; this step is equivalent to weighting the geometric mean level of each type parameter according to the degree of association with the overall health network, and obtain an indicator that can reflect the health contribution of this type; The average value of the type scale parameters corresponding to all data types is calculated to obtain the health scale parameters.

[0050] Specifically, the health scale parameter serves as an overall health score or index after integrating health data from multiple dimensions, reflecting the comprehensive level of an individual's health status in all aspects.

[0051] It can be seen that through the above optional embodiments, by calculating the geometric mean of the health parameter data of each data type, and combining the relationship weights of the corresponding parameter nodes, using power operations to determine the type scale parameters, and further averaging to obtain the health scale parameters, it is possible to comprehensively consider the intrinsic distribution characteristics of the data and the associated weights of different data types, improve the representativeness and robustness of the scale parameters in the health assessment process, and assist in combining multi-dimensional data with graph convolution and time series analysis to accurately capture the complex correlations and time series changes of health data, thereby improving the accuracy and reliability of health assessments.

[0052] As an optional embodiment, in the above steps, determining the health assessment result of the object to be assessed based on the graph feature parameters and the health scale parameters and the dynamic time series analysis algorithm includes: The health scale parameters corresponding to multiple time points of the object to be evaluated are sorted from early to late to obtain a time parameter sequence; Inputting the time parameter sequence into the trained LSTM neural network model to obtain the predicted future health scale parameter corresponding to the object to be evaluated; optionally, the LSTM neural network is trained by a training data set including a plurality of training time parameter sequences; Determine the evaluation algorithm model corresponding to the current evaluation scenario; According to at least one of the graph feature parameters, the future health scale parameters and the time parameter sequence, based on the evaluation algorithm model, a health evaluation result of the object to be evaluated is determined.

[0053] It can be seen that through the above optional embodiments, the health scale parameters are time-sorted and input into the trained LSTM neural network model to predict future health scale parameters, and a suitable evaluation algorithm model is selected in combination with the current evaluation scenario, and the health assessment result is finally determined based on at least one of the graph feature parameters, future health scale parameters and time parameter sequences, so as to achieve the combination of multi-dimensional data, graph convolution and time series analysis to accurately capture the complex correlation and time series changes of health data, thereby improving the accuracy and reliability of health assessment.

[0054] As an optional embodiment, in the above steps, the current evaluation scenario is a chronic management scenario, a sports health evaluation scenario, a sudden disease warning scenario or an Alzheimer's disease cognitive evaluation scenario; when the current evaluation scenario is a chronic management scenario, the evaluation algorithm model is used to calculate the stability parameters, abnormality rate parameters and trend slope parameters corresponding to the time parameter sequence to determine the health evaluation result of the object to be evaluated; when the current evaluation scenario is a sports health evaluation scenario, the evaluation algorithm model is used to calculate the difference between the health scale parameters of the object to be evaluated in the exercise state and the health scale parameters in the resting state to determine the health evaluation result of the object to be evaluated; when the current evaluation scenario is a sudden disease warning scenario, the evaluation algorithm model is used to calculate the change rate and parameter proportion of the health scale parameters exceeding the parameter threshold in the time parameter sequence to determine the health evaluation result of the object to be evaluated; when the current evaluation scenario is an Alzheimer's disease cognitive evaluation scenario, the evaluation algorithm model is used to calculate the change trend of the average value of the health scale parameters corresponding to multiple time periods in the time parameter sequence to determine the health assessment result of the object to be evaluated.

[0055] In a specific embodiment, the stability parameter corresponding to the time parameter sequence is: in, For time point The health parameter data value, is the mean value of the parameter. The abnormal rate parameter corresponding to the time parameter series is the proportion of abnormal data points to the total data points.

[0056] It can be seen that through the above optional embodiments, the type of the current evaluation scenario and the calculation rules of the evaluation algorithm models corresponding to different scenarios are clarified, so as to realize the analysis of the time change pattern of the health scale parameters based on different evaluation algorithm models, so as to extract key indicators such as stability, abnormality rate, trend slope, motion state difference, change rate ratio and average value change trend, so as to accurately evaluate the health status of individuals, and realize the combination of multi-dimensional data, graph convolution and time series analysis to accurately capture the complex correlation and time series changes of health data, and improve the accuracy and reliability of health assessment.

[0057] As an optional embodiment, in the above steps, determining the health assessment result of the object to be assessed based on the assessment algorithm model according to at least one of the graph feature parameters, the future health scale parameters and the time parameter sequence includes: Input the graph feature parameters and the health scale parameters at the most recent time point into the evaluation algorithm model to obtain the current evaluation results; Input the graph feature parameters and future health scale parameters into the evaluation algorithm model to obtain future evaluation results; Calculate the weighted average of the current assessment result and the future assessment results to obtain the health assessment result of the object to be assessed; optionally, the weighted calculation weight corresponding to the current assessment result includes a first weight and a second weight; the weighted calculation weight corresponding to the future assessment result includes a third weight and a fourth weight; the first weight and the third weight are both proportional to the difference between the corresponding assessment result value and the reference assessment result value; the second weight is inversely proportional to the time difference between the time point corresponding to the current assessment result and the current time point; the fourth weight is proportional to the number of parameters in the time parameter sequence corresponding to the future health scale parameter corresponding to the future assessment result; the weighted calculation weight corresponding to the current assessment result is greater than the weighted calculation weight corresponding to the future assessment result.

[0058] Specifically, the weights used in weighting can be dynamically adjusted according to the credibility and time relevance of the evaluation results. Preferably, the weight of the current evaluation result can be made greater than the weight of the future evaluation result, so as to take into account both real-time accuracy and trend prediction value. For example, the weight of the current result can be proportional to the degree to which its value deviates from the normal reference range (the greater the deviation, the higher the weight to attract attention), and inversely proportional to the time difference between the time point corresponding to the result and the current time (the newer the data, the greater the weight); and the weight of the future result can be proportional to the number of future time points used for prediction (the longer the prediction span, the more future indicators are considered comprehensively, and the weight is appropriately increased). Through this dynamic weighting, the present invention can comprehensively judge the current health status of an individual and the future trend of change, and obtain a more robust and reliable evaluation conclusion.

[0059] It can be seen that through the above optional embodiments, based on the graph feature parameters and the health scale parameters, the current evaluation results and the future evaluation results are calculated respectively, and the final health evaluation result is determined by weighted summation and average value, so as to achieve accurate judgment of the current health status and reasonable prediction of future health trends, wherein the weighted calculation weights are dynamically adjusted according to the deviation of the evaluation results, the time interval and the amount of data, so that the evaluation results take into account both real-time accuracy and long-term trend prediction capabilities, and realize the combination of multi-dimensional data, graph convolution and time series analysis to accurately capture the complex correlation and time series changes of health data, thereby improving the accuracy and reliability of health assessment.

[0060] Compared with the prior art, the scheme formed by the above-mentioned invention embodiment and its specific implementation scheme have the following beneficial effects: 1. Multimodal data fusion: The method of the present invention can integrate and process various types of health data, including physiological signals, behavioral data, and medical images, breaking through the limitations of traditional single indicator evaluation. By constructing a health parameter relationship graph and applying graph neural networks to extract features, the deep fusion of multimodal data is achieved, fully exploring the potential relationship between different indicators.

[0061] 2. Geometric mean: The geometric mean and associated weights are introduced to calculate the health scale parameters, which avoids the excessive impact of a single outlier on the overall assessment and ensures that the contribution of each dimension of information to the comprehensive health index is more balanced. This "" calculation improves the robustness and credibility of the health index and more accurately reflects the individual's global health status.

[0062] 3. Dynamic trend prediction: This invention incorporates time series analysis into the evaluation framework, and uses deep learning models such as LSTM to predict health scale parameters, thus realizing a forward-looking evaluation of individual future health change trends. Compared with traditional methods that can only statically describe the current status, this invention can detect abnormal trends in health indicators in advance, providing more sufficient response time for chronic disease management and disease warning.

[0063] 4. Contextualized Personalized Assessment: By customizing the assessment algorithm model for different application scenarios (such as specific analysis indicators for chronic diseases, exercise, acute events, cognitive impairment, etc.), the method and system of the present invention can output more targeted assessment results to meet the needs of personalized health management. This flexible architecture has wide application value in the fields of chronic disease follow-up, exercise guidance, and healthy elderly care.

[0064] 5. Commercial application prospects: The method and system of the present invention can be combined with real-time health monitoring equipment and cloud services, and integrated into the health management platform through standardized interfaces (such as API) to achieve automatic analysis and feedback of users' continuous health data. Its evaluation results can be used to assist doctors in decision-making, personal health risk warning, customized health intervention plans, etc., and have significant commercial potential. The system adopts a modular design and is easy to deploy on wearable devices, mobile applications or cloud servers, providing an innovative and feasible technical solution for the field of digital medicine and smart health.

[0065] Embodiment 2 See also Figure 2 , Figure 2 is a schematic diagram of the structure of an individual health assessment system based on geometric deep learning disclosed in an embodiment of the present invention. Figure 2 The individual health assessment system based on geometric deep learning described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the individual health assessment system based on geometric deep learning may include: The data acquisition module 201 is used to acquire health parameter data of multiple dimensions of the object to be evaluated.

[0066] The graph feature extraction module 202 is used to extract graph feature parameters corresponding to health parameter data of multiple dimensions based on a graph structure algorithm and a graph convolutional neural network. The geometric calculation module 203 is used to calculate the health scale parameters corresponding to the health parameter data of multiple dimensions based on the geometric mean algorithm. The health assessment module 204 is used to determine the health assessment result of the object to be assessed based on the graph feature parameters and the health scale parameters and on the basis of a dynamic time series analysis algorithm.

[0067] It can be seen that the above-mentioned embodiments of the invention can obtain health parameter data of multiple dimensions of the object to be evaluated, and use the graph structure algorithm and the graph convolution neural network to extract the corresponding graph feature parameters, and calculate the health scale parameters in combination with the geometric mean algorithm, and then comprehensively analyze these data based on the dynamic timing analysis algorithm, so as to combine multi-dimensional data and graph convolution and timing analysis to accurately capture the complex correlation and timing changes of health data, thereby improving the accuracy and reliability of health assessment.

[0068] As an optional embodiment, the data type of the health parameter data includes at least one of blood sugar, blood pressure, heart rate, body temperature, activity level, number of steps, exercise time, energy consumption, blood oxygen saturation, heart rate variability, electrocardiogram, MRI image features, cognitive test scores and brain metabolism data.

[0069] It can be seen that through the above optional embodiments, the content of the health parameter data is limited to comprehensively characterize the physiological-related characteristics of the object to be evaluated, so as to facilitate the subsequent accurate evaluation of the health status, and assist in combining multi-dimensional data and graph convolution and time series analysis to accurately capture the complex correlation and time series changes of health data, thereby improving the accuracy and reliability of health assessment.

[0070] As an optional embodiment, the graph feature extraction module extracts graph feature parameters corresponding to health parameter data of multiple dimensions based on the graph structure algorithm and the graph convolutional neural network, including: Based on the graph structure algorithm, the graph structure information corresponding to the health parameter data of multiple dimensions is calculated; The health parameter data and graph structure information of multiple dimensions are input into the trained graph convolutional neural network to obtain the output graph feature parameters.

[0071] It can be seen that through the above optional embodiments, the graph structure information of health parameter data of multiple dimensions is calculated based on the graph structure algorithm, and the health parameter data and the graph structure information are input into the trained graph convolutional neural network to extract graph feature parameters, which can effectively capture the complex relationship between health parameters and improve the accuracy of feature extraction. It can also assist in combining multi-dimensional data with graph convolution and time series analysis to accurately capture the complex correlation and time series changes of health data, thereby improving the accuracy and reliability of health assessment.

[0072] As an optional embodiment, the graph feature extraction module calculates the specific manner of the graph structure information corresponding to the health parameter data of multiple dimensions based on the graph structure algorithm, including: Determine each health parameter data in the health parameter data of multiple dimensions as a parameter node in a preset graph structure; For any two parameter nodes, determine the type association between the data types of the health parameter data corresponding to the two parameter nodes respectively; optionally, the type association is determined by a preset association rule between different data types; Calculate the data similarity of the health parameter data corresponding to the two parameter nodes respectively; Determine the historical data sequences of multiple historical time points corresponding to the health parameter data respectively corresponding to the two parameter nodes; Calculate the change rate similarity between the data change rates of the historical data sequences respectively corresponding to the two parameter nodes; Calculate the weighted sum average of type association, data similarity and change rate similarity to obtain the inter-node relationship value between the two parameter nodes; All parameter nodes and the corresponding inter-node relationship values ​​are output as graph structure information corresponding to health parameter data in multiple dimensions.

[0073] It can be seen that through the above optional embodiments, health parameter data of multiple dimensions are mapped into parameter nodes in a preset graph structure, and the relationship values ​​between nodes are calculated in combination with type association rules, data similarity and historical data change rate similarity to construct the graph structure information of health parameter data. This can accurately model the complex associations between different health parameters, and assist in combining multi-dimensional data with graph convolution and time series analysis to accurately capture the complex associations and time series changes of health data, thereby improving the accuracy and reliability of health assessments.

[0074] As an optional embodiment, the specific method of the geometric calculation module to calculate the health scale parameters corresponding to the health parameter data of multiple dimensions based on the geometric mean algorithm includes: For each data type, calculate the geometric mean of all health parameter data corresponding to the data type; Obtaining the relationship weight corresponding to the data type according to the average value of all inter-node relationship values ​​corresponding to the parameter nodes corresponding to all health parameter data corresponding to the data type; Calculate the power with the geometric mean as the base and the relationship weight as the exponent to obtain the type scale parameter corresponding to the data type; The average value of the type scale parameters corresponding to all data types is calculated to obtain the health scale parameters.

[0075] It can be seen that through the above optional embodiments, by calculating the geometric mean of the health parameter data of each data type, and combining the relationship weights of the corresponding parameter nodes, using power operations to determine the type scale parameters, and further averaging to obtain the health scale parameters, it is possible to comprehensively consider the intrinsic distribution characteristics of the data and the associated weights of different data types, improve the representativeness and robustness of the scale parameters in the health assessment process, and assist in combining multi-dimensional data with graph convolution and time series analysis to accurately capture the complex correlations and time series changes of health data, thereby improving the accuracy and reliability of health assessments.

[0076] As an optional embodiment, the health assessment module determines the specific manner of the health assessment result of the object to be assessed based on the graph feature parameters and the health scale parameters and the dynamic time series analysis algorithm, including: The health scale parameters corresponding to multiple time points of the object to be evaluated are sorted from early to late to obtain a time parameter sequence; Inputting the time parameter sequence into the trained LSTM neural network model to obtain the predicted future health scale parameter corresponding to the object to be evaluated; optionally, the LSTM neural network is trained by a training data set including a plurality of training time parameter sequences; Determine the evaluation algorithm model corresponding to the current evaluation scenario; According to at least one of the graph feature parameters, the future health scale parameters and the time parameter sequence, based on the evaluation algorithm model, a health evaluation result of the object to be evaluated is determined.

[0077] It can be seen that through the above optional embodiments, the health scale parameters are time-sorted and input into the trained LSTM neural network model to predict future health scale parameters, and a suitable evaluation algorithm model is selected in combination with the current evaluation scenario, and the health assessment result is finally determined based on at least one of the graph feature parameters, future health scale parameters and time parameter sequences, so as to achieve the combination of multi-dimensional data, graph convolution and time series analysis to accurately capture the complex correlation and time series changes of health data, thereby improving the accuracy and reliability of health assessment.

[0078] As an optional embodiment, the current evaluation scenario is a chronic management scenario, a sports health evaluation scenario, a sudden disease warning scenario or an Alzheimer's disease cognitive evaluation scenario; when the current evaluation scenario is a chronic management scenario, the evaluation algorithm model is used to calculate the stability parameters, abnormality rate parameters and trend slope parameters corresponding to the time parameter sequence to determine the health evaluation results of the object to be evaluated; when the current evaluation scenario is a sports health evaluation scenario, the evaluation algorithm model is used to calculate the difference between the health scale parameters of the object to be evaluated in a motion state and the health scale parameters in a resting state to determine the health evaluation results of the object to be evaluated; when the current evaluation scenario is a sudden disease warning scenario, the evaluation algorithm model is used to calculate the change rate and parameter proportion of the health scale parameters that exceed the parameter threshold in the time parameter sequence to determine the health evaluation results of the object to be evaluated; when the current evaluation scenario is an Alzheimer's disease cognitive evaluation scenario, the evaluation algorithm model is used to calculate the change trend of the average value of the health scale parameters corresponding to multiple time periods in the time parameter sequence to determine the health assessment results of the object to be evaluated.

[0079] It can be seen that through the above optional embodiments, the type of the current evaluation scenario and the calculation rules of the evaluation algorithm models corresponding to different scenarios are clarified, so as to realize the analysis of the time change pattern of the health scale parameters based on different evaluation algorithm models, so as to extract key indicators such as stability, abnormality rate, trend slope, motion state difference, change rate ratio and average value change trend, so as to accurately evaluate the health status of individuals, and realize the combination of multi-dimensional data, graph convolution and time series analysis to accurately capture the complex correlation and time series changes of health data, and improve the accuracy and reliability of health assessment.

[0080] As an optional embodiment, the health assessment module determines the specific manner of the health assessment result of the object to be assessed based on the assessment algorithm model according to at least one of the graph feature parameter, the future health scale parameter and the time parameter sequence, including: Input the graph feature parameters and the health scale parameters at the most recent time point into the evaluation algorithm model to obtain the current evaluation results; Input the graph feature parameters and future health scale parameters into the evaluation algorithm model to obtain future evaluation results; Calculate the weighted average of the current assessment result and the future assessment results to obtain the health assessment result of the object to be assessed; optionally, the weighted calculation weight corresponding to the current assessment result includes a first weight and a second weight; the weighted calculation weight corresponding to the future assessment result includes a third weight and a fourth weight; the first weight and the third weight are both proportional to the difference between the corresponding assessment result value and the reference assessment result value; the second weight is inversely proportional to the time difference between the time point corresponding to the current assessment result and the current time point; the fourth weight is proportional to the number of parameters in the time parameter sequence corresponding to the future health scale parameter corresponding to the future assessment result; the weighted calculation weight corresponding to the current assessment result is greater than the weighted calculation weight corresponding to the future assessment result.

[0081] It can be seen that through the above optional embodiments, based on the graph feature parameters and the health scale parameters, the current evaluation results and the future evaluation results are calculated respectively, and the final health evaluation result is determined by weighted summation and average value, so as to achieve accurate judgment of the current health status and reasonable prediction of future health trends, wherein the weighted calculation weights are dynamically adjusted according to the deviation of the evaluation results, the time interval and the amount of data, so that the evaluation results take into account both real-time accuracy and long-term trend prediction capabilities, and realize the combination of multi-dimensional data, graph convolution and time series analysis to accurately capture the complex correlation and time series changes of health data, thereby improving the accuracy and reliability of health assessment.

[0082] Embodiment 3 See also Figure 3 , Figure 3 It is another individual health assessment system based on geometric deep learning disclosed in an embodiment of the present invention. Figure 3 The individual health assessment system based on geometric deep learning is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the individual health assessment system based on geometric deep learning may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the individual health assessment method based on geometric deep learning described in the first embodiment.

[0083] Embodiment 4 An embodiment of the present invention discloses a computer-readable storage medium, which stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the individual health assessment method based on geometric deep learning described in Embodiment 1.

[0084] Embodiment 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the individual health assessment method based on geometric deep learning described in Example 1.

[0085] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0086] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0087] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0088] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0089] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0090] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0092] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0093] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0094] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0095] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0096] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media including storage devices.

[0097] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0098] Finally, it should be noted that the individual health assessment method and system based on geometric deep learning disclosed in the embodiment of the present invention only discloses the preferred embodiment of the present invention, which is only used to illustrate the technical scheme of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical schemes described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical schemes from the spirit and scope of the technical schemes of the embodiments of the present invention.

Claims

1. An individual health assessment method based on geometric deep learning, characterized in that: The method comprises: Obtain health parameter data of multiple dimensions of the object to be evaluated; Based on a graph structure algorithm and a graph convolutional neural network, graph feature parameters corresponding to the health parameter data of the multiple dimensions are extracted; Based on the geometric mean algorithm, calculate the health scale parameters corresponding to the health parameter data of the multiple dimensions; According to the graph feature parameters and the health scale parameters, based on a dynamic time series analysis algorithm, a health assessment result of the object to be assessed is determined.

2. The individual health assessment method based on geometric deep learning according to claim 1, characterized in that: The data type of the health parameter data includes at least one of blood sugar, blood pressure, heart rate, body temperature, activity level, number of steps, exercise time, energy consumption, blood oxygen saturation, heart rate variability, electrocardiogram, MRI image features, cognitive test scores and brain metabolism data.

3. The individual health assessment method based on geometric deep learning according to claim 1, characterized in that: The graph feature parameters corresponding to the health parameter data of the multiple dimensions are extracted based on the graph structure algorithm and the graph convolutional neural network, including: Based on the graph structure algorithm, calculate the graph structure information corresponding to the health parameter data of the multiple dimensions; The health parameter data of the multiple dimensions and the graph structure information are input into a trained graph convolutional neural network to obtain output graph feature parameters.

4. The individual health assessment method based on geometric deep learning according to claim 3 is characterized in that: The step of calculating the graph structure information corresponding to the health parameter data of the multiple dimensions based on the graph structure algorithm includes: Determine each health parameter data in the multiple dimensions of health parameter data as a parameter node in a preset graph structure; For any two of the parameter nodes, determining the type association between the data types of the health parameter data respectively corresponding to the two parameter nodes; the type association is determined by a preset association rule between different data types; Calculating the data similarity of the health parameter data respectively corresponding to the two parameter nodes; Determine the historical data sequences of multiple historical time points corresponding to the health parameter data respectively corresponding to the two parameter nodes; Calculating the change rate similarity between the data change rates of the historical data sequences respectively corresponding to the two parameter nodes; Calculating a weighted average of the type association degree, the data similarity and the change rate similarity to obtain an inter-node relationship value between the two parameter nodes; All the parameter nodes and the corresponding inter-node relationship values ​​are output as graph structure information corresponding to the health parameter data of the multiple dimensions.

5. The individual health assessment method based on geometric deep learning according to claim 4 is characterized in that: The step of calculating the health scale parameters corresponding to the health parameter data of the multiple dimensions based on the geometric mean algorithm includes: For each data type, calculating the geometric mean of all the health parameter data corresponding to the data type; Obtaining the relationship weight corresponding to the data type according to the average value of all the inter-node relationship values ​​corresponding to the parameter nodes corresponding to all the health parameter data corresponding to the data type; Calculate a power with the geometric mean as the base and the relationship weight as the exponent to obtain a type scale parameter corresponding to the data type; The average value of the type scale parameters corresponding to all the data types is calculated to obtain the health scale parameter.

6. The individual health assessment method based on geometric deep learning according to claim 1, characterized in that: The determining the health assessment result of the object to be assessed based on the graph feature parameters and the health scale parameters and a dynamic time series analysis algorithm includes: Sorting the health scale parameters corresponding to multiple time points of the subject to be evaluated from early to late according to time to obtain a time parameter sequence; Inputting the time parameter sequence into a trained LSTM neural network model to obtain predicted future health scale parameters corresponding to the object to be evaluated; the LSTM neural network is trained by a training data set including a plurality of training time parameter sequences; Determine the evaluation algorithm model corresponding to the current evaluation scenario; According to at least one of the graph feature parameter, the future health scale parameter and the time parameter sequence, based on the evaluation algorithm model, a health evaluation result of the object to be evaluated is determined.

7. The individual health assessment method based on geometric deep learning according to claim 6, characterized in that: The current assessment scenario is a chronic management scenario, a sports health assessment scenario, a sudden disease warning scenario, or an Alzheimer's disease cognitive assessment scenario; when the current assessment scenario is a chronic management scenario, the assessment algorithm model is used to calculate the stability parameter, the abnormal rate parameter, and the trend slope parameter corresponding to the time parameter sequence to determine the health assessment result of the subject to be assessed; When the current assessment scenario is a sports health assessment scenario, the assessment algorithm model is used to calculate the difference between the health scale parameters of the object to be assessed in a moving state and the health scale parameters in a resting state, so as to determine the health assessment result of the object to be assessed; when the current assessment scenario is a sudden disease warning scenario, the assessment algorithm model is used to calculate the change rate and parameter proportion of the health scale parameters that exceed the parameter threshold in the time parameter sequence, so as to determine the health assessment result of the object to be assessed; when the current assessment scenario is an Alzheimer's disease cognitive assessment scenario, the assessment algorithm model is used to calculate the change trend of the average value of the health scale parameters corresponding to multiple time periods in the time parameter sequence, so as to determine the health assessment result of the object to be assessed.

8. The individual health assessment method based on geometric deep learning according to claim 6, characterized in that: The step of determining the health assessment result of the subject to be assessed based on the assessment algorithm model according to at least one of the graph feature parameter, the future health scale parameter and the time parameter sequence includes: Input the graph feature parameters and the health scale parameters at the most recent time point into the evaluation algorithm model to obtain the current evaluation result; Inputting the graph feature parameters and the future health scale parameters into the evaluation algorithm model to obtain future evaluation results; Calculate the weighted average of the current assessment result and the future assessment result to obtain the health assessment result of the object to be assessed; wherein the weighted calculation weight corresponding to the current assessment result includes a first weight and a second weight; the weighted calculation weight corresponding to the future assessment result includes a third weight and a fourth weight; the first weight and the third weight are both proportional to the difference between the corresponding assessment result value and the reference assessment result value; the second weight is inversely proportional to the time difference between the time point corresponding to the current assessment result and the current time point; the fourth weight is proportional to the number of parameters in the time parameter sequence corresponding to the future health scale parameter corresponding to the future assessment result; the weighted calculation weight corresponding to the current assessment result is greater than the weighted calculation weight corresponding to the future assessment result.

9. An individual health assessment system based on geometric deep learning, characterized in that: The system comprises: A data acquisition module, used to acquire health parameter data of multiple dimensions of the object to be evaluated; A graph feature extraction module, used to extract graph feature parameters corresponding to the health parameter data of the multiple dimensions based on a graph structure algorithm and a graph convolutional neural network; A geometric calculation module, used for calculating the health scale parameters corresponding to the health parameter data of the multiple dimensions based on a geometric mean algorithm; The health assessment module is used to determine the health assessment result of the object to be assessed based on the graph feature parameters and the health scale parameters and on a dynamic time series analysis algorithm.

10. An individual health assessment system based on geometric deep learning, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the individual health assessment method based on geometric deep learning as described in any one of claims 1-8.

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