Metabolic energy storage method based on multi-mode and dynamic metabolism analysis

Abstract

The invention provides a metabolic energy storage method based on multi-mode and dynamic metabolism analysis, in the weight losing process of a user, the metabolic characteristics of the user during energy excretion are determined through the multi-mode metabolic parameters in excretion information, the metabolic characteristics are dynamically corrected based on an energy consumption model, and the energy utilization rate of the user is improved. Obtaining an energy consumption value of the user in a specified weight losing stage; based on an energy intake model, extracting an intake energy value of the user in the specified weight-losing stage from the diet information, and determining an energy net storage amount of the user in the specified weight-losing stage through the intake energy value and the energy consumption value; and acquiring efficiency correction factors of various physiological mechanisms of the user on energy storage, and performing multi-dimensional correction on the net energy storage amount based on each efficiency correction factor to obtain the actual storage amount of metabolic energy of the user in the specified weight losing stage. Based on the scheme, multi-mode fusion quantification of the energy metabolism state can be realized, so that the weight losing efficiency of the user can be improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent health management, and more specifically, to a metabolic energy storage method based on multimodal and dynamic metabolic analysis. Background Art

[0002] With the changes in modern lifestyles, obesity has become a global health challenge. Factors such as high-calorie diet, lack of exercise and stress have led to an increasing obesity rate year by year. Obesity not only affects appearance and self-confidence, but is also closely related to a variety of chronic diseases (such as diabetes, cardiovascular disease and hypertension). Losing weight is not only a need to pursue a healthy weight, but also an important means to improve overall health.

[0003] Traditional weight loss calculation methods lack metabolic dynamics. Basal metabolic rate typically uses a fixed formula (e.g., the Harris-Benedict equation), without accounting for the dynamic effects of hormonal fluctuations (e.g., thyroid hormone, cortisol) and environmental compensation (e.g., temperature, humidity) on metabolic rate. This leads to biased energy consumption estimates and simplified storage efficiency. Traditional methods ignore the nonlinear regulatory effects of age, gender, sleep quality, and psychological stress on energy conversion efficiency. Therefore, achieving multimodal fusion quantification of energy metabolic status to improve users' weight loss efficiency has become a challenging issue facing the industry. Summary of the Invention

[0004] The present application provides a metabolic energy storage method based on multimodal and dynamic metabolic analysis, which can realize multimodal fusion quantification of energy metabolic status, thereby improving the user's weight loss efficiency.

[0005] In a first aspect, the present application provides a metabolic energy storage method based on multimodal and dynamic metabolic analysis, comprising: During the user's weight loss process, the user's dietary information and excretion information are collected during the specified weight loss phase; Extracting multimodal metabolic parameters from the excretion information, and then determining the user's metabolic characteristics during energy excretion based on the metabolic parameters of each modality, and dynamically correcting the metabolic characteristics based on an energy expenditure model to obtain the user's energy expenditure value during a specified weight loss phase; extracting the energy intake value of the user in the specified weight loss stage from the diet information based on the energy intake model, and determining the net energy storage of the user in the specified weight loss stage by using the energy intake value and the energy expenditure value; The efficiency correction factors of energy storage of various physiological mechanisms of the user are obtained, and the net energy storage amount is corrected in multiple dimensions based on each efficiency correction factor to obtain the actual storage amount of metabolic energy of the user in the specified weight loss stage.

[0006] In some embodiments, extracting multimodal metabolic parameters from the excretion information specifically includes: Obtain all data modalities in the excretion information; extracting parameter values of each data modality from the excretion information; The multimodal metabolic parameters are determined from all parameter values.

[0007] In some embodiments, determining the user's metabolic characteristics during energy excretion using metabolic parameters of each modality specifically includes: Obtain stool volume, stool color value, stool shape value, stool odor level, stool duration, dietary fiber ratio and digestion cycle from the metabolic parameters of each modality; The user's metabolic characteristics during energy excretion are determined based on the amount of stool, stool color value, stool shape value, stool odor level, stool duration, dietary fiber ratio, and digestion cycle. The metabolic characteristics are determined according to the following formula: ; in, Represents metabolic characteristics, Indicates the proportion of dietary fiber, Represents the digestive cycle, Indicates energy intake value, Indicates the amount of bowel movements, Indicates the defecation color value, Indicates the defecation shape value, Indicates the level of defecation odor, Indicates the duration of bowel movements.

[0008] In some embodiments, dynamically modifying the metabolic characteristics based on the energy expenditure model to obtain the energy expenditure value of the user in a specified weight loss stage specifically includes: Construct a user's energy expenditure model during a specified weight loss phase; Calculating a metabolic correction value for the user in a specified weight loss phase based on the energy expenditure model; The energy consumption value of the user in a specified weight loss stage is determined according to the metabolic correction value and the metabolic characteristic.

[0009] In some embodiments, extracting the energy intake value of the user in a specified weight loss phase from the diet information based on the energy intake model specifically includes: Obtaining food caloric value, intake weight and chewing times during each diet from the dietary information; Based on the energy intake model, all food calorific values, intake weight and chewing times are integrated and verified to obtain the user's energy intake value in the specified weight loss stage.

[0010] In some embodiments, determining the net energy storage of the user in a specified weight loss phase using the energy intake value and the energy expenditure value specifically includes: The difference between the energy intake value and the energy expenditure value is taken as the user's net energy storage during the specified weight loss phase.

[0011] In some embodiments, performing multi-dimensional correction on the net energy storage based on various efficiency correction factors to obtain the actual metabolic energy storage of the user in a specified weight loss phase specifically includes: ; in, Indicates the actual storage capacity. represents the net energy storage, Indicates the number of types of physiological mechanisms, Indicates the Efficiency correction factor for this physiological mechanism.

[0012] In some embodiments, the dietary information includes food caloric value, intake weight, and number of chews.

[0013] In some embodiments, the excretion information includes stool volume, stool color, stool shape, stool odor and stool duration.

[0014] In some embodiments, the efficiency correction factor represents the extent to which various physiological mechanisms affect energy storage.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The present application provides a metabolic energy storage method based on multimodal and dynamic metabolic analysis. During the user's weight loss process, the user's diet information and excretion information in a specified weight loss stage are collected; the multimodal metabolic parameters 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, and 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; the energy intake 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 of the user in the specified weight loss stage is determined through the energy intake value and the energy consumption value; the efficiency correction factors of the user's various physiological mechanisms for energy storage are obtained, and the net energy storage is multi-dimensionally corrected based on each efficiency correction factor to obtain the actual storage of the user's metabolic energy in the specified weight loss stage.

[0016] It can be seen that in this application, the efficiency correction factors of energy storage caused by various physiological mechanisms of the user are obtained, and the net energy storage is multi-dimensionally corrected based on each efficiency correction factor to obtain the actual storage of metabolic energy of the user in a specified weight loss stage; first, determining the energy consumption value can capture the changes in the user's metabolic state in real time (for example, the energy consumption is reduced due to the extension of the digestive cycle), thereby dynamically adjusting the energy consumption value, which not only improves the accuracy of energy consumption assessment, but also provides users with more scientific exercise suggestions (such as adjusting exercise intensity or duration), thereby significantly improving weight loss efficiency; then, the energy intake value is extracted from the dietary information through the energy intake model, and the net energy storage is calculated in combination with the dynamically corrected energy consumption value, which can fully reflect the user's energy balance state. By introducing multi-dimensional efficiency correction factors to accurately correct the net energy storage, it can more realistically reflect the actual storage of energy in the body, thereby helping users to more scientifically adjust their diet and exercise strategies, significantly improve weight loss efficiency and reduce health risks; in summary, based on the above scheme, multimodal fusion quantification of energy metabolic state can be achieved, thereby improving the user's weight loss efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] Figure 1 is an exemplary flow chart of a metabolic energy storage method based on multimodal and dynamic metabolic analysis according to some embodiments of the present application; Figure 2 is a schematic diagram of a process for determining energy consumption values according to some embodiments of the present application; Figure 3 This is a schematic diagram of the process of determining the energy intake value according to some embodiments of the present application. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] The embodiment of the present application provides a metabolic energy storage method based on multimodal and dynamic metabolic analysis. The core of the method is to collect the user's diet information and excretion information in a specified weight loss stage during the user's weight loss process; extract the multimodal metabolic parameters in the excretion information, and then determine the user's metabolic characteristics during energy excretion through the metabolic parameters of each modality, and dynamically correct the metabolic characteristics based on the energy consumption model to obtain the user's energy consumption value in the specified weight loss stage; extract the user's energy intake value in the specified weight loss stage from the diet information based on the energy intake model, and determine the user's net energy storage in the specified weight loss stage through the energy intake value and the energy consumption value; obtain the efficiency correction factors of the user's various physiological mechanisms for energy storage, and perform multi-dimensional correction on the net energy storage based on each efficiency correction factor to obtain the user's actual metabolic energy storage in the specified weight loss stage. The above scheme can realize multimodal fusion quantification of energy metabolic state, thereby improving the user's weight loss efficiency.

[0021] In order to better understand the above technical solution, the following will be described in detail with reference to the accompanying drawings and specific implementation methods. Figure 1 , which is an exemplary flow chart of a metabolic energy storage method based on multimodal and dynamic metabolic analysis according to some embodiments of the present application. The metabolic energy storage method based on multimodal and dynamic metabolic analysis mainly includes the following steps: In step 101 , during the user's weight loss process, the user's dietary information and excretion information in a specified weight loss stage are collected.

[0022] It should be noted that, in the present application, the dietary information includes food calorific value, intake weight and number of chewings, and the excretion information includes defecation volume, 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 user's chewing times, and the jaw motion sensor count (automatic optimization ≥15 times per mouthful) can be used to measure the number of chewings of the user's diet, and a weighing instrument can be used to measure the weight of the food before and after the diet, and the difference in the weight of the food before and after the diet is used as the intake weight, so that the collection of food calorific value, intake weight and number of chewings is used as the diet record of the diet. The diet record of each diet can be obtained in the above manner, so that the collection of all diet records can be used as the diet information of the user in the specified weight loss stage; for the user in the specified For each defecation in a given weight loss stage, the amount of stool can be counted by user weighing, and a photo can be taken to automatically identify the contrast between the stool color and a 7-level color chart (1=black, 7=light yellow) as the stool color value. At the same time, a photo can be taken to automatically identify the contrast between the stool shape and the Bristol grade (1-7 numerical classification) as the stool shape value. The 5-level Likert subjective score (1=odorless, 5=pungent) of the user's subjective evaluation is input through the slider as the stool odor level, and a timer can be used to count the defecation duration of the defecation, so that the collection of stool volume, stool color, stool shape, stool odor and defecation duration can be used as the defecation record of the defecation. The above method can be used to obtain the defecation record of the user's defecation in the specified weight loss stage, and the collection of all defecation records can be used as the user's excretion information in the specified weight loss stage.

[0023] In step 102, the multimodal metabolic parameters 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 to obtain the energy consumption value of the user in the specified weight loss stage.

[0024] In some embodiments, extracting multimodal metabolic parameters from the excretion information may be achieved by the following steps: Obtain all data modalities in the excretion information; extracting parameter values of each data modality from the excretion information; The multimodal metabolic parameters are determined from all parameter values.

[0025] In specific implementation, first, the data modalities in the excretion information include stool volume, stool color, stool shape, stool odor, stool duration, dietary fiber ratio and digestion cycle; then, for each excretion, the stool volume, stool color value, stool shape value, stool odor level and defecation duration are obtained from the excretion information. The dietary fiber ratio in this application is 0.12±0.03, and the digestion cycle in this application is ln(2) / 48h-1 (corresponding to a 48-hour digestion cycle); finally, the set of value ranges of all parameter values can be used as multimodal metabolic parameters.

[0026] It should be noted that in this application, metabolic parameters are quantitative indicators that reflect the functional status of the user's digestive system and energy metabolism efficiency; data modalities refer to different types of data representations in excretion information; and parameter values of data modalities refer to specific quantitative values extracted from each data modality.

[0027] In some embodiments, determining the user's metabolic characteristics during energy excretion using metabolic parameters of each modality may be achieved by the following steps: Obtain stool volume, stool color value, stool shape value, stool odor level, stool duration, dietary fiber ratio and digestion cycle from the metabolic parameters of each modality; The user's metabolic characteristics during energy excretion are determined based on the amount of stool, stool color value, stool shape value, stool odor level, stool duration, dietary fiber ratio, and digestion cycle. The metabolic characteristics are determined according to the following formula: ; in, Represents metabolic characteristics, Indicates the proportion of dietary fiber, Represents the digestive cycle, Indicates energy intake value, Indicates the amount of bowel movements, Indicates the defecation color value, Indicates the defecation shape value, Indicates the level of defecation odor, Indicates the duration of bowel movements.

[0028] It should be noted that in this application, metabolic characteristics are quantitative indicators that reflect the functional state of the digestive system and the efficiency of energy metabolism. By analyzing multi-dimensional data such as stool volume, color, shape, odor, and duration, combined with dietary fiber proportion and digestion cycle, the user's digestive system function and energy excretion pattern can be comprehensively evaluated. For example, stool volume and shape can reflect intestinal transport efficiency, color and odor can indicate abnormal digestion and absorption (such as fat malabsorption), and bowel movement duration and digestion cycle can reveal intestinal peristalsis speed, which provides key input for the construction of energy excretion model and helps to calculate the net energy storage more accurately. At the same time, it provides users with personalized dietary recommendations (such as adjusting dietary fiber intake) and health warnings (such as abnormal digestive function), which not only optimizes the accuracy of energy metabolism assessment, but also provides a scientific basis for improving intestinal health and weight loss effects.

[0029] In some embodiments, the metabolic characteristics are dynamically modified based on the energy consumption model to obtain the energy consumption value of the user in the specified weight loss stage, and the reference Figure 2 As shown in the figure, this figure is a schematic diagram of the process of determining the energy consumption value in some embodiments of the present application. In this embodiment, the energy consumption value can be determined by the following steps: In step 1021, an energy consumption model of the user in a specified weight loss stage is constructed; In step 1022, a metabolic correction value of the user in a specified weight loss phase is calculated based on the energy expenditure model; In step 1023 , the energy consumption value of the user in the specified weight loss stage is determined based on the metabolic correction value and the metabolic characteristics.

[0030] 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.

[0031] It should be noted that, in this application, energy expenditure value; energy expenditure model; metabolic correction value; dynamic correction algorithm, the dynamic correction algorithm is: ;

[0032] in, represents 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, Indicates the impedance loss value; represents the ambient heat loss coefficient, Indicates the environmental loss value.

[0033] In specific implementation, basal metabolic rate refers to the minimum energy consumption required for an individual to maintain basic physiological functions in a resting state. The user's basal metabolic rate can be collected using indirect calorimetry (for example, respiratory quotient RQ calibration); the hormone fluctuation factor is a parameter used to correct changes in energy consumption due to hormone fluctuations, which can be obtained through blood tests (such as thyroid hormone, cortisol, insulin levels) or wearable devices monitoring hormone-related physiological indicators (such as skin conductivity); the heart rate-energy consumption curve refers to the quantitative relationship curve between an individual's heart rate and energy consumption, which is obtained by combining exercise experiments (such as treadmill tests) with data fitting of heart rate monitoring equipment (such as smart bracelets); the environmental loss value is a quantitative value used to measure the degree of influence of environmental factors on energy consumption, which can be obtained through air pressure sensors (high altitude), wind Environmental parameters are obtained by devices such as anemometers (strong wind) or underwater pressure sensors and estimated in combination with energy consumption experimental data; the muscle recruitment coefficient refers to a quantitative indicator of the degree of activation and efficiency of an individual's muscle fibers during exercise. The intensity of muscle activity can be measured by electromyography (EMG) and estimated based on an empirical model of exercise type and intensity; the impedance loss value refers to the proportion of energy loss caused by internal body friction, joint resistance or ineffective movements during exercise. The exercise efficiency can be evaluated through a motion analysis system (such as motion capture technology) or through sensor data analysis based on the user's movement posture; the environmental heat loss coefficient refers to the proportion of additional energy consumed by an individual due to body temperature regulation under specific environmental conditions. It can be calculated using environmental temperature and humidity sensors combined with body temperature change data (such as infrared temperature measurement).

[0034] In step 103, the energy intake 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 by the energy intake value and the energy consumption value.

[0035] In some embodiments, the energy intake value of the user in the specified weight loss stage is extracted from the diet information based on the energy intake model, and the energy intake value of the user in the specified weight loss stage is extracted based on the energy intake model. Figure 3 As shown in the figure, this figure is a schematic diagram of the process of determining the intake energy value in some embodiments of the present application. In this embodiment, the intake energy value can be determined by the following steps: In step 1031, the caloric value, intake weight, and chewing times of each meal are obtained from the dietary information; In step 1032, all food caloric values, intake weight, and chewing times are integrated and verified based on the energy intake model to obtain the energy intake value of the user in the specified weight loss stage.

[0036] It should be noted that, in this application, the energy intake value refers to the total energy value consumed by the user through diet during a specified weight loss stage. The energy intake model is used to calculate the energy intake value, which comprehensively considers the energy density of the food, the actual intake amount, and the effect of chewing behavior on digestion and absorption, thereby more accurately reflecting the actual energy obtained by the user from the diet. In specific implementation, all food calorific values, intake weight, and chewing times are integrated and verified based on the energy intake model to obtain the energy intake value of the user in the specified weight loss stage, wherein the energy intake value can be determined using the following formula: ; in, Indicates energy intake value, Indicates the calorific value of food. Indicates intake weight, It represents the digestion efficiency factor. It should be noted that the digestion efficiency factor refers to the efficiency index of the decomposition, absorption and utilization of food in the digestive tract during the user's energy intake process. The digestion efficiency factor at the time of energy intake can be obtained through gastric electrogram signal analysis (for example, EGG monitoring of gastric peristalsis frequency). For users with low digestion efficiency, even if they consume food with the same calories, the actual energy obtained may be lower than expected. Therefore, it is necessary to dynamically adjust the intake energy value according to the digestion efficiency factor, which not only helps to more accurately assess the energy balance state, but also provides support for personalized dietary recommendations (such as choosing easily digestible foods or adjusting the frequency of eating), thereby optimizing weight loss effects and improving digestive health.

[0037] In some embodiments, determining the net energy storage of the user in a specified weight loss phase using the energy intake value and the energy expenditure value can be achieved by using the following steps: The difference between the energy intake value and the energy expenditure value is taken as the user's net energy storage during the specified weight loss phase.

[0038] It should be noted that in this application, the net energy storage reflects the user's energy surplus and deficit in a specific time period. If the difference is positive, it indicates an energy surplus, which may be converted into fat or glycogen storage; if the difference is negative, it indicates an energy deficit, which helps fat decomposition and weight loss. By calculating the net energy storage, the user's energy balance status can be intuitively quantified, providing key data support for the weight loss process, helping users understand the impact of diet and exercise on weight changes, and thus formulate a more reasonable weight loss plan.

[0039] In step 104, efficiency correction factors of energy storage of various physiological mechanisms of the user are obtained, and the net energy storage amount is corrected in multiple dimensions based on each efficiency correction factor to obtain the actual storage amount of metabolic energy of the user in the specified weight loss stage.

[0040] In some embodiments, obtaining the efficiency correction factors of energy storage due to various physiological mechanisms of the user can be achieved in the following manner: the physiological mechanisms include age-related mitochondrial function decline, differences in sex hormone regulation, lack of sleep suppressing growth hormone secretion, and stress promoting gluconeogenesis. The efficiency correction factor for age-related mitochondrial function decline can be determined using the following formula: ; in, represents the efficiency correction factor for age-related decline in mitochondrial function, represents age; the efficiency correction factor for differences in sex hormone regulation can be determined using the following formula: ; in, The efficiency correction factor that represents the difference in sex hormone regulation is positive for men and negative for women. The efficiency correction factor for insufficient sleep to inhibit growth hormone secretion can be determined using the following formula: ; in, The efficiency correction factor representing the difference in sex hormone regulation; the efficiency correction factor for stress-induced gluconeogenesis can be determined using the following formula: ; in, It represents the efficiency correction factor of pressure-induced gluconeogenesis. It should be noted that, in the present application, the efficiency correction factor represents the degree of influence of various physiological mechanisms on energy storage.

[0041] In some embodiments, performing multi-dimensional correction on the net energy storage based on various efficiency correction factors to obtain the actual metabolic energy storage of the user in a specified weight loss phase can be achieved by the following steps: ; in, Indicates the actual storage capacity. represents the net energy storage, Indicates the number of types of physiological mechanisms, Indicates the Efficiency correction factor for this physiological mechanism.

[0042] It should be noted that in this application, the actual storage capacity reflects the energy value actually used by the user's body for metabolism, storage, or consumption. The actual storage capacity not only takes into account the difference between intake and consumption, but also integrates correction factors of multiple physiological mechanisms such as digestive efficiency, metabolic priority, exercise adaptability, and microbiome influence, thereby more accurately quantifying the actual distribution and storage of energy in the body. By performing multi-dimensional correction on the net energy storage capacity, the system can more accurately reflect the user's energy metabolism state, avoid errors caused by individual differences (such as digestion and absorption efficiency, metabolic abnormalities), and help to formulate more scientific weight loss strategies, such as adjusting diet structure and optimizing exercise plans, thereby improving weight loss efficiency and reducing health risks. At the same time, the introduction of dynamic correction factors enables the model to have stronger personalized adaptability, and can adjust energy balance assessment in real time according to the user's physiological changes, providing a reliable basis for long-term weight management.

[0043] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0044] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program. The program can be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, magnetic disk storage, or magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0045] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

Claims

1. A metabolic energy storage method based on multimodal and dynamic metabolic analysis, characterized in that: The steps include: During the user's weight loss process, the user's dietary information and excretion information are collected during the specified weight loss phase; Extracting multimodal metabolic parameters from the excretion information, and then determining the user's metabolic characteristics during energy excretion based on the metabolic parameters of each modality, and dynamically correcting the metabolic characteristics based on an energy expenditure model to obtain the user's energy expenditure value during a specified weight loss phase; extracting the energy intake value of the user in the specified weight loss stage from the diet information based on the energy intake model, and determining the net energy storage of the user in the specified weight loss stage by using the energy intake value and the energy expenditure value; The efficiency correction factors of energy storage of various physiological mechanisms of the user are obtained, and the net energy storage amount is corrected in multiple dimensions based on each efficiency correction factor to obtain the actual storage amount of metabolic energy of the user in the specified weight loss stage.

2. The method according to claim 1, wherein Extracting multimodal metabolic parameters from the excretion information specifically includes: Obtain all data modalities in the excretion information; extracting parameter values of each data modality from the excretion information; The multimodal metabolic parameters are determined from all parameter values.

3. The method according to claim 1, wherein The metabolic parameters of each modality are used to determine the user's metabolic characteristics during energy excretion, including: Obtain stool volume, stool color value, stool shape value, stool odor level, stool duration, dietary fiber ratio and digestion cycle from the metabolic parameters of each modality; The user's metabolic characteristics during energy excretion are determined based on the amount of stool, stool color value, stool shape value, stool odor level, stool duration, dietary fiber ratio, and digestion cycle. The metabolic characteristics are determined according to the following formula: ； in, Represents metabolic characteristics, Indicates the proportion of dietary fiber, Represents the digestive cycle, Indicates energy intake value, Indicates the amount of bowel movements, Indicates the defecation color value, Indicates the defecation shape value, Indicates the level of defecation odor, Indicates the duration of bowel movements.

4. The method according to claim 1, wherein Dynamically modifying the metabolic characteristics based on the energy consumption model to obtain the energy consumption value of the user in the specified weight loss stage specifically includes: Construct a user's energy expenditure model during a specified weight loss phase; Calculating a metabolic correction value for the user in a specified weight loss phase based on the energy expenditure model; The energy consumption value of the user in a specified weight loss stage is determined according to the metabolic correction value and the metabolic characteristic.

5. The method according to claim 1, wherein Extracting the energy intake value of the user in the specified weight loss stage from the diet information based on the energy intake model specifically includes: Obtaining food caloric value, intake weight and chewing times during each diet from the dietary information; Based on the energy intake model, all food calorific values, intake weight and chewing times are integrated and verified to obtain the user's energy intake value in the specified weight loss stage.

6. The method according to claim 1, wherein Determining the net energy storage of the user in a specified weight loss phase by using the energy intake value and the energy expenditure value specifically includes: The difference between the energy intake value and the energy expenditure value is taken as the user's net energy storage during the specified weight loss phase.

7. The method according to claim 1, wherein The net energy storage is corrected in multiple dimensions based on various efficiency correction factors to obtain the actual metabolic energy storage of the user in a specified weight loss phase, specifically including: ； in, Indicates the actual storage capacity. represents the net energy storage, Indicates the number of types of physiological mechanisms, Indicates the Efficiency correction factor for this physiological mechanism.

8. The method according to claim 1, wherein The dietary information includes food caloric value, intake weight and chewing times.

9. The method according to claim 1, wherein The excretion information includes the amount of stool, stool color, stool shape, stool odor and stool duration.

10. The method according to claim 1, wherein The efficiency correction factor represents the extent to which various physiological mechanisms affect energy storage.

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