A metabolic energy storage method based on multi-modal and dynamic metabolic analysis

Abstract

The application provides a metabolic energy storage method based on multi-modal and dynamic metabolic analysis. In the weight loss process of a user, metabolic characteristics of the user during energy excretion are determined through metabolic parameters of multi-modal in excretion information, the metabolic characteristics are dynamically corrected based on an energy consumption model, and an energy consumption value of the user in a specified weight loss stage is obtained. An intake energy value of the user in the specified weight loss stage is extracted from diet information based on an energy intake model, and the energy net storage amount of the user in the specified weight loss stage is determined through the intake energy value and the energy consumption value. Efficiency correction factors of various physiological mechanisms of the user for energy storage are obtained, the energy net storage amount is corrected in multiple dimensions based on the various efficiency correction factors, and the actual storage amount of metabolic energy of the user in the specified weight loss stage is obtained. Based on the above scheme, multi-modal fusion quantification of energy metabolic state can be realized, so that the weight loss efficiency of the user can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent health management, more particularly, the present application relates to a metabolic energy storage method based on multi-modal and dynamic metabolic analysis. BACKGROUND

[0002] With the change of modern lifestyle, obesity has become a global health challenge, high-calorie diet, lack of exercise and stress and other factors lead to the rising obesity rate year by year, obesity not only affects appearance and self-confidence, but also is closely related to a variety of chronic diseases (such as diabetes, cardiovascular disease and hypertension), weight loss is not only the demand for healthy weight, but also an important means to improve overall health status.

[0003] The metabolic dynamics in the traditional weight loss calculation method is insufficient, the basal metabolic rate usually adopts a fixed formula (for example: Harris-Benedict equation), without introducing the dynamic influence of hormone fluctuation (for example: thyroid hormone, cortisol) and environmental compensation (for example: temperature, humidity) on metabolic rate, leading to energy consumption estimation deviation, and storage efficiency simplification, the traditional method ignores the nonlinear regulation of age, gender, sleep quality and psychological stress on energy conversion efficiency, therefore, how to realize the multi-modal fusion quantization of energy metabolism state, so as to improve the weight loss efficiency of users has become a difficult problem in the industry. SUMMARY

[0004] The present application provides a metabolic energy storage method based on multi-modal and dynamic metabolic analysis, which can realize multi-modal fusion quantization of energy metabolism state, so as to improve the weight loss efficiency of users.

[0005] In the first aspect, the present application provides a metabolic energy storage method based on multi-modal and dynamic metabolic analysis, comprising:

[0006] In the weight loss process of the user, the diet information and excretion information of the user in the specified weight loss stage are collected;

[0007] The metabolic parameters of multi-modal in the excretion information are extracted, and then the metabolic characteristics of the user when energy is excreted are determined through the metabolic parameters of each mode, the metabolic characteristics are dynamically corrected based on the energy consumption model, and the energy consumption value of the user in the specified weight loss stage is obtained;

[0008] The intake energy value of the user in the specified weight loss stage is extracted from the diet information based on the energy intake model, and the net energy storage amount of the user in the specified weight loss stage is determined through the intake energy value and the energy consumption value;

[0009] Obtaining efficiency correction factors of various physiological mechanisms of the user on energy storage, correcting the net energy storage amount in multiple dimensions based on the efficiency correction factors to obtain an actual storage amount of metabolic energy of the user in a specified weight loss stage.

[0010] In some embodiments, extracting the multi-modal metabolic parameters in the excretion information specifically includes:

[0011] Obtaining all data modalities in the excretion information;

[0012] Extracting parameter values of each data modality from the excretion information;

[0013] Determining the multi-modal metabolic parameters through all the parameter values.

[0014] In some embodiments, determining the metabolic characteristics of the user in energy excretion through the metabolic parameters of each modality specifically includes:

[0015] Obtaining the defecation amount, defecation color value, defecation shape value, defecation odor grade, defecation duration, dietary fiber proportion, and digestion cycle from the metabolic parameters of each modality;

[0016] Determining the metabolic characteristics of the user in energy excretion according to the defecation amount, defecation color value, defecation shape value, defecation odor grade, defecation duration, dietary fiber proportion, and digestion cycle, wherein the metabolic characteristics are determined according to the following formula:

[0017] ;

[0018] Wherein, represents the metabolic characteristics, represents the dietary fiber proportion, represents the digestion cycle, represents the intake energy value, represents the defecation amount, represents the defecation color value, represents the defecation shape value, represents the defecation odor grade, represents the defecation duration.

[0019] In some embodiments, dynamically correcting the metabolic characteristics based on an energy consumption model to obtain an energy consumption value of the user in a specified weight loss stage specifically includes:

[0020] Constructing an energy consumption model of the user in a specified weight loss stage;

[0021] Calculating a metabolic correction value of the user in a specified weight loss stage based on the energy consumption model;

[0022] determine an energy consumption value of the user in a specified weight loss stage based on the metabolic correction value and the metabolic characteristics.

[0023] In some embodiments, extracting an intake energy value of the user in the specified weight loss stage from the diet information based on an energy intake model specifically comprises:

[0024] obtaining food caloric value, intake weight and chewing times for each meal from the diet information;

[0025] integrating and verifying all the food caloric value, intake weight and chewing times based on an energy intake model to obtain the intake energy value of the user in the specified weight loss stage.

[0026] In some embodiments, determining an energy net storage amount of the user in the specified weight loss stage based on the intake energy value and the energy consumption value specifically comprises:

[0027] taking the difference between the intake energy value and the energy consumption value as the energy net storage amount of the user in the specified weight loss stage.

[0028] In some embodiments, performing multi-dimensional correction on the energy net storage amount based on respective efficiency correction factors to obtain actual storage amount of metabolic energy of the user in the specified weight loss stage specifically comprises:

[0029] ;

[0030] wherein, represents the actual storage amount, represents the energy net storage amount, represents the number of physiological mechanisms, represents the efficiency correction factor of the i-th physiological mechanism.

[0031] In some embodiments, the diet information contains food caloric value, intake weight and chewing times.

[0032] In some embodiments, the excretion information contains excretion amount, excretion color, excretion shape, excretion smell and excretion duration.

[0033] In some embodiments, the efficiency correction factor represents the degree of influence of various physiological mechanisms on energy storage.

[0034] The technical scheme provided by the embodiments disclosed in the present application has the following beneficial effects:

[0035] ​The method for metabolic energy storage based on multi-modal and dynamic metabolic analysis provided in the application comprises the following steps: collecting diet information and excretion information of a user in a specified weight loss stage during the weight loss process of the user; extracting metabolic parameters of multiple modes in the excretion information, and then determining metabolic characteristics of the user when energy is excreted through the metabolic parameters of each mode; dynamically correcting the metabolic characteristics based on an energy consumption model to obtain an energy consumption value of the user in the specified weight loss stage; extracting an intake energy value of the user in the specified weight loss stage from the diet information based on an energy intake model, and determining a net energy storage amount of the user in the specified weight loss stage through the intake energy value and the energy consumption value; obtaining efficiency correction factors of various physiological mechanisms of the user for energy storage, and correcting the net energy storage amount in multiple dimensions based on each efficiency correction factor to obtain an actual storage amount of metabolic energy of the user in the specified weight loss stage.

[0036] As can be seen, in the application, the efficiency correction factors of various physiological mechanisms of the user for energy storage are obtained, the net energy storage amount is corrected in multiple dimensions based on each efficiency correction factor, and the actual storage amount of metabolic energy of the user in the specified weight loss stage is obtained. First, the energy consumption value can capture the change of the metabolic state of the user in real time (for example, energy consumption reduction caused by digestive cycle extension), so as to dynamically adjust the energy consumption value, which not only improves the accuracy of energy consumption evaluation, but also provides more scientific exercise suggestions (such as adjusting exercise intensity or duration) for the user, thereby significantly improving the weight loss efficiency. Then, the intake energy value is extracted from the diet information through the energy intake model, and the net energy storage amount is calculated by combining the dynamically corrected energy consumption value, which can comprehensively reflect the energy balance state of the user. The net energy storage amount is accurately corrected by introducing the multi-dimensional efficiency correction factor, so as to more truly reflect the actual storage situation of energy in the body, thereby helping the user to more scientifically adjust the diet and exercise strategy, significantly improving the weight loss efficiency and reducing the health risk. In summary, based on the above scheme, multi-modal fusion quantification of the energy metabolic state can be realized, thereby improving the weight loss efficiency of the user. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only some embodiments of the application, and for those skilled in the art, other drawings can be obtained without creative labor.

[0038] Figure 1 is an exemplary flowchart of the method for metabolic energy storage based on multi-modal and dynamic metabolic analysis according to some embodiments of the application;

[0039] Figure 2is a flowchart of a process for determining an energy consumption value according to some embodiments of the present application;

[0040] Figure 3 is a flowchart of a process for determining an intake energy value according to some embodiments of the present application. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the scope of protection of the present application.

[0042] The embodiments of the present application provide a metabolic energy storage method based on multi-modal and dynamic metabolic analysis. The core of the method is that, in the weight loss process of a user, dietary information and excretion information of the user in a specified weight loss stage are collected; metabolic parameters of multiple modes in the excretion information are extracted, and then metabolic characteristics of the user when energy is excreted are determined through the metabolic parameters of each mode; the metabolic characteristics are dynamically corrected based on an energy consumption model, and an energy consumption value of the user in the specified weight loss stage is obtained; an intake energy value of the user in the specified weight loss stage is extracted from the dietary information based on an energy intake model, and a net energy storage amount of the user in the specified weight loss stage is determined through the intake energy value and the energy consumption value; efficiency correction factors of various physiological mechanisms of the user for energy storage are obtained, and the net energy storage amount is corrected in multiple dimensions based on each efficiency correction factor, and an actual storage amount of metabolic energy of the user in the specified weight loss stage is obtained. The above scheme can realize multi-modal fusion quantification of energy metabolic state, thereby improving the weight loss efficiency of the user.

[0043] In order to better understand the above technical solutions, the above technical solutions will be described in detail below with reference to the drawings in the specification and specific embodiments. Referring to Figure 1 The figure is an exemplary flowchart of a metabolic energy storage method based on multi-modal and dynamic metabolic analysis according to some embodiments of the present application. The metabolic energy storage method based on multi-modal and dynamic metabolic analysis mainly includes the following steps:

[0044] In step 101, in the weight loss process of a user, dietary information and excretion information of the user in a specified weight loss stage are collected.

[0045] It should be noted that in the present application, the diet information contains food heat value, intake weight and chewing frequency, and the excretion information contains defecation amount, defecation color, defecation shape, defecation odor and defecation duration; in specific implementation, for each diet of the user in the specified weight loss stage, the USDA standard library + infrared spectrum verification can be used to collect the chewing frequency of the user, the jaw movement sensor can be used to count (≥ 15 times per mouth to automatically optimize) to measure the chewing frequency of the user diet, the weighing instrument can be used to measure the weight of the food before and after the diet, and the difference between the weight of the food before and after the diet is taken as the intake weight, so that the set of food heat value, intake weight and chewing frequency is taken as the diet record of the diet, and the diet record of each diet can be obtained through the above-mentioned manner, so that the set of all diet records can be taken as the diet information of the user in the specified weight loss stage; for each excretion of the user in the specified weight loss stage, the user weighing method can be used to count the defecation amount during excretion, the contrast color between the defecation color and the 7-level color comparison card (1 = black, 7 = light yellow) can be automatically identified by taking a photo as the defecation color value, the contrast value between the defecation shape and the Bristol classification (1-7 numerical values) can be automatically identified by taking a photo as the defecation shape value, the 5-level Likert subjective score (1 = no smell, 5 = pungent) input by the user through the sliding bar can be taken as the defecation odor grade, and the timer can be used to count the defecation duration, so that the set of defecation amount, defecation color, defecation shape, defecation odor and defecation duration can be taken as the defecation record of the defecation, and the defecation record of the user in the specified weight loss stage can be obtained through the above-mentioned manner, and the set of all defecation records can be taken as the excretion information of the user in the specified weight loss stage.

[0046] In step 102, the metabolic parameters of multiple modalities in the excretion information are extracted, and then the metabolic characteristics of the user during energy excretion are determined through the metabolic parameters of each modality, the metabolic characteristics are dynamically corrected based on the energy consumption model, and the energy consumption value of the user in the specified weight loss stage is obtained.

[0047] In some embodiments, the extraction of the metabolic parameters of multiple modalities in the excretion information can be implemented by the following steps:

[0048] All data modalities in the excretion information are acquired;

[0049] The parameter values of each data modality are extracted from the excretion information;

[0050] The metabolic parameters of multiple modalities are determined through all the parameter values.

[0051] In a specific implementation, first, the data modalities in the excretion information include the excretion amount, excretion color, excretion shape, excretion odor, excretion duration, dietary fiber proportion, and digestion period; then, for each excretion, the excretion amount, excretion color value, excretion shape value, excretion odor grade, and excretion duration of the excretion are obtained from the excretion information, the dietary fiber proportion in the present application is 0.12±0.03, and the digestion period in the present application is ln(2) / 48h-1(corresponding to a 48-hour digestion period); finally, the set of the value ranges of all parameter values can be used as the multi-modal metabolic parameters.

[0052] It should be noted that in the present application, the metabolic parameters are quantitative indicators reflecting the functional state of the user's digestive system and the energy metabolism efficiency; the data modalities refer to different types of data representations in the excretion information; and the parameter values of the data modalities refer to specific quantitative values extracted from each data modality.

[0053] In some embodiments, the determination of the metabolic characteristics of the user during energy excretion through the metabolic parameters of each modality can be achieved by the following steps:

[0054] Obtaining the excretion amount, excretion color value, excretion shape value, excretion odor grade, excretion duration, dietary fiber proportion, and digestion period from the metabolic parameters of each modality;

[0055] Determining the metabolic characteristics of the user during energy excretion according to the excretion amount, excretion color value, excretion shape value, excretion odor grade, excretion duration, dietary fiber proportion, and digestion period, wherein the metabolic characteristics are determined according to the following formula:

[0056] ;

[0057] wherein, represents the metabolic characteristics, represents the dietary fiber proportion, represents the digestion period, represents the intake energy value, represents the excretion amount, represents the excretion color value, represents the excretion shape value, represents the excretion odor grade, represents the excretion duration.

[0058] It should be noted that in the present application, the metabolic characteristics are quantitative indicators reflecting the functional state of the digestive system and the energy metabolism efficiency. By analyzing multi-dimensional data such as defecation amount, color, shape, odor, and duration, combined with the proportion of dietary fiber and the digestive cycle, the user's digestive system function and energy excretion mode can be comprehensively evaluated. For example, the defecation amount and shape can reflect the intestinal transport efficiency, the color and odor can indicate abnormal digestion and absorption (such as fat malabsorption), and the defecation duration and digestive cycle can reveal the intestinal peristalsis speed, which helps to provide key inputs for the construction of the energy excretion model, helps to more accurately calculate the net energy storage amount, and at the same time provides personalized dietary recommendations (such as adjusting dietary fiber intake) and health warnings (such as abnormal digestive function) for users. Not only optimizes the accuracy of energy metabolism evaluation, but also provides a scientific basis for improving intestinal health and weight loss effect.

[0059] In some embodiments, the metabolic characteristics are dynamically corrected based on the energy consumption model to obtain the energy consumption value of the user in the specified weight loss stage, and the energy consumption value of the user in the specified weight loss stage is calculated by referring to the following formula: Figure 2 As shown in the figure, the figure is a flowchart for determining the energy consumption value in some embodiments of the present application. In the present embodiment, the energy consumption value can be realized by the following steps:

[0060] In step 1021, an energy consumption model of the user in the specified weight loss stage is constructed;

[0061] In step 1022, a metabolic correction value of the user in the specified weight loss stage is calculated based on the energy consumption model;

[0062] In step 1023, the energy consumption value of the user in the specified weight loss stage is determined by the metabolic correction value and the metabolic characteristics.

[0063] In specific implementation, first, the dynamic correction algorithm is used as the energy consumption model of the user in the specified weight loss stage; then, the energy consumption model can be used to calculate the metabolic correction value of the user in the specified weight loss stage; finally, the sum of the metabolic correction value and the metabolic characteristics can be used as the energy consumption value of the user in the specified weight loss stage.

[0064] It should be noted that in the present application, the energy consumption value; the energy consumption model; the metabolic correction value; the dynamic correction algorithm, the dynamic correction algorithm is:

[0065] ;

[0066] Wherein, represents the metabolic correction value; represents the basal metabolic rate; represents the hormone fluctuation factor; represents the heart rate-energy consumption curve; represents the muscle recruitment coefficient, represents an impedance loss value; represents an environmental heat loss coefficient, represents an environmental loss value.

[0067] In a specific implementation, the basal metabolic rate refers to the minimum energy consumption required by an individual to maintain basic physiological functions in a resting state, and the basal metabolic rate of the user can be collected using an indirect calorimetry method (for example, respiratory quotient RQ calibration); the hormone fluctuation factor is a parameter for correcting the change in energy consumption caused by hormone fluctuation, and can be obtained by blood detection (such as thyroid hormone, cortisol, and insulin level) or monitoring of hormone-related physiological indicators (such as skin conductivity) by a wearable device; the heart rate-energy consumption curve refers to a quantitative relationship curve between the heart rate and energy consumption of an individual, and is fitted by combining data of a motion experiment (such as a treadmill test) and a heart rate monitoring device (such as a smart bracelet); the environmental loss value is a quantitative value for measuring the degree of influence of environmental factors on energy consumption, and environmental parameters can be obtained by using devices such as an air pressure sensor (high altitude), an anemometer (strong wind), or an underwater pressure sensor, and the environmental loss value is estimated in combination with experimental data of energy consumption; the muscle recruitment coefficient refers to a quantitative indicator of the activation degree and efficiency of muscle fibers of an individual during exercise, and the muscle activity intensity can be measured by electromyography (EMG), and the muscle recruitment coefficient is estimated based on an empirical model of the type and intensity of exercise; the impedance loss value refers to the proportion of energy loss of an individual during exercise due to internal friction of the body, joint resistance, or non-effective action, and the motion efficiency can be evaluated by using a motion analysis system (such as a motion capture technology), or the motion efficiency is analyzed based on sensor data of the user's motion posture; and the environmental heat loss coefficient refers to the proportion of additional energy consumption of an individual due to body temperature regulation under specific environmental conditions, and the environmental heat loss coefficient can be calculated by using environmental temperature and humidity sensors in combination with body temperature change data (such as infrared temperature measurement).

[0068] In step 103, the energy intake value of the user in the specified weight loss stage is extracted from the diet information based on an energy intake model, and the energy net storage amount of the user in the specified weight loss stage is determined by using the energy intake value and the energy consumption value.

[0069] In some embodiments, the energy intake value of the user in the specified weight loss stage is extracted from the diet information based on an energy intake model, and the energy net storage amount of the user in the specified weight loss stage is determined by using the energy intake value and the energy consumption value. Figure 3 As shown in the figure, the figure is a flowchart for determining the energy intake value in some embodiments of the present application, and the energy intake value in the present embodiment can be determined by using the following steps:

[0070] In step 1031, the food heat value, intake weight, and chewing frequency at each diet are obtained from the diet information.

[0071] In step 1032, all the food heat values, intake weights, and chewing frequencies are fused and verified based on an energy intake model to obtain the energy intake value of the user in the specified weight loss stage.

[0072] It should be noted that in the present application, the intake energy value refers to the total energy value that the user intakes through diet in a specified weight loss period, and the energy intake model is used to calculate the intake energy value, which comprehensively considers the energy density of food, the actual intake amount and the influence of chewing behavior on digestion and absorption, so as to more accurately reflect the actual energy obtained by the user from diet; in specific implementation, based on the energy intake model, all food calorific values, intake weights and chewing frequencies are fused and verified to obtain the intake energy value of the user in the specified weight loss period, wherein the intake energy value can be determined by the following formula:

[0073] ;

[0074] Among them, represents the intake energy value, represents the food calorific value, represents the intake weight, represents the digestion efficiency factor, it should be noted that the digestion efficiency factor refers to the efficiency index of food being decomposed, absorbed and utilized in the digestive tract during energy intake, which can be obtained by analyzing the gastric electrical signal (for example: EGG monitoring gastric peristalsis frequency) to obtain the digestion efficiency factor during energy intake. For users with low digestion efficiency, even if the same amount of food is ingested, the actual energy obtained may be lower than expected, so it is necessary to dynamically adjust the intake energy value according to the digestion efficiency factor, which not only helps to more accurately evaluate the energy balance state, but also provides support for personalized dietary recommendations (such as selecting easily digestible food or adjusting eating frequency), thereby optimizing the weight loss effect and improving digestive health.

[0075] In some embodiments, the determination of the energy net storage amount of the user in the specified weight loss period by the intake energy value and the energy consumption value can be achieved by the following steps:

[0076] The difference between the intake energy value and the energy consumption value is taken as the energy net storage amount of the user in the specified weight loss period.

[0077] It should be noted that in the present application, the energy net storage amount reflects the energy surplus and shortage of the user in a specific period of time, if the difference is positive, it means energy surplus, which may be converted into fat or glycogen storage; if the difference is negative, it means energy shortage, which is helpful for fat decomposition and weight loss. By calculating the energy net storage amount, the energy balance state of the user can be intuitively quantified, which provides key data support for the weight loss process, and helps the user to understand the influence of diet and exercise on body weight change, so as to make a more reasonable weight loss plan.

[0078] In step 104, efficiency correction factors of various physiological mechanisms of the user on energy storage are obtained, and the net energy storage amount is corrected in multiple dimensions based on the efficiency correction factors to obtain the actual storage amount of metabolic energy of the user in a specified weight loss stage.

[0079] In some embodiments, the efficiency correction factors of various physiological mechanisms of the user on energy storage can be obtained in the following manner: the physiological mechanisms include age-related mitochondrial function decline, sex hormone regulation difference, sleep deficiency inhibiting growth hormone secretion, and stress promoting gluconeogenesis, wherein the efficiency correction factor of the age-related mitochondrial function decline can be determined by the following formula:

[0080] ;

[0081] wherein, represents the efficiency correction factor of the age-related mitochondrial function decline, represents the age; the efficiency correction factor of the sex hormone regulation difference can be determined by the following formula:

[0082] ;

[0083] wherein, represents the efficiency correction factor of the sex hormone regulation difference, which is positive for males and negative for females; the efficiency correction factor of sleep deficiency inhibiting growth hormone secretion can be determined by the following formula:

[0084] ;

[0085] wherein, represents the efficiency correction factor of the sex hormone regulation difference; the efficiency correction factor of stress promoting gluconeogenesis can be determined by the following formula:

[0086] ;

[0087] wherein, represents the efficiency correction factor of stress promoting gluconeogenesis, and it is to be noted that the efficiency correction factor in the present application represents the influence degree of various physiological mechanisms on energy storage.

[0088] In some embodiments, the actual storage amount of metabolic energy of the user in a specified weight loss stage can be obtained by correcting the net energy storage amount in multiple dimensions based on the efficiency correction factors in the following steps:

[0089] ;

[0090] wherein, represents the actual storage amount, represents the net energy storage amount, a number of types of physiological mechanisms, an efficiency correction factor of the type of physiological mechanism.

[0091] It should be noted that in this application, the actual storage amount reflects the energy value actually used by the user's body for metabolism, storage or consumption. The actual storage amount not only considers the difference between intake and consumption, but also integrates correction factors of various physiological mechanisms such as digestion efficiency, metabolic priority, exercise adaptability, microbiome influence, etc., so as to more accurately quantify the real allocation and storage of energy in the body. By correcting the net storage amount of energy in multiple dimensions, the system can more accurately reflect the energy metabolism state of the user, avoid errors caused by individual differences (such as digestion and absorption efficiency, metabolic abnormalities), and help develop more scientific weight loss strategies, such as adjusting the dietary structure, optimizing the exercise plan, thereby improving the weight loss efficiency and reducing the health risks. At the same time, the introduction of dynamic correction factors enables the model to have stronger personalized adaptation ability, which can adjust the energy balance assessment in real time according to the physiological changes of the user, and provide a reliable basis for long-term weight management.

[0092] The present application is described with reference to flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as a combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows or one or more blocks in the flowcharts and / or block diagrams can be implemented by an apparatus. Figure 1

[0093] ​Those skilled in the art can understand that all or part of the steps of various methods in the above embodiments can be completed by instructing the relevant hardware by means of a program, and the program can be stored in a computer readable storage medium, including Read-Only Memory (ROM), Random Access Memory (RAM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), One-time Programmable Read-Only Memory (OTPROM), Electrically-Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage, magnetic disk storage, magnetic tape storage, or any other medium that can be used to carry or store data in a computer readable manner.

[0094] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements, but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the statement "comprising a" does not exclude the existence of additional identical elements in the process, method, article, or apparatus that includes the element.

Claims

1. A method for metabolic energy storage based on multi-modal and dynamic metabolic analysis, characterized in that, The method comprises the following steps: During the weight loss process of a user, diet information and excretion information of the user in a specified weight loss stage are collected; Metabolic parameters of multiple modes in the excretion information are extracted, and metabolic characteristics of the user during energy excretion are determined through the metabolic parameters of each mode, the metabolic characteristics are dynamically corrected based on an energy consumption model, and an energy consumption value of the user in the specified weight loss stage is obtained; An intake energy value of the user in the specified weight loss stage is extracted from the diet information based on an energy intake model, and an energy net storage amount of the user in the specified weight loss stage is determined through the intake energy value and the energy consumption value; Efficiency correction factors of various physiological mechanisms of the user on energy storage are obtained, the energy net storage amount is corrected in multiple dimensions based on each efficiency correction factor, and an actual storage amount of metabolic energy of the user in the specified weight loss stage is obtained; Wherein, the metabolic characteristics of the user during energy excretion are determined through the metabolic parameters of each mode, which specifically comprises: The amount of defecation, defecation color value, defecation shape value, defecation odor grade, defecation duration, dietary fiber proportion and digestion cycle are obtained from the metabolic parameters of each mode; The metabolic characteristics of the user during energy excretion are determined according to the amount of defecation, defecation color value, defecation shape value, defecation odor grade, defecation duration, dietary fiber proportion and digestion cycle, wherein the metabolic characteristics are determined according to the following formula: ； wherein, represents a metabolic characteristic, represents a dietary fiber ratio, represents a digestive cycle, represents a manually recorded number of excretions, represents an intake energy value, represents a stool volume of the excretion, represents a stool color value of the excretion, represents a stool shape value of the excretion, represents a stool odor grade of the excretion, represents a stool duration of the excretion; Wherein, the actual storage amount of metabolic energy of the user in the specified weight loss stage is obtained by correcting the energy net storage amount in multiple dimensions based on each efficiency correction factor, which specifically comprises: ； wherein, represents the actual storage amount, represents the net energy storage amount, represents the number of physiological mechanisms, represents the efficiency correction factor of the th physiological mechanism.

2. The method of claim 1, wherein, The extraction of the metabolic parameters of multiple modes in the excretion information specifically comprises: All data modes in the excretion information are obtained; Parameter values of each data mode are extracted from the excretion information; The metabolic parameters of multiple modes are determined through all the parameter values.

3. The method of claim 1, wherein, The energy consumption value of the user in the specified weight loss stage is obtained by dynamically correcting the metabolic characteristics based on the energy consumption model, which specifically comprises: An energy consumption model of the user in the specified weight loss stage is constructed; A metabolic correction value of the user in the specified weight loss stage is calculated based on the energy consumption model; The energy consumption value of the user in the specified weight loss stage is determined through the metabolic correction value and the metabolic characteristics.

4. The method of claim 1, wherein, The intake energy value of the user in the specified weight loss stage is extracted from the diet information based on the energy intake model, which specifically comprises: The food heat value, intake weight and chewing frequency at each diet are obtained from the diet information; All food heat values, intake weights and chewing frequencies are fused and verified based on the energy intake model, and the intake energy value of the user in the specified weight loss stage is obtained.

5. The method of claim 1, wherein, The energy net storage amount of the user in the specified weight loss stage is determined through the intake energy value and the energy consumption value, which specifically comprises: The difference between the intake energy value and the energy consumption value is taken as the energy net storage amount of the user in the specified weight loss stage.

6. The method of claim 1, wherein, The diet information includes food heat value, intake weight and chewing frequency.

7. The method of claim 1, wherein, The excretion information includes the amount of defecation, defecation color, defecation shape, defecation odor and defecation duration.

8. The method of claim 1, wherein, The efficiency correction factor represents the influence degree of various physiological mechanisms on energy storage.

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