CN117814775B

CN117814775B - Three-dimensional human body sign data management method and system

Info

Publication number: CN117814775B
Application number: CN202311867422.4A
Authority: CN
Inventors: 黎旭; 刘俭; 袁壮; 陈树青; 林逸
Original assignee: Shenzhen Xianku Intelligent Co ltd
Current assignee: Shenzhen Xianku Intelligent Co ltd
Priority date: 2023-12-29
Filing date: 2023-12-29
Publication date: 2024-06-21
Anticipated expiration: 2043-12-29
Also published as: CN117814775A

Abstract

The invention relates to the technical field of human body sign data management systems, in particular to a three-dimensional human body sign data management method and system. The method comprises the following steps: acquiring 3D data of a human body through 3D scanning equipment; performing physical sign model guidance on the 3D data of the human body to obtain a curved surface fitting 3D human body model; performing model calibration processing on the curved surface fitting 3D human body model and performing equal proportion reconstruction on the human body 3D model to obtain human body 3D model data; the impedance measuring equipment is utilized to carry out multi-frequency impedance scanning on the tested person, so that multi-frequency resistance data are obtained; conducting three-dimensional tissue resistance conduction analysis on the multi-frequency resistance data so as to obtain three-dimensional tissue resistance conductivity parameters; and generating a three-dimensional body composition spectrogram by utilizing the three-dimensional tissue resistivity conductivity parameters, so as to obtain three-dimensional body composition spectrogram data. The invention solves the problems that the existing sign data management system is difficult to accept in privacy and has insufficient data accuracy.

Description

Three-dimensional human body sign data management method and system

Technical Field

The invention relates to the technical field of human body sign data management systems, in particular to a three-dimensional human body sign data management method and system.

Background

With the continuous development of technology, human health monitoring and management are important directions for scientific research and medical health management. Traditional health monitoring relies mainly on two-dimensional data such as body temperature, blood pressure, heart rate, which, while providing certain information, lack a comprehensive characterization of the human body. With the rapid development of computer vision, deep learning and Internet of things technology, three-dimensional human body sign data can be acquired, processed and managed, and opportunities are provided for more comprehensive and deep knowledge of individual health conditions. The existing human body sign data system relates to user privacy, relies on color information based on 2D judgment, is unfavorable for protecting the user privacy and is not easy to accept by users; the data accuracy is insufficient, 3D depth information is absent based on 2D judgment, or overall body feature analysis is absent based on local judgment, and the acquired data feature result is inaccurate; the physical sign content is single, and the physical components, the physical state, the human body characteristics of the spine, the chest and the foot are scattered in different systems, so that unified management cannot be realized.

Disclosure of Invention

Based on the foregoing, it is necessary to provide a three-dimensional human body sign data management method and system to solve at least one of the above technical problems.

To achieve the above object, a three-dimensional human body sign data management method includes the steps of:

Step S1: acquiring 3D data of a human body through 3D scanning equipment; performing physical sign model guidance on the 3D data of the human body so as to obtain a curved surface fitting 3D human body model; performing model calibration processing on the curved surface fitting 3D human body model and performing equal proportion reconstruction on the human body 3D model so as to acquire human body 3D model data;

Step S2: the impedance measuring equipment is utilized to carry out multi-frequency impedance scanning on the tested person, so that multi-frequency resistance data are obtained; conducting three-dimensional tissue resistance conduction analysis on the multi-frequency resistance data so as to obtain three-dimensional tissue resistance conductivity parameters; generating a three-dimensional body composition spectrogram by utilizing the three-dimensional tissue resistivity conductivity parameters, thereby acquiring three-dimensional body composition spectrogram data;

Step S3: correcting the Cobb angle predicted value according to the three-dimensional body composition spectrogram data and performing scoliosis risk level assessment, so as to obtain scoliosis risk level assessment data; performing foot morphology 3D depth analysis according to the three-dimensional body composition spectrogram data, so as to obtain foot morphology 3D depth analysis data; carrying out overall three-dimensional human body sign analysis according to the scoliosis risk level evaluation data and the foot morphology 3D depth analysis data, thereby obtaining three-dimensional human body sign analysis data;

step S4: performing privacy protection coding processing on the three-dimensional human body sign analysis data so as to obtain privacy protection coding data; performing characteristic database storage processing on the three-dimensional human body sign analysis data according to the privacy protection coding data, so as to obtain sign encryption storage data;

step S5: the physical sign encrypted storage data is subjected to mobile terminal physical sign data presentation, so that physical sign data of a mobile terminal user are obtained;

Step S6: carrying out background management processing on the physical sign encrypted storage data so as to obtain background management interface data; and generating a paper report according to the background management interface data, thereby acquiring the paper report data of the body test and the foot test.

According to the invention, the 3D human body model is obtained by combining the 3D data acquired by the 3D scanning equipment with the sign model guidance and the surface fitting; model calibration and equal proportion reconstruction further improve the accuracy and the authenticity of the model, and are helpful for more accurately analyzing the human morphology in the subsequent steps. The multi-frequency resistance data obtained by the impedance measurement equipment is utilized to conduct three-dimensional tissue resistance conduction analysis, so that more comprehensive tissue resistance conductivity parameters are obtained; the method is favorable for more accurately knowing the electrical characteristics of the internal tissues of the human body, and provides more accurate basic data for the generation of the follow-up body composition spectrogram. By correcting the Cobb angle predicted value of the three-dimensional body composition spectrogram data, the risk level of scoliosis can be estimated more accurately, and early warning and intervention opportunities are provided. And by combining the scoliosis risk level evaluation and the foot morphology 3D depth analysis data, the overall three-dimensional human body sign analysis is performed, so that the sign analysis is more comprehensive and comprehensive. The privacy protection coding processing is helpful for protecting sensitive information of users, and ensures that individual privacy is effectively protected in physical sign data processing; the coded three-dimensional human body sign analysis data is stored in the feature database, so that a high-efficiency and feasible way is provided for large-scale data analysis and mining, and meanwhile, the safety of the data is ensured. By presenting the physical sign data of the mobile terminal on the physical sign encrypted storage data, personalized physical sign data presentation of the user is realized, and the cognition of the user on the health condition of the user is improved. The background management interface data provides management and monitoring of the whole system, including data storage and user management, and ensures the running stability and safety of the system; through background management interface data, a paper report is generated, and detailed body measurement and foot measurement reports are provided for users, so that the users can know the body conditions and foot health more conveniently, and health management is promoted. Therefore, in order to solve the problems that the existing physical sign data management system is difficult to accept due to privacy, insufficient in data accuracy, single in content and single in system presentation mode, the invention provides a three-dimensional physical sign data management system, human body 3D information is collected through equipment, a human body three-dimensional model is reconstructed, a human body is restored in real equal proportion, on the basis, the physical characteristics of human body components, physical states, spines, breasts and feet are extracted, and analysis and management are performed, so that an omnibearing physical sign data management system for a measurer and a merchant is formed.

Preferably, step S1 comprises the steps of:

Step S11: acquiring 3D data of a human body through 3D scanning equipment; performing discrete point set extraction on the human body 3D data so as to obtain human body 3D point cloud data;

Step S12: performing physical sign point screening on the human body 3D point cloud data so as to obtain human body characteristic point data; constructing an initial 3D human body model by utilizing human body characteristic point data, thereby acquiring an initial 3D human body model;

Step S13: performing model and point cloud alignment on the initial 3D human body model and human body 3D point cloud data, so as to obtain a point cloud aligned 3D human body model; performing surface fitting on the point cloud aligned 3D human body model, so as to obtain a surface fitting 3D human body model;

Step S14: performing physical sign model calibration on the curved surface fitting 3D human body model so as to obtain physical sign model calibration point data; 3D space calibration point coordinate extraction is carried out on the calibration point data of the physical sign model, so that calibration point coordinate data are obtained;

Step S15: performing 3D human body model calibration calculation according to the coordinate data of the calibration points, so as to obtain 3D human body model calibration parameters;

Step S16: and performing human body 3D model equal proportion reconstruction on the surface fitting 3D human body model by using the 3D human body model calibration parameters, thereby obtaining human body 3D model data.

According to the invention, the high-precision three-dimensional data of the human body is obtained through the 3D scanning equipment, so that the accurate capture of the human body morphology is realized; the discrete point set extraction further converts the continuous 3D data into discrete point cloud data, and a data basis is provided for subsequent processing. The screening of the sign points enables only key human body feature points to be extracted from massive point clouds, and complexity of subsequent calculation is reduced; through these feature points, the initial 3D mannequin is able to more accurately capture the basic shape and features of the human body. The relative positions of the initial model and the point cloud are adjusted to enable the 3D human model to be more in line with the actual form; the smoothness and accuracy of the model are further improved through surface fitting, and the obtained surface fitting 3D human model is more real. The physical sign model calibration enables the model to better reflect the physiological characteristics of the human body, and the extraction of the coordinates of the calibration points provides an accurate reference for subsequent calculation; the data lay a foundation for the accurate construction of the sign model. The 3D human body model is calibrated by using the calibration point coordinates, and the calibration parameters obtained by mathematical operation can better reflect the human body morphology, so that the accuracy and fidelity of the model are improved. Performing equal-proportion reconstruction on the curve fitting 3D human body model by using 3D human body model calibration parameters, and ensuring that the model is highly matched with the actual human body form; the 3D model data of the human body obtained in the step can be used for subsequent physical sign analysis.

Preferably, step S2 comprises the steps of:

step S21: the impedance measuring equipment is utilized to carry out multi-frequency impedance scanning on the tested person, so that multi-frequency resistance data are obtained; carrying out resistance spectrum analysis on the multi-frequency resistance data so as to obtain resistance spectrum characteristic data;

Step S22: constructing a tissue impedance initial model according to the resistance spectrum characteristic data, so as to obtain the tissue impedance initial model; performing resistance and body composition conversion by using a tissue electrical impedance initial model so as to obtain initial body composition data;

Step S23: performing three-dimensional tissue structure analysis on the human body 3D model data according to the initial body composition data so as to obtain three-dimensional tissue structure analysis data, wherein the three-dimensional tissue structure analysis data comprises three-dimensional tissue structure data, blood vessel position data, bone structure data and fat structure data;

Step S24: performing tissue density adjustment on the 3D model data of the human body according to the three-dimensional tissue structure data, thereby obtaining tissue density adjustment parameters; optimizing model parameters of the tissue impedance initial model by utilizing the tissue density adjustment parameters so as to obtain a tissue impedance model;

Step S25: three-dimensional tissue resistivity parameter extraction is carried out according to the blood vessel position data, the bone structure data and the fat structure data, so that three-dimensional tissue resistivity parameter is obtained, wherein the three-dimensional tissue resistivity parameter comprises a blood conductivity parameter and a local conductivity difference parameter;

Step S26: performing three-dimensional tissue resistivity conductivity compensation on the initial volume component data by utilizing the three-dimensional tissue resistivity parameter and the tissue electrical impedance model, thereby acquiring three-dimensional volume component data; and generating a three-dimensional body composition spectrogram of the three-dimensional body composition data, thereby acquiring the three-dimensional body composition spectrogram data.

According to the invention, through multi-frequency impedance scanning and resistance spectrum analysis, the change condition of the resistance of the tissue of the tested person under different frequencies can be obtained, the frequency response of the tissue to the current can be captured, and more detailed electrical characteristic information is provided for subsequent modeling; the resistance data at different frequencies reflect the complex structure of the tissue and its complex response to current, providing a basis for further building tissue electrical impedance models. The tissue electrical impedance initial model is constructed through the resistance spectrum characteristic data, so that the electrical characteristics of the tissue of the tested person can be more accurately simulated; the method is beneficial to establishing an initial model and provides a basis for the conversion of the subsequent resistance and the body composition; the complex characteristics of the multi-frequency resistance data are considered in the establishment of the initial model, and the fidelity and accuracy of the model are improved. The three-dimensional tissue structure analysis is carried out on the initial body composition data to obtain rich three-dimensional tissue structure information, wherein the information comprises blood vessel positions, bone structures and fat distribution; the method is helpful for more comprehensively understanding the tissue constitution of the testee, and provides detailed basic data for subsequent model adjustment and resistivity and conductivity parameter extraction. The tissue density is adjusted according to the three-dimensional tissue structure data, so that the tissue electrical impedance initial model can be further optimized; the adjusted tissue density parameters are more in line with the actual situation, so that the simulation precision and fidelity of the model are improved; the modeling error is reduced, and the model is more close to the actual physiological state of the tested person. Three-dimensional tissue resistivity parameters including blood conductivity parameters and local conductivity difference parameters are extracted through blood vessel position data, bone structure data and fat structure data; the parameters comprehensively consider the structural difference of tissues, and more accurate parameter input is provided for three-dimensional tissue resistivity and conductivity compensation. Compensating the initial volume component data by utilizing the three-dimensional tissue resistivity parameter and the optimized tissue electrical impedance model to obtain more accurate three-dimensional volume component data; the generated three-dimensional body composition spectrogram data comprehensively considers the electrical characteristics and tissue structure information, and provides powerful support for more comprehensively analyzing the body composition of the tested person.

Preferably, in step S26, the three-dimensional tissue resistivity is compensated, wherein the calculation formula of the three-dimensional tissue resistivity is specifically:

Where R _C represents the compensated tissue resistance, ρ ₀ represents the initial resistivity, G ₀ represents the initial conductivity, pi represents the circumference, R ₀ represents the initial resistance, H represents the local three-dimensional tissue height, z ₀ represents the initial impedance, n represents a positive integer approaching infinity, Q represents the tissue resistivity weight parameter, and S represents the conductivity weight parameter.

The invention constructs a calculation formula of three-dimensional tissue resistivity conductivity compensation, which is used for carrying out three-dimensional tissue resistivity conductivity compensation on initial body composition data by utilizing three-dimensional tissue resistivity parameters and a tissue electrical impedance model; in the formulaThe ratio of the initial resistivity to the initial conductivity is partially represented, the ratio reflects the electrical property of the tissue, and more comprehensive electrical information can be obtained by dividing the resistivity and the conductivity, so that the conductivity of the tissue can be more comprehensively understood; part of the three-dimensional function comprises a trigonometric function item related to the tissue morphology; by using sine and cosine functions, the effect of the height variation of the tissue on the electrical properties can be taken into account; the calculation of resistance compensation is more flexible, and the method can adapt to tissues with different forms. /(I) The initial resistance, the conductivity and the impedance are partially subjected to logarithmic operation, so that the relative influence of the parameters is more visual; the logarithmic operation is helpful for adjusting the magnitude of the electrical parameter, and numerical instability in calculation is avoided.The method is characterized in that a limit value when approaching infinity is partially expressed, and the compensation formula is more robust by incorporating the weight parameters of tissue resistivity and conductivity into limit calculation; helps smooth calculations and prevents numerical instability that may result in some situations.

Drawings

FIG. 1 is a flow chart illustrating the steps of a three-dimensional human body sign data management method;

FIG. 2 is a flowchart illustrating the detailed implementation of step S2 in FIG. 1;

FIG. 3 is a flowchart illustrating the detailed implementation of step S3 in FIG. 1;

FIG. 4 is a flowchart illustrating the detailed implementation of step S32 in FIG. 3;

fig. 5 is a system flow block diagram of a three-dimensional human body sign data management method.

The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.

Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.

It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

In order to achieve the above objective, referring to fig. 1 to 4, the present invention provides a three-dimensional human body sign data management method, comprising the following steps:

According to the invention, the 3D human body model is obtained by combining the 3D data acquired by the 3D scanning equipment with the sign model guidance and the surface fitting; model calibration and equal proportion reconstruction further improve the accuracy and the authenticity of the model, and are helpful for more accurately analyzing the human morphology in the subsequent steps. The multi-frequency resistance data obtained by the impedance measurement equipment is utilized to conduct three-dimensional tissue resistance conduction analysis, so that more comprehensive tissue resistance conductivity parameters are obtained; the method is favorable for more accurately knowing the electrical characteristics of the internal tissues of the human body, and provides more accurate basic data for the generation of the follow-up body composition spectrogram. By correcting the Cobb angle predicted value of the three-dimensional body composition spectrogram data, the risk level of scoliosis can be estimated more accurately, and early warning and intervention opportunities are provided. And by combining the scoliosis risk level evaluation and the foot morphology 3D depth analysis data, the overall three-dimensional human body sign analysis is performed, so that the sign analysis is more comprehensive and comprehensive. The privacy protection coding processing is helpful for protecting sensitive information of users, and ensures that individual privacy is effectively protected in physical sign data processing; the coded three-dimensional human body sign analysis data is stored in the feature database, so that a high-efficiency and feasible way is provided for large-scale data analysis and mining, and meanwhile, the safety of the data is ensured. By presenting the physical sign data of the mobile terminal on the physical sign encrypted storage data, personalized physical sign data presentation of the user is realized, and the cognition of the user on the health condition of the user is improved. The background management interface data provides management and monitoring of the whole system, including data storage and user management, and ensures the running stability and safety of the system; through background management interface data, a paper report is generated, and detailed body measurement and foot measurement reports are provided for users, so that the users can know the body conditions and foot health more conveniently, and health management is promoted. Therefore, in order to solve the problems that the existing physical sign data management system is difficult to accept due to privacy, insufficient in data accuracy, single in content and single in system presentation mode, the invention provides a three-dimensional physical sign data management system, human body 3D information is collected through equipment, a human body three-dimensional model is reconstructed, a human body is restored in real equal proportion, on the basis, body characteristics such as human body components, body states, spines, breasts and feet are extracted, analysis and management are carried out, and an omnibearing physical sign data management system for a measurer and a merchant is formed.

In the embodiment of the present invention, as described with reference to fig. 1, a step flow diagram of a three-dimensional human body sign data management method according to the present invention is provided, and in this example, the three-dimensional human body sign data management method includes the following steps:

In the embodiment of the invention, a 3D scanning device is used for scanning a tested person to acquire 3D data of a human body; guiding the 3D data by utilizing a previously established sign model guiding algorithm to extract key sign information; performing surface fitting on the guided data to generate a 3D human body model, and performing model calibration processing to ensure the proportion and accuracy of the model; and based on the model calibration parameters, performing equal-proportion 3D human body model reconstruction to obtain final human body 3D model data.

In the embodiment of the invention, the impedance measuring equipment is utilized to carry out multi-frequency impedance scanning on the tested person, and multi-frequency resistance data are obtained; carrying out resistance spectrum analysis on the multi-frequency resistance data, and obtaining resistance spectrum characteristic data through a mathematical algorithm; carrying out three-dimensional tissue structure analysis on the human body 3D model to obtain three-dimensional tissue structure analysis data, wherein the three-dimensional tissue structure analysis data comprises a three-dimensional tissue structure, a blood vessel position, a bone structure and a fat structure; compensating the initial volume component data by using a calculation formula of three-dimensional tissue resistivity compensation, and taking the influence of the three-dimensional tissue resistivity into consideration, thereby obtaining three-dimensional volume component data after resistivity conductivity compensation; extracting characteristic information of the compensated data; converting the characteristic information into three-dimensional body component spectrogram data by utilizing a spectrogram generation algorithm; and finally, three-dimensional body composition spectrogram data are obtained, and the spatial distribution condition of different compositions in the tissue is reflected.

In the embodiment of the invention, the predicted value of the Cobb angle of the spine is calculated by utilizing the three-dimensional body composition spectrogram data, and is corrected; based on the corrected Cobb angle, performing scoliosis risk level assessment, determining whether the scoliosis risk exists or not, and classifying the risk level; making a depth correction strategy by making an arch region, a heel region, a plantar edge region, a toe region and overall balance in the foot, and performing depth analysis on the form of the foot through the strategy, wherein the depth analysis comprises the height of the arch, the shape of the toe and the curve of the sole; and (5) examining the data comprehensive conditions of all aspects through overall three-dimensional human body physical sign analysis.

In the embodiment of the invention, the three-dimensional human body sign analysis data is subjected to privacy protection coding treatment, so that the safety of individual privacy information is ensured; and carrying out characteristic database storage processing on the three-dimensional human body sign analysis data by utilizing the privacy protection coding data to obtain sign encryption storage data.

in the embodiment of the invention, the physical sign encryption is utilized to encrypt the stored data, and decryption and processing are carried out on the mobile terminal to obtain the physical sign data of the user of the mobile terminal.

In the embodiment of the invention, the background management processing is carried out on the physical sign encrypted storage data for subsequent data management and analysis; and generating a paper report by using the background management interface data, wherein the paper report comprises detailed report data of body test and foot test.

Preferably, step S1 comprises the steps of:

In the embodiment of the invention, a professional 3D scanning device is used for scanning a human body to obtain high-density point cloud data; then, the point cloud data are extracted from the continuous data stream into discrete point sets through a discrete point set extraction algorithm, so that 3D point cloud data of the human body are formed. Screening the human body 3D point cloud data through a conventional algorithm, selecting key points with physical sign information, and forming human body characteristic point data; then, with these feature point data, the construction of the initial 3D mannequin is performed using triangulation or other reconstruction algorithms. Aligning the initial 3D human body model with the point cloud data through an Iterative Closest Point (ICP) algorithm or other point cloud alignment algorithms to obtain a 3D human body model with aligned point cloud; and then, carrying out surface fitting on the aligned 3D model to obtain a smoother and finer surface fitting 3D human body model. Extracting key points with physiological or morphological significance from the surface fitting 3D human body model through a physical sign model calibration algorithm to form physical sign model calibration point data; and then, carrying out 3D space calibration on the calibration points, extracting the coordinate information of each point, and obtaining the coordinate data of the calibration points. And (3) carrying out mathematical calculation and optimization algorithm by using the coordinate data of the calibration points to deduce the calibration parameters of the 3D human body model, wherein the parameters are used for accurately describing the form and the sign information of the human body. Performing accurate equal-proportion reconstruction on the 3D human body model fitted by using the acquired 3D human body model calibration parameters to obtain final human body 3D model data; this model will more accurately reflect the morphology and sign information of the individual.

Preferably, step S2 comprises the steps of:

As an example of the present invention, referring to fig. 2, the step S2 in this example includes:

in the embodiment of the invention, the impedance measuring equipment is used for carrying out multi-frequency impedance scanning on the tested person to obtain multi-frequency resistance data; and carrying out resistance spectrum analysis on the multi-frequency resistance data, and obtaining resistance spectrum characteristic data through a mathematical algorithm.

In the embodiment of the invention, a tissue electrical impedance initial model is constructed by utilizing the resistance spectrum characteristic data; and converting the resistance and the body composition by using the tissue electrical impedance initial model to obtain initial body composition data.

In the embodiment of the invention, three-dimensional tissue structure analysis is performed on a human body 3D model according to initial body composition data, and three-dimensional tissue structure analysis data including a three-dimensional tissue structure, a blood vessel position, a bone structure and a fat structure is obtained.

In the embodiment of the invention, the density adjustment parameters are calculated on the three-dimensional tissue structure data, the tissue types are classified and distinguished, and the densities of various tissues at different positions are adjusted; the original tissue density is adjusted by using the calculated tissue density adjustment parameters, which can be realized by linear or nonlinear mathematical functions, so that the adjustment of the density is reasonable and accords with physiological characteristics; selecting a gradient descent optimization algorithm to find an optimal solution in a parameter space; setting an optimized objective function, and actually measuring the fitting degree of data; introducing the density adjustment parameters obtained in the tissue density adjustment step into an objective function to ensure that the influence of the tissue density is considered in the optimization process; and minimizing or maximizing the objective function by using the selected optimization algorithm to obtain a more accurate tissue electrical impedance model subjected to tissue density adjustment.

In the embodiment of the invention, three-dimensional data of the blood vessel position is acquired firstly through a medical imaging technology (such as CT or MRI scanning). Subsequently, using Computational Fluid Dynamics (CFD) methods, the flow of blood within the vessel was simulated using the Navier-Stokes equation, taking into account vessel geometry, viscosity of the blood, and pulsatility of the heart. Through numerical simulation, blood flow rate, pressure distribution and hydrodynamic parameters are extracted. Next, the electrophysiological model is used in combination with the blood flow simulation data to extract conductivity parameters of the blood. Meanwhile, CT or MRI data of the bone structure are subjected to depth segmentation, bone depth distribution data are obtained, and local bone density calibration is performed by combining known bone density calibration data. And constructing a three-dimensional image of the bone density through interpolation or reconstruction operation. And then, carrying out local density difference analysis to obtain local density difference data. And carrying out dynamic bone density simulation through a biomechanical model to obtain dynamic bone density simulation data. Meanwhile, a three-dimensional image of fat distribution is constructed by utilizing fat structure data, region division is carried out, thickness data of a fat layer is calculated, a fat density gradient model is established, and fat density gradient data are obtained. Finally, considering the mixing condition of bones and fat in local tissues, synthesizing a local conductivity parameter extraction formula, expressing the formula as a combination of bone conductivity and fat conductivity, and extracting local conductivity difference parameters. The series of steps realizes multi-level multi-mode data fusion and analysis of complex biological tissue structures.

Step S26: performing three-dimensional tissue resistivity conductivity compensation on the initial volume component data by utilizing the three-dimensional tissue resistivity parameter and the tissue electrical impedance model, thereby acquiring three-dimensional volume component data; generating a three-dimensional body composition spectrogram of the three-dimensional body composition data, thereby obtaining three-dimensional body composition spectrogram data;

In the embodiment of the invention, the initial body composition data is compensated by using a calculation formula for compensating the resistivity of the three-dimensional tissue, and the influence of the resistivity of the three-dimensional tissue is considered, so that the three-dimensional body composition data after the resistivity compensation is obtained; extracting characteristic information of the compensated data; converting the characteristic information into three-dimensional body component spectrogram data by utilizing a spectrogram generation algorithm; and finally, three-dimensional body composition spectrogram data are obtained, and the spatial distribution condition of different compositions in the tissue is reflected.

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Preferably, step S25 comprises the steps of:

Step S251: performing blood flow simulation according to the blood vessel position data, thereby obtaining blood flow simulation data; extracting blood conductivity parameters according to the blood flow simulation data, thereby obtaining blood conductivity parameters;

step S252: performing depth segmentation on the bone structure data so as to obtain bone depth distribution data; calibrating the local bone density according to the bone depth distribution data, thereby obtaining local bone density calibration data;

step S253: constructing a bone density three-dimensional image by utilizing bone depth distribution data and local bone density calibration data, so as to obtain bone density three-dimensional image data;

step S254: performing local density difference analysis on the bone density three-dimensional image data so as to obtain local density difference data; performing dynamic bone density simulation according to the local density difference data, so as to obtain dynamic bone density simulation data;

Step S255: generating a fat distribution three-dimensional image according to the fat structure data, thereby acquiring fat distribution three-dimensional image data; performing region division on the fat distribution three-dimensional image data so as to obtain fat distribution region data; fat layer thickness calculations are performed on the fat distribution area data, thereby acquiring fat layer thickness data; performing fat density gradient modeling according to the fat layer thickness data, thereby obtaining fat density gradient data;

Step S256: and carrying out local conductivity difference analysis according to the dynamic bone density simulation data and the fat density gradient data, thereby obtaining local conductivity difference parameters.

The invention can improve the simulation precision of the physiological vascular system behaviors, including the parameters of blood flow speed and pressure, through the blood flow simulation data, thereby being beneficial to more truly reflecting the internal condition of the blood vessel; the blood flow simulation data provides conductivity parameters related to blood flow, which are closely related to the conductivity of blood, helping to more accurately estimate conductivity within tissue, providing a finer data base for subsequent electrophysiological simulations. The depth segmentation and the local density calibration are beneficial to capturing the form and the density distribution of the bone structure more accurately, so that the precision of the bone model is improved; the local density calibration considers the local bone density change of the individual, so that the bone density calibration is more personalized, and the physiological accuracy of the model is improved. The density distribution of the whole skeleton system can be intuitively displayed through three-dimensional image construction, and global skeleton density information is provided; the three-dimensional image plays an important role in medicine, helps doctors to know the bone condition of patients more clearly, and provides support for bone related diseases. The local density differential analysis provides detailed local bone density information so that individual bone changes can be monitored and analyzed; dynamic bone density simulation utilizes density difference data to simulate the density change of bones at different time points, and provides a beneficial tool for solving the dynamic change of individual bones. Three-dimensional image generation provides a clear presentation of fat distribution, including spatial distribution and morphology of fat layers; the regional division takes into account differences in individual fat distribution, so that subsequent analysis is more personalized, and customized medical evaluation is facilitated. The local conductivity difference analysis is combined with dynamic bone density simulation and fat density gradient data, so that the conductivity of the tissue can be determined more personally; analysis of the local conductivity difference is helpful for understanding the influence of bone density and fat distribution factors on tissue conductivity, and provides basis for more accurate estimation of conductivity parameters.

In the embodiment of the invention, three-dimensional data of the blood vessel position is obtained by utilizing a medical imaging technology, such as CT or MRI scanning; using a Computational Fluid Dynamics (CFD) method, adopting a Navier-Stokes equation to perform blood flow simulation, and simulating and considering the geometric shape of a blood vessel, the viscosity of blood and the pulsation of a heart; conversion from pulsations of the heart, bends and bifurcation of the blood vessel into numerical simulations, which can be performed on a computer, simulate the flow of blood within the blood vessel by numerical methods; extracting flow velocity, pressure distribution and hydrodynamic parameters of blood from the results of the numerical simulation; and extracting conductivity parameters of blood by utilizing an electrophysiology model and combining blood flow simulation data. Performing depth segmentation on CT or MRI data of a bone structure by using an image segmentation algorithm to obtain bone depth distribution data; based on the depth segmentation data, local bone density calibration is performed in combination with known bone density calibration data (e.g., standard density bone fragments) to obtain local bone density calibration data. And performing interpolation or reconstruction operation by utilizing the bone depth distribution data and the local bone density calibration data to construct a three-dimensional image of bone density. And carrying out local density difference analysis by utilizing the bone density three-dimensional image data to obtain local density difference data. And combining the local density difference data, adopting a biomechanical model to perform dynamic bone density simulation, and obtaining dynamic bone density simulation data. Interpolation or reconstruction is carried out by utilizing the fat structure data, and a three-dimensional image of fat distribution is constructed; performing region division on the fat distribution three-dimensional image data to obtain fat distribution region data; calculating thickness data of the fat layer based on the fat distribution area data; and establishing a fat density gradient model according to the fat layer thickness data to obtain fat density gradient data. The dynamic bone density simulation data comprise the change condition of bone density at different time points, and the fat density gradient data comprise gradient distribution of density in the fat layer; considering the simultaneous presence of bone and fat in local tissue, the local conductivity σ can be expressed as a combination of bone conductivity σ _b and fat conductivity σ _f, where ρ _b and ρ _f are the densities of bone and fat, respectively: σ=f·σ _b+(1―f)·σ_f, where f represents the volume fraction of bone in the local tissue, which can be estimated from bone density and fat density. According to the formula, the local conductivity difference parameter can be extracted.

Preferably, step S3 comprises the steps of:

step S31: key physical sign extraction is carried out on the 3D model data of the human body through a human body measurement algorithm according to the spectrogram data of the three-dimensional physical components, so that three-dimensional physical sign data are obtained, wherein the three-dimensional physical sign data comprise human body circumference data, skeleton characteristic data, human body shape data and foot data;

Step S32: correcting the Cobb angle predicted value according to the skeleton characteristic data and the human body morphological data and evaluating the risk level of scoliosis, so as to obtain the risk level evaluation data of scoliosis;

Step S33: performing foot morphology 3D depth analysis on the foot data so as to obtain foot morphology 3D depth analysis data;

Step S34: and carrying out overall three-dimensional human body sign analysis according to the human body circumference data, the scoliosis risk level evaluation data and the foot morphology 3D depth analysis data, thereby obtaining three-dimensional human body sign analysis data.

The invention can realize the comprehensive analysis of the physical characteristics of the individual through the three-dimensional physical sign data. The predicted Cobb angle value is corrected by using skeleton characteristic data through a human body measurement algorithm, so that the prediction accuracy is improved; the assessment of the risk level of scoliosis is performed in combination with the corrected Cobb angle predictions and other spine related characteristic data, such as bone length, joint angle, to provide a more detailed assessment and advice for individuals at risk of scoliosis. Extracting key features of foot shape, such as concave-convex of foot shape and height of arch of foot, by using foot data and anthropometric algorithm; according to foot data, dynamic analysis of foot morphology is carried out, morphology change of the foot in movement is known, and data support is provided for foot movement assessment.

As an example of the present invention, referring to fig. 3, the step S3 in this example includes:

In the embodiment of the invention, the advanced anthropometric algorithm is utilized to analyze the 3D model data of the human body and extract the key sign data; extracting key points from the three-dimensional model, and measuring the circumference of each part, including chest circumference, waistline and hip circumference; extracting skeleton key points, and acquiring skeleton structure information, wherein the skeleton structure information comprises coordinates and connection relations of the joint points; analyzing the body type curve, and extracting the integral characteristics of the human body shape, such as the body curve and muscle line; key features of the foot including arch height, foot length are extracted from the three-dimensional model.

In the embodiment of the invention, the predicted value of the Cobb angle of the spine is calculated by utilizing the skeleton characteristic data and corrected; and based on the corrected Cobb angle, performing scoliosis risk level assessment, determining whether the scoliosis risk exists or not, and classifying the risk level.

In the embodiment of the invention, the depth correction strategy is formulated by carrying out depth analysis on the shape of the foot through the strategy, including the height of the foot arch, the shape of the toes and the curve of the soles, and the foot arch region, the heel region, the plantar edge region, the toe region and the overall balance in the foot.

Step S34: carrying out overall three-dimensional human body sign analysis according to the human body circumference data, the scoliosis risk level evaluation data and the foot morphology 3D depth analysis data, thereby obtaining three-dimensional human body sign analysis data;

in the embodiment of the invention, the overall three-dimensional human body sign analysis is carried out by combining the human body circumference data, the scoliosis risk level evaluation data and the foot form 3D depth analysis data, and the data comprehensive conditions of all aspects are examined.

Preferably, step S32 comprises the steps of:

Step S321: labeling the key nodes of the spine according to the skeleton characteristic data, so as to obtain the key node data of the spine; performing curvature tensor calculation on the spine key node data so as to obtain curvature tensor data;

Step S322: defining the curvature direction of the spine according to the curvature tensor data so as to acquire the curvature direction data of the spine; performing spine bending degree evaluation according to the spine curvature direction data and the curvature tensor data so as to obtain spine bending degree evaluation data; curvature data integration is carried out according to the spinal curvature evaluation data, so that a three-dimensional spinal curvature data set is obtained;

Step S323: calculating the inclination angles of the left shoulder peak point and the right shoulder peak point by using the skeleton characteristic data, so as to obtain shoulder inclination angle parameters; calculating the transverse offset distance between the cervical vertebra and the coccyx by using the skeleton characteristic data, so as to obtain the transverse offset parameters of the spine;

step S324: performing Cobb angle prediction based on the human body morphological data, so as to obtain an initial Cobb angle prediction value; performing angle parameter correction on the initial Cobb angle predicted value according to the shoulder inclination angle parameter and the spine transverse offset parameter, so as to obtain the Cobb angle predicted value;

Step S325: and performing scoliosis risk level assessment according to the three-dimensional spinal curvature data set and the Cobb angle predicted value, so as to obtain scoliosis risk level assessment data.

According to the curvature tensor calculation method, curvature tensor data is obtained by calculating the curvature tensor of the spine key node data, the curvature tensor data is helpful for revealing the curvature condition of the spine in the three-dimensional space, and rich curvature information is provided for subsequent spine curvature degree evaluation. The definition of the curvature direction fully considers the space trend of the spine; the spine bending degree evaluation combines curvature direction data, and the spine bending degree is quantitatively evaluated, so that more objective and accurate spine morphological information is provided; integration of curvature data provides the basis for subsequent multidimensional analysis, enabling a comprehensive understanding of spinal curvature. The shoulder peak point inclination angle parameter provides quantitative measurement on the inclination condition of the upper body of the spine, and is helpful for judging the integral posture of the spine; the transverse offset parameters of the spine provide the offset condition of the spine in the transverse direction by measuring the transverse offset distance between the cervical vertebra and the coccyx, and provide important information for subsequent lateral bending risk assessment. The initial Cobb angle prediction provides an initial estimate of the spine curvature angle by being based on the body morphology data; the angle parameter correction utilizes the shoulder inclination angle and the spine transverse offset parameter, so that the prediction of the Cobb angle is more accurately adjusted, and the angle accuracy is improved. The three-dimensional spinal curvature dataset provides an omnidirectional description of the overall spinal morphology, providing sufficient information for lateral risk assessment.

As an example of the present invention, referring to fig. 4, the step S32 in this example includes:

In the embodiment of the invention, traversing skeleton characteristic data to identify key nodes on the spine, including the top and the bottom of the vertebral body; for each key node, a curvature tensor and a derivative operation of the skeleton curve are calculated using a mathematical method.

In the embodiment of the invention, for each key node, curvature tensor data is used for calculating the main curvature direction at the node; the principal curvature direction is the principal eigenvector of the curvature tensor, representing the curvature direction of the spine at that node. Calculating the bending degree of the spine at each key node by using the spine curvature direction data; calculating the bending degree of the spine at each key node by using the spine curvature direction data; the curvature data at each key node is integrated to form a three-dimensional spinal curvature dataset.

The specific implementation process is as follows:

For each key node i: calculating a curvature tensor c _i, and calculating eigenvalues and eigenvectors of the curvature tensor c _i, wherein eigenvalues gamma ₁ and gamma ₂ represent principal curvatures, and eigenvectors v ₁ and v ₂ represent corresponding principal curvature directions; selection of the principal curvature direction: selecting a feature vector v ₁ corresponding to |gamma ₁|>|γ₂ | as a main curvature direction; and recording the main curvature direction of each node to obtain the spine curvature direction data.

Using the principal curvature value to evaluate the degree of spinal curvature: calculating the bending degree of the spine by using the principal bending values |gamma ₁ | and |gamma ₂ |; the degree of bending can be expressed by |γ ₁|―|γ₂ |; spinal curvature assessment data was recorded at each node.

Finally, integrating the curvature direction data and the curvature degree evaluation data of the spine at each node to form a three-dimensional curvature data set of the spine, wherein the data set contains comprehensive information of the curvature direction and the curvature degree of the spine in space.

In the embodiment of the invention, the shoulder inclination angle is calculated: identifying coordinates of key points of the left shoulder and the right shoulder from the skeleton characteristic data; calculating the center point coordinates of the shoulders; calculating vectors between the key points of the left shoulder and the right shoulder and the center point of the shoulder; and calculating the inclination angles of the left shoulder and the right shoulder. Spinal lateral offset calculation: identifying key point coordinates of the cervical vertebra and the coccyx from the skeleton feature data; and calculating the transverse offset distance between the cervical vertebra and the coccyx.

In the embodiment of the invention, the characteristics related to the Cobb angle are extracted from the human morphological data, including the curvature of the spine and the rotation degree of the vertebral body; training a machine learning model (such as a regression model or a neural network) by using the marked Cobb angle data, and establishing a relation between the features and the Cobb angles; and inputting the characteristics of the current human body form data by using the trained model to obtain an initial Cobb angle predicted value.

Shoulder inclination angle correction: and correcting the initial Cobb angle according to the positive and negative sum of the shoulder inclination angles. For example, if the shoulder tilt angle is positive, it may be desirable to increase the Cobb angle; if negative, it may be desirable to reduce the Cobb angle.

Correction of spinal lateral offset: the initial Cobb angle is corrected taking into account the effect of the lateral offset of the spine on the overall morphology of the spine. For example, if laterally offset to one side, cobb angles may be caused to be larger; conversely, a small Cobb angle may result.

Integrating the corrected Cobb angle predicted value: the effects of the shoulder tilt angle correction and the spine lateral offset correction are combined with appropriate weights: corrected Cobb angle = initial Cobb angle + omega ₁ x shoulder tilt angle correction + omega ₂ x lateral offset correction; wherein ω ₁ and ω ₂ are the weight for the shoulder tilt angle correction and the weight for the spinal lateral offset correction, respectively. And integrating the influence of the two correction factors into the initial Cobb angle predicted value to form a final corrected Cobb angle predicted value.

Step S325: performing scoliosis risk level assessment according to the three-dimensional spinal curvature data set and the Cobb angle predicted value, so as to obtain scoliosis risk level assessment data;

In the embodiment of the invention, a risk level evaluation standard of scoliosis is formulated; according to the formulated evaluation standard, evaluating the integrated data to determine the risk level of scoliosis, wherein the risk level comprises normal, slight, moderate and serious; and outputting the evaluation result of the scoliosis risk level.

Preferably, in step S323, the calculation of the lateral offset distance between the cervical vertebra and the coccyx is specifically:

Where δ represents the lateral distance between the cervical vertebra and the coccyx, L ₁ represents the length of the cervical vertebra, L ₂ represents the length of the coccyx, θ represents the relative angle of the cervical vertebra and coccyx, E represents the modulus of elasticity of the skeletal tissue, a ₁ area of the cervical cross section, a ₂ represents the area of the coccyx cross section, I ₁ represents the moment of inertia of the cervical cross section about the axis, I ₂ represents the moment of inertia of the coccyx cross section about the axis, and h represents the height of the cervical vertebra.

The invention constructs a formula for calculating the transverse offset distance between the cervical vertebra and the coccyx, which is used for calculating the transverse offset distance between the cervical vertebra and the coccyx by utilizing skeleton characteristic data; in the formulaThe transverse distance is adjusted through the lengths of the cervical vertebra and the coccyx and the relative angle between the cervical vertebra and the coccyx, and the influence of the relative positions of the cervical vertebra and the coccyx on the transverse offset is considered; /(I)The elastic modulus of the bone tissue, the area of the cross section of the cervical vertebra and the coccyx and the moment of inertia are partially combined, and the transverse distance is adjusted through logarithmic operation, so that the influence of the bone elasticity and the cross section characteristic on the transverse deflection is considered; when the cervical vertebra height approaches infinity, the term represents the influence of the cervical vertebra on the lateral offset, and the term is used for processing boundary conditions which can occur in practical application, so that the robustness of a formula is ensured. The formula is helpful for more accurately reflecting the relative positions of the cervical vertebra and the coccyx, and provides a powerful mathematical foundation for three-dimensional analysis of bone structures.

Preferably, step S33 includes the steps of:

Step S331: extracting arch three-dimensional grid data from the foot data, thereby obtaining arch three-dimensional grid data; constructing a sole finite element grid model according to the arch three-dimensional grid data and embedding deformation sensing nodes so as to obtain the sole finite element grid model; measuring the stress state of the sole by utilizing a sole finite element grid model, thereby obtaining the stress state data of the sole;

Step S332: acquiring weight data in the three-dimensional body composition spectrogram data; setting load simulation parameters by utilizing the weight data so as to obtain plantar load simulation parameters, wherein the plantar load simulation parameters comprise static load simulation parameters and dynamic load simulation parameters;

step S333: according to the static load simulation parameters and the plantar finite element grid model, human body standing simulation is carried out, plantar stress analysis is carried out, and accordingly standing plantar support force analysis data are obtained;

Step S334: human walking simulation and plantar stress analysis are carried out according to the dynamic load simulation parameters and the plantar finite element grid model, so that walking plantar support force analysis data are obtained;

Step S335: predicting the plantar support force according to plantar stress state data, standing plantar support force analysis data and walking plantar support force analysis data, so as to obtain plantar support force data; and performing foot morphology 3D depth correction according to the plantar support force data, so as to obtain foot morphology 3D depth analysis data.

According to the foot arch three-dimensional grid data extraction method, the morphological characteristics of the foot can be more comprehensively captured by extracting the foot arch three-dimensional grid data; the method comprises the steps of constructing a sole finite element grid model and embedding deformation sensing nodes, and is beneficial to simulating the deformation of the foot under different stress states, so that the structure and the function of the sole are more truly simulated. The obtained weight data is used for setting static and dynamic load simulation parameters, so that the method is beneficial to more accurately simulating the stress states of the soles under different load conditions; the simulation of the plantar load can provide a real load condition for subsequent analysis, and lays a foundation for the plantar stress analysis of standing and walking. The human body standing simulation is carried out according to the static load simulation parameters, the stress state of the sole is analyzed, and standing sole supporting force analysis data are obtained; the support force distribution condition of each part of the sole in the standing state is helped to be known, and references are provided for the comfort of the standing posture and the health of the sole. The human walking simulation is carried out according to the dynamic load simulation parameters, the stress state of the sole is analyzed, and walking sole supporting force analysis data are obtained; the method is helpful for knowing the distribution of supporting force of each sole part in the walking state, and provides data support for the walking sole function and stability evaluation. Based on the plantar stress state data, the standing plantar support force analysis data and the walking plantar support force analysis data, plantar support force prediction is performed, and the support force information of the sole under the unknown load condition can be provided.

In the embodiment of the invention, three-dimensional grid data extraction is performed by utilizing foot data, and an arch region is gridded by adopting an advanced graphic processing algorithm to obtain arch three-dimensional grid data; and constructing a plantar finite element grid model based on the arch three-dimensional grid data. Dividing the arch grid into finite element units by adopting a grid generation algorithm, and establishing a finite element model; and embedding deformation sensing nodes in the plantar finite element grid model. The nodes are used for sensing the deformation condition of the sole under different load states so as to facilitate subsequent load simulation and force analysis. Extracting weight data from the three-dimensional body composition spectrogram data, the data reflecting an actual weight of the individual; setting plantar load simulation parameters including static load simulation parameters and dynamic load simulation parameters by using the obtained weight data; static parameters are used for standing simulation and dynamic parameters are used for walking simulation. According to the static load simulation parameters, simulating the standing of the human body through a finite element model; and recording the deformation condition of the sole on the deformation sensing node. Carrying out human body walking simulation by using dynamic load simulation parameters through a finite element model; recording the dynamic deformation condition of the sole on the deformation sensing node; and analyzing the stress condition of the sole in the walking state, and considering the plantar force distribution change of the supporting phase and the swinging phase in the gait process. Correlating the predicted plantar support force data with a three-dimensional model of the foot; and determining the change rule of the foot shape under different stress states by establishing a mapping relation between the supporting force and the shape. And (5) making a depth correction strategy, and considering the deformation condition of the sole in the standing and walking processes. And carrying out depth correction on the three-dimensional model of the foot according to the established correction strategy.

The strategy for making depth correction is as follows:

Arch region: when standing, the following steps: increasing the depth of the arch region to simulate natural bending of the arch while standing; when walking: according to gait analysis, the arch may slightly rise during the supporting phase of walking, thus requiring a corresponding reduction in the depth of the arch region.

Heel area: when standing, the following steps: adding a modest depth to the heel area to simulate the loading pressure of the heel when standing; when walking: in the walking support phase, the heel may be subjected to greater pressure, requiring a further increase in the depth of the heel area.

Plantar edge region: when standing, the following steps: the edge area may sink slightly, requiring a modest reduction in depth of the edge area; when walking: considering the twisting of the plantar edge during walking, a local depth increase in the plantar edge area may be required.

Toe area: when standing, the following steps: moderately increasing the depth in the toe region to simulate the natural bending of the toe when standing; when walking: the advancing phase of walking, the toes may be subjected to more pressure, taking into account the further increase in depth in the toe area.

Overall balance: the overall balance of depth adjustment is ensured in the whole sole model so as to keep the coordination of the sole morphology consistent.

Preferably, the present invention further provides a three-dimensional human body sign data management system, configured to execute the three-dimensional human body sign data management method, including:

The human body 3D model equal proportion reconstruction module is used for acquiring human body 3D data through the 3D scanning equipment; performing physical sign model guidance on the 3D data of the human body so as to obtain a curved surface fitting 3D human body model; performing model calibration processing on the curved surface fitting 3D human body model and performing equal proportion reconstruction on the human body 3D model so as to acquire human body 3D model data;

the body composition acquisition module is used for carrying out multi-frequency impedance scanning on the tested person by utilizing the impedance measurement equipment so as to acquire multi-frequency resistance data; conducting three-dimensional tissue resistance conduction analysis on the multi-frequency resistance data so as to obtain three-dimensional tissue resistance conductivity parameters; generating a three-dimensional body composition spectrogram by utilizing the three-dimensional tissue resistivity conductivity parameters, thereby acquiring three-dimensional body composition spectrogram data;

The physical sign data analysis module is used for correcting the Cobb angle predicted value according to the three-dimensional body composition spectrogram data and evaluating the scoliosis risk level so as to acquire scoliosis risk level evaluation data; performing foot morphology 3D depth analysis according to the three-dimensional body composition spectrogram data, so as to obtain foot morphology 3D depth analysis data; carrying out overall three-dimensional human body sign analysis according to the scoliosis risk level evaluation data and the foot morphology 3D depth analysis data, thereby obtaining three-dimensional human body sign analysis data;

The characteristic data encryption storage module is used for carrying out privacy protection coding processing on the three-dimensional human body sign analysis data so as to obtain privacy protection coding data; performing characteristic database storage processing on the three-dimensional human body sign analysis data according to the privacy protection coding data, so as to obtain sign encryption storage data;

The mobile terminal sign data management module is used for presenting the mobile terminal sign data to the sign encrypted storage data so as to acquire mobile terminal user sign data;

The background management system module is used for carrying out background management processing on the physical sign encrypted storage data so as to acquire background management interface data; and generating a paper report according to the background management interface data, thereby acquiring the paper report data of the body test and the foot test.

Referring to fig. 5, as an operation flow of the three-dimensional human body physical sign data management system platform, 3D human body information and body composition teaching data are collected by using a device, human body data and morphological characteristics are extracted by a server-side calling human body measurement algorithm, and then stored in a three-dimensional human body characteristic database; the three-dimensional human body characteristic database can be used by a user mobile terminal management physical sign data flow or a back terminal management physical sign data flow, and a user WeChat or a mobile phone logs in the user mobile terminal management physical sign data flow, so that a three-dimensional physical sign data report of the user mobile terminal management physical sign data flow can be checked; and logging in through a background account in the process of managing the sign data by the back-end management end, and managing and checking the three-dimensional sign data of the user measured by the authorization equipment.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The three-dimensional human body sign data management method is characterized by comprising the following steps of:

Step S3: cobb angle predicted value correction is carried out according to three-dimensional body composition spectrogram data, scoliosis risk level assessment is carried out, and therefore scoliosis risk and the like are obtained

Stage evaluation data; performing foot morphology 3D depth analysis according to the three-dimensional body composition spectrogram data, so as to obtain foot morphology 3D depth analysis data; carrying out overall three-dimensional human body sign analysis according to the scoliosis risk level evaluation data and the foot morphology 3D depth analysis data, thereby obtaining three-dimensional human body sign analysis data;

2. The three-dimensional human body sign data management method according to claim 1, wherein the step S1 comprises the steps of:

3. The three-dimensional human body sign data management method according to claim 1, wherein the step S2 comprises the steps of:

4. The method for three-dimensional human body sign data management according to claim 3, wherein the three-dimensional tissue resistivity and conductivity compensation in step S26 is specifically calculated according to the following formula:

5. The three-dimensional human body sign data management method according to claim 3, wherein step S25 comprises the steps of:

6. The three-dimensional human body sign data management method according to claim 1, wherein the step S3 comprises the steps of:

7. The three-dimensional human body sign data management method according to claim 6, wherein the step S32 comprises the steps of:

8. The method according to claim 7, wherein in step S323, the calculation of the lateral offset distance between the cervical vertebra and the coccyx is performed according to the following calculation formula:

9. The three-dimensional human body sign data management method according to claim 6, wherein step S33 comprises the steps of:

10. A three-dimensional human body sign data management system for performing the three-dimensional human body sign data management method of claim 1, the three-dimensional human body sign data management system comprising: