A measurement data correction method and system for an intelligent body fat scale

Abstract

This application relates to the technical field of body fat scales, and in particular to a method and system for correcting measurement data for an intelligent body fat scale. The method includes: obtaining weighing data; analyzing the weighing data to determine the weighing time, the information of the person being weighed, and the body fat data of the weighing; analyzing the weighing time to determine whether the weighing is in a preset optimal period; if it is not in the preset optimal period, obtaining eating data, analyzing the eating data and the information of the person being weighed, and determining the hydration state at the current moment; analyzing the hydration state, correcting the body fat data of the weighing to obtain the actual body fat data and displaying it. By analyzing the hydration state, the original body fat data is corrected to eliminate the error caused by the change in hydration. It ensures that accurate body fat percentage can be obtained even when measuring in a non-optimal period. Finally, the corrected body fat data is displayed to the user, providing a reliable monitoring result for the user, which helps the user better understand their health status and perform corresponding health management.

Description

Technical Field

[0001] This application relates to the technical field of body fat scales, and in particular to a method and system for calibrating measurement data 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 families and fitness venues.

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

[0004] This application provides a method and system for calibrating measurement data for an intelligent body fat scale to solve the above problems.

[0005] In a first aspect, this application provides a method for calibrating measurement data for an intelligent body fat scale, the method comprising:

[0006] Obtain weighing data; analyze the weighing data to determine the weighing time, weighing personnel information, and weighing body fat data;

[0007] Analyze the weighing time to determine whether the weighing is in a preset optimal period;

[0008] If not in the preset optimal period, obtain eating data, analyze the eating data and the weighing personnel information to determine the hydration state at the current moment;

[0009] Analyze the hydration state, correct the weighing body fat data, obtain the actual body fat data and display it.

[0010] Through this solution, weight data of the user is obtained by a high-precision sensor, providing basic data for subsequent analysis and calibration. 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 synchronously recorded, which is crucial for analyzing the user's health status and habits. By determining the weighing time, suggestions on when to measure most accurately can be provided to the user. By comparing the current measurement time with the preset optimal measurement period, it can be judged whether further calibration steps are required. The selection of the optimal period helps to reduce measurement errors caused by time fluctuations. If the measurement time is not within the optimal period, the user is required to input eating data, which helps to understand the user's eating habits and hydration status, so as to more accurately calibrate the body fat data. Combining the eating data and user information, the user's current hydration status can be estimated. This is crucial for calibrating the body fat data because changes in the hydration status will directly affect the measurement results of the body fat percentage. Through the analysis of the hydration status, the original body fat data is calibrated to eliminate errors caused by changes in hydration. This step ensures that even when measured during non-optimal periods, the user can obtain accurate body fat percentage. Finally, the calibrated body fat data is presented to the user, reflecting the user's true body fat percentage, providing reliable monitoring results for the user, and helping the user to better understand their health status and conduct corresponding health management.

[0011] Optionally, the obtaining of the weighing data includes:

[0012] Obtaining a weighing electrical signal; analyzing the weighing electrical signal to determine a single-foot electrical signal and a lower-limb electrical signal;

[0013] According to the single-foot electrical signal, determining the body fat data of the non-digestive structure of the weighing person;

[0014] Retrieving historical body fat data, comparing the historical body fat data with the body fat data to determine the body fat change;

[0015] According to the body fat change and the lower-limb electrical signal, determining the weighing data.

[0016] Through this solution, the weighing electrical signal obtained through 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 a single foot and the lower limbs can be separated, which is crucial for evaluating the body fat condition of the user's lower limbs. The electrical signal of a single foot can help determine the body fat data of non-digestive structures, while the electrical signal of the lower limbs helps correct the body fat percentage. Using the impedance information in the electrical signal of a single foot and combining it with the user's body characteristics, the body fat data of non-digestive structures can be calculated; it is very important for accurately estimating the body fat percentage. By comparing the user's historical body fat data and the current body fat data, the change trend of body fat can be monitored. This trend analysis helps the user understand their health condition and the effect of body fat control. Combining the information of body fat change and the electrical signal of the lower limbs, the weighing data of the user can be corrected and optimized. This step ensures that even when the measurement is carried out during a non-optimal period, the user can obtain accurate body fat percentage data after correction.

[0017] Optionally, the eating data includes the eating situation and the drinking situation. Analyzing the eating data and the weighing personnel information to determine the hydration status includes:

[0018] Analyze the eating situation within a first preset period to determine the eating amount and the types of food eaten;

[0019] According to the eating amount, determine the intake of components for each type of food eaten;

[0020] Analyze the drinking situation within the first preset period to determine the type of drink and the amount of water drunk;

[0021] According to the amount of water drunk, determine the water intake for each type of drink;

[0022] According to the weighing personnel information, determine the basic body weight data of the weighing personnel;

[0023] According to the basic body weight data, determine the initial state of the weighing personnel;

[0024] According to the intake of components, the water intake, and the basic body weight, determine the hydration status.

[0025] Through this solution, by analyzing the user's eating situation, the eating habits of the user within a specific period can be understood, which is crucial for evaluating the hydration status and nutritional intake. Information on the amount and type of food consumed helps predict the water content in the food, thereby affecting the assessment of the hydration status. By analyzing the amount of food consumed, the intake of each food component can be calculated, including water, protein, fat, carbohydrates, etc.; this is crucial for evaluating the impact of food on the hydration status. By analyzing the user's drinking situation, the water intake of the user within a specific period can be understood. The type and amount of water consumed are crucial for evaluating the user's hydration status. By analyzing the water intake, the water intake of each type of drinking can be calculated. Different types of drinking may have different impacts on the hydration status, so this step helps to more accurately evaluate the hydration status. Through the user's personal information, such as age, gender, height, weight, etc., the basic body weight data of the user can be determined; this is crucial for evaluating the user's hydration status and body fat percentage. Based on the basic body weight data, the initial hydration status of the user before eating and drinking can be evaluated. This step helps to more accurately calculate the impact of eating and drinking on the hydration status. By comprehensively analyzing the user's component intake, water intake, and basic body weight, the user's hydration status can be more accurately evaluated. This step is crucial for correcting the measurement data of the body fat scale to improve the accuracy of the body fat percentage.

[0026] Optionally, determining the hydration status according to the component intake, the water intake, and the basic body weight includes:

[0027] Obtain the exercise information within a second preset period; analyze the exercise information to determine the exercise type and exercise time;

[0028] Obtain the exercise image and analyze the exercise image to determine the sweating situation;

[0029] Determine the consumption level of the weighing person for the exercise type according to the sweating situation;

[0030] Determine the hydration status according to the consumption level, the component intake, the water intake, and the basic body weight.

[0031] Through this solution, by collecting the user's motion information within 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 motion information helps identify the types of exercises the user has performed and the duration of the exercise, which is very important for calculating the energy and water consumption during the exercise. By analyzing the user's images during the exercise, the user's sweating condition can be evaluated, so as to more accurately estimate the water loss during the exercise. The sweating condition reflects the user's water consumption during the exercise. Combining the type of exercise, the energy and water consumption levels of the user during a specific exercise can be calculated, which is very important for evaluating the user's hydration status. By comprehensively considering various 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 according to the user's hydration status to help the user improve their health. By continuously monitoring the user's hydration status, the user can be helped to track changes in their health condition and detect potential health problems in a timely manner. Considering the impact of exercise and sweating on the hydration status, the measurement error caused by unstable hydration status can be reduced.

[0032] Optionally, the determining the weighing data according to the body fat change and the lower limb electrical signal includes:

[0033] Obtain the environmental humidity at the current moment;

[0034] Determine the foot base impedance according to the basic body weight data;

[0035] Analyze the environmental humidity and the foot base impedance to determine the foot resistivity at the current moment;

[0036] Determine the lower limb resistivity according to the lower limb electrical signal;

[0037] Predict the current body resistivity according to the foot resistivity and the lower limb resistivity;

[0038] Determine the historical body fat percentage of the most recent time when the optimal hydration state was satisfied from the historical body fat data up to the current moment;

[0039] Determine 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.

[0040] Through this solution, by obtaining environmental humidity data, the impact of environmental factors on the user's hydration status can be evaluated. Environmental humidity is related to the user's sweating amount and water evaporation, thus affecting the bioelectrical impedance measurement results. Using the user's basal body weight data and body characteristics, the basal impedance value of the feet can be calculated as a benchmark for subsequent analysis. By analyzing the environmental humidity and the basal impedance of the feet, the foot resistivity at the current moment can be calculated, which reflects the water content and bioelectrical impedance of the user's feet. Considering comprehensively the foot resistivity, the data of the user's body fat change, and the lower limb electrical signals, the user's weighing data can be calculated through an algorithm formula. This process involves the calibration of the bioelectrical impedance value to eliminate the influence brought by the change of hydration status.

[0041] Optionally, to determine the hydration status based on the consumption level, the component intake, the water intake, and the basal body weight, the following formula is used:

[0042] ;

[0043] where, is the hydration status; is the water intake; is the consumption level; is the weight coefficient of water loss caused by each unit of consumption level; is the component intake; is the weight coefficient of the impact of component intake on the hydration status; is the basal body weight; is the weight coefficient of the impact of basal body weight on the hydration status.

[0044] Through this solution, by comprehensively considering multiple factors, especially the basal body weight, the consumption level, and the component intake, the formula can more precisely reflect the individual's hydration status. This is particularly important for athletes, the elderly, or people with chronic diseases, as their hydration needs are different from those of ordinary people. The formula can help understand how to adjust the hydration status through diet (especially the intake of water and electrolytes). For example, excessive salty foods may increase water retention, while strenuous exercise will cause more water loss. Timely replenishing water according to the consumption before and after exercise can effectively prevent problems such as dehydration or overhydration. If the water intake, the consumption level, the component intake, and the change of basal body weight can be monitored in real time, the formula will be able to dynamically adjust the prediction of the hydration status, enabling the user to adjust their hydration strategy according to their daily life, diet, and exercise situation.

[0045] Optionally, to determine the weighing data based on the foot resistivity, the body fat change, the lower limb resistivity, the historical body fat rate, and the current body resistivity, the following formula is used:

[0046] ;

[0047] Among them, is the current theoretical body fat percentage; is the weight coefficient of the weighing data on the body fat percentage; is the real-time weighing data; is the weight coefficient of the electrical signal data on the body fat percentage; is the lower limb resistivity; is the foot resistivity; is the weight coefficient of the hydration state on the body fat percentage; is the hydration state; is the basic body weight; is the correction parameter of the resistivity combined with the environmental humidity correction for the current measured body fat percentage; is the current body resistivity; is the correction parameter of the historical body fat data for the current measured body fat; is the historical body fat percentage; is the correction parameter of the amount of body fat change for the current measured body fat percentage; is the amount of body fat change. [[ID=3�]]

[0048] Through this solution, by integrating data from multiple dimensions, the measurement of body fat percentage becomes more accurate, especially with the support of real-time data and multiple correction factors; it has strong adaptability and can be customized according to the specific situation of individuals (such as basic body weight, historical body fat, etc.). This is especially important for people with different body types and health conditions. Considering the impact of the hydration state on the body fat percentage, this formula helps to provide a more reasonable estimate of the body fat percentage when the hydration is unstable (such as before and after exercise or when the climate changes). It avoids measurement errors caused by unbalanced hydration.

[0049] Optionally, before determining the basic body weight of the weighing person according to the weighing person information, it further includes:

[0050] Obtain a weighing request, analyze the weighing request, and determine the request person information;

[0051] Based on the request person information, analyze the stored data to determine whether the stored data contains the request person information;

[0052] If not, send an information filling signal to the request person and receive the information filling result to obtain the weighing person information;

[0053] Determining the basic body weight of the weighing person according to the weighing person information includes:

[0054] Send a weighing signal to the request person at the preset best time period and receive the weighing result;

[0055] Determine the basic body weight data according to the weighing result.

[0056] Through this solution, it is ensured that the weighing requirements of users can be responded to in a timely manner, providing instant health management services for users. By analyzing the request, it can be identified which user requests the weighing, which is crucial for subsequent data processing and personalized services. Determining whether the requested personnel information is included helps quickly locate the user's personal profile and measurement history. If the user information already exists, subsequent measurements and analyses can be directly carried out, avoiding duplicate data collection. For new users or users without records, this step ensures that the necessary personal information can be collected for accurate measurement and data analysis. Selecting the best time period for weighing can reduce measurement errors caused by changes in the hydration state and other factors, improving the accuracy of the measurement results. Weighing at the best time period, combined with the user's personal information, can more accurately determine the user's basic body weight, providing reliable data for subsequent body fat percentage calculation. It avoids measurement result deviations caused by improper measurement time, unstable hydration state and other factors, improving the measurement consistency of the body fat scale. Accurate basic body weight data helps provide personalized health management suggestions for users, including diet adjustment and exercise plans.

[0057] Optionally, the method further includes:

[0058] If the eating data is empty and the exercise information is empty, it is determined that the current hydration state is optimal;

[0059] If the current hydration state is optimal, update the basic body weight data according to the weighing data.

[0060] Through this solution, when the user has not eaten and exercised, the user's hydration state is stable because both eating and exercise affect the body's hydration state. A stable hydration state helps obtain more accurate body weight and body fat percentage measurement results. When it is determined that the user's hydration state is optimal, it uses the current weighing data to update the user's basic body weight. This ensures the accuracy of the basic body weight data because it reflects the weight in a stable hydration state.

[0061] In a second aspect, the present application provides a measurement data correction system for an intelligent body fat scale, and the system includes:

[0062] A data analysis module, configured to obtain weighing data; analyze the weighing data to determine the weighing time, weighing personnel information and weighing body fat data;

[0063] A time analysis module, configured to analyze the weighing time to determine whether the weighing is in a preset best time period;

[0064] A status analysis module, configured to obtain eating data if it is not in the preset optimal time period, analyze the eating data and the weighing personnel information, and determine the hydration status at the current moment;

[0065] A data correction module, configured to analyze the hydration status, correct the weighed body fat data, and obtain and display the actual body fat data. Description of the Drawings

[0066] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0067] Figure 1 It is a schematic diagram of an application scenario provided by an embodiment of the present application;

[0068] Figure 2 It is a flowchart of a method for correcting measurement data of an intelligent body fat scale provided by an embodiment of the present application;

[0069] Figure 3 It is a schematic structural diagram of a measurement data correction system for an intelligent body fat scale provided by an embodiment of the present application. Detailed Embodiments

[0070] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present application belong to the scope of protection of the present application.

[0071] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, unless otherwise specified.

[0072] The following will further describe the embodiments of the present application in detail with reference to the drawings of the specification.

[0073] As people pay more and more attention to health management, body fat scales, as a common health monitoring tool, have been widely used in families and fitness venues.

[0074] Traditional body fat scales mainly measure body fat percentage through Bioelectrical Impedance Analysis (BIA) technology. This technology calculates the body fat percentage based on the different impedances of different tissues in the human body to electric current. However, when measuring body fat percentage, existing body fat scales are usually affected by various factors, resulting in large errors in the measurement results.

[0075] Based on this, the present application provides a method and system for correcting measurement data for an intelligent body fat scale, which obtains weighing data; analyzes the weighing data to determine the weighing time, the information of the person being weighed, and the weighing body fat data; analyzes the weighing time to determine whether the weighing is in a preset optimal period; if it is not in the preset optimal period, it obtains eating data, analyzes the eating data and the information of the person being weighed to determine the hydration state at the current moment; analyzes the hydration state, corrects the weighing body fat data, obtains the actual body fat data and displays it. The weight data of the user is obtained through a high-precision sensor, providing basic data for subsequent analysis and correction. This step ensures that all subsequent processing is based on accurate weight measurement values. It not only records the user's weight but also synchronously records the measurement time and user information, which is crucial for analyzing the user's health status and habits. By determining the weighing time, suggestions on when to measure most accurately can be provided to the user. By comparing the current measurement time with the preset optimal measurement period, it can be judged whether further correction steps are needed. 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 input eating data; this helps to understand the user's eating habits and hydration state, thereby more accurately correcting the body fat data. Combining the eating data and the user information, the user's current hydration state can be estimated. This is crucial for correcting the body fat data because changes in the hydration state will directly affect the measurement result of the body fat percentage. By analyzing the hydration state, the original body fat data is corrected to eliminate the errors caused by changes in hydration. This step ensures that even when measured in a non-optimal period, the user can obtain an accurate body fat percentage. Finally, the corrected body fat data is displayed to the user; it reflects the user's true body fat percentage, provides a reliable monitoring result for the user, and helps the user better understand their health status and carry out corresponding health management.

[0076] Figure 1A schematic diagram of an application scenario provided for this application. When the body fat scale measures the body fat percentage, the method provided for this application is applied. Specifically, the method provided for this application is applied to any server, and the server interacts with the body fat scale. The server obtains the user's weight data through the high-precision sensor of the body fat scale, providing basic data for subsequent analysis and calibration. 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 synchronized, which is crucial for analyzing the user's health status and habits. By determining the weighing time, advice on when to measure most accurately can be provided to the user. By comparing the current measurement time with the preset optimal measurement period, it can be judged whether further calibration 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, asking the user to input eating data helps to understand the user's eating habits and hydration status, thereby more accurately calibrating the body fat data. Combining the eating data and user information, the user's current hydration status can be estimated. This is crucial for calibrating the body fat data because changes in the hydration status directly affect the measurement result of the body fat percentage. Through the analysis of the hydration status, the original body fat data is calibrated to eliminate errors caused by changes in hydration. This step ensures that even when measured outside the optimal period, the user can obtain an accurate body fat percentage. Finally, the calibrated body fat data is presented to the user, reflecting the user's true body fat percentage, providing reliable monitoring results for the user, and helping the user better understand their health status and conduct corresponding health management.

[0077] For the specific implementation method, reference can be made to the following embodiments.

[0078] Figure 2 A flowchart of a method for calibrating measurement data for an intelligent body fat scale provided in an embodiment of this application. The method of this embodiment can be applied to the server in the above scenario. As Figure 2 shown, the method includes:

[0079] S201. Obtain weighing data; analyze the weighing data to determine the weighing time, weighing personnel information, and weighing body fat data.

[0080] The weighing data can be the weight value of the user measured by the body fat scale and various impedance data.

[0081] The weighing time can be the specific time point when the user measures their weight, usually including the date and clock time.

[0082] The weighing personnel information can be the personal information of the user who uses the body fat scale for measurement, such as name, gender, age, height, weight, etc.

[0083] The body fat data measured can be body composition data such as body fat percentage, muscle mass, bone mass, etc. obtained through a body fat scale.

[0084] Specifically, first, the intelligent body fat scale obtains the user's weight data through its built-in sensor. The sensor can be a pressure sensor or other devices that can accurately measure weight. When the user stands on the body fat scale, the sensor transmits the weight data to the built-in calculation system. The calculation system processes the obtained weighing data to determine the time when the measurement occurs, the user 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's identity (through user accounts or biometric technologies), and the preliminary calculation of the body fat percentage.

[0085] S202. Analyze the weighing time to determine whether the weighing is within the preset optimal period.

[0086] The preset optimal period can be the time period set according to the user's living habits and the suggestions of the body fat scale, which is the most accurate time period for measuring weight and body fat. Generally, it is when the user wakes up in the morning and has not eaten or drunk water, and the body's hydration state is normal, which is suitable as the preset optimal period.

[0087] Specifically, compare the current measurement time with the preset optimal measurement period. The optimal measurement period is usually a relatively stable time period in the user's daily activity pattern, such as when fasting after waking up in the morning.

[0088] S203. If it is not within the preset optimal period, obtain the eating data, analyze the eating data and the weighing personnel information, and determine the hydration state at the current moment.

[0089] The eating data can be the user's diet situation in a period of time before the measurement, including the types, quantities of food, and drinking water situation.

[0090] The weighing personnel information can be various types of personal data, and can also include name, gender, age, height, weight, health status, etc.

[0091] The hydration state can be the condition of the body's water content, which reflects the adequacy of the body's water.

[0092] Specifically, if the current measurement time is not within the preset optimal period, the user will be instructed to input relevant eating data, including the time of eating, the types and quantities of food, and the amount of drinking water; it can be obtained through manual input by the user or by synchronizing with other intelligent devices (such as smart watches, health applications). The calculation system will analyze the eating data, combine the user's basal metabolic rate and historical data, and estimate the current hydration state, including the calculation of the water content in food and the analysis of the amount of drinking water.

[0093] S204. Analyze the hydration status, correct the body fat data measured by weight, obtain the actual body fat data and display it.

[0094] The actual body fat data can be the body fat data after calibration, which is closer to the user's true body fat percentage.

[0095] Specifically, according to the analyzed hydration status, the preliminary body fat data is corrected to eliminate the influence of the hydration status on the measurement result of the body fat percentage. The corrected body fat data is closer to the user's true body fat percentage. Finally, the intelligent body fat scale displays the corrected actual body fat data to the user, which can be directly displayed on the display screen of the body fat scale or viewed through remote devices such as a smartphone application.

[0096] Through this solution, the user's weight data is obtained by a high-precision sensor, providing 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 synchronized, which is crucial for analyzing the user's health status and habits. By determining the weighing time, advice on when to measure most accurately can be provided to the user. By comparing the current measurement time with the preset optimal measurement period, it can be judged whether further correction steps are needed. 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 input 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 the body fat data because changes in the hydration status directly affect the measurement result of the body fat percentage. Through the analysis of the hydration status, the original body fat data is corrected to eliminate errors caused by changes in hydration. This step ensures that even when measured outside the optimal period, the user can obtain an accurate body fat percentage. Finally, the corrected body fat data is displayed to the user, reflecting the user's true body fat percentage, providing a reliable monitoring result for the user, and helping the user to better understand their health status and conduct corresponding health management.

[0097] 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; according to 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; according to the body fat change and the lower-limb electrical signal, weighing data is determined.

[0098] The weighing electrical signal can be a weak current signal sent by the body fat scale through electrodes to the user's body, and the impedance response of the body to the current is measured.

[0099] A single - foot electrical signal can be the current signal sent and received by a body fat scale through the electrodes of only one foot during the measurement process.

[0100] The lower - limb electrical signal can be the current signal sent and received by the body fat scale through the user's lower limbs (including both feet).

[0101] The non - digestive structure can be the structure in the body that does not participate in food digestion and absorption, such as muscles, bones, internal organs, etc.

[0102] The body fat data can be the data related to body composition measured and calculated by the body fat scale, including body fat percentage, muscle mass, bone mass, etc.

[0103] The historical body fat data can be the body fat data measured and recorded by the user through the body fat scale over a period of time in the past.

[0104] The body fat change can be the change in the body fat percentage of the user over a period of time, such as an increase or decrease in the body fat percentage.

[0105] 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 respectively contact different electrodes. The sensor will capture the response of the user's body to the current and generate corresponding electrical signals. The calculation system will analyze the captured electrical signals to distinguish between single - foot electrical signals and lower - limb electrical signals. This may involve signal - processing techniques such as filtering, amplification, and digital processing. Using the impedance information in the single - foot electrical signal, combined with the user's body characteristics (such as height, weight, etc.), the body fat data of non - digestive structures is calculated through the bioelectrical impedance analysis (BIA) algorithm. The historical body fat data of the user is retrieved from the database and compared with the currently measured body fat data. This may involve data - matching and synchronization techniques. Considering the body fat change and the information of the lower - limb electrical signal comprehensively, the weighing data of the user is finally determined. This may involve complex algorithms and formulas to correct and optimize the measurement results.

[0106] Through this solution, the weighing electrical signal obtained through the electrodes 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 a single foot and the lower limbs can be separated, which is crucial for evaluating the body fat condition of the user's lower limbs. The electrical signal of a single foot can help determine the body fat data of non-digestive structures, while the electrical signal of the lower limbs helps correct the body fat percentage. Using the impedance information in the electrical signal of a single foot and combining it with the user's body characteristics, the body fat data of non-digestive structures can be calculated, which is very important for accurately estimating the body fat percentage. By comparing the user's historical body fat data and the current body fat data, the change trend of body fat can be monitored. This trend analysis helps the user understand their health status and the effect of body fat control. Combining the information on body fat changes and the electrical signal of the lower limbs, the user's weighing data can be corrected and optimized. This step ensures that even when the measurement is carried out during a non-optimal period, the user can obtain accurate body fat percentage data after correction.

[0107] In some embodiments, analyze the eating situation within a first preset time period to determine the amount of food eaten and the types of food eaten; based on the amount of food eaten, determine the intake of each component of each type of food eaten; analyze the drinking situation within the first preset time period to determine the type of drink and the amount of drink; based on the amount of drink, determine the water intake of each type of drink; based on the weighing personnel information, determine the basic body weight data of the weighing personnel; based on the basic body weight data, determine the initial state of the weighing personnel; based on the component intake, water intake, and basic body weight, determine the hydration state.

[0108] The first preset time period can be a preset time period for collecting the user's eating and drinking data within this period, such as 4 hours before weighing.

[0109] The amount of food eaten can be the total amount of food ingested by the user within a specific time period.

[0110] The types of food eaten can be the types of food ingested by the user within a specific time period.

[0111] The component intake can be the amount of each component of each type of food ingested by the user within a specific time period, including water, protein, fat, carbohydrates, etc.

[0112] The drinking situation can be the user's drinking behavior within a specific time period, including the frequency, time, and environment of drinking, etc.

[0113] The type of drink can be the types of drinking water ingested by the user within a specific time period, such as purified water, mineral water, tea, coffee, etc.

[0114] The amount of drink can be the total amount of drinking water ingested by the user within a specific time period.

[0115] The water intake can be the total amount of water that the user ingests through food and beverages within a specific period of time.

[0116] The basic weight data can be the weight-related data in the user's personal information, including the user's height, weight, age, gender, etc.

[0117] The initial state can be the hydration state of the user before starting to eat and drink.

[0118] Specifically, the user is required to input the eating situation within the first preset period, including the types and quantities of food. This can be input through a smart device (such as a mobile application) or by automatically obtaining relevant information by scanning the barcode on the food packaging. According to the amount of food intake entered by the user and combined with the information in the food database, calculate the intake of components of each type of food, such as protein, fat, carbohydrates, etc. The user will be required to record the drinking situation within the first preset period, including the type of drink (such as pure water, tea, coffee, etc.) and the amount of water consumed. According to the type of drink and the amount of water consumed recorded by the user, calculate the water intake of each type of drink. Utilize the user's personal information (such as age, gender, height, weight, etc.) and combine 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, evaluate the user's initial hydration state, that is, the state before eating and drinking. Considering the user's component intake, water intake, and basic weight comprehensively, calculate the user's hydration state through an algorithm formula.

[0119] Through this solution, by analyzing the user's eating situation, it is possible to understand the user's eating habits during a specific period, which is crucial for evaluating the hydration status and nutritional intake. Information on the amount and type of food consumed helps predict the water content in the food, thus affecting the assessment of the hydration status. By analyzing the amount of food consumed, the intake of each food component can be calculated, including water, protein, fat, carbohydrates, etc.; this is crucial for evaluating the impact of food on the hydration status. By analyzing the user's drinking situation, it is possible to understand the user's water intake during a specific period. The type and amount of water consumed are crucial for evaluating the user's hydration status. By analyzing the water intake, the water intake of each type of drinking can be calculated. Different types of drinking may have different effects on the hydration status, so this step helps to more accurately evaluate the hydration status. Through the user's personal information, such as age, gender, height, weight, etc., the user's basic body weight data can be determined; this is crucial for evaluating the user's hydration status and body fat percentage. Through the basic body weight data, the initial hydration status of the user before eating and drinking can be evaluated. This step helps to more accurately calculate the impact of eating and drinking on the hydration status. By comprehensively analyzing the user's component intake, water intake, and basic body weight, the user's hydration status can be more accurately evaluated. This step is crucial for correcting the measurement data of the body fat scale to improve the accuracy of the body fat percentage.

[0120] In some embodiments, obtain the exercise information within a second preset period; analyze the exercise information to determine the exercise type and exercise time; obtain the exercise images, analyze the exercise images to determine the sweating situation; according to the sweating situation, determine the consumption level of the weighing person for the exercise type; according to the consumption level, component intake, water intake, and basic body weight, determine the hydration status.

[0121] The second preset period can be the set second time period for collecting the user's exercise information within this period, 12 hours before weighing.

[0122] The exercise information can be detailed information about the user's exercise behavior, including exercise type, exercise intensity, exercise duration, etc.

[0123] The exercise type can be the types of exercise performed by the user, such as walking, running, swimming, yoga, etc.

[0124] The exercise time can be the total duration of the user's exercise.

[0125] The exercise images can be photos or videos of the user taken after each exercise stage.

[0126] The sweating situation can be the amount of water excreted by the user's body through sweat during exercise.

[0127] The consumption level can be the amount of energy and water consumed by the user during exercise.

[0128] Specifically, the user is required to input exercise information within a second preset time period, including the type of exercise (such as walking, running, swimming, etc.) and the duration of the exercise; it can be automatically collected by a smart device (such as a smart watch, health application), or manually input by the user. Analyze the exercise information input by the user to determine the type and duration of the exercise. This may involve statistical analysis of the user's exercise habits. If possible, obtain images of the user during exercise, such as through a camera installed in the exercise venue. The images can be used to analyze the user's sweating condition. Based on the user's sweating condition and the type of exercise, combined with data from exercise science, calculate the energy consumption and water consumption of the user during exercise. Considering the user's consumption level, component intake, water intake, and basal body weight comprehensively, calculate the user's hydration status through an algorithm formula.

[0129] Through this solution, by collecting the user's exercise information within the second preset time 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 identify the types of exercise the user has performed and the duration of the exercise, which is very important for calculating the energy and water consumption during exercise. By analyzing the images of the user during exercise, the user's sweating condition can be evaluated, so as to more accurately estimate the amount of water loss during exercise. The sweating condition reflects the water consumption of the user during exercise. Combined with the type of exercise, the energy and water consumption levels of the user during a specific exercise can be calculated, which is very important for evaluating the user's hydration status. By comprehensively considering various 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 according to the user's hydration status to help the user improve their health. By continuously monitoring the user's hydration status, the user can be helped to track changes in their health condition and detect potential health problems in a timely manner. Considering the impact of exercise and sweating on the hydration status, measurement errors caused by unstable hydration status can be reduced.

[0130] In some embodiments, obtain the ambient humidity at the current moment; determine the foot basal impedance according to the basal body weight data; analyze the ambient humidity and the foot basal impedance to determine the foot resistivity at the current moment; predict the current body resistivity according to the foot resistivity and the lower limb resistivity; determine the historical body fat percentage of the most recent time that meets the optimal hydration status from the current moment according to the historical body fat data; determine the weighing data according to the foot resistivity, body fat change, lower limb resistivity, historical body fat percentage, and current body resistivity.

[0131] The ambient humidity can be the moisture content in the surrounding air.

[0132] The foot base impedance can be the degree of hindrance of the user's foot to current under specific conditions.

[0133] The foot resistivity can be the degree of hindrance of foot tissue to current.

[0134] The current body resistivity can be the resistance value of human tissue.

[0135] Specifically, the humidity data of the current environment is obtained through an internal or external humidity sensor. This data is very important for evaluating the user's hydration status and correcting body fat percentage measurements. Using the user's basic body weight data and known body characteristics (such as height, age, gender, etc.), the foot base impedance value is calculated through an algorithm formula. Combining the environmental humidity data and the foot base impedance value, the foot resistivity at the current moment is analyzed and calculated through a specific algorithm formula. Based on the foot resistivity and lower limb resistivity, the current body resistivity is predicted; according to the historical body fat data, the historical body fat percentage of the most recent time that meets the optimal hydration status from the current moment is determined. At this time, the body fat percentage is not the body fat percentage under normal estimated hydration status; comprehensively considering the foot resistivity, body fat change, 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 correcting the impedance value to eliminate measurement errors caused by changes in hydration status.

[0136] Through this solution, by obtaining the environmental humidity data, the influence of environmental factors on the user's hydration status can be evaluated. Environmental humidity is related to the user's sweating amount and water evaporation, thus affecting the impedance measurement results. Using the user's basic body weight data and body characteristics, the foot base impedance value can be calculated as a benchmark for subsequent analysis. By analyzing the environmental humidity and foot base impedance, the foot resistivity at the current moment can be calculated, which reflects the water content and impedance situation of the user's foot. Comprehensively considering the foot resistivity, data on the user's body fat change, and lower limb electrical signals, the user's weighing data is calculated through an algorithm formula. This process involves correcting the impedance value to eliminate the influence brought by changes in hydration status.

[0137] In some embodiments, the hydration status is determined according to the consumption level, component intake, water intake, and basic body weight, using the following formula (1):

[0138] (1);

[0139] Wherein, is the hydration status; is the water intake; is the consumption level; is the weight coefficient of water loss caused by each unit of consumption level; is the component intake; is the weight coefficient of the influence of component intake on the hydration status; is the basal body weight; is the weight coefficient of the influence of basal body weight on the hydration status.

[0140] Specifically, the hydration status is directly related to water intake, but is affected by the consumption level, component intake, and basal body weight. Basal body weight is related to the total body water volume, and people with different weights have different water requirements. When the consumption level increases, more body water is lost, resulting in a decrease in the hydration status. Component intake may affect the hydration status, especially when a large amount of salt and protein are ingested, which will increase the water requirement. Specifically, water intake directly contributes to the hydration status. represents the water loss caused by the consumption level, through to represent the water loss per unit consumption level. represents the influence of component intake on the hydration status. The intake of certain nutrients (such as high protein, salt, etc.) will affect the hydration status. represents that the basal body weight determines the body's water volume, which can represent the water volume corresponding to each kilogram of body weight.

[0141] Through this solution, by comprehensively considering multiple factors, especially the basal body weight, consumption level, and component intake, the formula can more precisely reflect the individual's hydration status. This is particularly important for athletes, the elderly, or people with chronic diseases, as their hydration needs are different from those of ordinary people. The formula can help understand how to regulate the hydration status through diet (especially the intake of water and electrolytes). For example, too much salty food may increase water retention, while strenuous exercise will cause more water loss. Timely replenishing water according to the consumption before and after exercise can effectively prevent problems such as dehydration or overhydration. If the water intake, consumption level, component intake, and changes in basal body weight can be monitored in real time, the formula will be able to dynamically adjust the hydration status prediction, enabling users to adjust their hydration strategies according to their daily life, diet, and exercise conditions.

[0142] In some embodiments, according to the foot resistivity, body fat change, lower limb resistivity, historical body fat percentage, and current body resistivity, the weighing data is determined, and the following formula (2) is used:

[0143] (2);

[0144] Wherein, is the current theoretical body fat percentage; is the weight coefficient of the influence of the weighing data on the body fat percentage; is the real-time weighing data; is the weight coefficient of the influence of electrical signal data on body fat percentage; is the lower limb resistivity; is the foot resistivity; is the weight coefficient of the influence of hydration status on body fat percentage; is the hydration status; is the basic body weight; is the correction parameter for the current measured body fat percentage by combining resistivity and environmental humidity correction; is the current body resistivity; is the correction parameter for the current measured body fat by historical body fat data; is the historical body fat percentage; is the correction parameter for the current measured body fat percentage by the amount of body fat change; is the amount of body fat change.

[0145] Specifically, indicates that the weighing data (real-time body weight) directly affects the body fat percentage, for adjusting the ratio. reflects body composition information through the reciprocal of resistance (conductivity), including the distribution of muscle and fat; mainly reflects body composition, especially the distribution of muscle and fat, by measuring the conductivity of the lower limbs and feet (i.e., the reciprocal of resistivity). Low resistivity (high conductivity) indicates a higher muscle mass, while high resistivity (low conductivity) usually indicates a higher fat mass. indicates that the hydration status corrects the body fat percentage, indicates the weight of the hydration status. Divide by for normalization to avoid the influence of differences between individuals. indicates the influence of body resistivity (combined with environmental humidity correction) on body fat percentage. indicates the reference correction of historical body fat data to the current body fat. indicates the dynamic correction of the amount of body fat change to the current measured data.

[0146] Through this solution, by integrating data from multiple dimensions, the measurement of body fat percentage becomes more accurate, especially with the support of real-time data and multiple correction factors; it has strong adaptability and can be customized according to the specific situation of individuals (such as basic body weight, historical body fat, etc.). This is particularly important for people with different body types and health conditions. Considering the influence of hydration status on body fat percentage, this formula helps to provide a more reasonable estimate of body fat percentage when hydration is unstable (such as before and after exercise or during climate change). Avoid measurement errors caused by unbalanced hydration.

[0147] In some embodiments, a weighing request is obtained, the weighing request is analyzed, and the requesting person information is determined; based on the requesting person information, the stored data is analyzed to determine whether the stored data contains the requesting person 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 information; based on the weighing person 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.

[0148] The weighing request may be a request sent by a user to the smart body fat scale to measure weight and body fat percentage.

[0149] 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.

[0150] The stored data may be user information, historical measurement data, and other relevant data that have been saved.

[0151] The information filling signal can be a prompt sent to the user, requiring the user to fill in or confirm personal information.

[0152] The information filling result can be the personal information data that the user fills in or confirms according to the prompts.

[0153] The base weight may be the user's stable weight under specific conditions.

[0154] The weighing signal may be a prompt sent to the user, informing the user of instructions for performing a weighing operation.

[0155] The weighing result may be weight data provided by the smart body fat scale after the user weighs, including weight value and possible body fat percentage.

[0156] The basic weight data may be a basic weight value calculated based on the user's weighing results and other relevant information.

[0157] Specifically, the system waits for a user to send a weigh-in request via a smart body fat scale or other device. The system analyzes the received weigh-in request and extracts the identity information of the requester, such as the user ID or device ID. The system queries 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, the system prompts the user to enter their personal information, including baseline weight, height, age, etc. A weigh-in signal is sent to the user during a preset optimal measurement period (such as after waking up in the morning), and the user is weighed during this period. The user's baseline weight is calculated based on the weigh-in results during the optimal period, combined with their personal information and historical measurement data.

[0158] Through this solution, it is ensured that the weighing needs of users can be responded to in a timely manner, providing instant health management services for users. By analyzing the requests, it is possible to identify which user is requesting the weighing, which is crucial for subsequent data processing and personalized services. Determining whether the requested personnel information is included helps quickly locate the user's personal profile and measurement history. If the user information already exists, subsequent measurements and analyses can be directly carried out, avoiding duplicate data collection. For new users or users without records, this step ensures that the necessary personal information can be collected for accurate measurement and data analysis. Selecting the best time period for weighing can reduce measurement errors caused by changes in the hydration state and other factors, improving the accuracy of measurement results. Weighing at the best time period, combined with the user's personal information, can more accurately determine the user's basal weight, providing reliable data for subsequent body fat percentage calculations. It avoids measurement result deviations caused by improper measurement time, unstable hydration state and other factors, improving the measurement consistency of the body fat scale. Accurate basal weight data helps provide personalized health management suggestions for users, including diet adjustments and exercise plans.

[0159] In some embodiments, if the eating data is empty and the exercise information is empty, it is determined that the current hydration state is optimal; if the current hydration state is optimal, the basal weight data is updated according to the weighing data.

[0160] Specifically, check whether the user has records of recent eating and exercise. Judge whether the user has not eaten or exercised recently. If the user's eating data and exercise information are empty, it is considered that the current is the moment when the user's hydration state is optimal. Use the weighing data obtained in the optimal hydration state to update the user's basal weight data. Ensure that the body weight is measured at the moment when the user's hydration state is optimal, thereby improving the accuracy and consistency of the measurement results.

[0161] Through this solution, when the user has not eaten and exercised, the user's hydration state is stable because both eating and exercise affect the body's hydration state. A stable hydration state helps obtain more accurate body weight and body fat percentage measurement results. When it is determined that the user's hydration state is optimal, it uses the current weighing data to update the user's basal weight. This ensures the accuracy of the basal weight data because it reflects the weight in a stable hydration state.

[0162] Figure 3 The structural schematic diagram of a measurement data correction system for an intelligent body fat scale provided by an embodiment of the present application is as Figure 3 shown. The measurement data correction system 300 for the intelligent body fat scale in this embodiment includes: a data analysis module 301, a time analysis module 302, a state analysis module 303, and a data correction module 304.

[0163] The data analysis module 301 is used to obtain weighing data, analyze the weighing data, and determine the weighing time, the weighing personnel information, and the weighing body fat data.

[0164] The time analysis module 302 is used to analyze the weighing time and determine whether the weighing is in a preset optimal period.

[0165] The status analysis module 303 is used to, if not in the preset optimal period, obtain eating data, analyze the eating data and the weighing personnel information, and determine the hydration status at the current moment.

[0166] The data correction module 304 is used to analyze the hydration status, correct the weighing body fat data, obtain the actual body fat data, and display it.

[0167] Optionally, when the data analysis module 301 obtains the weighing data, it is used to:

[0168] Obtain weighing electrical signals, analyze the weighing electrical signals, and determine single - foot electrical signals and lower - limb electrical signals.

[0169] According to the single - foot electrical signals, determine the body fat data of the non - digestive structures of the weighing personnel.

[0170] Retrieve historical body fat data, compare the historical body fat data with the body fat data, and determine the body fat change.

[0171] According to the body fat change and the lower - limb electrical signals, determine the weighing data.

[0172] Optionally, the eating data includes the eating situation and the drinking situation. When the status analysis module 303 analyzes the eating data and the weighing personnel information to determine the hydration status, it is used to:

[0173] Analyze the eating situation within a first preset period to determine the eating amount and the types of food eaten.

[0174] According to the eating amount, determine the intake of each component of the types of food eaten.

[0175] Analyze the drinking situation within the first preset period to determine the types of drinks and the drinking amount.

[0176] According to the drinking amount, determine the water intake of each type of drink.

[0177] According to the weighing personnel information, determine the basic body weight data of the weighing personnel.

[0178] According to the basic body weight data, determine the initial state of the weighing personnel.

[0179] Determine the hydration status based on the component intake, the water intake, and the baseline weight.

[0180] Optionally, when determining the hydration status according to the component intake, the water intake, and the baseline weight, the status analysis module 303 is configured to:

[0181] Obtain exercise information within a second preset period; analyze the exercise information to determine the exercise type and exercise time;

[0182] Obtain an exercise image, analyze the exercise image, and determine the sweating condition;

[0183] Determine the consumption level of the weighing person for the exercise type according to the sweating condition;

[0184] Determine the hydration status according to the consumption level, the component intake, the water intake, and the baseline weight.

[0185] Optionally, when determining the weighing data according to the body fat change and the lower limb electrical signal, the data analysis module 301 is configured to:

[0186] Obtain the ambient humidity at the current moment;

[0187] Determine the foot baseline impedance according to the baseline weight data;

[0188] Analyze the ambient humidity and the foot baseline impedance to determine the foot resistivity at the current moment;

[0189] Determine the lower limb resistivity according to the lower limb electrical signal;

[0190] Predict the current body resistivity according to the foot resistivity and the lower limb resistivity;

[0191] Determine the nearest historical body fat percentage that meets the optimal hydration status from the current moment according to the historical body fat data;

[0192] Determine 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.

[0193] Optionally, when determining the hydration status according to the consumption level, the component intake, the water intake, and the baseline weight, the status analysis module 303 uses the following formula:

[0194] ;

[0195] where is the hydration status; is the water intake; is the consumption level; is the weight coefficient of water loss caused by each unit of consumption level; is the intake of ingredients; is the weight coefficient of the influence of ingredient intake on the hydration state; is the basic body weight; is the weight coefficient of the influence of basic body weight on the hydration state.

[0196] 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 rate and the current body resistivity, using the following formula:

[0197] ;

[0198] wherein, is the current theoretical body fat rate; is the weight coefficient of the influence of weighing data on the body fat rate; is the real-time weighing data; is the weight coefficient of the influence of electrical signal data on the body fat rate; is the lower limb resistivity; is the foot resistivity; is the weight coefficient of the influence of the hydration state on the body fat rate; is the hydration state; is the basic body weight; is the correction parameter of the resistivity combined with environmental humidity correction for the currently measured body fat rate; is the current body resistivity; is the correction parameter of historical body fat data for the currently measured body fat; is the historical body fat rate; is the correction parameter of the amount of body fat change for the currently measured body fat rate; is the amount of body fat change.

[0199] Optionally, the measurement data correction system 300 of the intelligent body fat scale further includes an information verification module 305 for:

[0200] Obtain a weighing request, analyze the weighing request, and determine the request personnel information;

[0201] Based on the request personnel information, analyze the stored data to determine whether the stored data contains the request personnel information;

[0202] If not, send an information filling signal to the requesting personnel, receive the information filling result, and obtain the weighing personnel information;

[0203] When determining the basic body weight of the weighing personnel according to the weighing personnel information, it is used for:

[0204] During the preset optimal period, send a weighing signal to the requesting person and receive the weighing result;

[0205] Determine the basic body weight data according to the weighing result.

[0206] Optionally, the measurement data correction system 300 of the intelligent body fat scale further includes a data update module 306 for:

[0207] If the eating data is empty and the exercise information is empty, it is determined that the current hydration state is optimal;

[0208] If the current hydration state is optimal, update the basic body weight data according to the weighing data.

[0209] 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 elaborated here.

Claims

1. A calibration method for measurement data of an intelligent body fat scale, characterized in that, Including: Obtain weighing data; analyze the weighing data to determine the weighing time, weighing personnel information, and weighing body fat data; Analyze the weighing time to determine whether the weighing is in a preset optimal period; If it is not in the preset optimal period, obtain eating data, analyze the eating data and the weighing personnel information to determine the hydration status at the current moment; Analyze the hydration status, correct the weighing body fat data, obtain the actual body fat data and display it; The eating data includes the eating situation and the drinking situation. Analyzing the eating data and the weighing personnel information to determine the hydration status includes: Analyze the eating situation within a first preset period to determine the eating amount and eating types; According to the eating amount, determine the component intake of each eating type; Analyze the drinking situation within a first preset period to determine the drinking type and drinking amount; According to the drinking amount, determine the water intake of each drinking type; According to the weighing personnel information, determine the basic body weight data of the weighing personnel; According to the basic body weight data, determine the initial state of the weighing personnel; According to the component intake, the water intake, and the basic body weight, determine the hydration status; The determining the hydration status according to the component intake, the water intake, and the basic body weight includes: Obtain the exercise information within a second preset period; analyze the exercise information to determine the exercise type and exercise time; Obtain the exercise image, analyze the exercise image to determine the sweating situation; According to the sweating situation, determine the consumption level of the weighing personnel for the exercise type; According to the consumption level, the component intake, the water intake, and the basic body weight, determine the hydration status.

2. The method according to claim 1, wherein The obtaining the weighing data includes: Obtain the weighing electrical signal; analyze the weighing electrical signal to determine the single-foot electrical signal and the lower limb electrical signal; According to the single-foot electrical signal, determine the body fat data of the non-digestive structure of the weighing personnel; Retrieve the historical body fat data, compare the historical body fat data with the body fat data to determine the body fat change; According to the body fat change and the lower limb electrical signal, determine the weighing data.

3. The method according to claim 2, characterized in that, The determining the weighing data according to the body fat change and the lower limb electrical signal includes: Obtain the environmental humidity at the current moment; According to the basic body weight data, determine the basic foot impedance; Analyze the environmental humidity and the basic foot impedance to determine the foot resistivity at the current moment; According to the lower limb electrical signal, determine the lower limb resistivity; According to the foot resistivity and the lower limb resistivity, predict the current body resistivity; According to the historical body fat data, determine the nearest historical body fat rate that meets the optimal hydration status from the current moment; According to the foot resistivity, the body fat change, the lower limb resistivity, the historical body fat rate, and the current body resistivity, determine the weighing data.

4. The method according to claim 3, characterized in that The determining the hydration status according to the consumption level, the component intake, the water intake, and the basic body weight adopts the following formula: ； wherein, is in a hydrated state; is the water intake; is the consumption level; is the weight coefficient of water loss caused by each unit of consumption level; is the ingredient intake; is the weight coefficient of the influence of ingredient intake on the hydrated state; is the basal body weight; is the weight coefficient of the influence of basal body weight on the hydrated state.

5. The method according to claim 3, characterized in that, Determine the weighing data 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: ; Among them, is the current theoretical body fat percentage; is the weight coefficient of the weighing data on the body fat percentage; is the real-time weighing data; is the weight coefficient of the electrical signal data on the body fat percentage; is the lower limb resistivity; is the foot resistivity; is the weight coefficient of the hydration state on the body fat percentage; is the hydration state; is the basic body weight; is the correction parameter of the resistivity combined with environmental humidity correction for the current measured body fat percentage; is the current body resistivity; is the correction parameter of the historical body fat data for the current measured body fat; is the historical body fat percentage; is the correction parameter of the amount of body fat change for the current measured body fat percentage; is the amount of body fat change.

6. The method according to claim 3, characterized in that, Before determining the basic weight of the weighing person according to the weighing person information, it further includes: Obtain a weighing request, analyze the weighing request, and determine the request person information; Based on the request person information, analyze the stored data to determine whether the request person information is contained in the stored data; If not, send an information filling signal to the request person, receive the information filling result, and obtain the weighing person information; Determine the basic weight of the weighing person according to the weighing person information, including: During the preset optimal period, send a weighing signal to the request person and receive the weighing result; Determine the basic weight data according to the weighing result.

7. The method according to claim 6, characterized in that, The method further includes: If the eating data is empty and the exercise information is empty, it is determined that the current hydration state is optimal; If the current hydration state is optimal, update the basic weight data according to the weighing data.

8. An intelligent body fat scale measurement data correction system, which is applied to execute the method described in any one of claims 1-7, and is characterized in that It includes: A data analysis module for obtaining weighing data; Analyze the weighing data to determine the weighing time, the weighing person information, and the weighing body fat data; A time analysis module for analyzing the weighing time to determine whether the weighing is during the preset optimal period; A state analysis module for, if not during the preset optimal period, obtaining eating data, analyzing the eating data and the weighing person information, and determining the hydration state at the current moment; A data correction module for analyzing the hydration state, correcting the weighing body fat data, obtaining the actual body fat data, and displaying it.

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