CN107122584B - Outdoor exercise planning system and method based on big data - Google Patents
Outdoor exercise planning system and method based on big data Download PDFInfo
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- CN107122584B CN107122584B CN201710154571.4A CN201710154571A CN107122584B CN 107122584 B CN107122584 B CN 107122584B CN 201710154571 A CN201710154571 A CN 201710154571A CN 107122584 B CN107122584 B CN 107122584B
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Abstract
The invention relates to an outdoor exercise planning system based on big data and a using method thereof, wherein the system comprises: the weather monitoring system comprises an integrated microprocessor, a display screen and an operation panel, wherein the display screen and the operation panel are in communication connection with the integrated microprocessor, historical air quality data, historical weather forecast data and a map are stored in the integrated microprocessor, the weather monitoring system also comprises a real-time weather monitoring module and a real-time air quality monitoring module, and the real-time weather monitoring module and the real-time air quality monitoring module are in communication connection with the integrated microprocessor; the method comprises the following steps: (1) drawing up an initial exercise plan; (2) adjusting the exercise plan; (3) feedback and adjustment of exercise plans; the system is intelligent in planning, scientific in adjustment and convenient to use, and can plan the exercise plan based on the historical data of weather forecast and air quality according to the requirements of users, adjust the exercise plan according to the real-time weather and air quality every day and adjust the exercise plan according to the feedback of the users.
Description
Technical Field
The invention relates to the technical field of big data analysis, in particular to an outdoor exercise planning system based on big data and a using method thereof.
Background
With the improvement of the living standard of people, the physical health and daily exercise are increasingly paid attention, and people often schedule different physical exercises in daily life for the physical health and the daily exercise. In addition, during exercise, the sun can supplement vitamin D, promote calcium absorption, and strengthen body constitution. But it is too hesitant to do exercise or sun exposure. For this reason, it is often necessary to make a daily exercise plan for an individual, the main purpose of which is to make the exercise scientific and targeted. The training plan is a simple and complex process, and if scientific and professional help is provided, the training effect can be improved, and the harm to the body caused by blind training and blind solarization is avoided, so that adverse consequences are brought.
At present, there are a large number of exercise application methods based on mobile terminals such as mobile phones, for example, millet sports, joy sports, gurgling sports, spring rain pedometer, joy race, and the like, and the functions of the exercise application methods mainly focus on the recording, statistics, and ranking of a certain sport (for example, running and riding), reservation of a sport place, social contact of a friend circle, and the like. The method for assisting the planning of the comprehensive exercise plan for each user is less, only a few applications such as fitness owners aiming at the exercise of the gymnasium exist, and the method for assisting the planning of the exercise plan for integrating the indoor exercise and the outdoor exercise is a blank. The invention provides a novel air-conditioning device which can ensure that a user can obtain enough sunshine and fresh air as much as possible while performing physical exercise.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide an outdoor exercise planning system based on big data and a using method thereof, which can plan an exercise plan meeting the requirements of a user according to factors such as weather and air quality.
The invention provides a use method of an outdoor exercise planning system based on big data, which comprises the following steps:
(1) drawing up an initial exercise plan: firstly, selecting an exercise item, secondly selecting an exercise place, secondly selecting exercise time, and finally estimating exercise amount and solarization amount, and manually adjusting an exercise initial plan by a user according to the requirements of the user on the exercise amount and the solarization amount;
(2) adjusting the exercise plan: according to factors such as real-time conditions of weather forecast, numerical values of air quality monitoring and personal conditions of a user on the same day, machine learning methods such as an ant colony algorithm are adopted, and a daily exercise plan meeting exercise requirements of the user is dynamically adjusted and planned for the user on the basis of an exercise initial plan;
(3) feedback and adjustment of exercise program: according to feedback information provided by a user, the exercise plan is adjusted by adopting machine learning methods such as an artificial neural network and the like, the user verifies the effectiveness of the previous exercise plan by adapting the plan and observing the self state, and the exercise plan is further adjusted to adapt to the actual condition of the user.
Further, the exercise items selected in step (1) are selected by: the selectable exercise items are given in a classified list mode and are divided into two categories of outdoor exercises and indoor exercises, some items are recommended according to physical and mental conditions of users, the users can freely combine in an exercise plan according to own health level, exercise bases and the like, different exercise items are selected, and the outdoor exercises and the indoor exercises are selected to facilitate later-stage adjustment between the outdoor exercises and the indoor exercises.
Further, the exercise location is selected in step (1) by: the method is characterized in that a map is used, according to a living area and a working area which are positioned by a user, the user selects an exercise place, indoor movement is a fixed place, and distance, a route and the like can be selected for outdoor movement.
Further, the exercise time is selected in step (1) by: in a time-sharing calendar mode, a user selects exercise time according to personal hobbies, working properties, living conditions and the like, and the user selects different sports items, sports places and the like to fill in a calendar form by taking 0.5 hour as a unit every day to form a day plan, a week plan and a month plan.
Further, the estimation mode of the exercise amount and the insolation amount in the step (1) is as follows: the method comprises the steps of carrying out statistics according to different exercise amounts and exercise time of each exercise item, initially calculating exercise amount by adopting the item average exercise intensity of a crowd to which a user belongs, adjusting the exercise amount to a specific value of the item exercise intensity of the user along with accumulation of user data, synchronizing a place where the user expects exercise with weather forecast of the place where the user is located by utilizing geographic positioning, and estimating the sun exposure amount of the user according to sun exposure indexes of different time periods every day.
Further, the way of manually adjusting the exercise initial plan in step (1) is: on the basis of the estimated exercise amount and the estimated sunshine amount, the user manually adjusts the exercise items, the exercise places and the exercise time according to the requirements of the user on the exercise amount and the sunshine amount.
Further, in the step (2), the weather forecast includes weather conditions and sun exposure index, and the air quality includes AQI and PM 2.5.
The invention provides an outdoor exercise planning system based on big data, which comprises an integrated microprocessor, a display screen and an operation panel, wherein the display screen and the operation panel are in communication connection with the integrated microprocessor uniformly, and air quality historical data, weather forecast historical data and a map are stored in the integrated microprocessor;
an exercise item selection button, an exercise place selection button, an exercise time selection button and an age and gender setting button are arranged on the operation panel;
the system also comprises a real-time weather monitoring module and a real-time air quality monitoring module, wherein the real-time weather monitoring module and the real-time air quality monitoring module are both in communication connection with the integrated microprocessor;
the integrated microprocessor stores the average exercise intensity of each exercise item and can calculate the exercise amount and the insolation amount.
By the scheme, the invention at least has the following advantages: the outdoor exercise planning system based on the big data and the using method thereof can plan an exercise plan based on historical data of weather forecast and air quality according to the requirements of users, adjust the exercise plan according to the real-time weather and the air quality every day, and adjust the exercise plan according to the feedback of the users.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings.
Drawings
FIG. 1 is a schematic diagram illustrating the steps of a method for using a big data based outdoor exercise planning system according to the present invention;
FIG. 2 is a block diagram of a big data based outdoor exercise planning system according to the present invention.
Detailed Description
The following detailed description of the present invention will be made with reference to the accompanying drawings and examples, which are provided for illustration of the present invention and are not intended to limit the scope of the present invention.
Example (b): a method of using a big data based outdoor exercise planning system, comprising the steps of:
first, the initialization of an exercise program. The initial setting of the entire exercise program by the user and the estimation of the total exercise amount and the amount of insolation thereof include the following:
first is the selection of an exercise item: the selectable exercise items are given in a classified list mode, the exercise items are divided into two categories of outdoor exercises and indoor exercises, some items are recommended according to physical and mental conditions (including age, gender and other factors) of the user, and the user can freely combine in an exercise plan according to the health level, exercise basis and the like of the user to select different exercise items. Such as children recommending and selecting games, dancing, health exercises, etc.; teenagers recommend selecting running, skipping ropes, ball games and the like; the middle-aged and young people recommend selecting jogging, swimming, bicycle riding and the like; the elderly recommend selection of slow walking, taijiquan, etc. It is desirable that a solution should be selected for both outdoor and indoor activities to facilitate later adjustments between the two.
Secondly, selection of exercise sites: the user selects the exercise place according to the living area and the working area positioned by the user in a map mode. Indoor sports are fixed places, and outdoor sports can select distance, routes and the like. Because outdoor exercises are greatly influenced by environmental factors, the ideal environment is a place with fresh air and soft sunlight, historical statistical data of the air quality of corresponding areas in a map in corresponding seasons (months) and weather forecast data (including weather conditions and sun exposure indexes) of a future week can be provided, and planning of a user is assisted.
Again, selection of exercise time: the user selects the exercise time according to personal hobbies, working properties, living conditions and the like by using a time-sharing calendar mode. The user selects different sports items, sports sites and the like to fill in a calendar form by taking 0.5 hour as a unit every day, and a day plan, a week plan (different schedules are carried out on working days and weekends) and a month plan (different schedules are carried out on different months and seasons) are formed.
Finally, after the above setting is completed, the system automatically classifies and estimates the exercise amount (in calories) exercised by the user. And (4) counting according to different exercise amounts and exercise time of each exercise item, initially calculating the average exercise intensity (exercise amount per minute/hour) of the item of the crowd to which the user belongs, and adjusting the average exercise intensity to a specific value of the exercise intensity of the item of the user along with the accumulation of the user data. In addition, the geographical positioning is utilized to synchronize the expected movement place of the user with the weather forecast of the position, and the insolation amount of the user is estimated according to the insolation indexes of different time periods in each day so as to adjust the insolation level. The user may make some manual adjustments to the overall exercise program based on his or her own requirements for these two values.
Second, the daily dynamic adjustment of the exercise program. The system adopts machine learning methods such as ant colony algorithm and the like according to factors such as real-time conditions of weather forecast (including weather conditions and sun exposure indexes), numerical values (AQI and PM2.5 numerical values) of air quality monitoring, personal conditions (working hours and working hours) of users and the like every day, and dynamically adjusts and plans a daily exercise plan meeting exercise requirements (particularly exercise amount and sun exposure amount) for the users on the basis of setting an initial plan for the users.
Third, feedback and adjustment of the exercise program. And adjusting the exercise plan by adopting machine learning methods such as an artificial neural network and the like according to feedback information provided by the user. The user verifies the effectiveness of the previous exercise program by adapting the program and observing the status of the user, and further adjusts the exercise program (including the time and amount of exercise) to suit the user's actual situation.
Examples are: the user is a student and the exercise program is initialized as follows:
1. workday exercise program
7 am-7 am, run from home to school class (site a to site B);
run at school playground 4 o half-5 o half in the afternoon (site C);
at 6 o 'clock and half-7 o' clock at night, walk slowly on the body-building cloth path near home (site D to site E).
2. Weekend exercise program
Half 8 am-half 9 am, walk slowly in the park (spot F);
3-5 pm, and performing competitive sports in a gymnasium near home, such as badminton (site G);
at 8 o 'clock and half-9 o' clock in the evening, skipping rope in bedroom (site A).
When the exercise program is actually executed, the beginning of the monday afternoon rains, the system changes jogging to stadium swimming near home in advance according to weather forecast (4 o 'clock and half-5 o' clock, location G); friday is poor in air quality due to haze, and morning and afternoon running is cancelled, so that the gymnasium jumping gymnasiums near the house in the afternoon are changed into gymnasiums to jump in gymnasiums (4 o 'clock-5 o' clock, place G), and the slow walking time of saturday morning (8 o 'clock-10 o' clock, place F) is increased. The whole adjustment still meets the exercise amount and the sunshine amount required by the user.
After one month, since the user has adapted to the exercise program, there is an increase in physical fitness, with appropriate increases in exercise intensity and duration according to the user's feedback: the competitive sport time in the afternoon on the weekend is adjusted to 3-5 o' clock and half.
An outdoor exercise planning system based on big data comprises an integrated microprocessor 1, a display screen 2 and an operation panel 3, wherein the display screen and the operation panel are in communication connection with the integrated microprocessor, and air quality historical data, weather forecast historical data and a map are stored in the integrated microprocessor;
an exercise item selection button, an exercise place selection button, an exercise time selection button and an age and gender setting button are arranged on the operation panel;
the system also comprises a real-time weather monitoring module 4 and a real-time air quality monitoring module 5, wherein the real-time weather monitoring module and the real-time air quality monitoring module are both in communication connection with the integrated microprocessor;
the integrated microprocessor stores the average exercise intensity of each exercise item and can calculate the exercise amount and the insolation amount.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, it should be noted that, for those skilled in the art, many modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (3)
1. A use method of an outdoor exercise planning system based on big data is characterized in that: the method comprises the following steps:
(1) drawing up an initial exercise plan: firstly, selecting an exercise item, secondly selecting an exercise place, secondly selecting exercise time, and finally estimating exercise amount and solarization amount, and manually adjusting an exercise initial plan by a user according to the requirements of the user on the exercise amount and the solarization amount;
(2) adjusting the exercise plan: dynamically adjusting and planning a daily exercise plan meeting exercise requirements of a user by adopting an ant colony algorithm machine learning method on the basis of an exercise initial plan according to the real-time condition of weather forecast, the numerical value of air quality monitoring and personal condition factors of the user on the same day;
(3) feedback and adjustment of exercise program: adjusting the exercise plan by adopting an artificial neural network machine learning method according to feedback information provided by a user, checking the effectiveness of the previous exercise plan by adapting the plan and observing the self state of the user, and further adjusting the exercise plan to adapt to the actual condition of the user;
the mode of selecting the exercise place in the step (1) is as follows: the method is characterized in that a map is used, according to a living area and a working area positioned by a user, the user selects an exercise place, indoor movement is a fixed place, and outdoor movement can select a distance and a route;
the exercise time selection mode in the step (1) is as follows: selecting exercise time by a user according to personal hobbies, working properties and living conditions in a time-sharing calendar mode, taking 0.5 hour as a unit every day, and filling different sports items and sports places into a calendar form by the user to form a daily plan, a weekly plan and a monthly plan;
the estimation mode of the exercise amount and the sunshine amount in the step (1) is as follows: counting according to different exercise amounts and exercise time of each exercise item, initially calculating exercise amount by adopting the item average exercise intensity of a crowd to which a user belongs, adjusting the exercise amount to a specific value of the item exercise intensity of the user along with accumulation of user data, synchronizing a place where the user expects exercise with weather forecast of the place where the user is located by using geographic positioning, and estimating the sun exposure amount of the user according to sun exposure indexes of different time periods of each day;
the mode of manually adjusting the exercise initial plan in the step (1) is as follows: on the basis of the estimated exercise amount and the estimated sunshine amount, a user manually adjusts an exercise item, an exercise place and exercise time according to the requirements of the user on two numerical values of the exercise amount and the sunshine amount;
the big data based outdoor exercise planning system comprises: the system comprises an integrated microprocessor, a display screen and an operation panel, wherein the display screen and the operation panel are in communication connection with the integrated microprocessor, and air quality historical data, weather forecast historical data and a map are stored in the integrated microprocessor;
an exercise item selection button, an exercise place selection button, an exercise time selection button and an age and gender setting button are arranged on the operation panel;
the system also comprises a real-time weather monitoring module and a real-time air quality monitoring module, wherein the real-time weather monitoring module and the real-time air quality monitoring module are both in communication connection with the integrated microprocessor;
the integrated microprocessor stores the average exercise intensity of each exercise item and can calculate the exercise amount and the insolation amount.
2. The method of claim 1 for use with a big-data based outdoor exercise planning system, wherein: the mode of selecting the exercise items in the step (1) is as follows: the method comprises the steps of providing selectable exercise items in a classified list mode, dividing the exercise items into two categories of outdoor exercises and indoor exercises, recommending some items according to physical and mental conditions of users, freely combining the exercise items in an exercise plan according to the health level and exercise basis of the users, selecting different exercise items, and selecting the outdoor exercises and the indoor exercises so as to be convenient for later-stage adjustment between the outdoor exercises and the indoor exercises.
3. The method of claim 1 for use with a big-data based outdoor exercise planning system, wherein: in the step (2), the weather forecast comprises weather conditions and sun exposure indexes, and the air quality comprises AQI and PM 2.5.
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| CN110665206A (en) * | 2019-09-24 | 2020-01-10 | 绍兴文理学院 | Motion monitoring system and method based on wearable device |
| CN112133403B (en) * | 2020-09-30 | 2024-01-05 | 郑州大学 | Exercise control system based on edge calculation and cloud feedback |
| CN112071393A (en) * | 2020-09-30 | 2020-12-11 | 郑州大学 | Exercise guiding control system based on real-time and historical physiological data of patient |
| CN112308511A (en) * | 2020-10-27 | 2021-02-02 | 北京字节跳动网络技术有限公司 | Task plan generation method and device, electronic equipment and storage medium |
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