Fat reduction energy consumption estimation method and device

Abstract

The invention is suitable for the technical field of data identification, and provides a fat-reducing energy consumption estimation method and device, and the method comprises the steps: obtaining body basic data and environment temperature, and calculating basic metabolism parameters according to the body basic data and the environment temperature; acquiring a moving track point, a current heart rate and a body surface temperature, and matching the estimated motion state and the motion intensity in each preset time period according to the moving track point, the current heart rate and the body surface temperature; and calculating fat reduction energy consumption according to the basic metabolic parameters, the estimated motion state in each preset time period and the motion intensity in each preset time period. According to the scheme, multiple key variables and dynamic data are introduced, so that the accuracy, comprehensiveness and individuation level of energy consumption estimation are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of data recognition, and particularly relates to a fat-reducing energy consumption estimation method and device. BACKGROUND

[0002] With the change of modern lifestyle and the acceleration of work pace, obesity problem is increasingly serious, becoming a global health threat. Fat reduction has become the focus of many people, and scientifically and effectively estimating energy consumption in the fat reduction process is the key to developing a reasonable fat reduction plan. However, the existing energy consumption estimation methods on the market have certain limitations, and generally cannot comprehensively and accurately reflect the actual energy consumption of individuals.

[0003] Traditional energy consumption estimation methods mostly rely on static data. Although these data can reflect the energy consumption of individuals under static conditions to some extent, their accuracy and application range are insufficient. SUMMARY

[0004] Therefore, the embodiments of the present application provide a fat-reducing energy consumption estimation method and device to solve the technical problem of insufficient accuracy and application range of traditional energy consumption estimation methods.

[0005] The first aspect of the embodiments of the present application provides a fat-reducing energy consumption estimation method, which comprises: obtaining body basic data and environmental temperature, and calculating a basic metabolism parameter according to the body basic data and the environmental temperature; wherein the body basic data comprises age, gender, height and weight; obtaining a moving trajectory point, a current heart rate and a body surface temperature, and matching an estimated motion state and a motion intensity in each preset time period according to the moving trajectory point, the current heart rate and the body surface temperature; calculating fat-reducing energy consumption according to the basic metabolism parameter, the estimated motion state in each preset time period and the motion intensity in each preset time period.

[0006] Further, the step of obtaining body basic data and environmental temperature, and calculating a basic metabolism parameter according to the body basic data and the environmental temperature comprises: when the environmental temperature is in a neutral temperature interval, substituting age, gender, height and weight into a basic metabolism function to obtain an initial metabolism parameter; wherein the basic metabolism function is: initial metabolism parameter=(10*weight)+(6.25*height)-(5*age)+gender parameter, the male gender parameter is 5, and the female gender parameter is-161; the neutral temperature interval is 18 degrees Celsius to 24 degrees Celsius; when the environmental temperature is not in the neutral temperature interval, calculating a metabolism compensation parameter based on a compensation function; adding the initial metabolic parameter and the metabolic compensation parameter to obtain the basal metabolic parameter.

[0007] Further, the step of adding the initial metabolic parameter and the metabolic compensation parameter to obtain the basal metabolic parameter comprises: when the ambient temperature is lower than the neutral temperature interval, substituting the ambient temperature into a first compensation function to obtain a first metabolic compensation parameter; wherein the first compensation function is: first metabolic compensation parameter = K_low * (T_neutral_low - T_ambient) N , K_low represents a low-temperature compensation coefficient, T_low represents a lower limit value of the neutral temperature interval, T_ambient represents the ambient temperature, and N is an index coefficient; when the ambient temperature is higher than the neutral temperature interval, substituting the ambient temperature into a second compensation function to obtain a second metabolic compensation parameter; wherein the second compensation function is: second metabolic compensation parameter = K_high * (T_ambient - T_high), wherein K_high represents a high-temperature compensation coefficient, and T_high represents an upper limit value of the neutral temperature interval.

[0008] Further, the step of substituting the ambient temperature into the second compensation function to obtain the second metabolic compensation parameter when the ambient temperature is higher than the neutral temperature interval comprises: acquiring humidity data; substituting the ambient temperature into the second compensation function to obtain a current compensation parameter; substituting the humidity data into a humidity compensation function to obtain a humidity influence factor; wherein the humidity compensation function is: humidity compensation parameter = 1 + C_humid * max(0, (RH - 0.6)), C_humid represents a humidity influence coefficient, and RH represents the humidity data multiplying the current compensation parameter and the humidity influence factor to obtain the second metabolic compensation parameter.

[0009] Further, the step of acquiring the moving trajectory point, the current heart rate and the body surface temperature, and matching the estimated motion state and the motion intensity in each preset time period according to the moving trajectory point, the current heart rate and the body surface temperature comprises: acquiring the moving trajectory point, the current heart rate and the body surface temperature; calculating an average moving speed according to a plurality of moving trajectory points in the preset time period; calculating the farthest distance between a plurality of moving trajectory points in the preset time period; acquiring a standard data set corresponding to a plurality of motion states; calculating similarity between the current data set and the standard data set; wherein the current data set comprises the average moving speed, the farthest distance, the current heart rate and the body surface temperature; taking the motion state corresponding to the maximum similarity as the estimated motion state; calculating the exercise intensity according to the estimated motion state and the current heart rate.

[0010] Further, the step of calculating the exercise intensity according to the estimated motion state and the current heart rate comprises: obtaining a plurality of preset exercise intensity intervals corresponding to a plurality of motion states, and matching a current preset exercise intensity interval corresponding to the estimated motion state; wherein the plurality of motion states comprise sleep, resting state, aerobic exercise and anaerobic exercise; different motion states correspond to different preset exercise intensity intervals respectively; calculating a heart rate difference by subtracting the resting heart rate from the current heart rate; multiplying the heart rate difference by a conversion parameter to obtain an exercise intensity coefficient; multiplying the exercise intensity coefficient by a minimum value of the current preset exercise intensity interval to obtain a current numerical value; if the current numerical value is less than a maximum value of the current preset exercise intensity interval, taking the current numerical value as the exercise intensity; if the current numerical value is not less than the maximum value of the current preset exercise intensity interval, taking the maximum value as the exercise intensity.

[0011] Further, the step of calculating the fat loss energy consumption according to the basal metabolic parameter, the estimated motion state in each preset time period and the exercise intensity in each preset time period comprises: substituting the basal metabolic parameter, the estimated motion state in each preset time period and the exercise intensity in each preset time period into an energy consumption function to obtain the fat loss energy consumption; The energy consumption function is: ; wherein D represents the fat loss energy consumption, BMR represents the basal metabolic parameter, represents the i-th preset time period, represents the exercise intensity of the i-th preset time period, represents the estimated motion state of the i-th preset time period, and m represents the number of preset time periods in 24 hours, represents the fat loss adjustment factor term of the i-th preset time period, represents the body fat rate, and A represents the age, represents the age and constitution attenuation factor, represents the curve width parameter, represents the constant term.

[0012] The second aspect of the embodiment of the present application provides a fat-reducing energy consumption estimation device, comprising: An acquisition unit is configured to acquire body basic data and environmental temperature, and calculate a basic metabolism parameter according to the body basic data and the environmental temperature; wherein the body basic data comprises age, gender, height and weight; A matching unit is configured to acquire a moving trajectory point, a current heart rate and a body surface temperature, and match an estimated motion state and a motion intensity in each preset time period according to the moving trajectory point, the current heart rate and the body surface temperature; A calculation unit is configured to calculate fat-reducing energy consumption according to the basic metabolism parameter, the estimated motion state in each preset time period and the motion intensity in each preset time period.

[0013] The third aspect of the embodiment of the present application provides a terminal device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the fat-reducing energy consumption estimation method of the first aspect when executing the computer program.

[0014] The fourth aspect of the embodiment of the present application provides a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the steps in the fat-reducing energy consumption estimation method of the first aspect.

[0015] Compared with the prior art, the embodiment of the present application has the beneficial effects that not only the basic body data such as age, gender, height and weight are considered, but also the important variable of environmental temperature is introduced to calculate the basic metabolism parameter. This comprehensive method can more accurately reflect the energy consumption of an individual in a static state. By acquiring dynamic data such as moving trajectory points, current heart rates and body surface temperatures, the method can capture the actual state and changes of an individual in the process of exercise in real time. This makes the energy consumption estimation not only dependent on static parameters, but also capable of dynamic adjustment and accurate calculation. By combining the basic metabolism parameter, the motion state and the motion intensity to calculate the energy consumption, the energy consumption of an individual in different preset time periods can be more accurately reflected. Through real-time acquisition and analysis of dynamic data, the method can timely reflect the energy consumption changes of an individual in different motion states, improving the accuracy of the estimation. By introducing the environmental temperature, the present application can maintain high estimation accuracy under different environmental conditions, has good adaptability and a wide range of application scenarios. The overall performance of the battery is improved. In summary, the present application realizes fine management of the energy storage battery by real-time monitoring and dynamic adjustment of charging parameters, significantly improving the life, charging efficiency, safety and overall performance of the battery. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to make the technical solutions in the embodiments of the present application clearer, the accompanying drawings needed to be used in the embodiments or related description will be briefly introduced. Obviously, the accompanying drawings in the following description only show some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0017] Figure 1 A schematic flow chart of a method for estimating fat-free energy consumption is shown; Figure 2 A schematic diagram of a device for estimating fat-free energy consumption is shown; Figure 3 A schematic diagram of a terminal device is shown. DETAILED DESCRIPTION

[0018] In the following description, specific details are set forth in order to provide a thorough understanding of embodiments of the present application. However, persons of ordinary skill in the art will readily recognize that embodiments of the present application can be practiced without these specific details. In other instances, well-known structures, devices, circuits, and methods have not been described in detail in order to avoid obscuring the present application.

[0019] Embodiments of the present application provide a method and device for estimating fat-free energy consumption, to solve the technical problem of insufficient accuracy and application range of traditional energy consumption estimation methods.

[0020] First, the present application provides a method for estimating fat-free energy consumption. Please refer to Figure 1 , Figure 1 A schematic flow chart of a method for estimating fat-free energy consumption is shown. As shown in Figure 1 , the method for estimating fat-free energy consumption can include the following steps: Step 101: Obtain body basic data and environmental temperature, and calculate basic metabolic parameters according to the body basic data and environmental temperature; wherein the body basic data includes age, gender, height and weight; Body basic data is the most core parameter for calculating basal metabolic rate. The basal metabolic rates of people of different ages, genders and body types are significantly different.

[0021] Traditional calculations often ignore the impact of ambient temperature on metabolism. Ambient temperature (especially cold environment) can significantly affect basal metabolic rate, as the body needs to consume additional energy to maintain constant core body temperature (thermogenic effect). Incorporating ambient temperature into the calculation significantly improves the accuracy of basal energy consumption estimates in non-“thermal comfort zone” environments, expanding the application range (for example, in winter outdoor or air conditioning temperature settings). The basal metabolic parameter is not just the traditional BMR value, but a “corrected static metabolic benchmark” that integrates the impact of ambient temperature. This parameter represents the basal energy consumption rate (in kcal / day or kcal / hour) required to maintain basic life activities at a specific ambient temperature. It is the starting point for subsequent calculations of fat loss energy consumption.

[0022] Specifically, step 101 specifically includes steps 1011 to 1013: Step 1011: When the ambient temperature is in the neutral temperature range, age, gender, height and weight are substituted into the basal metabolic function to obtain an initial metabolic parameter; wherein the basal metabolic function is: initial metabolic parameter = (10*weight) + (6.25*height) - (5*age) + gender parameter, male gender parameter is 5, female gender parameter is -161; the neutral temperature range is 18 degrees Celsius to 24 degrees Celsius; Neutral temperature range (18 degrees Celsius to 24 degrees Celsius), this range is defined as the thermal comfort zone or temperature neutral zone. Within this range, the additional energy consumed by the human body to maintain core body temperature is minimal (mainly relying on physical regulation such as vasodilation), and the basal metabolism is relatively stable and minimally affected by temperature. 18 degrees Celsius to 24 degrees Celsius is a comfortable temperature range based on physiological research. When the ambient temperature is in the neutral range (18 degrees Celsius to 24 degrees Celsius), the value calculated by this formula is (or very close to) the true basal metabolic rate of the user in the current body state. Therefore, under this specific condition, the initial metabolic parameter can be directly regarded as the final basal metabolic parameter (or as its core part).

[0023] Body basic data includes but is not limited to age, gender, height, weight. Body weight directly reflects body mass and is one of the main determinants of energy consumption. Height affects body surface area and is related to heat dissipation. Metabolic rate decreases with age. Male (+5) and female (-161) in the gender parameter reflect gender differences (usually men have more muscle mass and faster metabolism).

[0024] Step 1012: When the ambient temperature is not in the neutral temperature range, calculate the metabolic compensation parameter based on the compensation function; At low temperatures (<18°C) the body needs to increase heat production (shivering and non-shivering thermogenesis) to maintain body temperature, leading to an increase in basal metabolic rate. Energy expenditure is primarily used to produce heat. At high temperatures (>24°C) the body needs to increase heat loss (sweating, vasodilation), a process that itself consumes energy (especially when sweating heavily). While basal metabolic rate itself can vary less dramatically than at low temperatures, the additional energy expenditure to maintain a stable body temperature increases significantly. High temperature environments can also cause a slight increase in basal metabolic rate.

[0025] Traditional methods do not take into account the effect of ambient temperature, they implicitly assume a neutral temperature environment. Therefore, at non-neutral temperatures, direct use of the formula results can be inaccurate, underestimating the actual basal metabolic requirements at low temperatures, and possibly underestimating (due to heat loss consumption) or requiring special consideration at high temperatures.

[0026] The metabolic compensation parameters represent the additional energy consumption values that need to be added (at low temperatures) or possibly adjusted (at high temperatures) relative to the initial metabolic parameters at neutral temperatures, due to the ambient temperature deviating from the neutral interval.

[0027] Specifically, step 1012 specifically comprises steps A1 to A2: Step A1: When the ambient temperature is lower than the neutral temperature interval, substitute the ambient temperature into the first compensation function to obtain the first metabolic compensation parameter; wherein the first compensation function is: first metabolic compensation parameter = K_low * (T_neutral_low - T_ambient) N , K_low represents a low-temperature compensation coefficient, T_low represents the lower limit value of the neutral temperature interval, T_ambient represents the ambient temperature, and N is an exponential coefficient; (T_neutral_low - T_ambient) is the temperature difference. For example, if the current ambient temperature T_ambient = 10°C, the difference is 18°C - 10°C = 8°C. The larger this difference, the colder the environment, the more compensation needed.

[0028] N is an exponential coefficient (N >= 1, exemplarily: N = 1.5), reflecting the nonlinearity of body temperature regulation (the greater the temperature difference, the more additional energy required for each 1 degree drop).

[0029] Physiological studies have shown that in a low-temperature environment, the increased metabolic rate of the human body to maintain body temperature (especially through non-shivering thermogenesis and shivering thermogenesis) is not a simple linear relationship with the temperature difference. As the temperature decreases, the rate of increase in metabolic rate will accelerate (i.e., non-linear growth). The exponential function (ΔT)^N (N>1) can better simulate this accelerated growth characteristic. For example: ΔT = 2°C, N=1.8 => (2)^1.8≈3.48; ΔT = 4°C (doubled temperature difference), (4)^1.8≈12.13 (result increased more than 3 times, not 2 times of non-linear growth).

[0030] K_low is to convert the calculated temperature difference effect (exponentially amplified) into the actual energy consumption increment (unit consistent with the initial metabolic parameters, such as kilocalories per day).

[0031] The determination of K_low is very critical and needs to be calibrated based on physiological experimental data, which comprehensively reflects the average additional energy consumption per unit temperature difference (after exponential transformation) of the human body at low temperature. Exemplarily, K_low is 0.67.

[0032] The first compensation function non-linearly captures the physiological phenomenon that the additional energy consumption required by the human body to maintain the basal body temperature accelerates to increase as the ambient temperature decreases in cold environments. This is an important improvement in the low-temperature compensation accuracy of the scheme.

[0033] Step A2: when the ambient temperature is higher than the neutral temperature interval, substituting the ambient temperature into the second compensation function to obtain the second metabolic compensation parameter; wherein the second compensation function is: second metabolic compensation parameter = K_high * (T_ambient - T_high), wherein K_high represents a high-temperature compensation coefficient, and T_high represents an upper limit value of the neutral temperature interval.

[0034] (T_ambient - T_high) is the temperature difference. For example, if the current ambient temperature T_ambient = 30°C, the difference is 30°C - 24°C = 6°C. The larger this difference, the hotter the environment, and the more compensation required.

[0035] K_high is to linearly convert the calculated temperature difference into the actual energy consumption increment (unit such as kilocalories per day). K_high is usually smaller than the low-temperature coefficient (heat dissipation mainly relies on physiological regulation rather than pure metabolic improvement), and exemplarily, K_high is 0.43.

[0036] Relative to the intense non-shivering / shivering heat production at low temperature, the increased energy consumption in high-temperature environment is mainly from the active heat dissipation process (such as sweating). Although sweating itself also consumes energy, the energy consumption and temperature difference are approximately linearly increasing in a certain high-temperature range outside the comfort zone (especially in a model where humidity is controllable or individual differences are relatively stable).

[0037] The basal metabolic rate of the human body itself changes less significantly at high temperatures (possibly slightly increasing), and the main additional energy consumption comes from heat dissipation. Using a linear function K_high * ΔT is a good trade-off between accuracy and complexity, which can effectively simulate the stable increase in energy consumption as the temperature rises at high temperatures.

[0038] The second compensation function captures the physiological phenomenon of the stable increase in additional energy consumption required by the human body to maintain body temperature stability (mainly through sweating to dissipate heat) as the ambient temperature rises in hot environments through a linear model.

[0039] In the embodiments corresponding to steps A1 to A2, the accuracy of basal metabolic rate estimation in extreme temperature environments (severe cold or scorching heat) is significantly improved through a function form that is more consistent with physiological reality (especially non-linear at low temperatures). The fat loss energy consumption estimation scheme can be more reliably applied to winter outdoor sports, cold regions, summer high-temperature work, tropical regions, and other scenarios where traditional methods have large errors.

[0040] Specifically, step A2 specifically includes steps A21 to A24: Step A21: Collect humidity data; In a high-temperature environment, humidity has a crucial impact on the efficiency of human body heat dissipation and energy consumption, often even greater than the impact of temperature itself.

[0041] In a high-temperature + low-humidity environment, the human body can dissipate heat through effective evaporation of sweat (explicit sweating), although it consumes energy, but the heat dissipation efficiency is high. In a high-temperature + high-humidity environment, the air humidity is high, and sweat is difficult to evaporate (low evaporation efficiency), the body cannot effectively dissipate heat, leading to a higher core body temperature, requiring more effort (such as faster and more sweating) to try to dissipate heat. Cardiovascular load increases, heart rate increases to promote blood flow to the skin to dissipate heat. Energy consumption increases significantly, a large amount of ineffective sweating itself consumes energy, and the body is in a stronger heat stress state. Traditional high-temperature compensation models based only on temperature will severely underestimate the real energy consumption in high-humidity environments.

[0042] Step A22: Substitute the ambient temperature into the second compensation function to obtain the current compensation parameter; The current compensation parameter = K_high * (T_ambient - T_high), where K_high represents the high-temperature compensation coefficient, and T_high represents the upper limit value of the neutral temperature interval.

[0043] Step A23: Substitute the humidity data into the humidity compensation function to obtain the humidity influence factor; wherein the humidity compensation function is: humidity compensation parameter = 1 + C_humid * max(0, (RH - 0.6)), C_humid represents the humidity influence coefficient, and RH represents the humidity data. RH (relative humidity) is an input value, ranging between 0 (0%) and 1 (100%). 0.6 (60%) is a critical threshold (as the "critical humidity" or "upper comfort limit"). Physiological studies have shown that when the relative humidity is below about 60%, the human body has a relatively high efficiency in dissipating heat through sweat evaporation, and the additional burden of humidity on energy consumption is relatively small. When the humidity exceeds 60%, the evaporation efficiency decreases significantly, and heat dissipation becomes difficult, leading to a non-linear significant increase in energy consumption.

[0044] max(0,...) is used to ensure that the humidity compensation term only takes effect and is positive when RH > 60%. If RH <= 60%, the term is 0, and the humidity impact factor = 1 (i.e., no additional compensation is added).

[0045] C_humid (humidity impact coefficient) is a positive proportional coefficient. It determines the degree of amplification of energy consumption compensation per unit of excess over the critical value (60%) when the humidity exceeds the critical value.

[0046] For example, if C_humid = 0.5 and RH = 0.8 (80%): (RH - 0.6) = 0.2; C_humid * max(0, (RH-0.6)) = 0.5 * 0.2 = 0.1; humidity impact factor = 1 + 0.1 = 1.1; The humidity impact factor is usually greater than or equal to 1. It represents the amplification factor of high humidity (RH > 60%) on the basic high temperature compensation parameter (compensation based only on temperature). The higher the humidity (the more it exceeds 60%), the larger the factor, meaning the heavier the additional energy consumption burden caused by humidity.

[0047] Step A24: multiply the current compensation parameter and the humidity impact factor to obtain a second metabolic compensation parameter.

[0048] The impact of humidity is not simply added to the temperature impact, but significantly amplifies (or multiplies) the additional energy consumption burden caused by high temperature itself. In a high temperature environment, high humidity greatly increases the heat stress and energy consumption caused by the same temperature increase. Therefore, the basic compensation amount calculated based only on temperature (the current compensation parameter) is multiplied by the humidity impact factor representing the amplification effect of humidity to obtain a second metabolic compensation parameter.

[0049] In the embodiments corresponding to steps A21 to A24, the key defect of serious underestimation of energy consumption in high-humidity hot environments (such as tropical rainforest climate, sauna, high-temperature and high-humidity working environment) is solved. Traditional methods ignore humidity or only make simple superposition, while the scheme accurately captures the destructive effect of humidity on heat dissipation efficiency and its nonlinear amplification effect on energy consumption through a multiplication model and a critical humidity threshold. The amplification effect of high humidity (RH > 60%) on the consumption is more accurately quantified (multiplied by a factor greater than 1), thereby providing an unprecedented accurate benchmark for the final calculation of basal metabolic parameters and subsequent fat loss energy consumption in hot and humid hot environments.

[0050] Step 1013: Adding the initial metabolic parameter to the metabolic compensation parameter to obtain the basal metabolic parameter.

[0051] The basal metabolism at neutral temperature (determined by body basic data) is combined with the additional metabolic demand caused by the environmental temperature (determined by the compensation function). The basal metabolic parameter calculated through this step is no longer the BMR considering only body data in the traditional sense, but contains a value closer to the user's current actual basal energy demand level under the influence of a specific environmental temperature (especially non-neutral temperature). It represents a more accurate energy consumption benchmark value (usually still in kilocalories / day) required to maintain basic life activities (such as breathing, circulation, and body temperature maintenance) at the current environmental temperature.

[0052] In the neutral temperature range (18-24°C): the metabolic compensation parameter should be 0 (or very close to 0) in theory, and the basal metabolic parameter ≈ the initial metabolic parameter.

[0053] In the non-neutral temperature range (<18°C or >24°C): the metabolic compensation parameter is not 0, and the basal metabolic parameter is adjusted to better reflect the true static metabolic demand under temperature stress.

[0054] In the embodiments corresponding to steps 1011 to 1013, the impact of environmental temperature on basal metabolism is clearly and operatively included in the calculation model by introducing the definition of "neutral temperature range" and the concept of "compensation function". This is the key to improving accuracy. Through the compensation mechanism, the scheme can be effectively applied to various environmental temperatures, especially in cold environments (such as winter outdoor, cold storage work) and hot environments (such as summer high temperature, tropical regions, high-temperature work) where traditional methods fail or have large errors. The basal energy consumption of users in these environments can also be relatively accurately estimated.

[0055] Step 102: Obtain the mobile trajectory point, the current heart rate and the body surface temperature, and match the estimated exercise state and exercise intensity in each preset time period according to the mobile trajectory point, the current heart rate and the body surface temperature; Capturing "activity increments" (estimated activity levels and intensity) aims to dynamically monitor a user's physical activity and quantify the extra energy expenditure resulting from activity. This portion represents the most volatile part of daily energy expenditure and is the primary target of fat loss interventions (such as exercise).

[0056] Mobile tracking points are acquired via GPS, Wi-Fi positioning, or indoor positioning technologies. This provides information about the user's location changes and is crucial for determining the user's activity patterns (such as walking, running, cycling, or stationary) and the distance / speed of movement. For example, changes in the density and speed of tracking points can distinguish between walking, running, or using transportation.

[0057] Current heart rate is one of the most direct physiological indicators for measuring exercise intensity. Heart rate typically has a linear relationship with oxygen uptake (VO2) and energy expenditure rate (especially at moderate to high intensity). Heart rate data can more accurately reflect the actual load the body experiences during activity, even for activities with minimal trajectory changes (such as weightlifting and yoga). Its introduction has greatly improved the accuracy of exercise intensity estimation.

[0058] High-intensity exercise is typically accompanied by an increase in body surface temperature (increased heat dissipation). During activity in extreme temperatures (especially high temperatures), the body needs to expend additional energy to dissipate heat through sweating to maintain a stable body temperature. Combining body surface temperature with ambient temperature allows for a more precise assessment of the energy expenditure from non-exercise activities, a component that traditional methods tend to underestimate. The inclusion of body surface temperature significantly enhances the accuracy of energy expenditure estimations for activities in specific environments (high temperature, high humidity).

[0059] The system sets preset time periods (e.g., every minute, every 5 minutes, or dynamically divided according to activity changes). Within these periods, it comprehensively analyzes trajectory data (pattern, speed, distance), heart rate data (average heart rate, peak heart rate, heart rate variability, etc.), body surface temperature data (trend, temperature difference with ambient temperature), and potentially pre-stored activity pattern databases or rules. It outputs the estimated activity state (e.g., sitting, slow walking, brisk walking, running, climbing stairs, strength training, etc.) and exercise intensity (e.g., low, medium, high, or expressed as metabolic equivalents (METs)). This matching process utilizes multi-sensor data fusion technology, which can more accurately identify activity states and intensities under complex or static postures than a single sensor (e.g., using only an accelerometer).

[0060] Specifically, step 102 includes steps 1021 to 1027: Step 1021: Collect movement trajectory points, current heart rate, and body surface temperature; The system continuously or periodically collects the following data within each preset time period (e.g., every minute, every 5 minutes): movement trajectory points, current heart rate, and body surface temperature.

[0061] Moving trajectory points come from GPS, Wi-Fi, Bluetooth beacon or cellular network positioning, recording the user's location information (latitude and longitude, possibly including height, accuracy). The current heart rate comes from a heart rate sensor (optical PPG or electrode type), reflecting the intensity of cardiovascular activity, which is a direct indicator of exercise intensity. Body surface temperature comes from skin temperature sensors (such as wrist, chest), reflecting body heat dissipation status and environmental thermal effects.

[0062] Step 1022: Calculate the average moving speed according to the plurality of moving trajectory points within the preset period; Calculate the average value of the speed between all adjacent trajectory points within the period. Speed is one of the most core indicators for distinguishing different activity types (such as standing, walking, running, cycling, driving). For example: standing ≈ 0 km / h, walking ≈ 4-6 km / h, running > 7 km / h.

[0063] Step 1023: Calculate the farthest distance between the plurality of moving trajectory points within the preset period; Calculate the maximum straight-line distance between any two points among all trajectory points within the period. The farthest distance is used to distinguish "stationary activities" or "displacement activities": Stationary activities: such as strength training, yoga, HIIT (high-intensity interval training), skipping, jumping in place, etc. Although such activities may have high intensity (high heart rate), the spatial position moves very little or reciprocally within a small range, resulting in a very small farthest distance value (close to 0 or a few meters).

[0064] Displacement activities: such as walking, running, cycling, etc. Such activities are accompanied by significant position changes, and the farthest distance value is relatively large (tens of meters to hundreds of meters or even several kilometers).

[0065] The traditional method of relying only on the traditional method of accelerometer may not be able to distinguish between high-intensity jumping in place (small range) and low-intensity slow walking (large range), as they may have similar acceleration patterns. The farthest distance feature clearly captures this spatial scale difference, significantly improving the accuracy of activity recognition, especially for non-displacement activities.

[0066] Step 1024: Obtain a plurality of standard data sets corresponding to a plurality of exercise states; The system needs to pre-store a standard dataset (or called knowledge base, template library). This dataset contains multiple predefined exercise states. Exemplarily, the standard dataset for anaerobic exercise is: [speed (m / s): 0.2, farthest distance (m): 5.0, heart rate (bpm): 125, temperature (°C): 36.3], and the standard dataset for aerobic exercise is: [speed (m / s): 2.8, farthest distance (m): 300.0, heart rate (bpm): 145, temperature (°C): 37.0]. Each exercise state can correspond to multiple different standard datasets.

[0067] For each exercise state, its corresponding standard dataset should contain the typical or average characteristic values in this state.

[0068] These standard datasets can be obtained through controlled experiments in a laboratory environment (let different subjects perform various activities and record data), large-scale real-world data collection and labeling, or machine learning model training, etc.

[0069] Step 1025: Calculate the similarity between the current dataset and the standard dataset; wherein the current dataset includes the average moving speed, the farthest distance, the current heart rate, and the body surface temperature. Combine the four key characteristic values calculated / acquired in the current preset period into a feature vector, called the current dataset: [average moving speed, farthest distance, current heart rate, body surface temperature].

[0070] Traverse the standard dataset, and for each predefined exercise state (such as "running", "strength training", "sitting still", etc.) in the standard dataset, obtain its corresponding standard feature vector (such as [running_average_speed, running_farthest_distance, running_heart_rate, running_body_surface_temperature]).

[0071] Use a similarity measure algorithm to calculate the similarity or distance between the current dataset (feature vector) and the feature vector of each standard exercise state. The similarity measure algorithm can be Euclidean distance (the smaller the better), cosine similarity (the larger the better), or Manhattan distance.

[0072] This step simultaneously combines spatial movement features (speed, distance), cardiovascular features (heart rate), and thermal features (body surface temperature) for comprehensive judgment. For example: A combination of very small farthest distance, medium average speed, high heart rate, and high body surface temperature is more likely to match "high-intensity in-place training" (such as jumping rope, burpee jumping) rather than "slow walking" or "riding a car".

[0073] A combination of high average speed, large maximum distance, high heart rate, and high skin temperature is matched to "running" or "cycling".

[0074] Skin temperature can help distinguish between being in a hot environment (higher temperature) or a cool environment (lower temperature) at similar speed and heart rate, or distinguish between activities with different heat dissipation efficiency (e.g. swimming vs. running, but swimming can have no trajectory points) in the same environment.

[0075] Step 1026: The motion state corresponding to the maximum similarity is determined as the estimated motion state. After calculating the similarity of the current period data with all standard motion states, the standard motion state with the highest similarity (or the smallest distance) is found. This state is determined as the estimated motion state in the preset period.

[0076] Step 1027: Calculate the exercise intensity according to the estimated motion state and the current heart rate.

[0077] In the embodiments corresponding to steps 1021 to 1027, the extraction and application of the maximum distance are significant innovations, effectively solving the confusion between "high-intensity activities in place" (small spatial range) and "displacement activities" (large spatial range), greatly improving the recognition accuracy of non-displacement movements such as strength training, HIIT, and yoga, which is a major weakness of traditional accelerometer-based solutions. It is emphasized that spatial movement features (speed, distance), cardiovascular features (heart rate), and thermal features (skin temperature) are used simultaneously for similarity calculation, and multiple complementary information sources are utilized to improve the robustness and accuracy of activity state classification, especially in complex or edge scenarios (such as when the environmental temperature is abnormal). First, identify the activity type (state), and then calculate the activity intensity based on the type and real-time physiological feedback (heart rate). This is more consistent with physiological logic (different types of activities have different heart rate-intensity relationships), and the results are more accurate.

[0078] Specifically, step 1027 specifically includes steps B1 to B6: Step B1: Obtain a plurality of motion state corresponding preset exercise intensity intervals, and match the current preset exercise intensity interval corresponding to the estimated motion state; wherein the plurality of motion states include sleep, resting state, aerobic exercise, and anaerobic exercise; different motion states correspond to different preset exercise intensity intervals; A motion intensity interval ([Min, Max]) that matches the physiological characteristics of each type of motion state is preset. For example: Sleep interval: [0.7, 1.0]; Resting state: [1.0, 1.5]; Aerobic exercise: [1.5, 12.0] (wide range); Anaerobic exercise: [6.0, 15.0] (higher starting point).

[0079] Step B2: Calculate the heart rate difference by subtracting the resting heart rate from the current heart rate. The resting heart rate refers to the lowest stable heart rate of an individual in a completely awake, extremely relaxed, and unstimulated state (e.g., just getting up without activity). It is an indicator of the individual's basic cardiovascular state. Using the heart rate difference can better eliminate individual differences (e.g., different resting heart rates among different people) and more accurately reflect the individual's relative exercise load than using the current heart rate or heart rate / maximum heart rate percentage.

[0080] Step B3: Multiply the heart rate difference by the conversion parameter to obtain the exercise intensity coefficient. The conversion parameter is a key scaling factor or unit conversion factor. Its role is to linearly map the heart rate difference (HRR, unit: bpm) to a dimensionless or specific unit (e.g., METs) intensity indicator (exercise intensity coefficient).

[0081] For example: In the moderate intensity range of aerobic exercise, exercise intensity (usually represented by oxygen consumption VO2 or METs) is approximately linearly related to heart rate difference (HRR) (Fick's principle). The conversion parameter K can be understood as the slope (for example, for every 1 MET increase in intensity, the heart rate difference increases by approximately X bpm).

[0082] The conversion parameter can be determined through individualized exercise testing (such as treadmill testing to measure VO2 and heart rate at different intensities), or using population average values / experience values according to state categories.

[0083] The conversion parameter K can be set according to the oxygen pulse (O2 Pulse). Example: VO2max=45ml / kg / min (≈12.8MET), resting metabolism=1 MET, maximum heart rate difference=190-55=135→ K = (12.8-1) / 135 ≈ 0.087 MET / bpm.

[0084] Step B4: Multiply the exercise intensity coefficient by the minimum value of the current preset exercise intensity interval to obtain the current value. The exercise intensity coefficient is designed as a scaling factor (for example, when the exercise intensity coefficient=1.0, it represents the "basic activity intensity"). Then the minimum value of the exercise intensity coefficient represents the minimum value multiplied by the exercise intensity coefficient. For example, if the exercise intensity coefficient of a certain aerobic activity is calculated to be 2.0, and the minimum value of the interval is 1.5, then the current value=2.0*1.5=3.0 (METs), which can be understood as doubling the intensity from the basic aerobic intensity of 1.5 METs to 3.0 METs.

[0085] Step B5: If the current value is less than the maximum value of the current preset exercise intensity interval, then the current value is taken as the exercise intensity; Step B6: If the current value is not less than the maximum value of the current preset exercise intensity interval, then the maximum value is taken as the exercise intensity.

[0086] The linear relationship between heart rate difference (HRR) and exercise intensity mainly holds in the range of moderate intensity aerobic exercise. At very high intensity (anaerobic exercise) or close to maximum heart rate, the heart rate rising speed can not keep up with the actual intensity (energy metabolism) increase (nonlinear). At the same time, there is a heart rate ceiling (maximum heart rate).

[0087] Each activity type has its inherent upper limit of intensity. For example, the intensity of sitting cannot reach that of running. After being identified as "sitting", the intensity must be constrained within [1.0, 1.5], even if the heart rate is temporarily elevated due to tension or caffeine.

[0088] Current value < Max considers that the calculated intensity value (current value) is within the physiological reasonable range ([Min, Max]), and accepts the value as the final exercise intensity.

[0089] Current value >= Max considers that the calculated value exceeds the physiological upper limit of intensity (Max) for this type of activity, and at this time the exercise intensity is set to the upper limit value Max.

[0090] Prevent the calculated value based on the linear model from being unreasonably overestimated in the actual intensity when the exercise is at high intensity / anaerobic or the heart rate reaches the plateau / maximum. Force to ensure that the identified exercise state matches its possible intensity range (such as the intensity cannot be >1.0 METs when identified as "sleep"). This improves the physiological reasonableness and robustness of the results.

[0091] In the embodiments corresponding to steps B1 to B6, (current heart rate - resting heart rate) is used as input, which more accurately reflects the individual relative load and eliminates the influence of individual differences in resting heart rate. Flexibility is provided to allow linear mapping of heart rate difference to intensity coefficient, and parameter K can be individualized or optimized by state category. In calculating the preliminary intensity (current value), the basic intensity level (Min) of this type of activity is integrated. Finally, this preliminary value is forced to not exceed the maximum physiological intensity (Max) of this type of state, so as to obtain a final exercise intensity value that reflects the real-time heart rate load, conforms to the physiological constraints of the activity type, and does not overestimate in extreme cases.

[0092] Step 103: Calculate the fat loss energy consumption according to the basal metabolism parameter, the estimated exercise state in each preset time period, and the exercise intensity in each preset time period.

[0093] The total energy consumption of all preset time periods is accumulated, and the total energy consumption of a period of time (such as one day) is obtained.

[0094] Specifically, step 103 specifically comprises: substituting the basal metabolism parameter, the estimated motion state in each preset time period and the motion intensity in each preset time period into an energy consumption function to obtain the fat-reducing energy consumption; The energy consumption function is: ; wherein D represents the fat-reducing energy consumption, BMR represents the basal metabolism parameter, represents the i th preset time period, represents the motion intensity of the i th preset time period, represents the estimated motion state of the i th preset time period, and m represents the number of preset time periods in 24 hours, represents the fat-reducing adjustment factor term of the i th preset time period, represents the body fat rate, and A represents the age, represents the age and constitution attenuation factor, represents the curve width parameter, represents the constant term.

[0095] is a time period energy consumption term. The energy consumption function calculates the fat-reducing energy consumption based on the motion intensity, the estimated motion state and the basal metabolism parameter of each minute (preset time period) in units of minutes. As a nonlinear synergistic factor, short-term heat combustion is converted into long-term fat-reducing benefits. represents the multiple of the active metabolic rate and the resting metabolism. BMR is used to provide an energy baseline, is used to introduce the time dimension, is used to superimpose the intensity variable, is used to capture the motion type specificity. is a fat-reducing adjustment factor term, which is a nonlinear function estimating the proportion of fat oxidation in the energy consumption of the preset time period. It depends on the motion intensity , the motion state and the basal metabolism parameter (body fat rate , age A). Among them, the proportion of fat oxidation is not a fixed value, but a nonlinear function dynamically adjusted by the motion intensity, the motion state and the individual basal parameters (body fat rate, age). Therefore, the fat-reducing adjustment factor combines a Gaussian peak function (simulating an intensity-dependent inverted U-shaped curve) and a linear correction term (integrating individual parameters). exp() is used to smooth the fitting of the inverted U-shaped curve, and the derivative is continuous, avoiding the jump of the segmented function.

[0096] The primary term is used to simulate the inverted U-shaped curve of fat oxidation ratio as a function of intensity; the peak position is affected by the motion state. Modulation. Among them, in In this context, a baseline value of 0.6 represents a normal body fat percentage. At this level, the proportion of fat oxidation under moderate intensity is approximately 60%. The coefficient of 0.2 is based on clinical research: for every 10% increase in body fat percentage, the fat oxidation potential increases by 2-5% (approximately linear).

[0097] Curve width parameter A fixed value of 0.05 is used for calibration to ensure the curve covers the typical MET range (1-12). When = 0.05: Deviation 2 MET, the proportion of lipid oxidation decreased by 25% (consistent with experimental data).

[0098] The baseline is based on the following: for every year after age 30, fat oxidation efficiency decreases by 1% (based on the annual decay rate of muscle mitochondrial density). Every 10% increase in body fat percentage partially offsets the age effect (+0.5%) due to the release of free fatty acids from adipose tissue. =0.2, to ensure extreme scenarios (such as...) =15MET) (Approximately 20% of the energy comes from fat, consistent with high-intensity interval training data). By comparing traditional prediction methods with energy expenditure function predictions, the data shown in Table 1 were obtained.

[0099] Table 1: ; Traditional methods have a prediction error of up to 27%. The energy consumption function prediction is less than 3%, demonstrating the biological necessity of multidimensional data coupling in the energy consumption function.

[0100] In the embodiments corresponding to steps 101 to 103, not only the basic physical data such as age, gender, height, weight, etc. are considered, but also the important variable of environmental temperature is introduced to calculate the basal metabolic parameter. This comprehensive method can more accurately reflect the energy consumption of the individual in a static state. By obtaining dynamic data such as moving trajectory points, current heart rate and body surface temperature, this method can capture the actual state and changes of the individual in the process of exercise in real time. This makes the energy consumption estimation not only dependent on static parameters, but also dynamically adjusted and accurately calculated. By combining the basal metabolic parameter, exercise state and exercise intensity to calculate the energy consumption, the energy consumption of the individual in different preset periods can be more accurately reflected. Through real-time acquisition and analysis of dynamic data, this method can timely reflect the energy consumption changes of the individual in different exercise states, improving the accuracy of the estimation. By introducing the environmental temperature, the present application can maintain high estimation accuracy under different environmental conditions, has good adaptability and wide application scenarios. The overall performance of the battery is improved. In summary, the present application realizes fine management of the energy storage battery by real-time monitoring and dynamic adjustment of the charging parameters, significantly improves the life, charging efficiency, safety and overall performance of the battery.

[0101] As Figure 2 The present application provides a kind of energy consumption estimation device for reducing fat, please see Figure 2 , Figure 2 A kind of energy consumption estimation device for reducing fat provided by the present application is shown, as Figure 2 A kind of energy consumption estimation device for reducing fat includes: The acquisition unit 21 is used to obtain body basic data and environmental temperature, and calculate basal metabolic parameter according to body basic data and environmental temperature;Wherein, the body basic data includes age, gender, height and weight; Matching unit 22 is used to obtain moving trajectory points, current heart rate and body surface temperature, and match the estimated exercise state and exercise intensity in each preset period according to moving trajectory points, current heart rate and body surface temperature; The calculation unit 23 is used to calculate the energy consumption for reducing fat according to the basal metabolic parameter, the estimated exercise state in each preset period and the exercise intensity in each preset period.

[0102] This invention provides a fat-loss energy consumption estimation device that considers not only basic body data such as age, gender, height, and weight, but also introduces ambient temperature as an important variable to calculate basal metabolic parameters. This comprehensive method can more accurately reflect an individual's energy consumption under static conditions. By acquiring dynamic data such as movement trajectory points, current heart rate, and body surface temperature, this method can capture the actual state and changes of an individual during exercise in real time. This allows energy consumption estimation to not only rely on static parameters but also to be dynamically adjusted and accurately calculated. By combining basal metabolic parameters, exercise state, and exercise intensity to calculate energy consumption, it can more accurately reflect an individual's energy consumption in different preset time periods. Through real-time acquisition and analysis of dynamic data, this method can promptly reflect changes in an individual's energy consumption under different exercise states, improving the accuracy of the estimation. By introducing ambient temperature, this invention can maintain high estimation accuracy under different environmental conditions, exhibiting good adaptability and a wide range of application scenarios. It also improves the overall performance of the battery. In summary, this invention, through real-time monitoring and dynamic adjustment of charging parameters, achieves refined management of energy storage batteries, significantly improving battery life, charging efficiency, safety, and overall performance.

[0103] Figure 3 This is a schematic diagram of a terminal device provided in an embodiment of the present invention. Figure 3 As shown, a terminal device 3 in this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as a fat-loss energy consumption estimation program. When the processor 30 executes the computer program 32, it implements the steps in each of the above-described fat-loss energy consumption estimation method embodiments, for example... Figure 1 Steps 101 to 103 are shown. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each unit in the above-described device embodiments, for example... Figure 2 The function of the unit shown.

[0104] For example, the computer program 32 can be divided into one or more units, which are stored in the memory 31 and executed by the processor 30 to complete the present invention. The one or more units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 32 in the terminal device 3. For example, the specific functions of each unit of the computer program 32 can be divided as follows: The acquisition unit is used to acquire basic body data and ambient temperature, and calculate basal metabolic parameters based on the basic body data and ambient temperature; wherein, the basic body data includes age, gender, height and weight; The matching unit is configured to acquire the mobile trajectory point, the current heart rate and the body surface temperature, and match the estimated motion state and the motion intensity in each preset period according to the mobile trajectory point, the current heart rate and the body surface temperature. The calculating unit is configured to calculate the fat-reducing energy consumption according to the basal metabolism parameter, the estimated motion state in each preset period and the motion intensity in each preset period.

[0105] The terminal device 3 includes but is not limited to a processor 30 and a memory 31. Those skilled in the art can understand that, Figure 3 The terminal device 3 is only an example and does not constitute a limitation on the terminal device 3, and can include more or fewer components than the illustration, or combine certain components, or different components, for example, the terminal device can also include an input / output device, a network access device, a bus, etc.

[0106] The processor 30 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0107] The memory 31 can be an internal storage unit of the terminal device 3, for example, a hard disk or a memory of the terminal device 3. The memory 31 can also be an external storage device of the terminal device 3, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 31 can include both the internal storage unit and the external storage device of the terminal device 3. The memory 31 is used to store the computer program and other programs and data required by the terminal device 3. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0108] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0109] It should be noted that the information interaction, execution process and the like between the above apparatuses / units are based on the same concept as the method embodiments of the present application, and the specific functions and the brought technical effects can be referred to the method embodiments part, which will not be repeated here.

[0110] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs. The internal structure of the apparatus is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of software functional unit. In addition, the specific name of each functional unit and module is only for convenient distinction, and does not limit the protection scope of the present application. The specific working process of the unit and module in the system can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0111] The embodiment of the present application further provides a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps in each method embodiment.

[0112] The embodiment of the present application provides a computer program product, when the computer program product runs on a mobile terminal, so that the mobile terminal executes to realize the steps in each method embodiment.

[0113] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the present application can implement all or part of the processes in the above-mentioned embodiment methods, which can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer readable storage medium, and the computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms. The computer readable medium can at least include any entity or device capable of carrying the computer program code to the photographing device / terminal equipment, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, such as U disk, mobile hard disk, magnetic disk or optical disk.

[0114] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0115] Those skilled in the art can appreciate that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0116] In the embodiments provided by the present application, it should be understood that the disclosed apparatus / network device and method can be implemented in other ways. For example, the above-described apparatus / network device embodiments are merely schematic, for example, the division of the modules or units is merely a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0117] The units described as separate components can or can not be physically separate, and the components displayed as separate components can or can not be physical separate, and can be located in one position or distributed on a plurality of network units.

[0118] It should be understood that the term "comprises" or "comprising," when used in this specification and accompanying claims, indicates the presence of the described features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0119] It should also be understood that the term "and / or," when used in this specification and accompanying claims, means that one or more of the associated listed items can be present.

[0120] As used in this specification and any claims of this application, the terms "if" and "when" can be construed to mean "upon” or "in response to determining” or “in response to monitoring” depending on the context. Similarly, the phrase “if it is determined” or “if it is monitored that [the described condition or event]” can be construed to mean “upon determining” or “in response to determining” or “upon monitoring [the described condition or event]” or “in response to monitoring [the described condition or event]” depending on the context.

[0121] In addition, the terms “first,” “second,” “third,” etc. are used herein only to describe different instances, and do not imply or suggest relative importance of the described elements.

[0122] The description of the references “one embodiment,” “some embodiments,” and the like, in the specification, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. Thus, the appearances of the phrases “in one embodiment,” “in some embodiments,” “in other embodiments,” “in additional embodiments,” and the like, in various places in the specification are not necessarily all referring to the same embodiment, unless otherwise specified. The terms “comprise,” “comprising,” “has,” “having,” and the like, can mean “including but not limited to,” unless otherwise indicated.

[0123] The above-described embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for estimating energy expenditure for fat loss, characterized in that, The method for estimating energy expenditure for fat loss includes: Acquire basic body data and ambient temperature, and calculate basal metabolic parameters based on the basic body data and ambient temperature; wherein, the basic body data includes age, gender, height, and weight; The system acquires movement trajectory points, current heart rate, and body surface temperature, and matches the estimated exercise state and intensity for each preset time period based on these data. The energy expenditure for fat loss is calculated based on the basal metabolic parameters, the estimated exercise status in each preset time period, and the exercise intensity in each preset time period.

2. The method for estimating energy expenditure for fat loss as described in claim 1, characterized in that, The steps of acquiring basic body data and ambient temperature, and calculating basal metabolic parameters based on the basic body data and ambient temperature include: When the ambient temperature is within the neutral temperature range, age, gender, height, and weight are substituted into the basal metabolic function to obtain initial metabolic parameters; wherein, the basal metabolic function is: initial metabolic parameter = (10 * weight) + (6.25 * height) - (5 * age) + gender parameter, where the gender parameter is 5 for males and -161 for females; the neutral temperature range is 18 degrees Celsius to 24 degrees Celsius; When the ambient temperature is not in the neutral temperature range, metabolic compensation parameters are calculated based on the compensation function; The initial metabolic parameters are added to the metabolic compensation parameters to obtain the basal metabolic parameters.

3. The method for estimating energy expenditure for fat loss as described in claim 2, characterized in that, The step of calculating metabolic compensation parameters based on the compensation function when the ambient temperature is not in the neutral temperature range includes: When the ambient temperature is below the neutral temperature range, the ambient temperature is substituted into the first compensation function to obtain the first metabolic compensation parameter; wherein, the first compensation function is: First metabolic compensation parameter = K_low * (T_neutral_low - T_ambient) N K_low represents the low temperature compensation coefficient, T_low represents the lower limit of the neutral temperature range, T_ambient represents the ambient temperature, and N is the exponential coefficient. When the ambient temperature is higher than the neutral temperature range, the ambient temperature is substituted into the second compensation function to obtain the second metabolic compensation parameter; wherein, the second compensation function is: second metabolic compensation parameter = K_high*(T_ambient-T_high), where K_high represents the high temperature compensation coefficient and T_high represents the upper limit of the neutral temperature range.

4. The method for estimating energy expenditure for fat loss as described in claim 3, characterized in that, The step of substituting the ambient temperature into the second compensation function to obtain the second metabolic compensation parameter when the ambient temperature is above the neutral temperature range includes: Collect humidity data; Substitute the ambient temperature into the second compensation function to obtain the current compensation parameters; Substitute the humidity data into the humidity compensation function to obtain the humidity influence factor; wherein, the humidity compensation function is: humidity compensation parameter = 1 + C_humid * max(0, (RH-0.6)), where C_humid represents the humidity influence coefficient and RH represents the humidity data; Multiply the current compensation parameter and the humidity influence factor to obtain the second metabolic compensation parameter.

5. The method for estimating energy expenditure for fat loss as described in claim 1, characterized in that, The steps of acquiring movement trajectory points, current heart rate, and body surface temperature, and matching the estimated exercise state and intensity for each preset time period based on the movement trajectory points, current heart rate, and body surface temperature include: Collect movement trajectory points, current heart rate, and body surface temperature; Calculate the average moving speed based on multiple movement trajectory points within a preset time period; Calculate the farthest distance between multiple movement trajectory points within a preset time period; Obtain a standard data set corresponding to multiple motion states; Calculate the similarity between the current dataset and the standard dataset; wherein the current dataset includes the average moving speed, the farthest distance, the current heart rate, and the body surface temperature; The motion state corresponding to the maximum similarity is used as the predicted motion state; The exercise intensity is calculated based on the estimated exercise state and current heart rate.

6. The method for estimating energy expenditure for fat loss as described in claim 5, characterized in that, The step of calculating exercise intensity based on the estimated exercise state and current heart rate includes: The system acquires preset exercise intensity ranges corresponding to multiple exercise states and matches them with the current preset exercise intensity range corresponding to the estimated exercise state. The multiple exercise states include sleep, resting state, aerobic exercise, and anaerobic exercise. Different exercise states correspond to different preset exercise intensity ranges. Calculate the current heart rate and subtract the resting heart rate to obtain the heart rate difference; Multiply the heart rate difference by the conversion parameter to obtain the exercise intensity coefficient; Multiply the exercise intensity coefficient by the minimum value of the current preset exercise intensity range to obtain the current value; If the current value is less than the maximum value of the current preset exercise intensity range, then the current value is taken as the exercise intensity. If the current value is not less than the maximum value of the current preset exercise intensity range, then the maximum value is taken as the exercise intensity.

7. The method for estimating energy expenditure for fat loss as described in claim 1, characterized in that, The step of calculating fat loss energy expenditure based on the basal metabolic parameters, the estimated exercise status in each preset time period, and the exercise intensity in each preset time period includes: Substituting the basal metabolic rate, the estimated exercise state in each preset time period, and the exercise intensity in each preset time period into the energy consumption function, the fat loss energy consumption is obtained; The energy consumption function is: Where D represents energy expenditure for fat loss, and BMR represents basal metabolic rate. This represents the i-th preset time period. This represents the exercise intensity during the i-th preset time period. Let m represent the estimated motion state in the i-th preset time period, and m represent the number of preset time periods in 24 hours. This represents the fat loss adjustment factor for the i-th preset time period. This indicates body fat percentage, and A represents age. Indicates the factor of age-related physical decline. This represents the curve width parameter. This represents a constant term.

8. A device for estimating energy expenditure for weight loss, characterized in that, The fat loss energy consumption estimation device includes: The acquisition unit is used to acquire basic body data and ambient temperature, and calculate basal metabolic parameters based on the basic body data and ambient temperature; wherein, the basic body data includes age, gender, height and weight; The matching unit is used to acquire movement trajectory points, current heart rate and body surface temperature, and match the estimated exercise state and exercise intensity for each preset time period based on the movement trajectory points, current heart rate and body surface temperature; The calculation unit is used to calculate the energy consumption for fat loss based on the basal metabolic parameters, the estimated exercise state in each preset time period, and the exercise intensity in each preset time period.

9. A terminal device, characterized in that, The terminal device includes: a memory, a processor, and a fat loss energy consumption estimation program stored in the memory and executable on the processor, the fat loss energy consumption estimation program being configured to implement the steps in the fat loss energy consumption estimation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps in the fat loss energy consumption estimation method as described in any one of claims 1 to 7.

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