Method and device for measuring body age

Abstract

The invention is suitable for the technical field of data identification, and provides a body age measuring method and device, and the method comprises the steps: obtaining basic body data and current body composition data of a to-be-detected user; calculating the matching degree of the to-be-detected user in a matching library according to the basic body data and the current body composition data; and calculating the body age of the to-be-detected user according to the matching degree. According to the body age measuring method provided by the invention, the problem that the real age of the body cannot be comprehensively reflected due to the fact that the body condition is evaluated only by depending on single or a small number of indexes in the prior art can be effectively solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data recognition, and particularly relates to a method and device for measuring body age. Background Art

[0002] With the improvement of people's living standards and the enhancement of health awareness, more and more people begin to pay attention to their own physical conditions, especially by various health monitoring means to evaluate and improve their physical health. In this context, as an intelligent device widely used in home health management, the body fat scale has become an important tool for many people to daily monitor their physical health data due to its convenience, speed, and non-invasive characteristics.

[0003] Traditional body fat scales mainly rely on measuring basic body components of the human body, such as body weight, body fat percentage, muscle mass, etc. However, although these data can provide a certain degree of health information, they cannot accurately reflect the actual health age of the human body, especially the difference between the body and the actual age. In fact, the "health age" of the human body is not only determined by the actual age, but also closely related to various components of the body, metabolic capacity and other factors. In the existing technologies, most health management methods only evaluate according to basic parameters such as age and gender, or simply rely on a certain type of body data, and cannot comprehensively measure the true state of the body. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method and device for measuring body age to solve the technical problem that the traditional technology cannot comprehensively measure the true state of the body.

[0005] The first aspect of the embodiments of the present invention provides a method for measuring body age, and the method for measuring body age includes: Obtain the basic body data and current body composition data of the user to be detected; the basic body data includes actual age, gender, height, and weight, and the current body composition data includes body fat percentage, muscle mass, water content, total fat, bone content, visceral fat content, and basal metabolic rate; Calculate the matching degree of the user to be detected in the matching library according to the basic body data and the current body composition data; Calculate the body age of the user to be detected according to the matching degree.

[0006] Further, the step of calculating the matching degree of the user to be detected in the matching library according to the basic body data and the current body composition data includes: Construct the actual age, gender, height, and weight in the basic body data into a first body feature vector; Construct the body fat percentage, muscle mass, basal metabolic rate, water content, total fat, bone content, visceral fat content, and basal metabolic rate in the current body composition data into a second body feature vector; Obtain the first preset feature vector and the second preset feature vector corresponding to each of the multiple preset sample data in the matching library; Calculate the first similarity between the first body feature vector and the first preset feature vector; Calculate the second similarity between the second body feature vector and the second preset feature vector; Calculate the matching degree corresponding to each of the multiple preset sample data according to the first similarity and the second similarity corresponding to each of the multiple preset sample data.

[0007] Further, the step of calculating the matching degree corresponding to each of the multiple preset sample data according to the first similarity and the second similarity corresponding to each of the multiple preset sample data includes: If the first similarity is greater than the first threshold, multiply the first similarity by the first preset weight to obtain a first value; Multiply the second similarity by the second preset weight to obtain a second value; Add the first value and the second value to obtain the matching degree; If the first similarity is not greater than the first threshold, set the matching degree to 0.

[0008] Further, the step of calculating the body age of the user to be detected according to the matching degree includes: When the matching degree is greater than the second threshold, use the reference body age corresponding to the maximum matching degree as the body age of the user to be detected; When the matching degree is not greater than the second threshold, calculate the body age of the user to be detected through a preset model.

[0009] Further, the step of using the reference body age corresponding to the maximum matching degree as the body age of the user to be detected when the matching degree is greater than the second threshold includes: When the matching degree is greater than the second threshold, obtain the initial body age and multiple reference body composition data corresponding to the maximum matching degree; Subtract the reference body composition data corresponding to the same body composition data from the current body composition data to obtain a data difference; If the data difference is greater than the third threshold, use the reference body composition data corresponding to the data difference as the reference body composition data to be adjusted; Obtain the body age influence factor corresponding to the reference body composition data to be adjusted; Adjust the initial body age according to the reference body composition data to be adjusted, the current body composition data, and the body age influencing factor to obtain the reference body age; Use the reference body age as the body age of the user to be detected.

[0010] Further, the step of adjusting the initial body age according to the reference body composition data to be adjusted, the current body composition data, and the body age influencing factor to obtain the reference body age includes: Multiply the reference body composition data to be adjusted by the body age influencing factor to obtain a first age influence value; Multiply the current body composition data by the body age influencing factor to obtain a second age influence value; Subtract the second age influence value from the first age influence value to obtain an age adjustment value; Subtract the age adjustment value from the initial body age to obtain the reference body age.

[0011] Further, the step of calculating the body age of the user to be detected through a preset model when the matching degree is not greater than the first threshold includes: Input the body fat percentage, muscle mass, water content, total fat, bone content, visceral fat content, and basal metabolic rate into the preset model to obtain the body age output by the preset model; The preset model is: ; ; where represents the body age, represents the actual age, represents the body fat percentage, represents the muscle mass, represents the basal metabolic rate, represents the water content, represents the total fat, represents the bone content, represents the visceral fat content, , , , , , and represent the tuning factors of each body composition data item, represents the tuning factor of the interaction term, represents the i-th current body composition data is the non-linear function of represents the i-th current body composition data, represents the standard value corresponding to the current body composition data, and represents the weight factor.

[0012] The second aspect of the embodiments of the present invention provides a device for measuring body age, including: An acquisition unit, configured to acquire the basic body data and the current body composition data of the user to be detected; the basic body data includes the actual age, gender, height, and weight, and the current body composition data includes the body fat percentage, muscle mass, water content, total fat, bone content, visceral fat content, and basal metabolic rate; A first calculation unit, configured to calculate the matching degree of the user to be detected in the matching library according to the basic body data and the current body composition data; A second calculation unit, configured to calculate the body age of the user to be detected according to the matching degree.

[0013] The third aspect of the embodiments of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the method for measuring body age described in the first aspect are implemented.

[0014] The fourth aspect of the embodiments of the present invention provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps in the method for measuring body age described in the first aspect are implemented.

[0015] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows: By comprehensively acquiring the basic body data of the user (including the actual age, gender, height, and weight) and the current body composition data (including the body fat percentage, muscle mass, water content, total fat, bone content, visceral fat content, and basal metabolic rate), and calculating the matching degree in combination with the data in the matching library, the present invention can evaluate the body state of the user from multiple dimensions. This method calculates the similarity between the user's current body state and a large number of standard samples, and then obtains a more representative "body age". Compared with the traditional evaluation method based only on weight or body fat percentage, the measurement method provided by the present invention is more comprehensive and scientific, and can significantly improve the accuracy of body age evaluation and the ability to identify individual differences. A method for measuring body age provided by the present invention can effectively overcome the problem in the prior art that only a single or a small number of indicators are relied on to evaluate the body condition and the true age of the body cannot be fully reflected. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of related technologies. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 The figure shows a schematic flowchart of a method for measuring body age provided by the present invention; Figure 2 The figure shows a schematic diagram of a device for measuring body age provided by an embodiment of the present invention; Figure 3 The figure shows a schematic diagram of a terminal device provided by an embodiment of the present invention. Detailed implementation manners

[0018] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system architectures and technologies are presented to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0019] The embodiments of the present invention provide a method and device for measuring body age to solve the technical problem that the traditional technology cannot comprehensively measure the true state of the body.

[0020] First, the present invention provides a method for measuring body age. Please refer to Figure 1 , Figure 1 The figure shows a schematic flowchart of a method for measuring body age provided by the present invention. As Figure 1 shown, the method for measuring body age may include the following steps: Step 101: Obtain the basic body data and current body composition data of the user to be detected; the basic body data includes actual age, gender, height, and weight, and the current body composition data includes body fat percentage, muscle mass, water content, total fat, bone content, visceral fat content, and basal metabolic rate; First, it is necessary to collect the basic information and body composition information of the user, and these data are the basis for evaluating physical health. The current body composition data includes: Body fat percentage refers to the proportion of body fat to body weight. A higher body fat percentage usually means a higher disease risk. Muscle mass refers to the total weight of muscles in the body. The higher the muscle mass, the higher the metabolic rate usually is, which helps maintain health. Water content refers to the proportion of water in the body. The water level affects the body's electrolyte balance, blood circulation and other health conditions. Total fat refers to the total weight of fat in the body. This is an important indicator for measuring body fat content. Bone content refers to the weight of bones in the body. Bone density is crucial for physical health, especially for preventing problems such as osteoporosis. Visceral fat content refers to the fat content around the internal organs in the body. More visceral fat increases the risk of diseases such as diabetes and cardiovascular diseases. Basal metabolic rate (BMR) refers to the energy consumed by the body to maintain basic physiological functions at rest. A higher BMR usually means a higher metabolic level and more energy consumption.

[0021] Step 102: Calculate the matching degree of the user to be detected in the matching library according to the basic body data and the current body composition data; This process is to compare the collected basic data and body composition data with the "matching library", which contains a large number of health standards, body ages and body composition data of different groups such as age and gender.

[0022] By comparing the user's body data with the data in the matching library, the similarity between the user's physical health status and the normal standard or target status can be evaluated.

[0023] Specifically, step 102 specifically includes steps 1021 to 1026: Step 1021: Construct a first body feature vector from the actual age, gender, height and weight in the basic body data; In this step, first, the user's basic body data (actual age, gender, height and weight) needs to be converted into a "feature vector". A feature vector is a numerical vector used to represent the user's body characteristics for data analysis.

[0024] Actual age, gender, height and weight are important data closely related to an individual's health status. By converting these data into a vector, it is convenient for subsequent calculations and comparisons.

[0025] The actual age can be directly added as a numerical value to the vector. Gender is encoded using binary, for example, 1 for male and 0 for female, or one-hot encoding can be used for encoding. Height and weight can be directly added as numerical values to the vector.

[0026] For example, if the actual age of a certain user is 30 years old, the gender is female, the height is 165 cm, and the weight is 60 kg, then the first body feature vector can be expressed as: [30, 0, 165, 60] (where 0 represents female, 30 is the actual age, and 165 and 60 are the numerical values of height and weight).

[0027] Step 1022: Construct a second body feature vector from the body fat percentage, muscle mass, basal metabolic rate, water content, total fat, bone content, visceral fat content, and basal metabolic rate in the current body composition data; Next, it is necessary to convert the user's body composition data (body fat percentage, muscle mass, basal metabolic rate, water content, total fat, bone content, visceral fat content) into another feature vector. These data are related to the specific manifestations of physical health. After being converted into feature vectors, they can help compare and evaluate the user's physical health level.

[0028] Data such as body fat percentage, muscle mass, and basal metabolic rate are usually presented in the form of percentages or weight numerical values and can be directly converted into numerical values. The water content, total fat, bone content, visceral fat content, etc. can also be expressed in numerical form and added to the second feature vector.

[0029] For example, if a certain user has a body fat percentage of 25%, a muscle mass of 35 kg, a basal metabolic rate of 1500 kcal, a water content of 50%, a total fat of 20 kg, a bone content of 5 kg, and a visceral fat content of 10, the second body feature vector may be expressed as: [25, 35, 1500, 50, 20, 5, 10].

[0030] Step 1023: Obtain the first preset feature vector and the second preset feature vector corresponding to each of the multiple preset sample data in the matching library; In this step, it is necessary to obtain multiple preset sample data from a pre-prepared "matching library". Each sample data already contains the corresponding "first preset feature vector" and "second preset feature vector".

[0031] The matching library is a collection of a large number of pre-collected individual data, which can be sourced from healthy populations or typical data extracted from medical research. Each sample contains a first body feature vector and a second body feature vector similar to the above.

[0032] Step 1024: Calculate the first similarity between the first body feature vector and the first preset feature vector; The goal of this step is to calculate the similarity between the "first body feature vector" of the user to be detected and the "first preset feature vector" of each preset sample in the matching library. Similarity calculation methods include Euclidean Distance, Cosine Similarity, etc.

[0033] Step 1025: Calculate the second similarity between the second body feature vector and the second preset feature vector; Similar to the first similarity, the similarity calculation between the second body feature vector and the second preset feature vector is based on body composition data (such as body fat percentage, muscle mass, basal metabolic rate, etc.).

[0034] The second similarity can quantify the differences between the user to be detected and the samples in the matching library in terms of these body components, and can evaluate the similarity degree of these two vectors. For example, if the user and the sample are similar in terms of body fat percentage, muscle mass, etc., their second similarity is higher.

[0035] Step 1026: Calculate the matching degree corresponding to each of the multiple preset sample data according to the first similarity and the second similarity corresponding to each of the multiple preset sample data.

[0036] Finally, according to the calculated first similarity and second similarity, combined with the weighted average or other methods of these similarities, calculate the matching degree of each matching sample.

[0037] The matching degree is a comprehensive indicator that measures the similarity between the body features of the user to be detected and the preset samples in the matching library. By combining the first similarity and the second similarity, a final matching degree value can be obtained, which reflects the overall similarity between the user and the matching sample.

[0038] In the embodiments corresponding to steps 1021 to 1026, through this calculation process, the matching degree between the user to be detected and multiple preset samples can finally be obtained, thus providing data support for the subsequent calculation of body age.

[0039] Specifically, step 1026 specifically includes steps A1 to A4: Step A1: If the first similarity is greater than the first threshold, multiply the first similarity by the first preset weight to obtain a first value; Here, the first similarity is the similarity between the user to be detected and the preset sample in the matching library in terms of basic body data. In order to assign different weights to different similarities when calculating the matching degree, it is necessary to weight it with a first preset weight. The first preset weight is a value used to adjust the importance or contribution of the similarity of basic body data in the calculation of the matching degree. The setting of the weight reflects the importance of basic body data in evaluating physical health or age.

[0040] The first threshold is a preset critical value. If the first similarity is greater than this threshold, it indicates that the user to be detected and the matching sample have a high similarity in terms of basic body data, and it is worth further calculating the matching degree. If the first similarity is less than or equal to the first threshold, the subsequent calculation will not be carried out, and the matching degree will be directly set to 0.

[0041] Step A2: Multiply the second similarity by the second preset weight to obtain a second value; Similarly, the second similarity is the similarity between the user to be detected and the preset sample in the matching library in terms of body composition data (such as body fat percentage, muscle mass, basal metabolic rate, etc.). If the second similarity is high, it indicates that the user and the sample have a large similarity in terms of body composition, which usually also plays an important role in the assessment of body age.

[0042] The second preset weight is used to adjust the weight of the similarity of body composition data in the calculation of the matching degree. Generally, body composition data (such as body fat percentage, muscle mass, etc.) may be more direct in evaluating physical health than basic data, so a certain weight may also be assigned.

[0043] Step A3: Add the first value and the second value to obtain the matching degree; The matching degree is the comprehensive similarity between the user to be detected and a certain preset sample. In this solution, the matching degree is calculated by combining the weighted similarities of basic body data and body composition data. Adding the weighted similarities (the first value and the second value) means that the relative importance of both is incorporated into the final result.

[0044] Step A4: If the first similarity is not greater than the first threshold, set the matching degree to 0.

[0045] This part is used to handle the situation where the first similarity does not meet the requirements. When the first similarity is less than or equal to the first threshold, it indicates that the performance of the user to be detected in terms of basic body data is quite different from the preset sample, and at this time it is not worth further calculating the matching degree. Therefore, the matching degree is set to 0.

[0046] The significance of this rule: By setting a threshold, it is possible to avoid invalid matching of samples with excessive differences in the basic data, ensuring that the calculated matching degree is meaningful. If the similarity of the basic data is too low, the result of calculating the body composition similarity may also be inaccurate. Therefore, the matching degree is directly set to 0.

[0047] In the embodiments corresponding to steps A1 to A4, this process can help the system more accurately evaluate the health similarity between the user to be detected and the sample, and ultimately provide a more reliable basis for calculating the body age of the subsequent body through the matching degree.

[0048] Step 103: Calculate the body age of the user to be detected according to the matching degree.

[0049] Body age refers to how an individual's physiological state compares to that of the same age group. If a user's body composition (such as body fat percentage, muscle mass, etc.) performs better than the standard for their actual age, their body age may be lower than their actual age; conversely, if their physical health is poor, their body age may be higher than their actual age.

[0050] For example, if a 30-year-old person has a body fat percentage of a 40-year-old and muscle mass below the normal range, the calculated body age may be greater than 30 years, reflecting the actual situation of their physical health status.

[0051] Specifically, step 103 specifically includes steps 1031 to 1032: Step 1031: When the matching degree is greater than the second threshold, use the reference body age corresponding to the maximum matching degree as the body age of the user to be detected; If the matching degree is high enough, that is, greater than the preset second threshold, it is considered that the body state of the user to be detected is highly similar to that of a certain sample.

[0052] Specifically, step 1031 specifically includes steps B1 to B6: Step B1: When the matching degree is greater than the second threshold, obtain the initial body age corresponding to the maximum matching degree and multiple reference body composition data; When the matching degree of the user to be detected is greater than the second threshold, it indicates that the similarity between the user to be detected and the matching sample is relatively high. Therefore, the system will select the sample with the highest similarity to the user to be detected and obtain the initial body age of this sample (that is, the reference body age of this sample) and multiple reference body composition data (such as body fat percentage, muscle mass, bone density, etc.).

[0053] Maximum matching degree: Corresponding to the sample that is most similar to the user to be detected, its reference body age and body composition data can best represent the body state of the user to be detected.

[0054] Initial body age: Before any adjustment is made, this value directly comes from the sample most similar to the user to be detected.

[0055] Reference body composition data: Includes the body composition of the sample (such as body fat percentage, muscle mass, bone density, etc.), and these data help to precisely adjust the body age.

[0056] Step B2: Subtract the reference body composition data corresponding to the same body composition data from the current body composition data to obtain a data difference. In this part, the system compares the body composition data of the selected sample with the current body composition data of the user to be detected. Specifically, the system calculates the difference for the same type of body composition (such as body fat percentage, muscle mass, etc.).

[0057] Step B3: If the data difference is greater than the third threshold, then use the reference body composition data corresponding to the data difference as the reference body composition data to be adjusted. If the difference is greater than the third threshold, the system will mark the body composition data as the reference body composition data to be adjusted.

[0058] Step B4: Obtain the body age impact factor corresponding to the reference body composition data to be adjusted. In this step, the system obtains a "body age impact factor" based on the body composition data with a large difference. This impact factor is a numerical value representing the degree of influence of a specific body composition (such as body fat percentage or muscle mass) on the body age.

[0059] The body age impact factor is calculated through statistical analysis or model calculation, indicating how the change in a certain body composition affects the body age. For example, a 1% increase in body fat percentage may increase the body age by 1 year.

[0060] Step B5: Adjust the initial body age according to the reference body composition data to be adjusted, the current body composition data, and the body age impact factor to obtain the reference body age. This adjustment process will appropriately correct the initial body age according to the differences in body composition to obtain a more accurate reference body age.

[0061] Specifically, step B5 specifically includes steps B51 to B54: Step B51: Multiply the reference body composition data to be adjusted by the body age impact factor to obtain a first age impact value. By multiplying the reference body composition data to be adjusted by the impact factor, a "first age impact value" can be obtained, which represents the adjustment amount of the body composition to be adjusted on the body age.

[0062] Step B52: Multiply the current body composition data by the body age influencing factor to obtain a second age influencing value; Multiply the current body composition data by the body age influencing factor to obtain a "second age influencing value", which represents the influence of the current body composition of the user to be detected on the body age.

[0063] Step B53: Subtract the first age influencing value from the second age influencing value to obtain an age adjustment value; In this step, the system subtracts the calculated first age influencing value from the second age influencing value to obtain an "age adjustment value". The first age influencing value and the second age influencing value reflect the influence of the reference body composition data and the current body composition data on the body age. The purpose of subtraction is to measure the difference between the current body composition of the user to be detected and the reference body composition, and then adjust the initial body age.

[0064] Step B54: Subtract the age adjustment value from the initial body age to obtain the reference body age.

[0065] Finally, the system will adjust the initial body age according to the "age adjustment value" to obtain the final reference body age. The initial body age is a preliminary body age assessment based on the sample with the highest matching degree, and the difference adjustment has not been considered yet. The age adjustment value is the amount of body age adjustment obtained by comparing the reference body composition and the current body composition. By subtracting the age adjustment value from the initial body age, the reference body age can be obtained, which can more accurately reflect the actual physical condition of the user to be detected.

[0066] In the embodiments corresponding to steps B51 to B54, this process mainly calculates the adjustment value of the body composition difference by comparing the body composition data of the user to be detected and the reference sample using the body age influencing factor, and finally adjusts the initial body age to obtain a more accurate reference body age. This method provides a fine adjustment mechanism, which can ensure that the final body age is more in line with the actual physical state of the user while considering the influence of different body compositions.

[0067] Step B6: Use the reference body age as the body age of the user to be detected.

[0068] The system will use the adjusted reference body age as the final body age result of the user to be detected. This body age can better reflect the true physical condition of the user to be detected because it has been adjusted according to the difference from the sample with the highest matching degree.

[0069] In the embodiments corresponding to steps B1 to B6, the goal of this implementation solution is to obtain a more accurate physical age assessment by comparing the differences in body composition data between the user to be detected and the reference sample, and adjusting the initial physical age based on these differences. Through these steps, the system can find a balance between accuracy and flexibility to ensure that the final physical age obtained is more in line with the actual health status of the user to be detected.

[0070] Step 1032: When the matching degree is not greater than the second threshold, calculate the physical age of the user to be detected through a preset model.

[0071] If the matching degree of the user to be detected is lower than or equal to the second threshold, it means that the physical characteristics of the user to be detected have a low similarity with the samples in the matching library. At this time, the physical age cannot be directly obtained through the reference physical age. Therefore, the system will use a preset model to calculate the physical age of the user to be detected.

[0072] The preset model is a pre-trained model that can predict the physical age based on the user's body data (such as basic body data, body composition data, etc.).

[0073] Specifically, step 1032 specifically includes: Input the body fat percentage, muscle mass, water content, total fat, bone content, visceral fat content, and basal metabolic rate into the preset model to obtain the physical age output by the preset model; The preset model is: ; ; where represents the physical age, represents the actual age, represents the body fat percentage, represents the muscle mass, represents the basal metabolic rate, represents the water content, represents the total fat, represents the bone content, represents the visceral fat content, 、 、 、 、 、 and represent the tuning factors for each body composition data item, represents the tuning factor for the interaction term, represents the i-th current body composition data of the non-linear function, represents the i-th current body composition data, represents the standard value corresponding to the current body composition data, and represents the weight factor. The design of the preset model is based on a multi-dimensional, non-linear model, aiming to comprehensively consider various body composition data (such as body fat percentage, muscle mass, water content, etc.) and their complex interrelationships, so as to accurately calculate the body age.

[0074] Each current body composition data is fed into a non-linear function to amplify the asymmetric impact of its change on the body age. Handle the normalization problem of different data scales. Each component term is processed through a non-linear function, aiming to transform the relationship between each component and the body age into a mathematical form more suitable for physiological changes. The logarithmic function is used to process variables with different dimensions and scales (such as body fat percentage and muscle mass), avoiding certain variables from dominating the calculation due to excessive order of magnitude. The impact of "deviation from the health standard" is asymmetrically increased through the square term. For example, when the body fat percentage exceeds the healthy level, it will have a greater impact on the body age. Use to quantify the relative deviation of each body component from its healthy reference value. The greater the deviation, the more significant the impact of this item on the body age. The cross-term is designed to capture the interactions between different body components, that is: Combination of body fat percentage and visceral fat (VF): The body fat percentage and visceral fat are closely related. Especially in individuals with high visceral fat, the ratio of fat to muscle will exacerbate the negative impact on physical health.

[0075] Ratio of total fat to water: Excessive fat will affect the body's water balance. An increase in fat may lead to edema or dehydration, thus accelerating body aging.

[0076] This part of the interaction is to enhance the expression ability of the model, enabling it to accurately simulate the complex physiological connections between various components in the human body.

[0077] In this formula, the tuning factors , , , , , , , and are the key parts that are adaptively adjusted according to the individual's specific physiological characteristics, gender, age, activity level, etc. The functions of these tuning factors include: Personalized adjustment: Automatically adjust the impact weight of each component on biological age according to an individual's gender, age, health status, etc. For example, the fat distribution, muscle mass, and metabolic characteristics of men and women are different, so different tuning factors are required.

[0078] Optimize the impact of body components: Through tuning factors, the preset model can accurately evaluate the impact of each body component on an individual, avoiding over-simplifying the judgment of physical health.

[0079] Since the relationship between each body component and components is modeled through complex non-linear functions and interaction terms, this preset model can accurately reflect the actual health status and physiological age of the body. By introducing tuning factors, the preset model can perform personalized adjustments for different individuals to adapt to the influence of factors such as different ages, genders, and lifestyles. Not only the impact of a single component, the preset model also takes into account the interaction relationship between multiple body components and further optimizes the calculation of biological age through health markers such as metabolism and bone density.

[0080] In the embodiments corresponding to steps 1031 to 1032, in this way, the system can provide a more accurate biological age assessment according to different situations, making use of the advantages of highly matched samples and ensuring the rationality of the assessment results through the model in the case of low matching degree.

[0081] It should be noted that since the sample data in the matching library is biological age data measured by precise instruments, it has a high advantage in data accuracy. The accuracy of the preset model is lower than that of the matching library. Therefore, in this embodiment, the matching library is preferentially used to calculate the biological age. When no highly similar sample data can be matched in the matching library, the preset model is then used to calculate the biological age to ensure the calculation accuracy to the greatest extent.

[0082] In the embodiments corresponding to steps 101 to 103, by comprehensively obtaining the user's basic body data (including actual age, gender, height, and weight) and current body component data (including body fat percentage, muscle mass, water content, total fat, bone content, visceral fat content, and basal metabolic rate), and calculating the matching degree in combination with the data in the matching library, the present invention can evaluate the user's body state in multiple dimensions. This method calculates the similarity between the user's current body state and a large number of standard samples, and then obtains a more representative "biological age". Compared with the traditional assessment method based only on weight or body fat percentage, the measurement method provided by the present invention is more comprehensive and scientific, and can significantly improve the accuracy of biological age assessment and the ability to identify individual differences. A method for measuring biological age provided by the present invention can effectively overcome the problem in the prior art that only single or a small number of indicators are relied on to evaluate the body condition and the true age of the body cannot be fully reflected.

[0083] As Figure 2 The present invention provides a device for measuring body age. Please refer to Figure 2 , Figure 2 , which shows a schematic diagram of a device for measuring body age provided by the present invention. As Figure 2 shown, a device for measuring body age includes: An acquisition unit 21, configured to acquire basic body data and current body composition data of a user to be detected; the basic body data includes actual age, gender, height, and weight, and the current body composition data includes body fat percentage, muscle mass, water content, total fat, bone content, visceral fat content, and basal metabolic rate; A first calculation unit 22, configured to calculate a matching degree of the user to be detected in a matching library according to the basic body data and the current body composition data; A second calculation unit 23, configured to calculate the body age of the user to be detected according to the matching degree.

[0084] For the device for measuring body age provided by the present invention, by comprehensively acquiring the user's basic body data (including actual age, gender, height, and weight) and current body composition data (including body fat percentage, muscle mass, water content, total fat, bone content, visceral fat content, and basal metabolic rate), and calculating the matching degree in combination with the data in the matching library, the present invention can evaluate the user's body state from multiple dimensions. This method calculates the similarity between the user's current body state and a large number of standard samples, and then obtains a more representative "body age". Compared with the traditional evaluation method based only on body weight or body fat percentage, the measurement method provided by the present invention is more comprehensive and scientific, and can significantly improve the accuracy of body age evaluation and the ability to identify individual differences. The method for measuring body age provided by the present invention can effectively overcome the problem in the prior art that only a single or a small number of indicators are relied on to evaluate the body condition and the true body age cannot be fully reflected. Figure 3 is a schematic diagram of a terminal device provided by an embodiment of the present invention. As Figure 3 shown, a terminal device 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as a program for measuring body age. When the processor 30 executes the computer program 32, the steps in the above-mentioned various embodiments of the method for measuring body age are implemented, such as Figure 1 the steps 101 to 103 shown. Alternatively, when the processor 30 executes the computer program 32, the functions of each unit in the above-mentioned device embodiments are implemented, such as Figure 2 the functions of the units shown.

[0085] Exemplarily, the computer program 32 can be divided into one or more units, which are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 32 in the terminal device 3. For example, the specific functions of the computer program 32 that can be divided into each unit are as follows: An acquisition unit, configured to acquire the basic body data and current body composition data of the user to be detected; the basic body data includes actual age, gender, height, and weight, and the current body composition data includes body fat percentage, muscle mass, water content, total fat, bone content, visceral fat content, and basal metabolic rate; A first calculation unit, configured to calculate the matching degree of the user to be detected in the matching library according to the basic body data and the current body composition data; A second calculation unit, configured to calculate the body age of the user to be detected according to the matching degree.

[0086] The terminal device includes, but is not limited to, the processor 30 and the memory 31. Those skilled in the art can understand that Figure 3 This is only an example of a terminal device 3, and does not constitute a limitation on a terminal device 3. It may include more or fewer components than shown in the figure, or combine some components, or different components. For example, the terminal device may further include input / output devices, network access devices, buses, etc.

[0087] The processor 30 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor, etc.

[0088] The memory 31 may be an internal storage unit of the terminal device 3, such as a hard disk or memory of the terminal device 3. The memory 31 may also be an external storage device of the terminal device 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the terminal device 3. Further, the memory 31 may also include both the internal storage unit and the external storage device of the terminal device 3. The memory 31 is used to store the computer program and other programs and data required by the roaming control device. The memory 31 may also be used to temporarily store the data that has been output or will be output.

[0089] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0090] It should be noted that for the information interaction, execution process, etc. between the above-mentioned device / units, since it is based on the same concept as the method embodiment of the present invention, for its specific functions and the technical effects brought, reference can be specifically made to the method embodiment part, and details are not described herein again.

[0091] Those skilled in the art can clearly understand that for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In practical applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit exists physically alone, or two or more units are integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present invention. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiment, and details are not described herein again.

[0092] The embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0093] An embodiment of the present invention provides a computer program product. When the computer program product runs on a mobile terminal, it enables the mobile terminal to execute the steps in the above-mentioned method embodiments when executed.

[0094] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-mentioned method embodiments of the present invention, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the photographing device / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disc, etc.

[0095] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0096] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0097] In the embodiments provided by the present invention, it should be understood that the disclosed device / network device and method can be implemented in other ways. For example, the device / network device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0098] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, and it may be located in one place or distributed across multiple network units.

[0099] It should be understood that when used in the specification and appended claims of the present invention, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0100] It should also be understood that the term "and / or" used in the specification and appended claims of the present invention refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0101] As used in the specification and appended claims of the present invention, the term "if" may be interpreted as "when", "once", "in response to determining", or "in response to detecting" depending on the context. Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be interpreted as meaning "once determined", "in response to determining", "once [the described condition or event] is detected", or "in response to detecting [the described condition or event]" depending on the context.

[0102] In addition, in the description of the specification and appended claims of the present invention, the terms "first", "second", "third", etc. are only used for differentiating descriptions and cannot be understood as indicating or implying relative importance.

[0103] Reference to "one embodiment" or "some embodiments" or the like described in the specification of the present invention means that a specific feature, structure, or characteristic described in connection with that embodiment is included in one or more embodiments of the present invention. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized.

[0104] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for measuring body age, characterized in that: The method for measuring the body age includes: Obtaining basic body data and current body composition data of the user to be tested; the basic body data includes actual age, gender, height and weight, and the current body composition data includes body fat percentage, muscle mass, water content, total fat, bone content, visceral fat content and basal metabolic rate; Calculating the matching degree of the user to be detected in the matching library according to the basic body data and the current body composition data; The physical age of the user to be detected is calculated according to the matching degree.

2. The method for measuring body age as claimed in claim 1, characterized in that: The step of calculating the matching degree of the user to be detected in the matching library according to the basic body data and the current body composition data comprises: Constructing the actual age, gender, height and weight in the basic body data into a first body feature vector; constructing the body fat percentage, muscle mass, basal metabolic rate, water content, total fat, bone content, visceral fat content and basal metabolic rate in the current body composition data into a second body feature vector; Obtaining a first preset feature vector and a second preset feature vector corresponding to each of a plurality of preset sample data in the matching library; Calculating a first similarity between the first body feature vector and the first preset feature vector; Calculating a second similarity between the second body feature vector and the second preset feature vector; The matching degree corresponding to each of the plurality of preset sample data is calculated according to the first similarity and the second similarity corresponding to each of the plurality of preset sample data.

3. The method for measuring body age as claimed in claim 2, characterized in that: The step of calculating the matching degree corresponding to each of the plurality of preset sample data according to the first similarity and the second similarity corresponding to each of the plurality of preset sample data comprises: If the first similarity is greater than a first threshold, multiplying the first similarity by a first preset weight to obtain a first value; Multiplying the second similarity by the second preset weight to obtain a second value; Adding the first value and the second value to obtain the matching degree; If the first similarity is not greater than a first threshold, the matching degree is set to 0.

4. The method for measuring body age as claimed in claim 1, characterized in that: The step of calculating the physical age of the user to be detected according to the matching degree comprises: When the matching degree is greater than a second threshold, taking the reference physical age corresponding to the maximum matching degree as the physical age of the user to be detected; When the matching degree is not greater than a second threshold, the physical age of the user to be detected is calculated using a preset model.

5. The method for measuring body age as claimed in claim 4, characterized in that: When the matching degree is greater than a second threshold, the step of using the reference physical age corresponding to the maximum matching degree as the physical age of the user to be detected includes: When the matching degree is greater than a second threshold, obtaining an initial body age and a plurality of reference body composition data corresponding to a maximum matching degree; Subtracting the reference body composition data and the current body composition data corresponding to the same body composition data to obtain a data difference; If the data difference is greater than a third threshold, the reference body composition data corresponding to the data difference is used as the reference body composition data to be adjusted; Obtaining a body age influence factor corresponding to the reference body composition data to be adjusted; According to the reference body composition data to be adjusted, the current body composition data and the body age influencing factor, adjusting the initial body age to obtain the reference body age; The reference body age is used as the body age of the user to be detected.

6. The method for measuring body age as claimed in claim 5, characterized in that: The step of adjusting the initial body age to obtain the reference body age according to the reference body composition data to be adjusted, the current body composition data and the body age influencing factor comprises: Multiplying the reference body composition data to be adjusted by the body age influence factor to obtain a first age influence value; Multiplying the current body composition data by the body age influence factor to obtain a second age influence value; Subtracting the first age impact value from the second age impact value to obtain an age adjustment value; The initial body age is subtracted from the age adjustment value to obtain the reference body age.

7. The method for measuring body age as claimed in claim 4, characterized in that: When the matching degree is not greater than the first threshold, the step of calculating the physical age of the user to be detected by using a preset model includes: Input body fat percentage, muscle mass, water content, total fat, bone content, visceral fat content and basal metabolic rate into a preset model to obtain the body age output by the preset model; The preset model is: ； ;in, Indicates body age, Indicates actual age, Indicates body fat percentage, indicates muscle mass, represents the basal metabolic rate, Indicates the moisture content, The total amount of fat, Indicates bone content, Indicates the visceral fat content, , , , , , and Represents the parameter adjustment factor of each body composition data item, represents the tuning factor of the interaction term, represents the nonlinear function of the i-th current body composition data, represents the i-th current body composition data, Indicates the standard value corresponding to the current body composition data. and Represents the weight factor.

8. A device for measuring body age, characterized in that: The device for measuring body age comprises: An acquisition unit, used to acquire basic body data and current body composition data of the user to be detected; the basic body data includes actual age, gender, height and weight, and the current body composition data includes body fat percentage, muscle mass, water content, total fat, bone content, visceral fat content and basal metabolic rate; A first calculation unit, configured to calculate a matching degree of the user to be detected in a matching library according to the basic body data and the current body composition data; The second calculation unit is used to calculate the physical age of the user to be detected according to the matching degree.

9. A terminal device, characterized in that: The terminal device includes: a memory, a processor, and a physical age measurement program stored in the memory and executable on the processor, wherein the physical age measurement program is configured to implement the steps in the physical age measurement method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps in the method for measuring physical age as claimed in any one of claims 1 to 7 are implemented.

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