Method and system for health intervention of diet structure based on physical data of obese children

Abstract

The present application relates to the technical field of data processing and strategy optimization, and particularly relates to a diet structure health intervention method and system based on physical data of obese children. The present application firstly acquires physical data and nutrition data and groups children; further acquires intervention synchronism of each physical data according to deviation characteristics of each physical data compared with corresponding preset physical standard and similarity of changes in time domain of deviation characteristics of all physical data; further adjusts the diet structure of the target child according to the difference in intervention synchronism of each physical data of the target child and the children in the same group at the current period, in combination with the difference between the nutrition data and the preset recommended diet structure. By analyzing the individualized differences in the physical angle and the diet intake angle of the children, the diet structure of the children is continuously adjusted, the diet intervention adaptability is improved, and the intervention effect is ensured.

Description

Technical Field

[0001] This invention relates to the field of data processing and strategy optimization technology, specifically to a method and system for dietary structure health intervention based on the physical condition data of obese children. Background Technology

[0002] With economic development, accelerated urbanization, and changing lifestyles, the incidence of childhood overweight and obesity is rising rapidly, directly impacting children's growth and development, motor skills, and mental health. Currently, real-time monitoring and dynamic analysis of obese children's physical condition data enable health interventions in their diets. By optimizing the proportions and quality of various foods and controlling total energy intake, nutritional balance can be achieved, thereby improving physical condition and preventing obesity and related diseases.

[0003] In existing technologies, dietary structure health intervention systems automatically group and generate dietary structure intervention plans based on real-time collected physical condition data. However, due to significant individual differences among children in energy consumption, nutrient metabolism, and food adaptability, the same dietary intervention plan may produce different effects on different children, and may even prevent some children from achieving the expected health improvements. Summary of the Invention

[0004] To address the technical problem of unsatisfactory dietary intervention effects due to individual differences in obese children, the present invention aims to provide a method and system for dietary structure health intervention based on the body composition data of obese children. The specific technical solution adopted is as follows:

[0005] A dietary health intervention method based on body composition data of obese children, the method comprising:

[0006] We obtained physical fitness data and nutritional data of food intake of obese children at different times, and grouped children with different degrees of obesity; we selected any child as the target child for individual analysis.

[0007] For the target child, the intervention synchronicity of each physical fitness data point is obtained based on the deviation characteristics of each physical fitness data point from the corresponding preset physical fitness standard and the similarity of the changes in the deviation characteristics of all physical fitness data points in the time domain; the intervention acceptance rate of the target child for each physical fitness data point in the current period is obtained based on the difference in the intervention synchronicity of each physical fitness data point between the target child and the children in the same group at the current period.

[0008] Based on the difference between the target child's current nutritional intake data and the preset recommended dietary structure, and in conjunction with the corresponding intervention acceptance rate, the target child's dietary structure is adjusted.

[0009] Furthermore, the method for obtaining the intervention synchronicity includes:

[0010] In the current period, a deviation coefficient is obtained based on the difference between each of the aforementioned physical fitness data and the preset physical fitness standard; the average of the deviation coefficients of all the aforementioned physical fitness data of the target child in the current period is taken as the overall deviation coefficient.

[0011] For each of the aforementioned physical fitness data, an intervention effect sequence for each of the aforementioned physical fitness data is constructed based on the ratio of the change in the deviation coefficient of the aforementioned physical fitness data in each period to the change in the deviation coefficient of the historical adjacent period; an overall intervention effect sequence for all of the aforementioned physical fitness data is constructed based on the ratio of the change in the overall deviation coefficient of the aforementioned physical fitness data in each period to the change in the overall deviation coefficient of the historical adjacent period.

[0012] The intervention synchronicity of each physical fitness data point is obtained by comparing the latest intervention effect sequence of each physical fitness data point with the overall intervention effect sequence.

[0013] Furthermore, the method for adjusting the dietary structure of the target child includes:

[0014] Both the nutritional data and the preset recommended dietary structure include the percentage of each nutrient component.

[0015] Based on the difference between the proportion of each nutrient consumed by the target child in the current period and the recommended proportion of each nutrient, the difference in the proportion of each nutrient is obtained; based on the difference in the proportion, it is determined whether the target child is following the preset recommended dietary structure in the current period.

[0016] Based on the target children's adherence to the preset recommended dietary structure and in conjunction with the corresponding intervention acceptance rate, the target children's dietary structure is adjusted.

[0017] Furthermore, the method for determining whether the target child follows the preset recommended dietary structure in the current period based on the percentage difference includes:

[0018] For any nutrient, if the difference in the proportion is greater than a preset threshold, it is determined that the target child's intake of the corresponding nutrient does not follow the preset recommended dietary structure.

[0019] Furthermore, the method for adjusting the dietary structure of the target child includes:

[0020] Select any one of the nutrients as the target nutrient; select any one of the aforementioned body constitution data as the target body constitution data;

[0021] When the target child's intake of the target nutrients follows the preset recommended dietary structure during the current period, a first adjustment coefficient is obtained based on the correlation between the time series of the differences in the proportions of the target nutrients and the time series of the intervention acceptance of the target physical condition data. In the current period, the proportions of the target nutrients are adjusted based on the intervention acceptance of the target physical condition data and the first adjustment coefficient to obtain an initial adjusted component proportion. Both the intervention acceptance and the nutrient proportions are positively correlated with the initial adjusted component proportion; the first adjustment coefficient is negatively correlated with the initial adjusted component proportion.

[0022] When the target child does not follow the preset recommended dietary structure in terms of the intake of the target nutrients during the current period, other foods with the same proportion of the target nutrients are recommended, and the proportion of the target nutrients in the current period is directly used as the final component proportion.

[0023] The final component percentage for each nutrient is obtained by combining the initial adjusted component percentages based on the recommended nutrient composition and all types of body composition data, with the final component percentages based on the nutrient composition that did not follow the recommendations.

[0024] Furthermore, the method for obtaining the intervention acceptance rate includes:

[0025] In the current period, the overall intervention synchronicity is obtained based on the overall characteristics of the intervention synchronicity of each physical fitness data of the target child in the same group of children; the intervention acceptance rate of each physical fitness data of the target child is obtained based on the difference between the intervention synchronicity of each physical fitness data of the target child and the corresponding overall intervention synchronicity.

[0026] Furthermore, the method for grouping children with different degrees of obesity includes:

[0027] The physical fitness data includes the BMI index, and children are grouped according to their different degrees of obesity based on the BMI index.

[0028] Furthermore, the DTW algorithm was used to analyze the similarity of changes between the intervention effect sequence and the overall intervention effect sequence.

[0029] Furthermore, the correlation between the time series of the proportion difference of the target nutrient and the time series of the intervention acceptance of the target body composition data was analyzed using Pearson correlation coefficient.

[0030] The present invention also proposes a dietary structure health intervention system based on the body composition data of obese children. The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the steps of the dietary structure health intervention method based on the body composition data of obese children.

[0031] The present invention has the following beneficial effects:

[0032] This invention first acquires physical condition data and nutritional data of obese children at different times to establish a foundation for data analysis. Considering the different characteristics of physical condition data changes among children with varying degrees of obesity, children with different degrees of obesity are grouped to more accurately analyze the effectiveness of dietary intervention. Furthermore, for the target children, the intervention synchronicity of each physical condition data point is obtained from the perspective of the synchronous changes in single and multiple physical condition factors in response to dietary intervention. This quantifies the individual characteristics of physical condition data changes, facilitating subsequent dietary adjustments. Further, based on the differences in the intervention synchronicity of each physical condition data point between the target children and their group members at the current time, the intervention acceptance rate of each physical condition data point for the target children at the current time is obtained from the perspective of individual physical condition differences. This assesses the individual's adaptation to the dietary intervention plan, providing a basis for subsequent dietary adjustments. Finally, based on the differences between the nutritional data of the target children at the current time and the preset recommended dietary structure, the differences in children's eating habits are analyzed. Combined with the corresponding intervention acceptance rate, the dietary structure of the target children is adjusted to improve the adaptability of the dietary intervention and ensure its effectiveness. Attached Figure Description

[0033] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 A flowchart illustrating a dietary structure health intervention method based on body mass data of obese children, provided in one embodiment of the present invention;

[0035] Figure 2 This is a flowchart illustrating a method for adjusting the dietary structure of a target child, as provided in one embodiment of the present invention. Detailed Implementation

[0036] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a dietary structure health intervention method and system based on the body composition data of obese children proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0038] The following description, in conjunction with the accompanying drawings, details a specific scheme for a dietary structure health intervention method and system based on the body composition data of obese children provided by this invention.

[0039] Please see Figure 1 The diagram illustrates a flowchart of a dietary structure health intervention method based on body mass data of obese children, according to an embodiment of the present invention, specifically including:

[0040] Step S1: Obtain physical condition data and nutritional data of food intake of obese children at different times, and group children with different degrees of obesity; select any child as the target child for individual analysis.

[0041] In one embodiment of the invention, daily monitoring is performed using an eight-electrode body fat scale. Measurements are taken every morning after the child wakes up, on an empty stomach and after urination, to minimize the impact of food and water intake on body composition data. During measurement, the child must stand barefoot on the scale, holding the electrode handles with both hands, to ensure that the eight-electrode technology can simultaneously measure the impedance values ​​of the limbs and torso, improving the accuracy of body composition analysis. The collected body composition data includes weight, body fat percentage, and waist circumference, and is combined with height data to obtain the BMI (Body Mass Index).

[0042] Meanwhile, considering that children and adolescents are in the school stage, their energy consumption on school days and rest days is different, but their daily activities and diet are relatively stable every week. In order to avoid short-term fluctuations in physical fitness data, we take one week as a period and obtain physical fitness data for one period based on the physical fitness data of each day within a week. Specifically, we take the average of the physical fitness data measured each day within a week (from Monday to Sunday) as the value of the physical fitness data for one period.

[0043] Furthermore, to improve data comparability and analytical accuracy, data cleaning is performed to remove measurement errors, including filling in missing values, eliminating outliers, and ensuring consistency in data format and units, thereby reducing the interference of non-physiological factors on the analysis results. Then, data normalization is performed, such as using maximum-minimum normalization, to convert indicators of different dimensions (such as weight, body fat percentage, BMI, etc.) into the same numerical range. The preprocessed data is archived and stored to establish a long-term physical fitness monitoring record, facilitating the tracking of changes in the physical fitness of obese children. Detailed records of daily diet and calorie intake are kept, and nutritional databases or websites are used to query the calories and nutrient composition of food, calculate total daily energy intake, and obtain nutritional data for ingested foods, which is stored synchronously with the physical fitness monitoring record.

[0044] In other embodiments of the present invention, the implementer may adjust the data acquisition frequency, the types of body composition data collected, and the length of the period corresponding to the body composition data; the method of using the eight-electrode body fat scale, the method of collecting body composition data, the data cleaning and preprocessing method, and the calories and nutritional components of food are all technical means well known to those skilled in the art, and will not be described in detail here.

[0045] Considering that children with different degrees of obesity face different difficulties in weight loss and have different characteristics of changes in body composition data, in order to more accurately analyze the effect of dietary structure intervention, children with different degrees of obesity were grouped and any one child was selected as the target child for individual analysis.

[0046] In one embodiment of the present invention, considering that the BMI index is an important basis for measuring the degree of obesity, children with different degrees of obesity are grouped according to the BMI index.

[0047] Specifically, according to the "Guidelines for the Diagnosis and Treatment of Obesity (2024 Edition)," a BMI of 28.0 kg / m² to less than 32.5 kg / m² is classified as mild obesity, 32.5 kg / m² to less than 37.5 kg / m² as moderate obesity, 37.5 kg / m² to less than 50 kg / m² as severe obesity, and 50 kg / m² or more as extremely severe obesity.

[0048] In another embodiment of the present invention, considering that obese children with similar physical fitness data have similar physical conditions, a physical fitness data vector is constructed from the physical fitness data before the start of dietary intervention. The K-means algorithm is used to cluster the physical fitness data vector, and the number of clusters is set to 4. Each cluster is a group, and the groups are grouped from the perspective of similar physical fitness data. The average BMI index of children in the cluster is used to distinguish different degrees of obesity. The higher the average BMI index, the higher the degree of obesity.

[0049] In another embodiment of the present invention, after grouping based on BMI index or clustering, further grouping is performed according to age and gender, grouping children with the same age, gender and degree of obesity into one group. More detailed grouping is more conducive to subsequent comparative analysis using the physical data of children in the same group.

[0050] It should be noted that the K-means algorithm is an existing technology and will not be described in detail here.

[0051] It should be noted that, in one embodiment of the present invention, the initial preset recommended dietary structure is set as follows: The "Dietary Guidelines for Obese Children and Adolescents (2024 Edition)" states that during weight loss in obese children and adolescents, dietary energy should be reduced by approximately 20% compared to the requirements of children and adolescents of normal weight. Therefore, when setting up the dietary intervention plan, the reduction ratios for mild, moderate, severe, and very severe cases are set as follows: 5%-10%, 7.5%-12.5%, 10%-15%, and 15%-20%, respectively. Setting the reduction ratio within a range is more conducive to food pairing; the dietary plan only needs to fall within the reduction ratio range, reducing the difficulty of food selection.

[0052] Based on Table 1.1 of the "Dietary Guidelines for Childhood and Adolescent Obesity (2024 Edition)," the recommended intake and energy requirements of various foods for children and adolescents aged 2 to 17 with normal weight are calculated. The intake and energy requirements of obese children are reduced according to the set reduction ratio based on their age. The preset recommended dietary structure for the target children is obtained. The specific dietary plan is adjusted and set in conjunction with the recipe examples in Appendix 3 of the "Dietary Guidelines for Childhood and Adolescent Obesity (2024 Edition)" and the existing food exchange table. It will not be elaborated further.

[0053] In other embodiments of the present invention, the implementer may adjust the reduction ratio as needed.

[0054] Step S2: For the target child, based on the deviation characteristics of each physical fitness data point from the corresponding preset physical fitness standard, and the similarity of the deviation characteristics of all physical fitness data points in the time domain, obtain the intervention synchronicity of each physical fitness data point; based on the difference in intervention synchronicity between the target child and other children in the same group at the current period, obtain the intervention acceptance rate of the target child for each physical fitness data point at the current period.

[0055] The goal of dietary intervention for obese children is to gradually restore various physical fitness data to normal. Therefore, it is necessary to monitor the deviation characteristics of each physical fitness data point from the corresponding preset physical fitness standard. Considering that different obese children may have different changes in physical fitness data due to factors such as their basal metabolism, daily activity level, and dietary habits, the similarity of the deviation characteristics of each physical fitness data point from the corresponding preset physical fitness standard to the overall deviation characteristics of all physical fitness data points in the time domain is used to obtain the intervention synchronicity of each physical fitness data point, quantify the changes in the physical fitness data of individual children, and facilitate subsequent dietary adjustments.

[0056] Preferably, in one embodiment of the present invention, since the physical condition data for each period is already fixed, whenever there is new data, only the newly added data is processed, and the historical data can be directly retrieved for analysis.

[0057] In the current period, a deviation coefficient is obtained based on the difference between each physical fitness data point and the preset physical fitness standard; the difference between the physical fitness data point and the preset physical fitness standard is positively correlated with the deviation coefficient.

[0058] As an example, the physical fitness data standard of normal children is taken as the preset physical fitness standard. For the range-type standard, the median value is taken as the preset physical fitness standard. The difference between the physical fitness data and the preset physical fitness standard is measured by the absolute value of the difference. The absolute value of the difference between each physical fitness data and the preset physical fitness standard is used as the deviation coefficient, which represents the deviation characteristics of the physical fitness data from the corresponding preset physical fitness standard.

[0059] The deviation coefficients of all physical fitness data are combined using a weighted summation method. Based on existing technology, BMI, which integrates height and weight, is the primary and universal standard for obesity screening and classification, with the highest weight (e.g., 0.4). Body fat percentage is one of the important indicators for judging obesity, with the second highest weight (e.g., 0.3). Waist circumference is the simplest and most practical indicator for assessing central obesity (abdominal obesity), serving as a supplement to BMI and body fat percentage, with the third highest weight (e.g., 0.2). Weight is a raw and absolute measurement, serving as a basic but auxiliary indicator, with the lowest weight (e.g., 0.1). The deviation coefficients of all physical fitness data of the target child at the current period are weighted and summed with corresponding preset weights. The weighted summation result serves as the overall deviation coefficient, representing the overall deviation characteristics of all physical fitness data. The preset weights for BMI, body fat percentage, waist circumference, and weight are 0.4, 0.3, 0.2, and 0.1, respectively.

[0060] It should be noted that in other embodiments of the present invention, the implementer may adjust the preset weighting weights as needed, but the relative size of the weights must be satisfied, that is, BMI has the highest weight, body fat percentage has the second highest weight, waist circumference has the third highest weight, and weight has the lowest weight.

[0061] Considering that the deviation coefficient of the physical fitness data represents the abnormal characteristics of the physical fitness data, and the change of the deviation coefficient represents the effect of dietary intervention, for each physical fitness data, an intervention effect sequence for each physical fitness data is constructed based on the change ratio of the deviation coefficient of the physical fitness data in each period to the deviation coefficient of adjacent historical periods; similarly, an overall intervention effect sequence for all physical fitness data is constructed based on the change ratio of the overall deviation coefficient of each period to the overall deviation coefficient of adjacent historical periods.

[0062] As an example, the difference between the deviation coefficient of each physical fitness data point and the deviation coefficient of adjacent historical periods is used as the numerator, the sum of the deviation coefficient of adjacent historical periods and the preset positive parameter 0.05 (divided by zero) is used as the denominator, and the ratio of the fractions is used as the intervention effect coefficient of the corresponding physical fitness data point. The intervention effect sequence of each physical fitness data point is constructed in chronological order.

[0063] The difference between the overall deviation coefficient and the overall deviation coefficient of adjacent historical periods is used as the numerator, and the sum of the overall deviation coefficient of adjacent historical periods and the preset positive parameter 0.05 (divided by zero) is used as the denominator. The ratio of the fractions is used as the overall intervention effect coefficient for all types of physical fitness data, and the overall intervention effect sequence is constructed in chronological order.

[0064] Considering that the more similar the changes in data of a single dimension are to the changes in data of all dimensions, the more synchronized the changes are, and the higher the synchronicity of dietary structure intervention, the intervention synchronicity of each physical condition data is obtained by comparing the changes in the latest intervention effect sequence of each physical condition data with the overall intervention effect sequence; the similarity of the changes in the intervention effect sequence with the overall intervention effect sequence is positively correlated with the intervention synchronicity.

[0065] As an example, considering that the DTW algorithm is often used to measure the similarity of changes in time series data sequences, the DTW algorithm is used to analyze the similarity of changes between the intervention effect sequence and the overall intervention effect sequence. Specifically, the DTW distance between the intervention effect sequence of each physical fitness data item and the overall intervention effect sequence is used as the independent variable. After negative correlation mapping through the exp(-x) function, the mapping result is used as the intervention synchronicity of each physical fitness data item, thus obtaining the intervention synchronicity of the target child's various physical fitness data items each week, where x represents the independent variable.

[0066] If, after dietary intervention for obese children, a particular indicator does not change in sync with the overall body composition data, it indicates that the intervention was not very effective for that indicator, and the lower the synchronicity of the intervention, the better. For example, waist circumference may not change significantly, while the child's overall body composition data returns to the normal range.

[0067] It should be noted that the DTW algorithm is an existing technology; preset physical fitness standards can be obtained by consulting the official website of the National Health Commission, the "Dietary Guidelines for Childhood and Adolescent Obesity", etc., to obtain the standard for each physical fitness data, and determine the preset physical fitness standard corresponding to each physical fitness data in combination with the age and gender of the target child, which will not be elaborated here.

[0068] Considering the individual differences in how children's physical fitness data are affected by dietary interventions, we further analyzed the differences in the synchronicity of interventions for each physical fitness data point between the target child and other children in the same group at the current period. This allowed us to obtain the target child's acceptance of the intervention for each physical fitness data point at the current period, assess the individual's adaptation to the dietary intervention plan, and thus achieve more precise and personalized dietary adjustments, providing a basis for subsequent adjustments to the dietary structure.

[0069] The smaller the difference in the synchronicity of intervention for each physical fitness data point between the target child and the children in the same group at the current period, the more consistent the response to the dietary structure intervention, the smaller the individual differences in the intervention for the corresponding physical fitness data, the more acceptable the target child is, and the greater the acceptance of the corresponding intervention.

[0070] Preferably, in one embodiment of the present invention, firstly, in the current period, the overall intervention synchronicity is obtained based on the overall characteristics of the intervention synchronicity of each physical fitness data of the target child in the same group of children, representing the overall intervention synchronicity characteristics of each physical fitness data of the same group of children, which is convenient for subsequent comparison of intervention synchronicity differences;

[0071] Based on the difference between the intervention synchronicity of each physical fitness data point of the target child and the corresponding overall intervention synchronicity, the intervention acceptance rate of each physical fitness data point of the target child is obtained.

[0072] As an example, the average value of the intervention synchronicity of each physical fitness data of the target child in the same group of children is taken as the overall intervention synchronicity. The absolute value of the difference between the intervention synchronicity of the target child's physical fitness data and the corresponding overall intervention synchronicity is taken as the independent variable. After negative correlation mapping through the exp(-x) function, the mapping result is taken as the intervention acceptance of each physical fitness data.

[0073] As another example, the mean, mode, and median of each physical fitness data point of the target child in the same group of children are weighted and summed with weights of 0.5, 0.25, and 0.25, respectively. The weighted sum is used as the overall intervention synchronicity, representing the overall characteristics of intervention synchronicity.

[0074] Step S3: Based on the difference between the target child's current nutritional intake data and the preset recommended dietary structure, and in combination with the corresponding intervention acceptance, adjust the target child's dietary structure.

[0075] When dietary interventions fail to produce satisfactory results for children's physical indicators, it doesn't necessarily mean the recommended diet is flawed; rather, it's related to the child's actual dietary intake. Since obese children receive their food from diverse sources, a standardized diet cannot be universally applied. Therefore, there may be discrepancies between a child's actual and recommended dietary intake. If there's a significant difference between the actual and recommended proportions of nutrients in a child's diet, it indicates that the poor intervention effect for the most strongly correlated nutrient is due to implementation issues. Furthermore, intervention acceptance represents the target child's level of acceptance of the pre-set recommended dietary structure based on their current nutritional intake and the discrepancy with the pre-set recommended dietary structure, along with the corresponding intervention acceptance rate, to adjust the target child's diet.

[0076] Preferably, in one embodiment of the present invention, considering that the proportion of nutrients reflects the relative contribution of various nutrients to the total intake, and that the absolute intake has been limited when setting the dietary structure intervention plan, and that the proportion of nutrients is more conducive to analyzing the dietary structure due to the complexity and variety of recipes, so as to more accurately reflect the dietary structure deviation; based on this, both the nutritional data and the preset recommended dietary structure include the proportion of each nutrient.

[0077] Based on the difference between the proportion of each nutrient consumed by the target child in the current period and the recommended proportion of each nutrient, the difference in the proportion of each nutrient is obtained; based on the difference in proportion, it is determined whether the target child is following the preset recommended dietary structure in the current period.

[0078] Considering that a large difference in the proportion indicates that the target child is not following the recommended plan, for any nutrient, if the difference in the proportion is greater than the preset judgment threshold, it is determined that the target child's intake of the corresponding nutrient is not following the preset recommended dietary structure.

[0079] In one embodiment of the present invention, the absolute value of the difference between the proportion of each nutrient consumed and the recommended proportion of each nutrient is divided by the recommended proportion of each nutrient. The quotient is then linearly normalized in the corresponding data dimension, and the normalized result is used as the proportion difference. Since the proportion difference after normalization is between 0 and 1, and the median value is the average distribution level, the median value is used as the threshold, and the preset judgment threshold is 0.5.

[0080] In another embodiment of the invention, considering that different nutrients have an acceptable range of macronutrient distribution, such as carbohydrates: 50%-65%, protein: 10%-15%, and fat: 20%-30%, the median value is taken as the recommended percentage. Taking fat as an example, the recommended percentage is 25%, and the allowable absolute deviation is... 5%, representing the percentage of the recommended deviation that can be tolerated, is used as the maximum permissible percentage difference. The preset threshold for judging fat is... The ratio is 0.2; similarly, carbohydrates are 0.13 and protein is 0.2. The absolute value of the difference between the proportion of each nutrient consumed and the recommended proportion is used as the numerator, and the recommended proportion is used as the denominator. The ratio of the fractions is used as the actual proportion difference. When the proportion difference exceeds the corresponding preset judgment threshold, it is determined that the corresponding nutrient does not follow the recommendation.

[0081] Once it is determined whether children follow the pre-set recommended dietary structure, their dietary structure can be adjusted according to the situation. Based on the children's compliance with the pre-set recommended dietary structure and the corresponding intervention acceptance, the children's dietary structure can be adjusted.

[0082] Please refer to the detailed process. Figure 2 The diagram illustrates a flowchart of a method for adjusting the dietary structure of a target child according to an embodiment of the present invention, specifically including:

[0083] Step S301: Select any nutrient as the target nutrient; select any body constitution data as the target body constitution data.

[0084] First, select the target nutrient components and target body constitution data to facilitate analysis by combining them one by one.

[0085] Step S302: When the target child's intake of target nutrients follows the preset recommended dietary structure in the current period, a first adjustment coefficient is obtained based on the correlation between the time series of the difference in the proportion of target nutrients and the time series of the intervention acceptance of the target physical condition data. In the current period, the proportion of target nutrients is adjusted according to the intervention acceptance of the target physical condition data and the first adjustment coefficient to obtain the initial adjusted component proportion.

[0086] Considering the strong correlation between the difference in the proportion of target nutrients and the intervention acceptance of target physical condition data, it indicates that the target nutrients have a greater impact on the target physical condition data, so the first adjustment coefficient is obtained accordingly. At the same time, the lower the intervention acceptance, the lower the acceptance of the target children's target physical condition data for dietary intervention of target nutrients, and the more necessary it is to further reduce the proportion of target nutrients to achieve the effect of dietary intervention. Since the adjustment is based on the existing proportion of nutrients, both the intervention acceptance and the proportion of nutrients are positively correlated with the initial proportion of adjusted components; the first adjustment coefficient is negatively correlated with the initial proportion of adjusted components.

[0087] As an example, considering that the Pearson correlation coefficient can measure the correlation between data series, the correlation between the time series of the difference in the proportion of target nutrient components and the time series of intervention acceptance of target body composition data is analyzed using the Pearson correlation coefficient. Furthermore, since a larger difference in proportion is associated with a smaller intervention acceptance, it better indicates that the intervention acceptance of target body composition data is influenced by the difference in the proportion of target nutrient components. Therefore, the Pearson correlation coefficient between the time series of the proportion difference and the time series of intervention acceptance of target body composition data is obtained as the independent variable, and then analyzed using… The function is negatively correlated and normalized, and the normalized result is used as the first adjustment coefficient;

[0088] The formulas for calculating the initial adjustment component percentages include: ;

[0089] in, This represents the initial adjustment component ratio between the j-th target nutrient and the k-th target physical condition data for the i-th target child. This represents the percentage of the j-th target nutrient and the k-th target physique data for the i-th target child before adjustment in the current period. This represents an exponential function with the natural constant e as the base. This represents the intervention acceptance rate of the k-th target physical condition data for the i-th target child in the current period; The first adjustment coefficient represents the data of the j-th target nutrient component and the k-th target physical condition for the i-th target child.

[0090] In the initial formula for calculating the proportion of adjusted components, by... As independent variables, negative correlation mapping is performed using the exp(-x) function, and the results are then fused through multiplication. and The larger the first adjustment factor, the greater the impact of the target nutrient on the target physical condition data. Conversely, a lower intervention acceptance indicates a lower acceptance of the target child's target physical condition data by the dietary intervention based on the target nutrient, necessitating a further reduction in the proportion of the target nutrient to achieve the desired dietary intervention effect. The greater the reduction, the smaller the proportion of the initial adjustment component; among which and After normalization, the product is less than or equal to 1. It will not be a negative value.

[0091] It should be noted that the Pearson correlation coefficient is existing technology and will not be discussed further.

[0092] Step S303: When the target child does not follow the preset recommended dietary structure in terms of the target nutrient intake during the current period, other foods with the same nutrient content are recommended, and the nutrient content of the target child during the current period is directly used as the final component content.

[0093] If the child does not follow the target nutrient composition or the recommended nutrient composition percentage in the preset recommended dietary structure, it indicates that the unsatisfactory intervention effect may be due to the child's eating habits. In this case, maintain the existing nutrient composition percentage and recommend other foods instead to facilitate subsequent monitoring of the effectiveness of the dietary structure intervention.

[0094] Step S304: Based on the initial adjusted component proportions according to the recommended nutrients and all types of body constitution data, and combined with the final component proportions of nutrients that were not followed, obtain the final component proportions for each nutrient.

[0095] Considering that the recommended nutrient components are not followed and each body constitution data corresponds to an initial adjustment component ratio, it is necessary to comprehensively consider the overall adjustment of all body constitution data to determine the component ratio parameters that are more suitable for the overall body constitution; in addition, it is also necessary to ensure that the sum of the ratios of all nutrients obtained in the end is 1.

[0096] As an example, the average of the initial adjusted component percentages for following recommended nutrients and all types of body composition data is used as the final adjusted component percentage for following recommended nutrients. All final adjusted component percentages for following recommended nutrients are scaled proportionally so that the sum of the scaled final adjusted component percentages for all following recommended nutrients and the sum of the final component percentages for all non-following recommended nutrients equals 1.

[0097] For example, if the sum of the final component percentages for all non-recommended nutrients is 0.6, then the sum of the final adjusted component percentages for all compliant nutrients after scaling should be 0.4. Assuming the compliant nutrients are A and B, with corresponding final adjusted component percentages of 0.15 and 0.1, they should be scaled down to 0.24 and 0.16, respectively. The unadjusted component percentage for A might not even be 0.24, indicating that the reduction in B's component percentage is greater than that of A, and therefore requires a greater reduction in B's component percentage.

[0098] As another example, to improve the effectiveness of dietary intervention, the body composition data with the largest deviation coefficient and the initial adjusted component ratio corresponding to the recommended nutrient composition are selected as the final adjusted component ratio, thereby obtaining the final component ratio.

[0099] In another embodiment of the present invention, considering that children do not follow the recommended dietary structure when their intake of target nutrients does not conform to the recommended dietary structure, the proportion of target nutrients can be finely adjusted to avoid a single adjustment being too drastic and affecting children's dietary acceptance, and to guide their nutrient intake toward the recommended structure, which is more gentle and adaptable.

[0100] Based on this, implementers can also directly use the proportion of the target nutrients in the current period as the final adjusted proportion when the target child does not follow the preset recommended dietary structure. Then, they can further obtain the final adjusted proportion of the recommended nutrients, use the final adjusted proportion of each nutrient as the numerator, the sum of the final adjusted proportions of all nutrients as the denominator, and the ratio of the fractions as the final proportion of each nutrient.

[0101] It should be noted that the nutritional components of food can be obtained through the "Chinese Food Composition Table" or food composition databases or food composition query platforms, thereby obtaining the percentage of each nutrient.

[0102] The final component ratio incorporates the intervention acceptance of various physical data, as well as the deviation between the intake of target nutrients and the preset recommended structure. This provides more data support for relevant personnel, enabling the intervention plan to be fine-tuned based on real-time data in each cycle, forming a closed-loop feedback mechanism. This promotes the continuous improvement of the dietary intervention plan, constantly adapts to the individual differences of different children and children at different stages of dietary intervention, improves the adaptability of dietary intervention, and ensures the intervention effect.

[0103] In another embodiment of the invention, the method further includes using the latest final ingredient ratios, combined with the child's corresponding food intake and energy requirements (obtained by the ratio of the recommended intake and energy requirements of various foods for children and adolescents of normal weight to the set reduction amount), as well as food composition data and food exchange tables, to recommend a new dietary intervention plan for the child; the dietary intervention plan can be obtained from a professional nutritionist or an existing dietary recommendation platform, and will not be elaborated further.

[0104] An embodiment of the present invention also provides a dietary structure health intervention system based on the body mass data of obese children. The system includes a memory, a processor, and a computer program, wherein the memory is used to store the corresponding computer program, the processor is used to run the corresponding computer program, and the computer program, when running in the processor, can implement the dietary structure health intervention method based on the body mass data of obese children described in steps S1-S3.

[0105] In summary, addressing the technical problem of unsatisfactory dietary intervention effects due to individual differences in obese children, this invention proposes a method and system for dietary health intervention based on the physical fitness data of obese children. This invention first acquires physical fitness data and nutritional data and groups the children accordingly; further, it obtains the intervention synchronicity of each physical fitness data point by comparing its deviation characteristics from the corresponding preset physical fitness standard with the temporal similarity of the overall deviation characteristics of all physical fitness data points; further, it obtains the intervention acceptance rate of the target child for each physical fitness data point at the current period by comparing the difference in intervention synchronicity between the target child and other children in the same group at the current period; finally, it adjusts the target child's dietary structure based on the difference between the target child's current nutritional intake and the preset recommended dietary structure, combined with the corresponding intervention acceptance rate.

[0106] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0107] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for dietary structure health intervention based on physical data of obese children, characterized in that, The method includes: We obtained physical fitness data and nutritional data of food intake of obese children at different times, and grouped children with different degrees of obesity; we selected any child as the target child for individual analysis. For the target child, the intervention synchronicity of each physical fitness data point is obtained based on the deviation characteristics of each physical fitness data point from the corresponding preset physical fitness standard and the similarity of the changes in the deviation characteristics of all physical fitness data points in the time domain; the intervention acceptance rate of the target child for each physical fitness data point in the current period is obtained based on the difference in the intervention synchronicity of each physical fitness data point between the target child and the children in the same group at the current period. Based on the difference between the target child's current nutritional intake data and the preset recommended dietary structure, and in conjunction with the corresponding intervention acceptance rate, the target child's dietary structure is adjusted.

2. The method of claim 1, wherein the method is based on physical data of obese children. The method for obtaining the intervention synchronicity includes: In the current period, a deviation coefficient is obtained based on the difference between each of the aforementioned physical fitness data and the preset physical fitness standard; the average of the deviation coefficients of all the aforementioned physical fitness data of the target child in the current period is taken as the overall deviation coefficient. For each of the aforementioned physical fitness data, an intervention effect sequence for each of the aforementioned physical fitness data is constructed based on the ratio of the change in the deviation coefficient of the aforementioned physical fitness data in each period to the change in the deviation coefficient of the historical adjacent period; an overall intervention effect sequence for all of the aforementioned physical fitness data is constructed based on the ratio of the change in the overall deviation coefficient of the aforementioned physical fitness data in each period to the change in the overall deviation coefficient of the historical adjacent period. The intervention synchronicity of each physical fitness data point is obtained by comparing the latest intervention effect sequence of each physical fitness data point with the overall intervention effect sequence.

3. The method of claim 1, wherein the method further comprises: The method for adjusting the dietary structure of the target children includes: Both the nutritional data and the preset recommended dietary structure include the percentage of each nutrient component. Based on the difference between the proportion of each nutrient consumed by the target child in the current period and the recommended proportion of each nutrient, the difference in the proportion of each nutrient is obtained; based on the difference in the proportion, it is determined whether the target child is following the preset recommended dietary structure in the current period. Based on the target children's adherence to the preset recommended dietary structure and in conjunction with the corresponding intervention acceptance rate, the target children's dietary structure is adjusted.

4. A dietary structure health intervention method based on the body composition data of obese children according to claim 3, characterized in that, The method for determining whether the target child follows the preset recommended dietary structure in the current period based on the percentage difference includes: For any nutrient, if the difference in the proportion is greater than a preset threshold, it is determined that the target child's intake of the corresponding nutrient does not follow the preset recommended dietary structure.

5. A dietary structure health intervention method based on the body composition data of obese children according to claim 3, characterized in that, The method for adjusting the dietary structure of the target children includes: Select any one of the nutrients as the target nutrient; select any one of the aforementioned body constitution data as the target body constitution data; When the target child's intake of the target nutrients follows the preset recommended dietary structure during the current period, a first adjustment coefficient is obtained based on the correlation between the time series of the differences in the proportions of the target nutrients and the time series of the intervention acceptance of the target physical condition data. In the current period, the proportions of the target nutrients are adjusted based on the intervention acceptance of the target physical condition data and the first adjustment coefficient to obtain an initial adjusted component proportion. Both the intervention acceptance and the nutrient proportions are positively correlated with the initial adjusted component proportion; the first adjustment coefficient is negatively correlated with the initial adjusted component proportion. When the target child does not follow the preset recommended dietary structure in terms of the intake of the target nutrients during the current period, other foods with the same proportion of the target nutrients are recommended, and the proportion of the target nutrients in the current period is directly used as the final component proportion. The final component percentage for each nutrient is obtained by combining the initial adjusted component percentages based on the recommended nutrient composition and all types of body composition data, with the final component percentages based on the nutrient composition that did not follow the recommendations.

6. A dietary structure health intervention method based on the body composition data of obese children according to claim 1, characterized in that, The methods for obtaining the intervention acceptance rate include: In the current period, the overall intervention synchronicity is obtained based on the overall characteristics of the intervention synchronicity of each physical fitness data of the target child in the same group of children; the intervention acceptance rate of each physical fitness data of the target child is obtained based on the difference between the intervention synchronicity of each physical fitness data of the target child and the corresponding overall intervention synchronicity.

7. A dietary structure health intervention method based on body composition data of obese children according to claim 1, characterized in that, The method for grouping children with different degrees of obesity includes: The physical fitness data includes the BMI index, and children are grouped according to their different degrees of obesity based on the BMI index.

8. A dietary structure health intervention method based on the body composition data of obese children according to claim 2, characterized in that, The DTW algorithm was used to analyze the similarity between the changes in the intervention effect sequence and the overall intervention effect sequence.

9. A dietary structure health intervention method based on body composition data of obese children according to claim 5, characterized in that, The correlation between the time series of the differences in the proportion of the target nutrient and the time series of the intervention acceptance of the target body composition data was analyzed using Pearson correlation coefficient.

10. A dietary structure health intervention system based on body composition data of obese children, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the dietary structure health intervention method based on the body mass data of obese children as described in any one of claims 1 to 9.

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