A weight management method and system

Abstract

The present application relates to a weight management method and system, and to the technical field of weight management, the method comprising: obtaining heart rate information sent by a first smart terminal and mentality information corresponding to the heart rate information; obtaining diet information of the day recorded by diet software in a second smart terminal; the diet information includes food types and food weights; the second smart terminal is a smart phone; obtaining exercise information sent by the first smart terminal or a bound exercise APP; obtaining excretion information recorded in the second smart terminal; the excretion information includes excretion type and excretion time; the excretion information is obtained by being filled in by the user and then saved; obtaining sleep information recorded in the first smart terminal; obtaining a weight management score and a weight management suggestion plan based on the mentality information, diet information, exercise information, excretion information and sleep information; and sending the weight management score and the weight management suggestion plan to the second smart terminal.

Description

Technical Field

[0001] The present application relates to the technical field of weight management, and in particular to a weight management method and system. Background Art

[0002] Obesity refers to a certain degree of obvious overweight and excessive fat layer. It is a state caused by excessive accumulation of body fat, especially triglycerides. It is a chronic disease caused by excessive accumulation of body fat due to excessive food intake or changes in body metabolism, which leads to excessive weight gain and harm to the human body. Obesity is not a criterion for judging a person's health status. The definition of overweight and obesity is the body mass index (BMI), which is a screening indicator for measuring obesity. Obesity affects the beauty of the body, seriously endangers people's health, reduces the quality of life, and shortens life expectancy. With the improvement of people's living standards, people's dietary structure and living habits have changed greatly. Coupled with the fast pace of work and life, many people have no time to exercise, and the problem of obesity has become increasingly prominent, especially for middle-aged and elderly people. The problem of obesity is more serious.

[0003] When controlling weight, in addition to controlling your diet, you also need to move your legs. There are many kinds of exercise equipment on the market. How to choose the exercise method that suits you is a difficult problem that bothers the public.

[0004] In the current fitness process, fitness people only exercise based on subjective judgment, and their evaluation of whether the corresponding amount of exercise has been achieved is also based on subjective judgment, and there is a complete lack of corresponding data to support it. Subjective judgment is uncertain. Due to different physical fitness, the same amount of exercise does not consume the same amount of energy for different people. Therefore, fitness people cannot accurately judge whether they have achieved their training goals after exercise.

[0005] The same is true in terms of diet. There is also a lack of guidance on how to determine whether a diet is healthy and which diet is suitable for bodybuilders.

[0006] Therefore, how to determine whether the fitness person's living habits have reached the health index and give corresponding improvement plans or suggestions according to the user's status is the problem to be solved by the present invention. Summary of the invention

[0007] In order to at least partially solve the above technical problems, the present application provides a weight management method and system.

[0008] In a first aspect, the present application provides a weight management method that adopts the following technical solution.

[0009] A weight management method, comprising:

[0010] Acquire heart rate information and mentality information corresponding to the heart rate information sent by a first smart terminal; the first smart terminal is a smart bracelet or a smart watch;

[0011] Obtaining dietary information of the day recorded by dietary software in a second intelligent terminal; the dietary information includes food types and food weights; the second intelligent terminal is a smart phone;

[0012] Obtaining sports information sent by the first smart terminal or a bound sports APP;

[0013] Acquire excretion information recorded in the second intelligent terminal; the excretion information includes excretion type and excretion time; the excretion information is filled in by the user and saved;

[0014] Obtaining sleep information recorded in the first smart terminal;

[0015] Obtaining a weight management score and a weight management suggestion plan based on the mentality information, diet information, exercise information, excretion information and sleep information;

[0016] The weight management score and the weight management suggestion plan are sent to the second smart terminal.

[0017] Optionally, a weight management score and a weight management suggestion plan are obtained based on the mentality information, diet information, exercise information, excretion information and sleep information, including:

[0018] Based on the mentality information, the recognition result of the emotional state is obtained to obtain the mentality score f1;

[0019] Based on the food types and weights in the dietary information, the total intake of various nutrients is obtained and a nutritional balance assessment is performed; based on the nutritional balance assessment results, a dietary score f2 is obtained;

[0020] Calculate the amount of exercise and evaluate the effect of exercise based on the exercise information; obtain an exercise score f3 based on the amount of exercise and the effect of exercise;

[0021] Based on the excretion information, the excretion frequency and type are analyzed to determine whether there is a constipation or diarrhea problem and obtain the metabolic status, and the metabolic score f4 is obtained according to the metabolic status;

[0022] Performing sleep quality assessment based on sleep information; the sleep quality assessment includes calculating the total sleep duration and analyzing the proportion of deep sleep, light sleep and REM sleep; obtaining a sleep score f5 according to the sleep quality assessment result;

[0023] The weight management score is obtained based on the mentality score f1, diet score f2, exercise score f3, metabolism score f4, sleep score f5 and the preset evaluation formula;

[0024] A weight management recommendation plan is generated based on abnormal items in the mentality score f1, diet score f2, exercise score f3, metabolic score f4 and sleep score f5.

[0025] Optionally, the method further includes:

[0026] Obtain user historical data based on user information;

[0027] Analyze user behavior patterns; the analysis of user behavior patterns includes: analyzing historical mentality information to identify the user's emotional fluctuation patterns; analyzing historical dietary information to identify the user's dietary preferences and habits; analyzing historical exercise information to identify the user's exercise frequency and intensity habits; analyzing historical excretion information to identify the user's excretion patterns; analyzing historical sleep information to identify the user's sleep patterns;

[0028] Predicting the user's weight change trend based on the user's historical data and user behavior patterns;

[0029] Adjusting the weight management suggestion plan based on the weight change trend prediction result to adapt to the user's weight change trend;

[0030] The adjusted weight management recommendation plan is sent to the second smart terminal.

[0031] Optionally, the amount of exercise is calculated and the effect of exercise is evaluated based on the exercise information, including:

[0032] Locating the exercise time, exercise mode and exercise intensity from the exercise information; the exercise mode includes walking, running and fitness; the exercise intensity is used to characterize the change of heart rate during exercise;

[0033] The exercise coefficient E1 is calculated based on the exercise time, exercise mode and exercise intensity; the larger the E1, the greater the amount of exercise;

[0034] Based on the weight management goal and the exercise coefficient E1, a predicted range of exercise intensity is obtained; the predicted range of exercise intensity includes a maximum exercise intensity Emax and a minimum exercise intensity Emin;

[0035] Determine whether the maximum load value measured during exercise is greater than the maximum exercise intensity Emax. If the maximum load value measured is greater than the maximum exercise intensity Emax, it is overload;

[0036] Obtain the number of overloads; determine whether it is the first overload, and if it is the first overload, generate a good exercise status score prompt;

[0037] If it is not the first overload, the preset fatigue recovery period will be entered. If the maximum load value measured after the fatigue recovery period is less than Emin, the overload recovery is good and the fatigue recovery period ends.

[0038] If the maximum load value is not less than Emin after the fatigue recovery period, it is determined that the current exercise intensity is not helpful for the exercise effect.

[0039] Optionally, the performing of emotional state recognition based on heart rate information and a preset heart rate fluctuation curve includes: receiving a heart rate curve sent by the first smart terminal, wherein the heart rate curve is generated based on the heart rate information of the user in a normal state;

[0040] Get the peaks and troughs of the heart rate curve;

[0041] According to a preset heart rate fluctuation curve, each peak and trough is compared with the preset heart rate fluctuation curve;

[0042] The degree of deviation between the peak and the trough is determined according to the comparison result; the emotional state is evaluated based on the degree of deviation to obtain the current emotional state information.

[0043] Optionally, the method further includes:

[0044] Extracting the emotion change trend in the emotion state information; the emotion change trend is calculated based on the heart rate information;

[0045] Determining the correlation between the user's emotion and the heart rate curve according to the emotion change trend and the historical emotion state information to correct the emotion change trend to obtain the correct user emotion;

[0046] Determine the corresponding relationship between the user emotion and the heart rate curve based on the correct user emotion and the peaks and troughs of the corresponding heart rate curve;

[0047] Determine whether the current emotion is in an abnormal state based on the corresponding relationship between the user's emotion and the heart rate curve and the rules of the user's emotion;

[0048] Based on the emotion influencing factors in the abnormal state, regularity analysis is performed to obtain information on the cause of the abnormal state.

[0049] Optionally, the user emotion influencing factors include diet information, exercise information, excretion information, behavioral habits, and sleep information;

[0050] The process of the rule analysis includes:

[0051] Eliminating the influencing factors affecting weight change from the influencing factor group to obtain an analysis group; the influencing factors include all factors in the influencing factor group and the corresponding influence degrees;

[0052] Arrange the influencing factors in the analysis group in order from small to large according to the degree of influence; randomly combine the influencing factors in the analysis group in pairs;

[0053] Match each combination obtained by combining with the user's historical behavior based on a preset relationship evaluation model to obtain the matching degree corresponding to each combination;

[0054] Determine whether each influencing factor of the analysis group is associated with an influencing factor with a high matching degree and / or below a preset matching degree threshold in at least one combination; if so, remove the influencing factor from the analysis group and add abnormal state cause information;

[0055] If there is no correlation, the influencing factor is saved as a suggestion factor, and the suggestion factor and the corresponding influence degree are used as part of the weight management suggestion plan; the influencing factor removed from the influencing factor group is analyzed separately, and if the matching degree obtained by the separate analysis meets the preset matching degree threshold, the influencing factor is included in the abnormal state cause information;

[0056] If the matching degree obtained by the separate analysis does not meet the preset matching degree threshold, the influencing factors are saved as suggested factors, and the suggested factors and the corresponding influence degrees are used as part of the weight management suggestion plan.

[0057] In a second aspect, the present application provides a weight management method that adopts the following technical solution.

[0058] In a third aspect, the present application discloses an electronic device, comprising a memory and a processor, wherein the memory stores a computer program that is loaded by the processor and executes any of the above methods.

[0059] In a fourth aspect, the present application discloses a computer-readable storage medium storing a computer program that can be loaded by a processor and execute any of the above methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a flow chart of a weight management method according to an embodiment of the present application;

[0061] Figure 2 This is a system block diagram of a weight management system according to an embodiment of the present application;

[0062] In the figure, 201 is a first processing module; 202 is a second processing module; 203 is a third processing module; 204 is a fourth processing module; 205 is a fifth processing module; and 206 is a sixth processing module. DETAILED DESCRIPTION

[0063] The following is combined with Figure 1-2 The present application is further described with specific embodiments:

[0064] First of all, it should be noted that in the description of this application, if the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside" and other directional words appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, which is only for the convenience of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of this application; in addition, if the terms "first", "second", "third" and other numerical quantifiers appear, they are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in this application, unless otherwise clearly specified and limited, if the terms "installed", "connected", and "connected" appear, they should be understood in a broad sense, for example, it can be a fixed connection, or a detachable connection, a limited connection such as an interference fit, a transition fit, or an integral connection; it can be directly connected or indirectly connected through an intermediate medium; therefore, for ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to the specific circumstances.

[0065] The present application embodiment discloses a weight management method. Figure 1 As an implementation of a weight management method, a weight management method includes the following steps:

[0066] Step 101: Acquire heart rate information and mentality information corresponding to the heart rate information sent by a first smart terminal; the first smart terminal is a smart bracelet or a smart watch.

[0067] Step 102: Obtain the dietary information of the day recorded by the dietary software in the second intelligent terminal; the dietary information includes food types and food weights; the second intelligent terminal is a smart phone.

[0068] Step 103: Obtain exercise information sent by the first smart terminal or the bound exercise APP.

[0069] Step 104, obtaining excretion information recorded in the second intelligent terminal; the excretion information includes excretion type and excretion time; the excretion information is filled in by the user and saved.

[0070] Step 105: Obtain sleep information recorded in the first smart terminal.

[0071] Step 106: Obtain a weight management score and a weight management suggestion plan based on the mentality information, diet information, exercise information, excretion information, and sleep information, and send the weight management score and the weight management suggestion plan to the second smart terminal.

[0072] Specifically, this application integrates multi-dimensional health data such as heart rate and mentality information, diet details, exercise volume, excretion status, and sleep quality from different smart terminals (such as smart bracelets, smart phones, etc.), and comprehensively analyzes them to obtain an individualized weight management score. Based on this, customized weight management recommendations are proposed, and finally these analysis results are fed back to the user's smart phone, which helps users better understand the impact of their own living habits on their weight, and take effective adjustment measures to achieve or maintain an ideal weight.

[0073] As a specific implementation of a weight management method, a weight management score and a weight management suggestion plan are obtained based on the mentality information, diet information, exercise information, excretion information and sleep information, including:

[0074] Based on the mentality information, the recognition result of the emotional state is obtained to obtain the mentality score f1;

[0075] Based on the food types and weights in the dietary information, the total intake of various nutrients is obtained and a nutritional balance assessment is performed; based on the nutritional balance assessment results, a dietary score f2 is obtained;

[0076] Calculate the amount of exercise and evaluate the effect of exercise based on the exercise information; obtain an exercise score f3 based on the amount of exercise and the effect of exercise;

[0077] Based on the excretion information, the excretion frequency and type are analyzed to determine whether there is a constipation or diarrhea problem and obtain the metabolic status, and the metabolic score f4 is obtained according to the metabolic status;

[0078] Performing sleep quality assessment based on sleep information; the sleep quality assessment includes calculating the total sleep duration and analyzing the proportion of deep sleep, light sleep and REM sleep; obtaining a sleep score f5 according to the sleep quality assessment result;

[0079] The weight management score is obtained based on the mentality score f1, diet score f2, exercise score f3, metabolism score f4, sleep score f5 and the preset evaluation formula;

[0080] A weight management recommendation plan is generated based on abnormal items in the mentality score f1, diet score f2, exercise score f3, metabolic score f4 and sleep score f5.

[0081] Specifically, by comprehensively analyzing mentality information, diet information, exercise information, excretion information and sleep information, we can accurately evaluate the overall health status and weight management effect of an individual. The mentality score f1 is calculated based on the emotional state recognition results of the mentality information to understand the individual's mental health level. The analysis of various types of food and their intake in the diet information, combined with the nutritional balance assessment, derives the diet score f2 to ensure the rationality of nutritional intake. Through the quantitative evaluation of exercise information, the exercise score f3 can be obtained to evaluate the individual's exercise effect and activity. Based on the analysis of excretion information, the metabolic state of the individual is judged, and the metabolic score f4 is obtained to identify potential digestive problems. Through the evaluation of sleep information, including sleep duration and the proportion of different sleep stages, the sleep score f5 is obtained to further reflect the individual's rest quality. These scores are combined with the preset evaluation formula for comprehensive analysis to form a comprehensive weight management score.

[0082] As a specific implementation of a weight management method, the method further includes:

[0083] Obtain user historical data based on user information;

[0084] Analyze user behavior patterns; the analysis of user behavior patterns includes: analyzing historical mentality information to identify the user's emotional fluctuation patterns; analyzing historical dietary information to identify the user's dietary preferences and habits; analyzing historical exercise information to identify the user's exercise frequency and intensity habits; analyzing historical excretion information to identify the user's excretion patterns; analyzing historical sleep information to identify the user's sleep patterns;

[0085] Predicting the user's weight change trend based on the user's historical data and user behavior patterns;

[0086] Adjusting the weight management suggestion plan based on the weight change trend prediction result to adapt to the user's weight change trend;

[0087] The adjusted weight management recommendation plan is sent to the second smart terminal.

[0088] Specifically, by collecting and analyzing multi-dimensional data such as the user's historical mentality, diet, exercise, excretion and sleep, we can identify individual behavior patterns and habits, predict the user's weight change trends based on this information, and dynamically adjust management suggestions accordingly. The customized plan can be conveyed to the user through the smart terminal, which helps the user achieve weight management goals more effectively.

[0089] As a specific implementation of a weight management method, the amount of exercise is calculated and the effect of exercise is evaluated based on exercise information, including:

[0090] Locating the exercise time, exercise mode and exercise intensity from the exercise information; the exercise mode includes walking, running and fitness; the exercise intensity is used to characterize the change of heart rate during exercise;

[0091] The exercise coefficient E1 is calculated based on the exercise time, exercise mode and exercise intensity; the larger the E1, the greater the amount of exercise;

[0092] Based on the weight management goal and the exercise coefficient E1, a predicted range of exercise intensity is obtained; the predicted range of exercise intensity includes a maximum exercise intensity Emax and a minimum exercise intensity Emin;

[0093] Determine whether the maximum load value measured during exercise is greater than the maximum exercise intensity Emax. If the maximum load value measured is greater than the maximum exercise intensity Emax, it is overload;

[0094] Obtain the number of overloads; determine whether it is the first overload, and if it is the first overload, generate a good exercise status score prompt;

[0095] If it is not the first overload, the preset fatigue recovery period will be entered. If the maximum load value measured after the fatigue recovery period is less than Emin, the overload recovery is good and the fatigue recovery period ends.

[0096] If the maximum load value is not less than Emin after the fatigue recovery period, it is determined that the current exercise intensity is not helpful for the exercise effect.

[0097] Specifically, the weight management method analyzes the exercise time, exercise mode and exercise intensity in the user's exercise information, calculates the exercise coefficient E1, and then evaluates the amount of exercise and its impact on the weight management goal; the larger the E1 value, the greater the amount of exercise, and accordingly sets a reasonable exercise intensity prediction range, including the maximum exercise intensity Emax and the minimum exercise intensity Emin, to monitor the load condition during exercise; when it is detected that the measured maximum load value exceeds Emax, the system determines it as an overload state and records the number of overloads. The first overload will generate a good score prompt to encourage the user, and the non-first time will enter the fatigue recovery period; if the maximum load value drops below Emin during the recovery period, it is considered to have recovered well, otherwise it is determined that the current exercise intensity is not conducive to weight management, ensuring that the exercise is both effective and safe and avoiding overtraining.

[0098] As a specific implementation of a weight management method, the emotional state recognition based on heart rate information and a preset heart rate fluctuation curve includes: receiving a heart rate curve sent by the first smart terminal, the heart rate curve being generated based on the heart rate information of the user in a normal state;

[0099] Get the peaks and troughs of the heart rate curve;

[0100] According to a preset heart rate fluctuation curve, each peak and trough is compared with the preset heart rate fluctuation curve;

[0101] The degree of deviation between the peak and the trough is determined according to the comparison result; the emotional state is evaluated based on the degree of deviation to obtain the current emotional state information.

[0102] As a specific implementation of a weight management method, the method further includes:

[0103] Extracting the emotion change trend in the emotion state information; the emotion change trend is calculated based on the heart rate information;

[0104] Determining the correlation between the user's emotion and the heart rate curve according to the emotion change trend and the historical emotion state information to correct the emotion change trend to obtain the correct user emotion;

[0105] Determine the corresponding relationship between the user emotion and the heart rate curve based on the correct user emotion and the peaks and troughs of the corresponding heart rate curve;

[0106] Determine whether the current emotion is in an abnormal state based on the corresponding relationship between the user's emotion and the heart rate curve and the rules of the user's emotion;

[0107] Based on the emotion influencing factors in the abnormal state, regularity analysis is performed to obtain information on the cause of the abnormal state.

[0108] As a specific implementation of a weight management method, the user's emotion influencing factors include diet information, exercise information, excretion information, behavioral habits, and sleep information;

[0109] The process of the rule analysis includes:

[0110] Eliminating the influencing factors affecting weight change from the influencing factor group to obtain an analysis group; the influencing factors include all factors in the influencing factor group and the corresponding influence degrees;

[0111] Arrange the influencing factors in the analysis group in order from small to large according to the degree of influence; randomly combine the influencing factors in the analysis group in pairs;

[0112] Match each combination obtained by combining with the user's historical behavior based on a preset relationship evaluation model to obtain the matching degree corresponding to each combination;

[0113] Determine whether each influencing factor of the analysis group is associated with an influencing factor with a high matching degree and / or below a preset matching degree threshold in at least one combination; if so, remove the influencing factor from the analysis group and add abnormal state cause information;

[0114] If there is no correlation, the influencing factor is saved as a suggestion factor, and the suggestion factor and the corresponding influence degree are used as part of the weight management suggestion plan; the influencing factor removed from the influencing factor group is analyzed separately, and if the matching degree obtained by the separate analysis meets the preset matching degree threshold, the influencing factor is included in the abnormal state cause information;

[0115] If the matching degree obtained by the separate analysis does not meet the preset matching degree threshold, the influencing factors are saved as suggested factors, and the suggested factors and the corresponding influence degrees are used as part of the weight management suggestion plan.

[0116] As one implementation of a weight management system, it includes:

[0117] The first processing module 201 is used to: obtain heart rate information sent by a first smart terminal and mentality information corresponding to the heart rate information; the first smart terminal is a smart bracelet or a smart watch;

[0118] The second processing module 202 is used to: obtain the dietary information of the day recorded by the dietary software in the second intelligent terminal; the dietary information includes food types and food weights; the second intelligent terminal is a smart phone;

[0119] The third processing module 203 is used to: obtain the exercise information sent by the first smart terminal or the bound exercise APP;

[0120] The fourth processing module 204 is used to: obtain excretion information recorded in the second intelligent terminal; the excretion information includes excretion type and excretion time; the excretion information is filled in by the user and saved;

[0121] The fifth processing module 205 is used to: obtain the sleep information recorded in the first smart terminal;

[0122] The sixth processing module 206 is used to: obtain a weight management score and a weight management suggestion plan based on the mentality information, diet information, exercise information, excretion information and sleep information; and send the weight management score and the weight management suggestion plan to the second smart terminal.

[0123] The embodiment of the present application also discloses an electronic device.

[0124] Specifically, the device includes a memory and a processor, and the memory stores a computer program that can be loaded by the processor and execute any one of the above-mentioned weight management methods.

[0125] The embodiment of the present application also discloses a computer-readable storage medium. Specifically, the computer-readable storage medium stores a computer program that can be loaded by a processor and execute any of the weight management methods described above, and the computer-readable storage medium includes, for example, a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0126] It should be noted that the above embodiments are only used to illustrate the present application and are not intended to limit the technical solutions described in the present application. Although the present application has been described in detail in this specification with reference to the above embodiments, a person of ordinary skill in the art should understand that a person of ordinary skill in the art can still modify or make equivalent substitutions to the present application, and all technical solutions and improvements thereof that do not depart from the spirit and scope of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for weight management, characterized in that: include: Acquire the heart rate information sent by the first smart terminal and the mentality information corresponding to the heart rate information; The first smart terminal is a smart bracelet or a smart watch; Obtaining dietary information of the day recorded by dietary software in a second intelligent terminal; the dietary information includes food types and food weights; the second intelligent terminal is a smart phone; Obtaining sports information sent by the first smart terminal or a bound sports APP; Acquire excretion information recorded in the second intelligent terminal; the excretion information includes excretion type and excretion time; the excretion information is filled in by the user and saved; Obtaining sleep information recorded in the first smart terminal; Obtaining a weight management score and a weight management suggestion plan based on the mentality information, diet information, exercise information, excretion information and sleep information; sending the weight management score and the weight management suggestion plan to the second smart terminal; Performing emotional state recognition based on heart rate information and a preset heart rate fluctuation curve includes: receiving a heart rate curve sent by the first smart terminal; Get the peaks and troughs of the heart rate curve; According to a preset heart rate fluctuation curve, each peak and trough is compared with the preset heart rate fluctuation curve; Determine the degree of deviation between the peak and the trough according to the comparison result; evaluate the emotional state based on the degree of deviation to obtain the current emotional state information; Also includes: Extracting the emotion change trend in the emotion state information; Determining the correlation between the user's emotion and the heart rate curve according to the emotion change trend and the historical emotion state information to correct the emotion change trend to obtain the correct user emotion; Determine the corresponding relationship between the user emotion and the heart rate curve based on the correct user emotion and the peaks and troughs of the corresponding heart rate curve; Determine whether the current emotion is in an abnormal state based on the corresponding relationship between the user's emotion and the heart rate curve and the rules of the user's emotion; Perform regular analysis based on the emotional influencing factors in the abnormal state to obtain information on the cause of the abnormal state; Factors that influence emotions include diet information, exercise information, excretion information, behavioral habits, and sleep information; The process of the rule analysis includes: Eliminating the influencing factors affecting weight change from the influencing factor group to obtain an analysis group; the influencing factors include all factors in the influencing factor group and the corresponding influence degrees; Arrange the influencing factors in the analysis group in order from small to large according to the degree of influence; randomly combine the influencing factors in the analysis group in pairs; Match each combination obtained by combining with the user's historical behavior based on a preset relationship evaluation model to obtain the matching degree corresponding to each combination; Determine whether each influencing factor of the analysis group is associated with an influencing factor with a high matching degree and / or below a preset matching degree threshold in at least one combination; if so, remove the influencing factor from the analysis group and add abnormal state cause information; If there is no correlation, the influencing factor is saved as a suggestion factor, and the suggestion factor and the corresponding influence degree are used as part of the weight management suggestion plan; the influencing factor removed from the influencing factor group is analyzed separately, and if the matching degree obtained by the separate analysis meets the preset matching degree threshold, the influencing factor is included in the abnormal state cause information; If the matching degree obtained by the separate analysis does not meet the preset matching degree threshold, the influencing factors are saved as suggested factors, and the suggested factors and the corresponding influence degrees are used as part of the weight management suggestion plan.

2. A weight management method according to claim 1, characterized in that: Based on the mentality information, diet information, exercise information, excretion information and sleep information, a weight management score and a weight management suggestion plan are obtained, including: Based on the mentality information, the recognition result of the emotional state is obtained to obtain the mentality score f1; Based on the food types and weights in the dietary information, the total intake of various nutrients is obtained and a nutritional balance assessment is performed; based on the nutritional balance assessment results, a dietary score f2 is obtained; Calculate the amount of exercise and evaluate the effect of exercise based on the exercise information; obtain an exercise score f3 based on the amount of exercise and the effect of exercise; Based on the excretion information, the excretion frequency and type are analyzed to determine whether there is a constipation or diarrhea problem and obtain the metabolic status, and the metabolic score f4 is obtained according to the metabolic status; Performing sleep quality assessment based on sleep information; the sleep quality assessment includes calculating the total sleep duration and analyzing the proportion of deep sleep, light sleep and REM sleep; obtaining a sleep score f5 according to the sleep quality assessment result; The weight management score is obtained based on the mentality score f1, diet score f2, exercise score f3, metabolism score f4, sleep score f5 and the preset evaluation formula; A weight management recommendation plan is generated based on abnormal items in the mentality score f1, diet score f2, exercise score f3, metabolic score f4 and sleep score f5.

3. A weight management method according to claim 2, characterized in that: The method further comprises: Obtain user historical data based on user information; Analyze user behavior patterns; the analysis of user behavior patterns includes: analyzing historical mentality information to identify the user's emotional fluctuation patterns; analyzing historical dietary information to identify the user's dietary preferences and habits; analyzing historical exercise information to identify the user's exercise frequency and intensity habits; analyzing historical excretion information to identify the user's excretion patterns; analyzing historical sleep information to identify the user's sleep patterns; Predicting the user's weight change trend based on the user's historical data and user behavior patterns; Adjusting the weight management suggestion plan based on the weight change trend prediction result to adapt to the user's weight change trend; The adjusted weight management recommendation plan is sent to the second smart terminal.

4. A weight management method according to claim 3, characterized in that: Calculate exercise volume and evaluate exercise effects based on exercise information, including: Locating the exercise time, exercise mode and exercise intensity from the exercise information; the exercise mode includes walking, running and fitness; the exercise intensity is used to characterize the change of heart rate during exercise; The exercise coefficient E1 is calculated based on the exercise time, exercise mode and exercise intensity; the larger the E1, the greater the amount of exercise; Based on the weight management goal and the exercise coefficient E1, the exercise intensity prediction range is obtained; the exercise intensity prediction range includes the maximum exercise intensity E max and minimum exercise intensity E min ; Determine whether the maximum load value measured during exercise is greater than the maximum exercise intensity Emax. max It is overloaded; Obtain the number of overloads; determine whether it is the first overload, and if it is the first overload, generate a good exercise status score prompt; If it is not the first overload, the preset fatigue recovery period will be entered. If the maximum load value measured after the fatigue recovery period is less than E min The overload recovery is good and the fatigue recovery period is over; If the maximum load value after the fatigue recovery period is not less than E min , it is determined that the current exercise intensity is not helpful for the exercise effect.

5. A weight management method according to claim 4, characterized in that: The heart rate curve is generated based on the heart rate information of the user in a normal state.

6. A weight management method according to claim 5, characterized in that: The emotion change trend is calculated based on the heart rate information.

7. A method for weight management, characterized in that: include: The first processing module is used to: obtain the heart rate information sent by the first intelligent terminal and the mentality information corresponding to the heart rate information; The first smart terminal is a smart bracelet or a smart watch; The second processing module is used to: obtain the dietary information of the day recorded by the dietary software in the second intelligent terminal; the dietary information includes food types and food weights; the second intelligent terminal is a smart phone; The third processing module is used to: obtain the sports information sent by the first smart terminal or the bound sports APP; The fourth processing module is used to: obtain the excretion information recorded in the second intelligent terminal; the excretion information includes the excretion type and the excretion time; the excretion information is filled in by the user and saved; A fifth processing module is used to: obtain sleep information recorded in the first smart terminal; A sixth processing module is used to obtain a weight management score and a weight management suggestion plan based on the mentality information, diet information, exercise information, excretion information and sleep information; sending the weight management score and the weight management suggestion plan to the second smart terminal; Performing emotional state recognition based on heart rate information and a preset heart rate fluctuation curve includes: receiving a heart rate curve sent by the first smart terminal; Get the peaks and troughs of the heart rate curve; According to a preset heart rate fluctuation curve, each peak and trough is compared with the preset heart rate fluctuation curve; Determine the degree of deviation between the peak and the trough according to the comparison result; evaluate the emotional state based on the degree of deviation to obtain the current emotional state information; Also includes: Extracting the emotion change trend in the emotion state information; Determining the correlation between the user's emotion and the heart rate curve according to the emotion change trend and the historical emotion state information to correct the emotion change trend to obtain the correct user emotion; Determine the corresponding relationship between the user emotion and the heart rate curve based on the correct user emotion and the peaks and troughs of the corresponding heart rate curve; Determine whether the current emotion is in an abnormal state based on the corresponding relationship between the user's emotion and the heart rate curve and the rules of the user's emotion; Perform regular analysis based on the emotional influencing factors in the abnormal state to obtain information on the cause of the abnormal state; Factors that influence emotions include diet information, exercise information, excretion information, behavioral habits, and sleep information; The process of the rule analysis includes: Eliminating the influencing factors affecting weight change from the influencing factor group to obtain an analysis group; the influencing factors include all factors in the influencing factor group and the corresponding influence degrees; Arrange the influencing factors in the analysis group in order from small to large according to the degree of influence; randomly combine the influencing factors in the analysis group in pairs; Match each combination obtained by combining with the user's historical behavior based on a preset relationship evaluation model to obtain the matching degree corresponding to each combination; Determine whether each influencing factor of the analysis group is associated with an influencing factor with a high matching degree and / or below a preset matching degree threshold in at least one combination; if so, remove the influencing factor from the analysis group and add abnormal state cause information; If there is no correlation, the influencing factor is saved as a suggestion factor, and the suggestion factor and the corresponding influence degree are used as part of the weight management suggestion plan; the influencing factor removed from the influencing factor group is analyzed separately, and if the matching degree obtained by the separate analysis meets the preset matching degree threshold, the influencing factor is included in the abnormal state cause information; If the matching degree obtained by the separate analysis does not meet the preset matching degree threshold, the influencing factors are saved as suggested factors, and the suggested factors and the corresponding influence degrees are used as part of the weight management suggestion plan.

8. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program of the method according to any one of claims 1 to 6 which is loaded and executed by the processor.

9. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 6.

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