Exercise plan planning method and system based on big data analysis

Abstract

The invention discloses an exercise plan planning method and system based on big data analysis, relates to the field of exercise planning, and solves the problem that a current user cannot obtain an exercise plan matched with the user, and the exercise plan planning method specifically comprises the following steps: obtaining physiological data of the user before exercise and the number of exercise times of the user per week; analyzing according to the physiological data to obtain a conditioning body fat rate of the user; analyzing according to the number of exercises per week to obtain the conventional heat consumption of the user, and comparing the conventional heat consumption with the standard heat consumption to judge the heat consumption condition of the user; formulating a screening exercise plan and a diet plan of the user, and combining the screening exercise plan with the diet plan to obtain an exercise planning scheme of the user; the user exercises in a fixed period according to the exercise planning scheme, and physiological data of the user after exercise is collected after the fixed period; and the exercise effect of the corresponding exercise planning scheme of the user is judged according to the physiological data after exercise. The adaptive exercise plan is provided for the user in combination with data analysis.

Description

Technical Field

[0001] The present invention belongs to the field of sports health and relates to big data analysis technology, specifically to a sports plan planning method and system based on big data analysis. Background Art

[0002] Sports are various activities that have been gradually developed in the process of human development to consciously cultivate one's physical fitness. They take various forms of physical activities such as walking, running, jumping, throwing and dancing. Since the emergence of sports, strengthening the body has always been the main function of sports. As people's health awareness improves, they pay more and more attention to the role of exercise in maintaining physical health. However, due to the lack of professional guidance and personalized plans, it is difficult to stick to or achieve the expected results. Although there are some sports software on the market, most of them cannot analyze the user's own situation and thus cannot provide users with adaptive exercise plans, resulting in poor user experience. To this end, we propose a motion planning method and system based on big data analysis. Summary of the invention

[0003] In view of the deficiencies in the prior art, the object of the present invention is to provide a motion planning method and system based on big data analysis.

[0004] The technical problems to be solved by the present invention are: How to provide users with adaptive exercise plans based on data analysis.

[0005] In the first aspect, the purpose of the present invention can be achieved by the following technical solutions: A motion planning method based on big data analysis, the motion planning method is as follows: Step S1, obtaining the user's physiological data before exercise and the user's weekly exercise frequency; Step S2, obtaining the user's adjusted body fat percentage based on physiological data analysis; Step S3, analyzing the user's regular calorie consumption according to the number of exercises per week, and comparing the regular calorie consumption with the standard calorie consumption to determine the user's calorie consumption; Step S4, formulating a screening exercise plan and a diet plan for the user, and combining the screening exercise plan and the diet plan to obtain an exercise plan for the user; Step S5, the user exercises within a fixed period according to the exercise plan, and the user's post-exercise physiological data is collected after the fixed period; Step S6, judging the exercise effect of the user's corresponding exercise plan according to the physiological data after the exercise.

[0006] Furthermore, the physiological data includes the user's gender, age, height, basic weight and basic waist circumference.

[0007] Furthermore, the step S2 includes the following sub-steps: Step S21, obtaining the user's gender, basic weight TZ and basic waist circumference YW; Step S22, using a formula to calculate the user's basic body fat percentage TZL before exercise, the formula is as follows: Female: TZL=[YW×0.74-(TZ×0.082+34.89)] / STZ×100%; Male: TZL=[YW×0.74-(TZ×0.082+44.74)] / STZ×100%; Step S23, comparing the user's basic body fat percentage TZL with the standard body fat percentage BTZ of the same gender; Step S24, if the user's basic body fat percentage is greater than the standard body fat percentage of the same gender, the user's reduced body fat percentage is obtained by subtracting the standard body fat percentage from the basic body fat percentage; If the user's basic body fat percentage is less than or equal to the standard body fat percentage of the same gender, the user's increased body fat percentage is calculated by subtracting the basic body fat percentage from the standard body fat percentage; If the user's body fat percentage is equal to the standard body fat percentage of the same gender, the user's adjusted body fat percentage TTL is zero; Step S25, marking the reduced body fat percentage and the increased body fat percentage as the user's adjusted body fat percentage.

[0008] Furthermore, the step S3 includes the following sub-steps: Step S31, obtaining the number of exercises per week of the user, and obtaining the corresponding exercise coefficient YD of the user according to the exercise coefficient table; wherein the number of exercises is proportional to the exercise coefficient, that is, the more the number of exercises, the greater the value of the exercise coefficient; Step S32, then obtaining the user's height SG and age NL; wherein the height unit is cm; Step S33, using a formula to calculate the user's regular calorie consumption CXH, the formula is as follows: Female: CXH = [(10 × TZ) + (6.25 × SG) - (5 × NL) - 161] × YD; Male: CXH=[(10×TZ)+(6.25×SG)-(5×NL)+5]×YD; Step S34, comparing the user's regular calorie consumption with the standard calorie consumption of the same gender; Step S35, if the user's regular calorie consumption is less than the standard calorie consumption of the same gender, the standard calorie consumption of the same gender is calibrated as the total calorie consumption ZXH, and the user's increased calorie consumption ZRL is obtained by subtracting the regular calorie consumption from the standard calorie consumption; If the user's regular calorie consumption is greater than or equal to the standard calorie consumption of the same gender, the user's regular calorie consumption is calibrated as the total calorie consumption ZXH, and the user's adjusted calorie consumption is zero.

[0009] Furthermore, the step S4 comprises the following steps: Step S41: if the user does not want to increase the calorie consumption, no operation is performed; If the user wants to increase calorie consumption, the average calorie consumption of all sports on the market is obtained, and multiple sports are selected to form a preliminary exercise plan. The exercise calories of the sports in the preliminary exercise plan must be greater than or equal to the increased calorie consumption; Step S42, obtaining the user's corresponding adjusted body fat percentage; If the user's adjusted body fat percentage is to reduce the body fat percentage, then adding exercise items to the preliminary exercise plan to obtain the user's screened exercise plan is the first screened exercise plan; If the user's adjusted body fat percentage is zero, then adding exercise items to the preliminary exercise plan to obtain the user's screened exercise plan is the second screened exercise plan; If the user's adjusted body fat percentage is to increase the body fat percentage, then adding exercise items to the preliminary exercise plan will result in the user's screened exercise plan being a third screened exercise plan; wherein the amount of exercise in the first screened exercise plan is greater than the amount of exercise in the second screened exercise plan, and the amount of exercise in the second screened exercise plan is greater than the amount of exercise in the third screened exercise plan.

[0010] Furthermore, the step S4 further includes the following sub-steps: Step S43, obtaining the total calorie consumption ZXH corresponding to the user, and converting the total calorie consumption of the user into the food intake calorie SWL of the user, specifically: If the user's adjusted body fat percentage is to reduce the body fat percentage, the user's food intake calories SWL is obtained by adding a fixed percentage to the total calorie consumption, specifically: food intake calories = total calorie consumption × 80%; If the user's adjusted body fat percentage is zero, the total calories consumed are recorded as the user's food intake calories SWL; If the user's adjusted body fat percentage is to increase the body fat percentage, then the user's food intake calories SWL is obtained by adding a fixed percentage to the total calorie consumption, specifically: food intake calories = total calorie consumption × 120%; Step S44, obtaining nutritional intake data of users of the same gender and age, namely, the daily standard protein intake DBZ, the standard fat intake ZF and the standard carbohydrate intake TS; Step S45, then obtain the calorie content KRLp, protein content KDBp, fat content KZFp and carbohydrate content KTSp per gram of all foods on the market, where p is the number of the food, p=1, 2, ..., m; Step S46, according to the food intake calorie and nutrient intake data, and in combination with the food configuration formula, a corresponding diet plan is formulated for the user. The specific food configuration formula is: SWL=(KRL1×k1)+(KRL2×k2)+……+(KRLm×km); DBZ=(DBZ1×k1)+(DBZ2×k2)+……+(DBZm×km); ZF=(ZF1×k1)+(ZF2×k2)+……+(ZFm×km); TS=(TS1×k1)+(TS2×k2)+……+(TSm×km);where k is the weight of each food in the diet plan; Step S47, screening the exercise plan and combining it with the diet plan to obtain the user's exercise plan.

[0011] Furthermore, the calculation process of exercise calories is: Step S411, marking the movement as i, i=1, 2, ..., n, where n is a positive integer; Step S412, calculating the exercise calories of all exercises in the preliminary exercise plan by using an exercise calorie formula, the exercise calorie formula is configured as: ZRL=(FXH1×t1)+(FXH2×t2)+…+(FXHn×tn); In the above formula, FXHi is the average calorie consumption of each exercise in the exercise plan, and ti is the exercise duration of each exercise.

[0012] Furthermore, the physiological data after exercise includes the user's gender, real-time weight and real-time waist circumference.

[0013] Furthermore, the step S6 includes the following sub-steps: Step S61, calculating the real-time body fat percentage of the user after exercise according to the above formula; Step S62, subtracting the absolute value of the real-time body fat percentage from the basic body fat percentage to obtain the changed body fat percentage of the user after exercise; Step S63, if the user's adjusted body fat percentage is a reduced body fat percentage, the changed body fat percentage is compared with the reduced body fat percentage; When the change in body fat percentage is greater than or equal to the decrease in body fat percentage, an effective exercise signal is generated; When the change in body fat percentage is less than the decrease in body fat percentage, an invalid exercise signal is generated; Step S64, if the user's adjusted body fat percentage is an increased body fat percentage, the changed body fat percentage is compared with the increased body fat percentage; When the change in body fat percentage is greater than or equal to the increase in body fat percentage, an effective exercise signal is generated; When the change in body fat percentage is less than the increase in body fat percentage, an invalid exercise signal is generated.

[0014] In a second aspect, the present invention further proposes a motion planning system based on big data analysis, comprising: The data collection module is used to obtain the user's physiological data before exercise and the number of exercises per week; The body fat percentage analysis module is used to obtain the user's adjusted body fat percentage based on physiological data analysis; The calorie analysis module is used to analyze the user's regular calorie consumption based on the number of exercises per week, and compare the regular calorie consumption with the standard calorie consumption to determine the user's calorie consumption; A plan generation module is used to formulate a user's screening exercise plan and diet plan, and combine the screening exercise plan with the diet plan to obtain the user's exercise planning plan; An execution module is used for the user to exercise within a fixed period according to the exercise plan, and collect the user's physiological data after the fixed period; The effect analysis module is used to determine the exercise effect of the user's corresponding exercise plan based on the physiological data after exercise.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention first obtains the physiological data of the user before exercise and the number of exercises per week of the user, and obtains the user's adjusted body fat rate based on the physiological data analysis, and then obtains the user's regular calorie consumption based on the number of exercises per week, and compares the regular calorie consumption with the standard calorie consumption to determine the user's calorie consumption. The user's screening exercise plan and diet plan are formulated by adjusting the body fat rate, regular calorie consumption and other data, and the screening exercise plan is combined with the diet plan to obtain the user's exercise planning scheme. The present invention provides an adaptive exercise plan for the user in combination with data analysis; 2. In the present invention, the user exercises within a fixed period according to the exercise planning plan, and the physiological data of the user after the exercise is collected after the fixed period; the exercise effect of the user corresponding to the exercise planning plan is judged based on the physiological data after the exercise. The present invention realizes the analysis of the exercise effect of the exercise plan, which is convenient for timely adjustment of the user's exercise plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0017] Figure 1 is a flow chart of the method of the present invention; Figure 2 is a method flow chart of the sub-steps corresponding to step S2 in the present invention; Figure 3 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example 1: Please refer to Figure 1 and Figure 2 As shown, the present invention provides a technical solution: a motion planning method based on big data analysis, comprising the following steps: Step S1, obtaining the user's physiological data before exercise and the user's weekly exercise frequency; Among them, physiological data includes the user's gender, age, height, basic weight and basic waist circumference, etc. Physiological data is obtained after the user's authorization and consent; Step S2, obtaining the user's adjusted body fat percentage based on physiological data analysis; In this embodiment, step S2 includes the following sub-steps: Step S21, obtaining the user's gender, basic weight TZ and basic waist circumference YW; wherein the unit of weight is kg; Step S22, using a formula to calculate the user's basic body fat percentage TZL before exercise, the formula is as follows: Female: TZL=[YW×0.74-(TZ×0.082+34.89)] / STZ×100%; Male: TZL=[YW×0.74-(TZ×0.082+44.74)] / STZ×100%; Step S23, comparing the user's basic body fat percentage TZL with the standard body fat percentage BTZ of the same gender; Step S24, if the user's basic body fat percentage is greater than the standard body fat percentage of the same gender, it means that the user's body fat percentage is too high, and the user's reduced body fat percentage is obtained by subtracting the standard body fat percentage from the basic body fat percentage; If the user's basic body fat percentage is less than or equal to the standard body fat percentage of the same gender, it means that the user's body fat percentage is too low. The user's increased body fat percentage is calculated by subtracting the basic body fat percentage from the standard body fat percentage. If the user's body fat percentage is equal to the standard body fat percentage of the same gender, it means that the user's body fat percentage is normal, and the user's adjusted body fat percentage TTL is zero; Step S25, marking the reduced body fat percentage and the increased body fat percentage as the user's adjusted body fat percentage.

[0020] Step S3, analyzing the user's regular calorie consumption according to the number of exercises per week, and comparing the regular calorie consumption with the standard calorie consumption to determine the user's calorie consumption; In this embodiment, step S3 includes the following sub-steps: Step S31, obtaining the number of exercises per week of the user, and obtaining the corresponding exercise coefficient YD of the user according to the exercise coefficient table; wherein the number of exercises is proportional to the exercise coefficient, that is, the more the number of exercises, the greater the value of the exercise coefficient; Exemplarily, the motion coefficient table is shown in Table 1, and the details of Table 1 are as follows: Weekly exercise frequency Motion coefficient YD 0 times per week 1.2 1-3 times per week 1.375 4-6 times a week 1.55 6-8 times per week 1.725 8-10 times per week 1.9 Step S32, then obtaining the user's height SG and age NL; wherein the height unit is cm; Step S33, using a formula to calculate the user's regular calorie consumption CXH, the formula is as follows: Female: CXH = [(10 × TZ) + (6.25 × SG) - (5 × NL) - 161] × YD; Male: CXH=[(10×TZ)+(6.25×SG)-(5×NL)+5]×YD; Step S34, comparing the user's regular calorie consumption with the standard calorie consumption of the same gender; Step S35, if the user's regular calorie consumption is less than the standard calorie consumption of the same gender, the standard calorie consumption of the same gender is calibrated as the total calorie consumption ZXH, and the user's increased calorie consumption ZRL is obtained by subtracting the regular calorie consumption from the standard calorie consumption; If the user's regular calorie consumption is greater than or equal to the standard calorie consumption of the same gender, the user's regular calorie consumption is calibrated as the total calorie consumption ZXH, and the user's adjusted calorie consumption is zero.

[0021] Step S4, formulating a screening exercise plan and a diet plan for the user, and combining the screening exercise plan and the diet plan to obtain an exercise plan for the user; In this embodiment, step S4 includes the following steps: Step S41: if the user does not want to increase the calorie consumption, no operation is performed; If the user has increased calorie consumption, the average calorie consumption of all sports on the market is obtained, and multiple sports are selected to form a preliminary exercise plan. The exercise calories of the corresponding sports in the preliminary exercise plan must be greater than or equal to the increased calorie consumption. The calculation process of exercise calories is: Step S411, marking the movement as i, i=1, 2, ..., n, where n is a positive integer; Step S412, calculating the exercise calories of all exercises in the preliminary exercise plan by using an exercise calorie formula, the exercise calorie formula is configured as: ZRL=(FXH1×t1)+(FXH2×t2)+…+(FXHn×tn); In the above formula, FXHi is the average calorie consumption of each exercise in the exercise plan, and ti is the exercise duration of each exercise; For example, when the user needs to increase the calorie consumption by 300 calories, he can choose to do housework for 20 minutes to consume 110 calories, plus yoga for 60 minutes to consume 150 calories, plus Pilates for 10 minutes to consume 30 calories. In practice, the user can also choose different sports with the same calorie consumption; Step S42, obtaining the user's corresponding adjusted body fat percentage; If the user's adjusted body fat percentage is to reduce the body fat percentage, then adding exercise items to the preliminary exercise plan to obtain the user's screened exercise plan is the first screened exercise plan; If the user's adjusted body fat percentage is zero, then adding exercise items to the preliminary exercise plan to obtain the user's screened exercise plan is the second screened exercise plan; If the user's adjusted body fat percentage is to increase the body fat percentage, then adding exercise items to the preliminary exercise plan to obtain the user's screened exercise plan is the third screened exercise plan; The amount of exercise in the first screening exercise plan is greater than the amount of exercise in the second screening exercise plan, and the amount of exercise in the second screening exercise plan is greater than the amount of exercise in the third screening exercise plan; In specific implementation, if the user adjusts the body fat percentage to reduce the body fat percentage, the sports items added to the initial exercise plan include but are not limited to aerobic exercise items, strength training items, etc. Aerobic exercise includes running, swimming and rope skipping, etc. Strength training includes squats, bench presses, deadlifts, etc. using fitness equipment, as well as push-ups, pull-ups and plank support, etc.; If the user's adjusted body fat percentage is zero, then the additional exercise items on the basis of the initial exercise plan include but are not limited to daily activities, flexibility training items, etc. The additional daily activities include walking up stairs, increasing walking distance and doing housework, etc., and the flexibility training includes yoga, Pilates and stretching, etc.; If the user's adjusted body fat percentage is to increase the body fat percentage, the sports items added to the initial exercise plan include but are not limited to strength training items; Step S43, obtaining the total calorie consumption ZXH corresponding to the user, and converting the total calorie consumption of the user into the food intake calorie SWL of the user, specifically: If the user's adjusted body fat percentage is to reduce the body fat percentage, the user's food intake calories SWL is obtained by adding a fixed percentage to the total calorie consumption, specifically: food intake calories = total calorie consumption × 80%; If the user's adjusted body fat percentage is zero, the total calories consumed are recorded as the user's food intake calories SWL; If the user's adjusted body fat percentage is to increase the body fat percentage, then the user's food intake calories SWL is obtained by adding a fixed percentage to the total calorie consumption, specifically: food intake calories = total calorie consumption × 120%; It should be explained that users can achieve muscle gain only when the calorie intake from food is greater than the total calorie consumption; users can achieve fat loss only when the calorie intake from food is less than the total calorie consumption; users can maintain their current body fat percentage only when the total calorie consumption is the same as the calorie intake from food. Step S44, obtaining nutritional intake data of users of the same gender and age, namely, the daily standard protein intake DBZ, the standard fat intake ZF and the standard carbohydrate intake TS; Step S45, then obtain the calorie content KRLp, protein content KDBp, fat content KZFp and carbohydrate content KTSp per gram of all foods on the market, where p is the number of the food, p=1, 2, ..., m; Step S46, according to the food intake calorie and nutrient intake data, and in combination with the food configuration formula, a corresponding diet plan is formulated for the user. The specific food configuration formula is: SWL=(KRL1×k1)+(KRL2×k2)+……+(KRLm×km); DBZ=(DBZ1×k1)+(DBZ2×k2)+……+(DBZm×km); ZF=(ZF1×k1)+(ZF2×k2)+……+(ZFm×km); TS=(TS1×k1)+(TS2×k2)+……+(TSm×km);where k is the weight of each food in the diet plan; Step S47, screening the exercise plan and combining it with the diet plan to obtain the user's exercise plan.

[0022] Step S5, the user exercises within a fixed period according to the exercise plan, and the user's post-exercise physiological data is collected after the fixed period; Specifically, the physiological data after exercise includes the user's gender, real-time weight and real-time waist circumference.

[0023] Step S6, judging the exercise effect of the user's corresponding exercise plan according to the physiological data after the exercise; In this embodiment, step S6 includes the following sub-steps: Step S61, calculating the real-time body fat percentage of the user after exercise according to the above formula; Step S62, subtracting the absolute value of the real-time body fat percentage from the basic body fat percentage to obtain the changed body fat percentage of the user after exercise; Step S63, if the user's adjusted body fat percentage is a reduced body fat percentage, the changed body fat percentage is compared with the reduced body fat percentage; When the change in body fat percentage is greater than or equal to the decrease in body fat percentage, an effective exercise signal is generated; When the change in body fat percentage is less than the decrease in body fat percentage, an invalid exercise signal is generated; Step S64, if the user's adjusted body fat percentage is an increased body fat percentage, the changed body fat percentage is compared with the increased body fat percentage; When the change in body fat percentage is greater than or equal to the increase in body fat percentage, an effective exercise signal is generated; When the change in body fat percentage is less than the increase in body fat percentage, an invalid exercise signal is generated.

[0024] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0025] Embodiment 2: Figure 3 As shown, this embodiment also provides a motion plan planning method based on big data analysis, including: The data collection module is used to obtain the user's physiological data before exercise and the number of exercises per week; The body fat percentage analysis module is used to obtain the user's adjusted body fat percentage based on physiological data analysis; The calorie analysis module is used to analyze the user's regular calorie consumption based on the number of exercises per week, and compare the regular calorie consumption with the standard calorie consumption to determine the user's calorie consumption; A plan generation module is used to formulate a user's screening exercise plan and diet plan, and combine the screening exercise plan with the diet plan to obtain the user's exercise planning plan; An execution module is used for the user to exercise within a fixed period according to the exercise plan, and collect the user's physiological data after the fixed period; The effect analysis module is used to determine the exercise effect of the user's corresponding exercise plan based on the physiological data after exercise.

[0026] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A motion planning method based on big data analysis, characterized in that: The specific method of exercise planning is as follows: Step S1, obtaining the user's physiological data before exercise and the user's weekly exercise frequency; Step S2, obtaining the user's adjusted body fat percentage based on physiological data analysis; Step S3, analyzing the user's regular calorie consumption according to the number of exercises per week, and comparing the regular calorie consumption with the standard calorie consumption to determine the user's calorie consumption; Step S4, formulating a screening exercise plan and a diet plan for the user, and combining the screening exercise plan and the diet plan to obtain an exercise plan for the user; Step S5, the user exercises within a fixed period according to the exercise plan, and the user's post-exercise physiological data is collected after the fixed period; Step S6, judging the exercise effect of the user's corresponding exercise plan according to the physiological data after the exercise.

2. The method for motion planning based on big data analysis according to claim 1, characterized in that: The physiological data include the user's gender, age, height, basic weight and basic waist circumference.

3. A method for motion planning based on big data analysis according to claim 2, characterized in that: The step S2 includes the following sub-steps: Step S21, obtaining the user's gender, basic weight TZ and basic waist circumference YW; Step S22, using a formula to calculate the user's basic body fat percentage TZL before exercise, the formula is as follows: Female: TZL=[YW×0.74-(TZ×0.082+34.89)] / STZ×100%; Male: TZL=[YW×0.74-(TZ×0.082+44.74)] / STZ×100%; Step S23, comparing the user's basic body fat percentage TZL with the standard body fat percentage BTZ of the same gender; Step S24, if the user's basic body fat percentage is greater than the standard body fat percentage of the same gender, the user's reduced body fat percentage is obtained by subtracting the standard body fat percentage from the basic body fat percentage; If the user's basic body fat percentage is less than or equal to the standard body fat percentage of the same gender, the user's increased body fat percentage is calculated by subtracting the basic body fat percentage from the standard body fat percentage; If the user's body fat percentage is equal to the standard body fat percentage of the same gender, the user's adjusted body fat percentage TTL is zero; Step S25, marking the reduced body fat percentage and the increased body fat percentage as the user's adjusted body fat percentage.

4. The method for motion planning based on big data analysis according to claim 3, characterized in that: The step S3 includes the following sub-steps: Step S31, obtaining the number of exercises per week of the user, and obtaining the corresponding exercise coefficient YD of the user according to the exercise coefficient table; wherein the number of exercises is proportional to the exercise coefficient, that is, the more the number of exercises, the greater the value of the exercise coefficient; Step S32, then obtaining the user's height SG and age NL; wherein the height unit is cm; Step S33, using a formula to calculate the user's regular calorie consumption CXH, the formula is as follows: Female: CXH = [(10 × TZ) + (6.25 × SG) - (5 × NL) - 161] × YD; Male: CXH=[(10×TZ)+(6.25×SG)-(5×NL)+5]×YD; Step S34, comparing the user's regular calorie consumption with the standard calorie consumption of the same gender; Step S35, if the user's regular calorie consumption is less than the standard calorie consumption of the same gender, the standard calorie consumption of the same gender is calibrated as the total calorie consumption ZXH, and the user's increased calorie consumption ZRL is obtained by subtracting the regular calorie consumption from the standard calorie consumption; If the user's regular calorie consumption is greater than or equal to the standard calorie consumption of the same gender, the user's regular calorie consumption is calibrated as the total calorie consumption ZXH, and the user's adjusted calorie consumption is zero.

5. The method for motion planning based on big data analysis according to claim 1, characterized in that: The step S4 comprises the following steps: Step S41: if the user does not want to increase the calorie consumption, no operation is performed; If the user wants to increase calorie consumption, the average calorie consumption of all sports on the market is obtained, and multiple sports are selected to form a preliminary exercise plan. The exercise calories of the sports in the preliminary exercise plan must be greater than or equal to the increased calorie consumption; Step S42, obtaining the user's corresponding adjusted body fat percentage; If the user's adjusted body fat percentage is to reduce the body fat percentage, then adding exercise items to the preliminary exercise plan to obtain the user's screened exercise plan is the first screened exercise plan; If the user's adjusted body fat percentage is zero, then adding exercise items to the preliminary exercise plan to obtain the user's screened exercise plan is the second screened exercise plan; If the user's adjusted body fat percentage is to increase the body fat percentage, then adding exercise items to the preliminary exercise plan will result in the user's screened exercise plan being a third screened exercise plan; wherein the amount of exercise in the first screened exercise plan is greater than the amount of exercise in the second screened exercise plan, and the amount of exercise in the second screened exercise plan is greater than the amount of exercise in the third screened exercise plan.

6. The method for motion planning based on big data analysis according to claim 5, characterized in that: The step S4 also includes the following sub-steps: Step S43, obtaining the total calorie consumption ZXH corresponding to the user, and converting the total calorie consumption of the user into the food intake calorie SWL of the user, specifically: If the user's adjusted body fat percentage is to reduce the body fat percentage, the user's food intake calories SWL is obtained by adding a fixed percentage to the total calorie consumption, specifically: food intake calories = total calorie consumption × 80%; If the user's adjusted body fat percentage is zero, the total calories consumed are recorded as the user's food intake calories SWL; If the user's adjusted body fat percentage is to increase the body fat percentage, then the user's food intake calories SWL is obtained by adding a fixed percentage to the total calorie consumption, specifically: food intake calories = total calorie consumption × 120%; Step S44, obtaining nutritional intake data of users of the same gender and age, namely, the daily standard protein intake DBZ, the standard fat intake ZF and the standard carbohydrate intake TS; Step S45, then obtain the calorie content KRLp, protein content KDBp, fat content KZFp and carbohydrate content KTSp per gram of all foods on the market, where p is the number of the food, p=1, 2, ..., m; Step S46, according to the food intake calorie and nutrient intake data, and in combination with the food configuration formula, a corresponding diet plan is formulated for the user. The specific food configuration formula is: SWL=(KRL1×k1)+(KRL2×k2)+……+(KRLm×km); DBZ=(DBZ1×k1)+(DBZ2×k2)+……+(DBZm×km); ZF=(ZF1×k1)+(ZF2×k2)+……+(ZFm×km); TS=(TS1×k1)+(TS2×k2)+……+(TSm×km);where k is the weight of each food in the diet plan; Step S47, screening the exercise plan and combining it with the diet plan to obtain the user's exercise plan.

7. The method for motion planning based on big data analysis according to claim 6, characterized in that: The calculation process of exercise calories is: Step S411, marking the movement as i, i=1, 2, ..., n, where n is a positive integer; Step S412, calculating the exercise calories of all exercises in the preliminary exercise plan by using an exercise calorie formula, the exercise calorie formula is configured as: ZRL=(FXH1×t1)+(FXH2×t2)+…+(FXHn×tn); In the above formula, FXHi is the average calorie consumption of each exercise in the exercise plan, and ti is the exercise duration of each exercise.

8. The method for sports planning based on big data analysis according to claim 6, characterized in that: The physiological data after exercise includes the user's gender, real-time weight and real-time waist circumference.

9. The method for sports planning based on big data analysis according to claim 8, characterized in that: The step S6 includes the following sub-steps: Step S61, calculating the real-time body fat percentage of the user after exercise according to the above formula; Step S62, subtracting the absolute value of the real-time body fat percentage from the basic body fat percentage to obtain the changed body fat percentage of the user after exercise; Step S63, if the user's adjusted body fat percentage is a reduced body fat percentage, the changed body fat percentage is compared with the reduced body fat percentage; When the change in body fat percentage is greater than or equal to the decrease in body fat percentage, an effective exercise signal is generated; When the change in body fat percentage is less than the decrease in body fat percentage, an invalid exercise signal is generated; Step S64, if the user's adjusted body fat percentage is an increased body fat percentage, the changed body fat percentage is compared with the increased body fat percentage; When the change in body fat percentage is greater than or equal to the increase in body fat percentage, an effective exercise signal is generated; When the change in body fat percentage is less than the increase in body fat percentage, an invalid exercise signal is generated.

10. A sports planning system based on big data analysis, characterized in that: In combination with a method for motion planning based on big data analysis as described in any one of claims 1 to 9, comprising: The data collection module is used to obtain the user's physiological data before exercise and the number of exercises per week; The body fat percentage analysis module is used to obtain the user's adjusted body fat percentage based on physiological data analysis; The calorie analysis module is used to analyze the user's regular calorie consumption based on the number of exercises per week, and compare the regular calorie consumption with the standard calorie consumption to determine the user's calorie consumption; A plan generation module is used to formulate a user's screening exercise plan and diet plan, and combine the screening exercise plan with the diet plan to obtain the user's exercise planning plan; An execution module is used for the user to exercise within a fixed period according to the exercise plan, and collect the user's physiological data after the fixed period; The effect analysis module is used to determine the exercise effect of the user's corresponding exercise plan based on the physiological data after exercise.

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