CN114496253B - Big data physique health monitoring system - Google Patents
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- CN114496253B CN114496253B CN202210086101.XA CN202210086101A CN114496253B CN 114496253 B CN114496253 B CN 114496253B CN 202210086101 A CN202210086101 A CN 202210086101A CN 114496253 B CN114496253 B CN 114496253B
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Abstract
The invention discloses a big data physique health monitoring system, which comprises: the system comprises a campus building classification module, a student position acquisition module, a position color marking module, a behavior image drawing module, an influence index determination module and a physique behavior suggestion module. The method combines the daily activity dynamic trajectory of the university student to obtain the influence index of the behavior of the university student on the physique, and combines the physique detection report of the university student, thereby realizing daily supervision on the physique health of the university student; according to the method, the activity track of the university student in the periodic time is updated according to the corresponding position, so that the activity track of the university student can be recorded at any time, the physique influence index of the student is obtained according to the behavior image, the real physical influence data of the university student are obtained according to the real activity of the university student in real time, and meanwhile, the influence data are distinguished positively and negatively so that a data manager can fully know.
Description
Technical Field
The invention relates to the field of health detection, in particular to a big data physique health monitoring system.
Background
Physical health is a focus of attention of modern people, people keep physical health by performing various activities, and meanwhile, real-time monitoring of physical health is a non-trivial part of life. While the university students, as the young seedlings of the Chinese ridge, have the constitution health as a crucial problem. In reality, in the physical ability test of college students every year, about 20% of students have unqualified side scores, and meanwhile, the data of student rest caused by physique problems are greatly increased. However, in the daily life of college students, students live in schools uniformly, all daily lives are arranged by themselves, and the role of physique supervision is lacked, but most students do not have correct knowledge on healthy life and do not know how to keep healthy physique, so that how to detect the physique of the college students in real time and give corresponding suggestions is an important research topic.
Disclosure of Invention
The invention aims to overcome the problems in the prior art and provide a big data physical health monitoring system, which combines the daily activity dynamic track of a university student to obtain the influence index of the behavior of the university student on the physical health, and combines the physical detection report of the university student to give reasonable suggestions to the living activity of the university student, thereby realizing daily supervision on the physical health of the university student.
Therefore, the invention provides a big data physique health monitoring system, which comprises:
the campus building classification module is used for marking the body mass indexes of different buildings according to the types of campus buildings and acquiring the fixed positions of the buildings at the same time;
the student position acquisition module is used for acquiring the campus card position of a student to obtain the real-time position of the student in a school;
the position color marking module is used for accumulating the multiple of the numerical value of the body mass index on the color value of the pixel point where the real-time position is located according to the body mass index corresponding to the fixed position where the real-time position is located;
the behavior image drawing module is used for establishing a blank picture, segmenting the blank picture according to the area of the fixed position, executing the position color marking module in real time and obtaining a behavior image after a period of time;
the influence index determining module is used for acquiring the characteristic value of the behavior image and calculating an influence index according to the characteristic value;
wherein k is a characteristic value, C is an influence index, and A and rho are constants;
the constitution behavior opinion module is used for extracting constitution data of a student physical examination report and calculating the current constitution index of the student according to the influence index;
T＝W+μC
wherein W is the constitution data, T is the current constitution index, and mu is a constant;
and giving student advice according to the influence index and the current body mass index.
Further, the fixed position and the real-time position are both represented using two-dimensional coordinates; the fixed position is a set of two-dimensional coordinates, and the real-time position is a single two-dimensional coordinate.
Further, the position color marking module determines whether the two-dimensional coordinate corresponding to the real-time position is in a set of two-dimensional coordinates corresponding to the fixed position according to the body mass index corresponding to the fixed position where the real-time position is located.
Further, still include:
an activity number detection module for detecting the number of times a student has arrived at a building within the time period;
a cumulative multiple determining module for calculating the multiple in the position color marking module according to the times obtained in the activity time detecting module;
B＝X·N
wherein B is the multiple, N is the number, and X follows normal distribution, namely X-N (eta, alpha) 2 ) Eta is an expected value, alpha is a standard deviation, and eta and alpha are constants;
and the position color marking module accumulates the multiple of the numerical value of the body mass index on the color value of the pixel point where the real-time position is located.
Further, the activity number detection module, when detecting the number of times the student has arrived at the building within the time period, includes the steps of:
obtaining the buildings of the students according to the relation between the real-time positions and the fixed positions;
monitoring the relation between the real-time position and the fixed position in real time, and recording the retention time of the real-time position in the area of the fixed position;
and obtaining the times according to the ratio of the residence time to the standard time.
Further, when the influence index determination module obtains the feature value of the behavior image, the influence index determination module includes the following steps:
segmenting the behavior image according to the area of the fixed position of each building to obtain the area image of each building;
enabling the center of the area of the fixed position of each building to be used as a building position, and enabling the building position to correspond to the building area image one by one;
respectively calculating the color value of each building area image, and arranging the color values according to the corresponding building positions to obtain a color matrix;
and calculating the characteristic value of the color matrix to obtain the characteristic value of the behavior image.
Further, when the constitution behavior opinion module extracts the constitution data of the student physical examination report, the constitution behavior opinion module comprises the following steps:
splitting the student physical examination report according to fragments to obtain a plurality of fragment reports;
converting each segment report into a text format, and extracting keywords of the segment reports by using a keyword extraction technology;
obtaining an index corresponding to the segment report according to the keyword;
and multiplying the index of each segment report by the corresponding weight and accumulating to obtain the constitution data of the student physical examination report.
Further, the corresponding weight value of the segment report is entered by the provider of the student physical examination report.
The big data physique health monitoring system provided by the invention has the following beneficial effects:
the method combines the daily activity dynamic track of the university student to obtain the influence index of the behavior of the university student on the physique, and simultaneously combines the physique detection report of the university student to give reasonable suggestions to the living activity of the university student, thereby realizing daily supervision on the physique health of the university student;
according to the method, the activity track of the university student in the periodic time is updated according to the corresponding position, so that the activity track of the university student can be recorded at any time, the physique influence index of the student is obtained according to the behavior image, the real physical influence data of the university student are obtained according to the real activity of the university student in real time, and meanwhile, the influence data are distinguished positively and negatively so that a data manager can fully know.
Drawings
FIG. 1 is a schematic block diagram of the overall system connection of the present invention;
FIG. 2 is a schematic block diagram of a system connection for determination of multiples in the position color-marking module of the present invention;
FIG. 3 is a schematic block diagram of the operation of the activity number detection module of the present invention;
FIG. 4 is a schematic block diagram of the operation of the impact index determination module of the present invention;
FIG. 5 is a block diagram illustrating the operation of the constitution behavior opinion module according to the present invention.
Detailed Description
An embodiment of the present invention will be described in detail below with reference to the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the embodiment.
Specifically, as shown in fig. 1 to 5, an embodiment of the present invention provides a big data physique health monitoring system, including: the system comprises a campus building classification module, a student position acquisition module, a position color marking module, a behavior image drawing module, an influence index determination module and a physique behavior suggestion module. The following is an introduction of the operational functions of the respective functional modules.
And the campus building classification module is used for marking the body mass indexes of different buildings according to the types of the campus buildings and acquiring the fixed positions of the buildings. The campus is provided with a plurality of buildings, such as a stadium, a gymnasium, a teaching building, an office building, a laboratory, a canteen, a dormitory and the like, the content borne by each building is inconsistent, for example, the teaching building bears a teaching task, and the stadium bears an exercise task, so that different buildings and students have inconsistent effects on the maintenance of physiques when using the campus, therefore, the campus can be mastered in real time according to marks of different physique indexes corresponding to different buildings and position areas (namely fixed positions of buildings) of each building in the school, and the modules can be obtained in an input mode.
The student position acquisition module is used for acquiring the positions of the campus cards of students to obtain the real-time positions of the students in schools. By monitoring the real-time positions of the students in the school in real time, the buildings used by the students in the school, namely the activity tracks of the students in the school, can be obtained. For the position of the campus card, as students need to hold the campus card to enter and exit when entering buildings, the use of the campus card can reflect the experience and time of the students in each building.
And the position color marking module accumulates the multiple of the numerical value of the physical index on the color value of the pixel point of the real-time position according to the physical index corresponding to the fixed position of the real-time position. The module is used for paving the behavior image drawing module, has an execution effect, and when the behavior image drawing module is executed, as long as students rebuild the building, the multiple of the numerical value of the body mass index corresponding to the building is accumulated on the fixed position of the building (the fixed position described by the invention, which is reflected by the region) as the color value worth mode of the pixel point, so that the execution step of the color of the pixel point is realized.
And the behavior image drawing module is used for establishing a blank picture, segmenting the blank picture according to the area of the fixed position, executing the position color marking module in real time and obtaining a behavior image after a period time. This module is through monitoring the student in cycle time, just can obtain cycle time's student's action image, according to the difference of the colour value of the colour that the region of difference shows, surveyability obtains the student to the frequency of utilization of each building, also can be the length of time of using to follow-up obtaining the degree of keeping to the physique. The cycle time refers to a time that can be repeated periodically, and can be set in various forms such as a day, a week, a month, and the like, as needed.
The influence index determining module is used for acquiring the characteristic value of the behavior image and calculating the influence index according to the characteristic value;
wherein k is a characteristic value, C is an influence index, and A and rho are constants.
The influence index determining module obtains the influence index of the physique of the student through calculation, namely, the numerical value of the rise and the fall of the physique of the student in the period time is generally expressed through the form of score. The above formula can accurately combine the image and the numerical value to obtain an accurate influence index through the form of calculus (the result of calculus may be positive number or negative number).
The constitution behavior opinion module is used for extracting constitution data of a student physical examination report and calculating the current constitution index of the student according to the influence index;
T＝W+μC
wherein W is the constitution data, T is the current constitution index, and mu is a constant;
and giving student advice according to the influence index and the current body mass index.
The constitution behavior opinion module gives some suggestions to students according to the comparison of the current constitution indexes of the students with standards, the suggestions can be obtained from a database, expert suggestions can also be obtained through the Internet, and finally the suggestions are fed back to the students.
Note that a, ρ, and μ in the present invention are constants, and these data are set by the student's situation and the school's request.
The invention obtains the activity condition of the student by the image mode, provides suggestions for the maintenance of the physique of the student according to the activity condition of the student, and can also enable the student and a manager to clearly know the dynamic of the student by visually watching the behavior image, thereby deeply knowing the school condition of the student. Therefore, the invention integrates the position, the image, the data and the physique of the student and other aspects to obtain the physique health data of the student, realizes the comprehensive monitoring of the health of the student, gives suggestions, and hopes that the physique of the student can be improved in the next style cycle time.
In an embodiment of the invention, in order to homogenize the position representation, both the fixed position and the real-time position are represented using two-dimensional coordinates; the fixed position is a set of two-dimensional coordinates, and the real-time position is a single two-dimensional coordinate. The format conversion of the position is that when the GPS coordinates are used, the position can still be calculated and used by the system provided by the invention only through the conversion.
Meanwhile, the position color marking module judges whether the two-dimensional coordinate corresponding to the real-time position is in the set of the two-dimensional coordinate corresponding to the fixed position according to the body mass index corresponding to the fixed position of the real-time position, so that the building of the school in which the student is located is obtained, and the using condition of the student on the building is obtained.
In the embodiment of the invention, in order to make the use measurement of the building more accurate for students, the invention further comprises: the device comprises an activity time detection module and an accumulation multiple determination module. The working processes of the activity time detection module and the accumulation multiple determination module are as follows:
and the activity frequency detection module is used for detecting the frequency that the students reach the building in the time period. This is to detect the number of times that the student has completed using the same building, e.g., the student has come to the library 3 times, to the stadium 8 times, to the teaching building 17 times, etc. the week.
A cumulative multiple determining module for calculating the multiple in the position color marking module according to the times obtained in the activity time detecting module;
B＝X·N
wherein B is the multiple, N is the number, and X follows normal distribution, namely X-N (eta, alpha) 2 ) Eta is an expected value, alpha is a standard deviation, and eta and alpha are constants;
and the position color marking module accumulates the multiple of the numerical value of the body mass index on the color value of the pixel point where the real-time position is located.
The accumulation multiple determining module is a process of obtaining the multiple according to the times, and because the general physique promotion is in a normal distribution form during the physique exercise, the effect is optimal when the number of times is optimal, namely the optimal multiple value is reached, the method of the invention is more accurate for detecting the physique promotion.
Meanwhile, the activity number detection module, when detecting the number of times that the student has arrived at the building within the time period, includes the steps of:
obtaining a building where the student is located according to the relation between the real-time position and the fixed position;
(II) monitoring the relation between the real-time position and the fixed position in real time, and recording the residence time of the real-time position in the area of the fixed position;
and (III) obtaining the times according to the ratio of the residence time to the standard time.
The steps (a) to (b) are sequentially carried out, the number of times of the students is obtained by obtaining the stay time of the students in the building, wherein the standard time is set by the students, the number of times of the students is obtained according to the standard time, so that the use frequency of the students for the building is obtained, the standard time can be inconsistent for different buildings, and the user of the system needs to set according to the actual situation.
In an embodiment of the present invention, in order to save computation time and increase computation speed, when the influence index determining module obtains the feature value of the behavior image, the method includes the following steps:
(1) Segmenting the behavior image according to the area of the fixed position of each building to obtain the area image of each building;
(2) Enabling the center of the area of the fixed position of each building to be used as a building position, and enabling the building position to correspond to the building area image one by one;
(3) Respectively calculating the color value of each building area image, and arranging the color values according to the corresponding building positions to obtain a color matrix;
(4) And calculating the characteristic value of the color matrix to obtain the characteristic value of the behavior image.
The steps (1) to (4) are sequentially performed according to the sequence, the step (1) divides the behavior image according to the position of each building, each divided building area image represents the use times and frequency of students, the color value of each building area image only needs to extract the color value of one pixel point in the color image, all the color values are arranged according to the position of the building area image in the behavior image, the missing part is filled with 0, the color matrix in the step (3) is obtained, and the characteristic value of the color matrix is calculated subsequently. Therefore, the data for calculating the characteristic value by using the method is reduced, so that the calculation efficiency of the system is effectively improved, and the result is ensured to be unchanged.
In an embodiment of the present invention, when the physique data of the student physical examination report is extracted, the physique behavior opinion module includes the following steps:
1, splitting the student physical examination report according to fragments to obtain a plurality of fragment reports;
2, converting each segment report into a text format, and extracting keywords of the segment report by using a keyword extraction technology;
3, obtaining an index corresponding to the fragment report according to the keyword;
and 4, multiplying the index of each segment report by the corresponding weight and accumulating to obtain the constitution data of the student physical examination report.
In the technical scheme, the step 1 and the step 4 are sequentially performed, the lecture physical examination report is split to obtain the constitution indexes (the constitution indexes are the dividing basis of the segments) in the student physical examination report, the grading data of the disease findings of the constitution indexes are weighted to obtain the constitution data of the final student physical examination report, so that the comprehensive constitution data of the student physical examination report is obtained, and the authenticity of the data is ensured. Meanwhile, by using the system mode, the judgment standard of the physique data of each student is single, and other actual standards of the fitting system, which are obtained by a subsequent system, are more suitable for other operations, so that the same standard exists, and the execution suggested by the subsequent students is facilitated to think about the directional development of physique improvement. In the invention, the corresponding weight value of the segment report is input by a provider of the student physical examination report.
The above disclosure is only for a few specific embodiments of the present invention, however, the present invention is not limited to the above embodiments, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present invention.
Claims (6)
1. A big data physical fitness monitoring system, comprising:
the campus building classification module is used for marking the body mass indexes of different buildings according to the types of campus buildings and acquiring the fixed positions of the buildings;
the student position acquisition module is used for acquiring the campus card position of a student to obtain the real-time position of the student in a school;
the position color marking module is used for accumulating the multiple of the numerical value of the body composition index on the color value of the pixel point of the real-time position according to the body composition index corresponding to the fixed position of the real-time position;
the activity frequency detection module is used for detecting the frequency of the students reaching the building within the period time;
the accumulation multiple determining module is used for calculating the multiple in the position color marking module according to the times obtained in the activity time detecting module;
B＝X·N
wherein B is the multiple, N is the number of times, and X follows normal distribution, namely X-N (eta, alpha) 2 ) Eta is an expected value, alpha is a standard deviation, and eta and alpha are constants;
the position color marking module accumulates the multiple of the numerical value of the body mass index on the color value of the pixel point where the real-time position is located;
the behavior image drawing module is used for establishing a blank picture, segmenting the blank picture according to the area of the fixed position, executing the position color marking module in real time and obtaining a behavior image after a period time;
the influence index determining module is used for acquiring the characteristic value of the behavior image and calculating an influence index according to the characteristic value;
wherein k is a characteristic value, C is an influence index, and A and rho are constants;
when the influence index determining module acquires the characteristic value of the behavior image, the influence index determining module comprises the following steps:
segmenting the behavior image according to the area of the fixed position of each building to obtain the area image of each building;
enabling the center of the area of the fixed position of each building to be used as a building position, and enabling the building position to correspond to the building area image one by one;
respectively calculating the color value of each building area image, and arranging the color values according to the corresponding building positions to obtain a color matrix;
calculating a characteristic value of the color matrix to obtain a characteristic value of the behavior image;
the constitution behavior opinion module is used for extracting constitution data of a student physical examination report and calculating the current constitution index of the student according to the influence index;
T＝W+μC
wherein, W is the constitution data, T is the current constitution index, and mu is a constant;
and giving student suggestions according to the influence indexes and the current body constitution indexes.
2. The big data physical fitness monitoring system of claim 1, wherein the fixed location and the real-time location are both expressed using two-dimensional coordinates; the fixed position is a set of two-dimensional coordinates, and the real-time position is a single two-dimensional coordinate.
3. The big data physical fitness monitoring system according to claim 2, wherein the position color marking module determines whether the two-dimensional coordinate corresponding to the real-time position is in the set of two-dimensional coordinates corresponding to the fixed position according to the physical fitness index corresponding to the fixed position of the real-time position.
4. The big data physical fitness monitoring system of claim 1, wherein the activity count detection module, when detecting the number of times a student has arrived at a building within the time period, comprises the steps of:
obtaining the buildings where the students are located according to the relation between the real-time positions and the fixed positions;
monitoring the relation between the real-time position and the fixed position in real time, and recording the residence time of the real-time position in the area of the fixed position;
and obtaining the times according to the ratio of the residence time to the standard time.
5. The system according to claim 1, wherein the physical activity opinion module, when extracting physical data of student physical examination report, comprises the following steps:
splitting the student physical examination report according to fragments to obtain a plurality of fragment reports;
converting each segment report into a text format, and extracting keywords of the segment report by using a keyword extraction technology;
obtaining an index corresponding to the segment report according to the keyword;
and multiplying the index of each segment report by the corresponding weight and accumulating to obtain the constitution data of the student physical examination report.
6. The big data physical fitness monitoring system of claim 5, wherein the corresponding weights of the segment reports are entered by a provider of the student health report.
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