Measurement data correction method and system for intelligent body fat scale

Abstract

The invention relates to the technical field of body fat scales, in particular to a measurement data correction method and system for an intelligent body fat scale. The method comprises the following steps: acquiring weighing data; analyzing the weighing data, and determining weighing time, weighing personnel information and weighing body fat data; the weighing time is analyzed, and whether weighing is in a preset optimal time period or not is determined; if not, acquiring feeding data, analyzing the feeding data and the weighing person information, and determining the hydration state at the current moment; the hydration state is analyzed, the weighed body fat data are corrected, and actual body fat data are obtained and displayed. By analyzing the hydration state, the original body fat data are corrected, so that errors caused by hydration change are eliminated. And the accurate body fat rate can be obtained even if measurement is carried out in a non-optimal time period. Finally, the corrected body fat data is displayed to the user, a reliable monitoring result is provided for the user, and the user can better understand the health state of himself and perform corresponding health management.

Description

Technical Field

[0001] The present application relates to the technical field of body fat scales, and in particular to a measurement data correction method and system for an intelligent body fat scale. Background Art

[0002] As people pay more and more attention to health management, body fat scales, as a common health monitoring tool, have been widely used in homes and fitness venues.

[0003] Traditional body fat scales mainly use bioelectrical impedance analysis (BIA) technology to measure body fat percentage. This technology is based on the different impedances of different tissues in the human body to electric current, and thus calculates body fat percentage. However, when measuring body fat percentage, existing body fat scales are usually affected by multiple factors, resulting in large errors in the measurement results. Summary of the invention

[0004] The present application provides a measurement data correction method and system for a smart body fat scale to solve the above-mentioned problems.

[0005] In a first aspect, the present application provides a method for calibrating measurement data for a smart body fat scale, the method comprising: Obtaining weighing data; analyzing the weighing data to determine weighing time, weighing personnel information, and weighing body fat data; Analyze the weighing time to determine whether the weighing is in a preset optimal time period; If it is not in the preset optimal time period, obtaining eating data, analyzing the eating data and the weighing person information, and determining the hydration status at the current moment; The hydration status is analyzed, the weighed body fat data is corrected, and actual body fat data is obtained and displayed.

[0006] Through this solution, the user's weight data is obtained through a high-precision sensor to provide basic data for subsequent analysis and correction. This step ensures that all subsequent processing is based on accurate weight measurements. Not only the user's weight is recorded, but also the measurement time and user information are recorded synchronously; this is essential for analyzing the user's health and habits. By determining the weighing time, the user can be given the most accurate advice on when to measure. By comparing the current measurement time with the preset optimal measurement period, it can be determined whether further correction steps are required. The selection of the optimal period helps to reduce measurement errors caused by time fluctuations. If the measurement time is not in the optimal period, the user is required to enter the eating data; this helps to understand the user's eating habits and hydration status, so as to more accurately correct the body fat data. Combining the eating data and user information, the user's current hydration status can be estimated. This is essential for correcting body fat data, because changes in hydration status will directly affect the measurement results of body fat percentage. Through the analysis of hydration status, the original body fat data is corrected to eliminate errors caused by changes in hydration. This step ensures that users can obtain accurate body fat percentage even when measuring at non-optimal periods. Finally, the corrected body fat data is displayed to the user, reflecting the user's true body fat percentage and providing the user with reliable monitoring results, which helps the user better understand their health status and perform corresponding health management.

[0007] Optionally, the obtaining of weighing data includes: Acquire a weighing electrical signal; analyze the weighing electrical signal to determine a single foot electrical signal and a lower limb electrical signal; determining body fat data of the non-digestive structure of the weighed person according to the single-foot electrical signal; Retrieving historical body fat data, comparing the historical body fat data with the body fat data, and determining body fat changes; Weighing data is determined based on the body fat changes and the lower limb electrical signals.

[0008] Through this scheme, the weighing electrical signal obtained by the electrode is the impedance response of the user's body to the current, reflecting the user's body composition information. This step is the basis of the entire measurement process because it provides the raw data required for subsequent analysis. By analyzing the weighing electrical signal, the electrical signals of the single foot and lower limbs can be separated, which is crucial for evaluating the body fat of the user's lower limbs. The electrical signal of the single foot can help determine the body fat data of the non-digestible structure, while the electrical signal of the lower limbs can help correct the body fat rate. Using the impedance information in the electrical signal of the single foot, combined with the user's physical characteristics, the body fat data of the non-digestible structure can be calculated; it is very important for accurately estimating the body fat rate. By comparing the user's historical body fat data and current body fat data, the trend of body fat changes can be monitored. This trend analysis helps users understand their health status and body fat control effects. Combining the information of body fat changes and lower limb electrical signals, the user's weighing data can be corrected and optimized. This step ensures that users can obtain corrected and accurate body fat rate data even when measuring at non-optimal times.

[0009] Optionally, the eating data includes eating conditions and drinking conditions, and the analyzing the eating data and the weighing personnel information to determine the hydration status includes: Analyze the eating situation during the first preset time period to determine the amount and type of food eaten; Determining the ingredient intake of each food type based on the food intake; Analyze the drinking water situation during the first preset time period to determine the type and amount of drinking water; Determine the water intake of each drinking water type according to the drinking water amount; Determining basic weight data of the weighing person according to the weighing person information; Determining the initial state of the weighing person according to the basic weight data; The hydration status is determined based on the ingredient intake, the water intake and the basal body weight.

[0010] Through this solution, by analyzing the user's eating situation, the user's eating habits in a specific period of time can be understood, which is crucial for evaluating hydration status and nutritional intake. Information on the amount and type of food eaten helps predict the water content in food, which affects the evaluation of hydration status. By analyzing the amount of food eaten, the component intake of each food can be calculated, including water, protein, fat, carbohydrates, etc.; it is crucial for evaluating the impact of food on hydration status. By analyzing the user's drinking water situation, the user's water intake in a specific period of time can be understood. The type and amount of water drinking are crucial for evaluating the user's hydration status. By analyzing the water intake, the water intake of each type of water drinking can be calculated. Different types of drinking water may have different effects on hydration status, so this step helps to more accurately evaluate hydration status. Through the user's personal information, such as age, gender, height, weight, etc., the user's basic weight data can be determined; it is crucial for evaluating the user's hydration status and body fat percentage. Through the basic weight data, the user's initial hydration status before eating and drinking can be evaluated. This step helps to more accurately calculate the impact of eating and drinking on hydration status. By comprehensively analyzing the user's food intake, water intake, and basic weight, a more accurate assessment of the user's hydration status can be made. This step is crucial for correcting the measurement data of the body fat scale to improve the accuracy of body fat percentage.

[0011] Optionally, determining the hydration status according to the ingredient intake, the water intake and the basal body weight includes: Acquire exercise information within a second preset time period; analyze the exercise information to determine the exercise type and exercise time; Acquire motion images, analyze the motion images, and determine perspiration conditions; Determining the consumption level of the person being weighed for the type of exercise according to the perspiration situation; A hydration status is determined based on the consumption level, the ingredient intake, the water intake, and the basal body weight.

[0012] Through this solution, by collecting the user's exercise information during the second preset period, the user's activity level can be better understood, which is crucial for evaluating the user's energy consumption and water loss. Analyzing the exercise information helps to identify what type of exercise the user has performed and the length of time the exercise has been performed, which is very important for calculating the energy and water consumption during exercise. By analyzing the image of the user during exercise, the user's perspiration can be evaluated, so as to more accurately estimate the amount of water loss during exercise. The perspiration situation reflects the user's water consumption during exercise. Combined with the type of exercise, the user's energy and water consumption level in a specific exercise can be calculated, which is very important for evaluating the user's hydration status. By comprehensively considering multiple factors, the user's hydration status can be more accurately evaluated, thereby improving the accuracy of the body fat percentage measurement results. Personalized diet and exercise recommendations can be provided based on the user's hydration status to help users improve their health. By continuously monitoring the user's hydration status, it can help users track changes in their health status and discover potential health problems in a timely manner. Taking into account the impact of exercise and perspiration on hydration status, measurement errors caused by unstable hydration status can be reduced.

[0013] Optionally, determining weighing data according to the body fat change and the lower limb electrical signal includes: Get the current ambient humidity; Determining the basic impedance of the foot according to the basic body weight data; Analyze the ambient humidity and the foot basic impedance to determine the foot resistivity at the current moment; determining the resistivity of the lower limb according to the lower limb electrical signal; predicting current body resistivity based on the resistivity of the foot and the resistivity of the lower limb; Determine, based on the historical body fat data, the most recent historical body fat percentage that meets the optimal hydration state at the current moment; Weighing data is determined based on the foot resistivity, the body fat change, the lower limb resistivity, the historical body fat percentage and the current body resistivity.

[0014] Through this solution, by obtaining environmental humidity data, the impact of environmental factors on the user's hydration status can be evaluated. Ambient humidity is related to the user's sweat volume and water evaporation, which affects the electrical impedance measurement results. Using the user's basic weight data and physical characteristics, the basic impedance value of the foot can be calculated as a benchmark for subsequent analysis. By analyzing the environmental humidity and the basic impedance of the foot, the foot resistivity at the current moment can be calculated. This value reflects the moisture content and electrical impedance of the user's foot. Taking into account the foot resistivity, the user's body fat change data, and the lower limb electrical signals, the user's weighing data is calculated through an algorithm formula. This process involves correcting the electrical impedance value to eliminate the impact of changes in hydration status.

[0015] Optionally, the hydration status is determined based on the consumption level, the ingredient intake, the water intake and the basal body weight using the following formula: ; in, In hydrated state; is water intake; is the consumption level; is the weighting factor for water loss per unit consumption level; is the ingredient intake; is the weight coefficient of the effect of ingredient intake on hydration status; is the base weight; is the weight coefficient of the effect of basal body weight on hydration status.

[0016] Through this solution, the formula can more accurately reflect the hydration status of an individual by taking into account multiple factors, especially basic body weight, consumption level and ingredient intake. This is especially important for athletes, the elderly or people with chronic diseases, because their hydration needs are different from those of ordinary people. The formula can help understand how to regulate hydration status through diet, especially water and electrolyte intake. For example, too much salty food may increase water retention, while strenuous exercise will lead to more water loss. Replenishing water in time according to consumption before and after exercise can effectively prevent dehydration or overhydration. If water intake, consumption level, ingredient intake and basic body weight changes can be monitored in real time, the formula will be able to dynamically adjust the hydration status prediction, allowing users to adjust their hydration strategy according to their daily life, diet and exercise.

[0017] Optionally, the weighing data is determined according to the foot resistivity, the body fat change, the lower limb resistivity, the historical body fat percentage and the current body resistivity, using the following formula: ; in, is the current theoretical body fat percentage; is the weight coefficient of the impact of weighing data on body fat percentage; Real-time weighing data; is the weight coefficient of the influence of electrical signal data on body fat percentage; is the resistivity of the lower limb; is the foot resistivity; is the weight coefficient of the effect of hydration status on body fat percentage; In hydrated state; is the base weight; It is the correction parameter of the current measured body fat percentage by combining resistivity with ambient humidity correction; is the current body resistivity; Correction parameters for current body fat measurements based on historical body fat data; is the historical body fat percentage; The correction parameter for the current measured body fat percentage based on the amount of body fat change; is the amount of body fat change.

[0018] This solution makes body fat percentage measurement more accurate by integrating data from multiple dimensions, especially with the support of real-time data and multiple correction factors; it is highly adaptable and can be customized according to individual circumstances (such as basal weight, historical body fat, etc.). This is especially important for people of different body types and health conditions. Considering the impact of hydration status on body fat percentage, this formula helps provide a more reasonable estimate of body fat percentage when hydration is unstable (such as before and after exercise or when the climate changes). Avoid measurement errors caused by unbalanced hydration.

[0019] Optionally, before determining the basic weight of the weighing person according to the weighing person information, the method further includes: Obtaining a weighing request, analyzing the weighing request, and determining information of the person making the request; Based on the requesting person information, analyzing the stored data to determine whether the stored data contains the requesting person information; If not, send an information filling signal to the requesting person, receive the information filling result, and obtain the weighing person information; The step of determining the basic weight of the weighing person according to the weighing person information includes: During the preset optimal time period, a weighing signal is sent to the requesting person, and a weighing result is received; According to the weighing result, basic body weight data is determined.

[0020] This solution ensures that the user's weighing needs can be responded to in a timely manner, providing users with instant health management services. By analyzing the request, it is possible to identify which user requested the weighing, which is essential for subsequent data processing and personalized services. Determining whether the requested person information is contained helps to quickly locate the user's personal information and measurement history. If the user information already exists, subsequent measurements and analysis can be performed directly, avoiding duplicate data collection. For new users or users without records, this step ensures that the necessary personal information can be collected to facilitate accurate measurement and data analysis. Choosing to weigh at the best time can reduce measurement errors caused by changes in hydration status and other factors, and improve the accuracy of measurement results. Weighing at the best time, combined with the user's personal information, can more accurately determine the user's basic weight and provide reliable data for subsequent body fat percentage calculations. It avoids measurement result deviations caused by factors such as improper measurement time and unstable hydration status, and improves the measurement consistency of body fat scales. Accurate basic weight data helps provide users with personalized health management suggestions, including diet adjustments and exercise plans.

[0021] Optionally, the method further includes: If the eating data is empty and the exercise information is empty, determining that the current hydration state is optimal; If the current hydration status is optimal, the basic weight data is updated according to the weighing data.

[0022] With this solution, when the user is not eating and exercising, the user's hydration status is stable, because eating and exercising will affect the body's hydration status. A stable hydration status helps to obtain more accurate weight and body fat percentage measurements. When it determines that the user's hydration status is optimal, it uses the current weigh-in data to update the user's base weight. This ensures the accuracy of the base weight data because it reflects a weight in a stable hydration state.

[0023] In a second aspect, the present application provides a measurement data correction system for an intelligent body fat scale, the system comprising: A data analysis module is used to obtain weighing data; analyze the weighing data to determine the weighing time, weighing personnel information and weighing body fat data; A time analysis module, used to analyze the weighing time and determine whether the weighing is in a preset optimal time period; A status analysis module, for obtaining eating data if the person is not in the preset optimal time period, analyzing the eating data and the weighing person information, and determining the hydration status at the current moment; The data correction module is used to analyze the hydration status, correct the weighed body fat data, obtain actual body fat data and display it. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are 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.

[0025] Figure 1 A schematic diagram of an application scenario provided for an embodiment of the present application; Figure 2 A flow chart of a method for calibrating measurement data for a smart body fat scale provided in one embodiment of the present application; Figure 3 A schematic diagram of the structure of a measurement data correction system for a smart body fat scale provided in one embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0028] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0029] As people pay more and more attention to health management, body fat scales, as a common health monitoring tool, have been widely used in homes and fitness venues.

[0030] Traditional body fat scales mainly use bioelectrical impedance analysis (BIA) technology to measure body fat percentage. This technology is based on the different impedances of different tissues in the human body to electric current, and thus calculates body fat percentage. However, when measuring body fat percentage, existing body fat scales are usually affected by multiple factors, resulting in large errors in the measurement results.

[0031] Based on this, the present application provides a measurement data correction method and system for an intelligent body fat scale, which obtains weighing data; analyzes weighing data, determines weighing time, weighing personnel information and weighing body fat data; analyzes weighing time, determines whether weighing is in a preset optimal period; if not in the preset optimal period, obtains eating data, analyzes eating data and weighing personnel information, determines the hydration state at the current moment; analyzes the hydration state, corrects weighing body fat data, obtains actual body fat data and displays it. The user's weight data is obtained through a high-precision sensor to provide basic data for subsequent analysis and correction. This step ensures that all subsequent processing is based on accurate weight measurement values. Not only the user's weight is recorded, but also the measurement time and user information are recorded synchronously, which is crucial for analyzing the user's health status and habits. By determining the weighing time, the user can be provided with the most accurate advice on when to measure. By comparing the current measurement time with the preset optimal measurement period, it can be determined whether further correction steps are required. The selection of the optimal period helps to reduce measurement errors caused by time fluctuations. If the measurement time is not during the optimal period, the user is required to enter the eating data; this helps to understand the user's eating habits and hydration status, so as to correct the body fat data more accurately. Combining the eating data and user information, the user's current hydration status can be estimated. This is crucial for correcting body fat data, because changes in hydration status will directly affect the measurement results of body fat percentage. Through the analysis of hydration status, the original body fat data is corrected to eliminate errors caused by changes in hydration. This step ensures that users can obtain accurate body fat percentage even when measuring during non-optimal periods. Finally, the corrected body fat data is displayed to the user; it reflects the user's true body fat percentage, provides users with reliable monitoring results, and helps users better understand their health status and conduct corresponding health management.

[0032] Figure 1A schematic diagram of an application scenario provided by the present application is provided. When a body fat scale measures body fat percentage, the method provided by the present application is applied. Specifically, the method provided by the present application is applied to any server, and the server interacts with the body fat scale, and obtains the user's weight data through the high-precision sensor of the body fat scale to provide basic data for subsequent analysis and correction. This step ensures that all subsequent processing is based on accurate weight measurement values. Not only the user's weight is recorded, but also the measurement time and user information are recorded synchronously, which is crucial for analyzing the user's health status and habits. By determining the weighing time, the user can be provided with the most accurate advice on when to measure. By comparing the current measurement time with the preset optimal measurement period, it can be determined whether further correction steps are required. The selection of the optimal period helps to reduce measurement errors caused by time fluctuations. If the measurement time is not in the optimal period, the user is required to enter the eating data, which helps to understand the user's eating habits and hydration status, so as to more accurately correct the body fat data. Combining the eating data and user information, the user's current hydration status can be estimated. This is crucial for correcting body fat data, because changes in hydration status will directly affect the measurement results of body fat percentage. By analyzing the hydration status, the original body fat data is corrected to eliminate errors caused by changes in hydration. This step ensures that users can obtain accurate body fat percentage even when measuring at non-optimal times. Finally, the corrected body fat data is displayed to the user, reflecting the user's true body fat percentage, providing users with reliable monitoring results, helping users to better understand their health status and conduct corresponding health management.

[0033] For specific implementation methods, please refer to the following embodiments.

[0034] Figure 2 This is a flow chart of a method for correcting measurement data for a smart body fat scale provided in one embodiment of the present application. The method of this embodiment can be applied to the server in the above scenario. Figure 2 As shown, the method includes: S201, obtaining weighing data; analyzing the weighing data, determining weighing time, weighing personnel information and weighing body fat data.

[0035] The weighing data may be the user's weight value and various electrical impedance data measured by a body fat scale.

[0036] The weighing time may be a specific time point when the user measures his / her weight, usually including the date and clock time.

[0037] The weighing person information may be the personal information of the user who uses the body fat scale for measurement, such as name, gender, age, height, weight, etc.

[0038] The weighed body fat data may be body composition data such as body fat percentage, muscle mass, bone mass, etc. measured by a body fat scale.

[0039] Specifically, first, the smart body fat scale obtains the user's weight data through its built-in sensor. The sensor can be a pressure sensor or other device that can accurately measure weight. When the user stands on the body fat scale, the sensor transmits the weight data to the built-in computing system. The computing system processes the acquired weighing data to determine the time when the measurement occurred, the information of the person using the body fat scale, and the preliminary body fat data. This step may involve the recording of timestamps, the identification of the user (through user accounts or biometrics), and the preliminary calculation of body fat percentage.

[0040] S202: Analyze the weighing time to determine whether the weighing is in a preset optimal time period.

[0041] The preset optimal time period can be the time period for the most accurate weight and body fat measurement set based on the user's living habits and the recommendations of the body fat scale. Generally, it is the time period in the morning when you have not eaten or drunk water after getting up, when the body's hydration status is normal, which is suitable as the preset optimal time period.

[0042] Specifically, the current measurement time is compared with a preset optimal measurement period, which is usually a relatively stable time period in the user's daily activity pattern, such as when the user is fasting after getting up in the morning.

[0043] S203: If it is not in the preset optimal time period, then obtain the eating data, analyze the eating data and the weighing personnel information, and determine the hydration status at the current moment.

[0044] The eating data may be the user's eating conditions in the period before measurement, including the type and quantity of food and water intake.

[0045] The weighing person information may include various types of personal data, and may also include name, gender, age, height, weight, health status, etc.

[0046] Hydration status can be the condition of water content in the body, which reflects the degree of sufficiency of water in the body.

[0047] Specifically, if the current measurement time is not within the preset optimal time period, the user will be instructed to enter relevant eating data, including eating time, food type and quantity, and water intake; this can be obtained through manual user input or synchronization with other smart devices (such as smart watches, health applications). The computing system will analyze the eating data, combined with the user's basal metabolic rate and historical data, to estimate the current hydration status, including the calculation of the water content in food and the analysis of water intake.

[0048] S204, analyzing the hydration status, correcting the weighed body fat data, obtaining the actual body fat data and displaying it.

[0049] The actual body fat data may be corrected body fat data, which is closer to the user's actual body fat percentage.

[0050] Specifically, based on the analyzed hydration status, the preliminary body fat data is corrected to eliminate the impact of hydration status on the body fat percentage measurement results. The corrected body fat data is closer to the user's actual body fat percentage. Finally, the smart body fat scale displays the corrected actual body fat data to the user; it can be displayed directly on the body fat scale's display screen or viewed remotely through a smartphone app or other remote device.

[0051] Through this solution, the user's weight data is obtained through a high-precision sensor to provide basic data for subsequent analysis and correction. This step ensures that all subsequent processing is based on accurate weight measurements. Not only the user's weight is recorded, but also the measurement time and user information are recorded synchronously; this is essential for analyzing the user's health and habits. By determining the weighing time, the user can be given the most accurate advice on when to measure. By comparing the current measurement time with the preset optimal measurement period, it can be determined whether further correction steps are required. The selection of the optimal period helps to reduce measurement errors caused by time fluctuations. If the measurement time is not in the optimal period, the user is required to enter the eating data; this helps to understand the user's eating habits and hydration status, so as to more accurately correct the body fat data. Combining the eating data and user information, the user's current hydration status can be estimated. This is essential for correcting body fat data, because changes in hydration status will directly affect the measurement results of body fat percentage. Through the analysis of hydration status, the original body fat data is corrected to eliminate errors caused by changes in hydration. This step ensures that users can obtain accurate body fat percentage even when measuring at non-optimal periods. Finally, the corrected body fat data is displayed to the user, reflecting the user's true body fat percentage and providing the user with reliable monitoring results, which helps the user better understand their health status and perform corresponding health management.

[0052] In some embodiments, a weighing electrical signal is obtained; the weighing electrical signal is analyzed to determine a single-foot electrical signal and a lower limb electrical signal; based on the single-foot electrical signal, the body fat data of the non-digestive structure of the weighing person is determined; historical body fat data is retrieved, and the historical body fat data is compared with the body fat data to determine the body fat change; and the weighing data is determined based on the body fat change and the lower limb electrical signal.

[0053] The weighing electrical signal can be a weak current signal sent by the body fat scale to the user's body through electrodes, and the body's impedance response to the current is measured.

[0054] The single-foot electrical signal can be the current signal sent and received by the body fat scale through the electrode of only one foot during the measurement process.

[0055] The lower limb electrical signal may be an electric current signal sent and received by the body fat scale through the user's lower limbs (including both feet).

[0056] Non-digestive structures can be structures in the body that do not participate in the digestion and absorption of food, such as muscles, bones, internal organs, etc.

[0057] Body fat data can be data related to body composition measured and calculated by a body fat scale, including body fat percentage, muscle mass, bone mass, etc.

[0058] The historical body fat data may be the body fat data measured and recorded by the user using a body fat scale over a period of time in the past.

[0059] The body fat change may be a change in the body fat percentage of a user over a period of time, such as an increase or decrease in the body fat percentage.

[0060] Specifically, when the user stands on the body fat scale, the built-in sensor will send a weak current signal to the user's body through the electrodes; the electrodes are usually located on the platform of the body fat scale, and the user's two feet touch different electrodes respectively. The sensor captures the user's body's response to the current and generates corresponding electrical signals. The computing system analyzes the captured electrical signals to distinguish between single-foot electrical signals and lower limb electrical signals. This may involve signal processing technologies such as filtering, amplification, and digital processing. Using the impedance information in the single-foot electrical signal, combined with the user's physical characteristics (such as height, weight, etc.), the non-digestible structure body fat data is calculated through the bioelectrical impedance analysis (BIA) algorithm. The user's historical body fat data is retrieved from the database and compared with the currently measured body fat data. This may involve data matching and synchronization technology. The user's weighing data is finally determined by comprehensively considering the changes in body fat and the information of the lower limb electrical signals. This may involve complex algorithms and formulas to correct and optimize the measurement results.

[0061] Through this scheme, the weighing electrical signal obtained by the electrode is the impedance response of the user's body to the current, reflecting the user's body composition information. This step is the basis of the entire measurement process because it provides the raw data required for subsequent analysis. By analyzing the weighing electrical signal, the electrical signals of the single foot and lower limbs can be separated, which is crucial for evaluating the body fat of the user's lower limbs. The electrical signal of the single foot can help determine the body fat data of the non-digestible structure, while the electrical signal of the lower limbs can help correct the body fat rate. Using the impedance information in the electrical signal of the single foot, combined with the user's physical characteristics, the body fat data of the non-digestible structure can be calculated; it is very important for accurately estimating the body fat rate. By comparing the user's historical body fat data and current body fat data, the trend of body fat changes can be monitored. This trend analysis helps users understand their health status and body fat control effects. Combining the information of body fat changes and lower limb electrical signals, the user's weighing data can be corrected and optimized. This step ensures that users can obtain corrected and accurate body fat rate data even when measuring at non-optimal times.

[0062] In some embodiments, the eating situation during the first preset time period is analyzed to determine the amount of food eaten and the type of food eaten; based on the amount of food eaten, the ingredient intake of each type of food eaten is determined; the drinking water situation during the first preset time period is analyzed to determine the type of water drunk and the amount of water drunk; based on the amount of water drunk, the water intake of each type of water drunk is determined; based on the information of the weighing person, the basic weight data of the weighing person is determined; based on the basic weight data, the initial state of the weighing person is determined; based on the ingredient intake, the water intake and the basic weight, the hydration state is determined.

[0063] The first preset time period may be a preset time period for collecting the user's eating and drinking data within the time period, such as 4 hours before weighing.

[0064] The food intake may be the total amount of food consumed by the user within a specific period of time.

[0065] The food types may be the food types consumed by the user within a specific period of time.

[0066] The ingredient intake may be the ingredient amount of each food consumed by the user within a specific period of time, including water, protein, fat, carbohydrates, etc.

[0067] The drinking situation may be the drinking behavior of a user in a specific period of time, including the frequency, time, and environment of drinking.

[0068] The drinking water type may be the type of drinking water consumed by the user in a specific period of time, such as purified water, mineral water, tea, coffee, etc.

[0069] The amount of water consumed may be the total amount of drinking water consumed by the user in a specific period of time.

[0070] Water intake may be the total amount of water a user consumes through food and drink in a specific period of time.

[0071] The basic weight data may be the weight-related data in the user's personal information, including the user's height, weight, age, gender, etc.

[0072] The initial state may be the user's hydration state before they start eating and drinking.

[0073] Specifically, the user is required to input the eating situation during the first preset period, including the type and amount of food. This may be input through a smart device (such as a mobile phone application), or the relevant information may be automatically obtained by scanning the barcode on the food package. According to the amount of food input by the user, combined with the information in the food database, the component intake of each food, such as protein, fat, carbohydrates, etc., is calculated. The user will be required to record the drinking situation during the first preset period, including the type of drinking water (such as pure water, tea, coffee, etc.) and the amount of drinking water. According to the type and amount of drinking water recorded by the user, the water intake of each type of drinking water is calculated. The user's personal information (such as age, gender, height, weight, etc.) is used in combination with historical body fat data to determine the user's basic weight data. Based on the basic weight data and the user's current weight, the user's initial hydration state, that is, the state before eating and drinking water, is evaluated. The user's hydration state is calculated by an algorithm formula by comprehensively considering the user's component intake, water intake and basic weight.

[0074] Through this solution, by analyzing the user's eating situation, the user's eating habits in a specific period of time can be understood, which is crucial for evaluating hydration status and nutritional intake. Information on the amount and type of food eaten helps predict the water content in food, which affects the evaluation of hydration status. By analyzing the amount of food eaten, the component intake of each food can be calculated, including water, protein, fat, carbohydrates, etc.; it is crucial for evaluating the impact of food on hydration status. By analyzing the user's drinking water situation, the user's water intake in a specific period of time can be understood. The type and amount of water drinking are crucial for evaluating the user's hydration status. By analyzing the water intake, the water intake of each type of water drinking can be calculated. Different types of drinking water may have different effects on hydration status, so this step helps to more accurately evaluate hydration status. Through the user's personal information, such as age, gender, height, weight, etc., the user's basic weight data can be determined; it is crucial for evaluating the user's hydration status and body fat percentage. Through the basic weight data, the user's initial hydration status before eating and drinking can be evaluated. This step helps to more accurately calculate the impact of eating and drinking on hydration status. By comprehensively analyzing the user's food intake, water intake, and basic weight, a more accurate assessment of the user's hydration status can be made. This step is crucial for correcting the measurement data of the body fat scale to improve the accuracy of body fat percentage.

[0075] In some embodiments, motion information within a second preset time period is obtained; the motion information is analyzed to determine the type of motion and the time of motion; motion images are obtained, analyzed to determine the perspiration situation; based on the perspiration situation, the consumption level of the person being weighed for the type of motion is determined; and the hydration status is determined based on the consumption level, ingredient intake, water intake and basal body weight.

[0076] The second preset period may be a second time period set to collect the user's exercise information within the period, 12 hours before weighing.

[0077] The exercise information may be detailed information about the user's exercise behavior, including exercise type, exercise intensity, exercise duration, etc.

[0078] The exercise type may be the type of exercise performed by the user, such as walking, running, swimming, yoga, etc.

[0079] The exercise time may be the total time the user performs exercise.

[0080] The motion images may be photos or videos taken of the user after each motion stage.

[0081] The perspiration condition may be the amount of water that a user loses from the body through sweat during exercise.

[0082] The consumption level may be the amount of energy and water consumed by the user during exercise.

[0083] Specifically, the user is required to input exercise information within the second preset time period, including the type of exercise (such as walking, running, swimming, etc.) and the length of exercise; it can be automatically collected through smart devices (such as smart watches, health applications), or manually entered by the user. Analyze the exercise information entered by the user to determine the type and duration of exercise. This may involve statistical analysis of the user's exercise habits. If possible, images of the user during exercise can be obtained, such as through cameras installed in sports venues. Images can be used to analyze the user's perspiration. Based on the user's perspiration and exercise type, combined with sports science data, calculate the user's energy consumption and water consumption during exercise. Taking into account the user's consumption level, ingredient intake, water intake, and basal weight, the user's hydration status is calculated through an algorithm formula.

[0084] Through this solution, by collecting the user's exercise information during the second preset period, the user's activity level can be better understood, which is crucial for evaluating the user's energy consumption and water loss. Analyzing the exercise information helps to identify what type of exercise the user has performed and the length of time the exercise has been performed, which is very important for calculating the energy and water consumption during exercise. By analyzing the image of the user during exercise, the user's perspiration can be evaluated, so as to more accurately estimate the amount of water loss during exercise. The perspiration situation reflects the user's water consumption during exercise. Combined with the type of exercise, the user's energy and water consumption level in a specific exercise can be calculated, which is very important for evaluating the user's hydration status. By comprehensively considering multiple factors, the user's hydration status can be more accurately evaluated, thereby improving the accuracy of the body fat percentage measurement results. Personalized diet and exercise recommendations can be provided based on the user's hydration status to help users improve their health. By continuously monitoring the user's hydration status, it can help users track changes in their health status and discover potential health problems in a timely manner. Taking into account the impact of exercise and perspiration on hydration status, measurement errors caused by unstable hydration status can be reduced.

[0085] In some embodiments, the current ambient humidity is obtained; the basic impedance of the feet is determined based on the basic body weight data; the ambient humidity and the basic impedance of the feet are analyzed to determine the current resistivity of the feet; the current body resistivity is predicted based on the foot resistivity and the lower limb resistivity; based on the historical body fat data, the most recent historical body fat rate that meets the optimal hydration state from the current moment is determined; the weighing data is determined based on the foot resistivity, body fat changes, lower limb resistivity, historical body fat rate and current body resistivity.

[0086] Ambient humidity can be the moisture content in the surrounding air.

[0087] The foot base impedance can be the degree of resistance of the user's foot to the current under certain conditions.

[0088] Foot resistivity is the degree to which foot tissue blocks the flow of electrical current.

[0089] The current body resistivity may be the electrical resistance magnitude of human tissue.

[0090] Specifically, the humidity data of the current environment is obtained through a built-in or external humidity sensor. This data is very important for evaluating the user's hydration status and correcting the body fat percentage measurement. The basic impedance value of the foot is calculated through an algorithm formula using the user's basic weight data and known physical characteristics (such as height, age, gender, etc.). Combined with the environmental humidity data and the basic impedance value of the foot, the resistivity of the foot at the current moment is analyzed and calculated through a specific algorithm formula. The current body resistivity is predicted based on the resistivity of the foot and the resistivity of the lower limbs; based on the historical body fat data, the most recent historical body fat percentage that meets the optimal hydration status at the current moment is determined, and the body fat percentage at this time is not the estimated body fat percentage under normal hydration status; based on the comprehensive consideration of the foot resistivity, body fat changes, lower limb resistivity, historical body fat percentage and current body resistivity, the user's weighing data is calculated through a complex algorithm formula. This process may involve the correction of the impedance value to eliminate the measurement error caused by the change in hydration status.

[0091] Through this solution, by obtaining environmental humidity data, the impact of environmental factors on the user's hydration status can be evaluated. Ambient humidity is related to the user's sweat volume and water evaporation, which affects the electrical impedance measurement results. Using the user's basic weight data and physical characteristics, the basic impedance value of the foot can be calculated as a benchmark for subsequent analysis. By analyzing the environmental humidity and the basic impedance of the foot, the foot resistivity at the current moment can be calculated. This value reflects the moisture content and electrical impedance of the user's foot. Taking into account the foot resistivity, the user's body fat change data, and the lower limb electrical signals, the user's weighing data is calculated through an algorithm formula. This process involves correcting the electrical impedance value to eliminate the impact of changes in hydration status.

[0092] In some embodiments, hydration status is determined based on consumption level, ingredient intake, water intake, and basal body weight using the following formula (1): (1); in, In hydrated state; is water intake; is the consumption level; is the weighting factor for water loss per unit consumption level; is the ingredient intake; is the weight coefficient of the effect of ingredient intake on hydration status; is the base weight; is the weight coefficient of the effect of basal body weight on hydration status.

[0093] Specifically, hydration status is directly related to water intake, but it can be affected by consumption level, ingredient intake, and basic body weight. Basic body weight is related to the body's total water capacity, and people of different weights have different water requirements. When consumption level increases, the body loses more water, resulting in a decrease in hydration status. Ingredient intake may affect hydration status, especially when consuming large amounts of salt and protein, which can increase water needs. Specifically, water intake directly contributes to hydration status. Indicates water loss due to consumption level, through to express the water loss per unit consumption level. Indicates the effect of ingredient intake on hydration status. The intake of certain nutrients (such as high protein, salt, etc.) will affect the hydration status. It means that the basic body weight determines the body's water capacity. It can express the water content per kilogram of body weight.

[0094] Through this solution, the formula can more accurately reflect the hydration status of an individual by taking into account multiple factors, especially basic body weight, consumption level and ingredient intake. This is especially important for athletes, the elderly or people with chronic diseases, because their hydration needs are different from those of ordinary people. The formula can help understand how to regulate hydration status through diet, especially water and electrolyte intake. For example, too much salty food may increase water retention, while strenuous exercise will lead to more water loss. Replenishing water in time according to consumption before and after exercise can effectively prevent dehydration or overhydration. If water intake, consumption level, ingredient intake and basic body weight changes can be monitored in real time, the formula will be able to dynamically adjust the hydration status prediction, allowing users to adjust their hydration strategy according to their daily life, diet and exercise.

[0095] In some embodiments, the weighing data is determined based on the foot resistivity, body fat change, lower limb resistivity, historical body fat percentage, and current body resistivity, using the following formula (2): (2); in, is the current theoretical body fat percentage; is the weight coefficient of the impact of weighing data on body fat percentage; Real-time weighing data; is the weight coefficient of the influence of electrical signal data on body fat percentage; is the resistivity of the lower limb; is the foot resistivity; is the weight coefficient of the effect of hydration status on body fat percentage; In hydrated state; is the base weight; It is the correction parameter of the current measured body fat percentage by combining resistivity with ambient humidity correction; is the current body resistivity; Correction parameters for current body fat measurements based on historical body fat data; is the historical body fat percentage; The correction parameter for the current measured body fat percentage based on the amount of body fat change; is the amount of body fat change.

[0096] Specifically, It means that the weighing data (real-time weight) directly affects the body fat percentage. Used to adjust the ratio. The inverse of resistance (conductivity) is used to reflect body composition information, including the distribution of muscle and fat; the conductivity (i.e. the inverse of resistivity) of the lower limbs and feet is mainly measured to reflect body composition, especially the distribution of muscle and fat. Low resistivity (high conductivity) indicates a higher muscle mass, while high resistivity (low conductivity) generally indicates a higher fat mass. Indicates hydration status corrected body fat percentage, Represents the weight of hydration status. Divided by to normalize the data to avoid the influence of differences among individuals. Represents the effect of body resistivity (corrected for ambient humidity) on body fat percentage. Indicates the reference correction of historical body fat data to current body fat. Indicates the dynamic correction of the current measurement data to the amount of body fat change.

[0097] This solution makes body fat percentage measurement more accurate by integrating data from multiple dimensions, especially with the support of real-time data and multiple correction factors; it is highly adaptable and can be customized according to individual circumstances (such as basal weight, historical body fat, etc.). This is especially important for people of different body types and health conditions. Considering the impact of hydration status on body fat percentage, this formula helps provide a more reasonable estimate of body fat percentage when hydration is unstable (such as before and after exercise or when the climate changes). Avoid measurement errors caused by unbalanced hydration.

[0098] In some embodiments, a weighing request is obtained, the weighing request is analyzed, and the requesting person's information is determined; based on the requesting person's information, the stored data is analyzed to determine whether the stored data contains the requesting person's information; if not, an information filling signal is sent to the requesting person, and the information filling result is received to obtain the weighing person's information; based on the weighing person's information, the basic weight of the weighing person is determined, including: sending a weighing signal to the requesting person during a preset optimal time period, and receiving the weighing result; based on the weighing result, the basic weight data is determined.

[0099] The weighing request may be a request sent by a user to the smart body fat scale to measure weight and body fat percentage.

[0100] The requesting person information may be the personal information of the user who initiates the weighing request, including but not limited to the user's name, user ID, height, weight, etc.

[0101] The stored data may be user information, historical measurement data, and other relevant data that have been saved.

[0102] The information filling signal may be a prompt sent to the user, requiring the user to fill in or confirm personal information.

[0103] The information filling result can be the personal information data that the user fills in or confirms according to the prompts.

[0104] The base weight may be a user's stable weight under specific conditions.

[0105] The weighing signal may be a prompt sent to the user, informing the user of instructions for performing a weighing operation.

[0106] The weighing result may be weight data provided by the smart body fat scale after the user weighs, including the weight value and possible body fat percentage.

[0107] The basic weight data may be a basic weight value calculated based on the user's weighing result and other relevant information.

[0108] Specifically, wait for the user to send a weighing request through a smart body fat scale or other device. Analyze the received weighing request and extract the identity information of the requester, such as the user ID or device ID. Query the database to see if there is a user record that matches the requester's information. If no matching user record is found in the database, prompt the user to fill in personal information, including basic weight, height, age, etc. Send a weighing signal to the user during the preset optimal measurement period (such as after getting up in the morning), and the user weighs during this period. According to the user's weighing results in the optimal period, combined with the user's personal information and measurement history data, calculate the user's basic weight.

[0109] This solution ensures that the user's weighing needs can be responded to in a timely manner, providing users with instant health management services. By analyzing the request, it is possible to identify which user requested the weighing, which is essential for subsequent data processing and personalized services. Determining whether the requested person information is contained helps to quickly locate the user's personal information and measurement history. If the user information already exists, subsequent measurements and analysis can be performed directly, avoiding duplicate data collection. For new users or users without records, this step ensures that the necessary personal information can be collected to facilitate accurate measurement and data analysis. Choosing to weigh at the best time can reduce measurement errors caused by changes in hydration status and other factors, and improve the accuracy of measurement results. Weighing at the best time, combined with the user's personal information, can more accurately determine the user's basic weight and provide reliable data for subsequent body fat percentage calculations. It avoids measurement result deviations caused by factors such as improper measurement time and unstable hydration status, and improves the measurement consistency of body fat scales. Accurate basic weight data helps provide users with personalized health management suggestions, including diet adjustments and exercise plans.

[0110] In some embodiments, if the eating data is empty and the exercise information is empty, it is determined that the current hydration status is optimal; if the current hydration status is optimal, the basic weight data is updated according to the weighing data.

[0111] Specifically, check whether the user has recent eating and exercise records. Determine whether the user has not eaten or exercised recently. If the user's eating data and exercise information are empty, it will be considered that the current time is the best time for the user to be hydrated. Use the weighing data obtained in the best hydration state to update the user's basic weight data. Ensure that the weight is measured at the time when the user is in the best hydration state, thereby improving the accuracy and consistency of the measurement results.

[0112] With this solution, when the user is not eating and exercising, the user's hydration status is stable, because eating and exercising will affect the body's hydration status. A stable hydration status helps to obtain more accurate weight and body fat percentage measurements. When it determines that the user's hydration status is optimal, it uses the current weigh-in data to update the user's base weight. This ensures the accuracy of the base weight data because it reflects a weight in a stable hydration state.

[0113] Figure 3 A structural diagram of a measurement data correction system for a smart body fat scale provided in one embodiment of the present application is shown in FIG. Figure 3 As shown, the measurement data correction system 300 for the smart body fat scale of this embodiment includes: a data analysis module 301 , a time analysis module 302 , a state analysis module 303 , and a data correction module 304 .

[0114] The data analysis module 301 is used to obtain weighing data; analyze the weighing data to determine the weighing time, weighing personnel information and weighing body fat data; A time analysis module 302 is used to analyze the weighing time and determine whether the weighing is in a preset optimal time period; The state analysis module 303 is used to obtain the eating data if it is not in the preset optimal time period, analyze the eating data and the weighing person information, and determine the hydration state at the current moment; The data correction module 304 is used to analyze the hydration status, correct the weighed body fat data, obtain actual body fat data and display it.

[0115] Optionally, when the data analysis module 301 obtains weighing data, it is used to: Acquire a weighing electrical signal; analyze the weighing electrical signal to determine a single foot electrical signal and a lower limb electrical signal; determining body fat data of the non-digestive structure of the weighed person according to the single-foot electrical signal; Retrieving historical body fat data, comparing the historical body fat data with the body fat data, and determining body fat changes; Weighing data is determined based on the body fat changes and the lower limb electrical signals.

[0116] Optionally, the eating data includes eating conditions and drinking conditions. The state analysis module 303 analyzes the eating data and the weighing person information to determine the hydration state, and is used to: Analyze the eating situation during the first preset time period to determine the amount and type of food eaten; Determining the ingredient intake of each food type based on the food intake; Analyze the drinking water situation during the first preset time period to determine the type and amount of drinking water; Determine the water intake of each drinking water type according to the drinking water amount; Determining basic weight data of the weighing person according to the weighing person information; Determining the initial state of the weighing person according to the basic weight data; The hydration status is determined based on the ingredient intake, the water intake and the basal body weight.

[0117] Optionally, when determining the hydration state according to the ingredient intake, the water intake and the basal body weight, the state analysis module 303 is used to: Acquire exercise information within a second preset time period; analyze the exercise information to determine the exercise type and exercise time; Acquire motion images, analyze the motion images, and determine perspiration conditions; Determining the consumption level of the person being weighed for the type of exercise according to the perspiration situation; A hydration status is determined based on the consumption level, the ingredient intake, the water intake, and the basal body weight.

[0118] Optionally, when the data analysis module 301 determines the weighing data according to the body fat change and the lower limb electrical signal, it is used to: Get the current ambient humidity; Determining the basic impedance of the foot according to the basic body weight data; Analyze the ambient humidity and the foot basic impedance to determine the foot resistivity at the current moment; determining the resistivity of the lower limb according to the lower limb electrical signal; predicting current body resistivity based on the resistivity of the foot and the resistivity of the lower limb; Determine the most recent historical body fat percentage that meets the optimal hydration state from the current moment according to the historical body fat data; Weighing data is determined based on the foot resistivity, the body fat change, the lower limb resistivity, the historical body fat percentage and the current body resistivity.

[0119] Optionally, the state analysis module 303 determines the hydration state according to the consumption level, the ingredient intake, the water intake and the basal body weight, using the following formula: ; in, In hydrated state; is water intake; is the consumption level; is the weighting factor for water loss per unit consumption level; is the ingredient intake; is the weight coefficient of the effect of ingredient intake on hydration status; is the base weight; is the weight coefficient of the effect of basal body weight on hydration status.

[0120] Optionally, the data analysis module 301 determines the weighing data according to the foot resistivity, the body fat change, the lower limb resistivity, the historical body fat percentage and the current body resistivity, using the following formula: ; in, is the current theoretical body fat percentage; is the weight coefficient of the impact of weighing data on body fat percentage; Real-time weighing data; is the weight coefficient of the influence of electrical signal data on body fat percentage; is the resistivity of the lower limb; is the foot resistivity; is the weight coefficient of the effect of hydration status on body fat percentage; In hydrated state; is the base weight; It is the correction parameter of the current measured body fat percentage by combining resistivity with ambient humidity correction; is the current body resistivity; Correction parameters for current body fat measurements based on historical body fat data; is the historical body fat percentage; The correction parameter for the current measured body fat percentage based on the amount of body fat change; is the amount of body fat change.

[0121] Optionally, the measurement data correction system 300 for the smart body fat scale further includes an information verification module 305, which is used to: Obtaining a weighing request, analyzing the weighing request, and determining information of the person making the request; Based on the requesting person information, analyzing the stored data to determine whether the stored data contains the requesting person information; If not, send an information filling signal to the requesting person, receive the information filling result, and obtain the weighing person information; When determining the basic weight of the weighing person according to the weighing person information, it is used to: During the preset optimal time period, a weighing signal is sent to the requesting person, and a weighing result is received; According to the weighing result, basic body weight data is determined.

[0122] Optionally, the measurement data correction system 300 for the smart body fat scale further includes a data updating module 306 for: If the eating data is empty and the exercise information is empty, determining that the current hydration state is optimal; If the current hydration status is optimal, the basic weight data is updated according to the weighing data.

[0123] The system of this embodiment can be used to execute the method of any of the above embodiments. The implementation principles and technical effects are similar and will not be described in detail here.

Claims

1. A method for correcting measurement data for an intelligent body fat scale, characterized in that: include: Obtaining weighing data; analyzing the weighing data to determine weighing time, weighing personnel information, and weighing body fat data; Analyze the weighing time to determine whether the weighing is in a preset optimal time period; If it is not in the preset optimal time period, obtaining eating data, analyzing the eating data and the weighing person information, and determining the hydration status at the current moment; The hydration status is analyzed, the weighed body fat data is corrected, and actual body fat data is obtained and displayed.

2. The method according to claim 1, characterized in that The step of obtaining weighing data comprises: Acquire a weighing electrical signal; analyze the weighing electrical signal to determine a single foot electrical signal and a lower limb electrical signal; Determining body fat data of the non-digestive structure of the weighed person according to the single-foot electrical signal; Retrieving historical body fat data, comparing the historical body fat data with the body fat data, and determining body fat changes; Weighing data is determined based on the body fat changes and the lower limb electrical signals.

3. The method according to claim 2, characterized in that The eating data includes eating conditions and drinking conditions, and the analyzing the eating data and the weighing person information to determine the hydration status includes: Analyze the eating situation during the first preset time period to determine the amount and type of food eaten; Determining the ingredient intake of each food type based on the food intake; Analyze the drinking water situation during the first preset time period to determine the type and amount of drinking water; Determine the water intake of each drinking water type according to the drinking water amount; Determining basic weight data of the weighing person according to the weighing person information; Determining the initial state of the weighing person according to the basic weight data; The hydration status is determined based on the ingredient intake, the water intake and the basal body weight.

4. The method according to claim 3, characterized in that The determining of the hydration status according to the ingredient intake, the water intake and the basal body weight comprises: Acquire exercise information within a second preset time period; analyze the exercise information to determine the exercise type and exercise time; Acquire motion images, analyze the motion images, and determine perspiration conditions; Determining the consumption level of the person being weighed for the type of exercise according to the perspiration situation; A hydration status is determined based on the consumption level, the ingredient intake, the water intake, and the basal body weight.

5. The method according to claim 3, characterized in that: Determining weighing data according to the body fat change and the lower limb electrical signal includes: Get the current ambient humidity; Determining the basic impedance of the foot according to the basic body weight data; Analyze the ambient humidity and the foot basic impedance to determine the foot resistivity at the current moment; determining the resistivity of the lower limb according to the lower limb electrical signal; predicting current body resistivity based on the resistivity of the foot and the resistivity of the lower limb; Determine, based on the historical body fat data, the most recent historical body fat percentage that meets the optimal hydration state at the current moment; Weighing data is determined based on the foot resistivity, the body fat change, the lower limb resistivity, the historical body fat percentage and the current body resistivity.

6. The method according to claim 4, characterized in that The hydration status is determined based on the consumption level, the ingredient intake, the water intake and the basal body weight using the following formula: ； in, In hydrated state; for water intake; is the consumption level; is the weighting factor for water loss per unit consumption level; is the ingredient intake; is the weight coefficient of the effect of ingredient intake on hydration status; is the base weight; is the weight coefficient of the effect of basal body weight on hydration status.

7. The method according to claim 5, characterized in that The weighing data is determined according to the foot resistivity, the body fat change, the lower limb resistivity, the historical body fat rate and the current body resistivity, using the following formula: ; in, is the current theoretical body fat percentage; is the weight coefficient of the impact of weighing data on body fat percentage; Real-time weighing data; is the weight coefficient of the influence of electrical signal data on body fat percentage; is the resistivity of the lower limb; is the foot resistivity; is the weight coefficient of the effect of hydration status on body fat percentage; In hydrated state; is the base weight; It is the correction parameter of the current measured body fat percentage by combining resistivity with ambient humidity correction; is the current body resistivity; Correction parameters for current body fat measurements based on historical body fat data; is the historical body fat percentage; The correction parameter for the current measured body fat percentage based on the amount of body fat change; is the amount of body fat change.

8. The method according to claim 4, characterized in that Before determining the basic weight of the weighing person according to the weighing person information, the method further includes: Obtaining a weighing request, analyzing the weighing request, and determining information of the person making the request; Based on the requesting person information, analyzing the stored data to determine whether the stored data contains the requesting person information; If not, send an information filling signal to the requesting person, receive the information filling result, and obtain the weighing person information; The step of determining the basic weight of the weighing person according to the weighing person information includes: During the preset optimal time period, a weighing signal is sent to the requesting person, and a weighing result is received; According to the weighing result, basic body weight data is determined.

9. The method according to claim 8, characterized in that The method further comprises: If the eating data is empty and the exercise information is empty, determining that the current hydration state is optimal; If the current hydration status is optimal, the basic weight data is updated according to the weighing data.

10. A measurement data correction system for an intelligent body fat scale, characterized in that: include: Data analysis module, used to obtain weighing data; Analyze the weighing data to determine the weighing time, weigher information, and weighing body fat data; A time analysis module, used to analyze the weighing time and determine whether the weighing is in a preset optimal time period; A status analysis module, for obtaining eating data if the person is not in the preset optimal time period, analyzing the eating data and the weighing person information, and determining the hydration status at the current moment; The data correction module is used to analyze the hydration status, correct the weighed body fat data, obtain actual body fat data and display it.

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