CN117438091B - Motion intensity detection system and method based on big data - Google Patents
Motion intensity detection system and method based on big data Download PDFInfo
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Abstract
The invention discloses a motion intensity detection system and method based on big data, and particularly relates to the field of big data. According to the invention, the exercise time of the user is divided and detected, so that the repetition of work and the waste of resources are effectively avoided, the exercise data are acquired through a big data technology, the acquired exercise data are more comprehensive and accurate, the characteristics related to the exercise intensity can be extracted from a large amount of data through the analysis of the exercise data, the detection accuracy is improved, the potential exercise risk and physical discomfort can be found in time through generating an exercise intensity report, the user is reminded to take necessary protective measures, the occurrence of exercise injury is effectively prevented, and the exercise machine is beneficial to promoting the development of the body-building industry and improving the public health level.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a motion intensity detection system and method based on big data.
Background
The exercise intensity refers to the intensity of force and the tension degree of the body during exercise, is one of main factors for determining the exercise load, and the main factors for influencing the exercise intensity include exercise density, intermittent time, action speed, weight born by exercise, difficulty and complexity of action, the exercise intensity has a great stimulation effect on the human body, and proper exercise can effectively promote the improvement of the body function, if the intensity is overlarge, the body bearing capacity is exceeded, the body can be reduced, and even the body health is damaged.
The exercise intensity detection system is a system for detecting and recording exercise intensity, collects exercise data through sensors and other data acquisition equipment, processes and analyzes the data by utilizing a data analysis technology, and the common data acquisition equipment mainly comprises a heart rate detector and a speedometer, is mainly used for acquiring heart rate, respiratory rate and acceleration of an exercise person, carries out deep analysis according to the acquired data, extracts characteristics related to the exercise intensity, judges the exercise intensity level according to the characteristics, and has wide application range including but not limited to the fields of body building, physical training, rehabilitation training and the like.
However, when the system is actually used, some defects still exist, such as problems in accuracy and reliability of the existing motion intensity detection system may exist, and problems in data distortion, false alarm, missing report and the like may occur due to limitations of a sensor and a data collector, so that accurate judgment of motion intensity is affected, and the existing motion intensity detection system lacks adaptability to different motion types of people and different age groups, lacks intelligence and self-adaptation capability, and also lacks comprehensive analysis and utilization of multidimensional data.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present invention provide a motion intensity detection system and method based on big data, which solve the problems set forth in the above-mentioned background art through the following schemes.
In order to achieve the above purpose, the present invention provides the following technical solutions: a motion intensity detection system and method based on big data comprises:
monitoring time dividing module: the method is used for determining the motion data of the user in the monitored time process as a target data area, dividing the motion time data of the user in the target time period into sub data areas in an equal time division mode, and marking the sub data areas as 1 and 2 … … n in sequence.
The motion data acquisition module: the system is used for collecting the breathing variation parameters, the heart rate variation parameters and the running speed variation parameters of each sub-data area and transmitting the collected data to the exercise data analysis module.
Motion data analysis module: the device comprises a respiration change data analysis unit, a heart rate change data analysis unit and a running speed change data analysis unit, wherein the respiration change data analysis unit, the heart rate change data analysis unit and the running speed change data analysis unit are used for analyzing parameters transmitted by the exercise data acquisition module, analyzing and obtaining respiration change coefficients, heart rate change coefficients and running speed change coefficients of all sub-data areas, and transmitting the respiration change coefficients, the heart rate change coefficients and the running speed change coefficients to the exercise intensity calculation module.
The motion intensity calculation module: the system comprises a respiration change coefficient, a heart rate change coefficient and a running speed change coefficient, wherein the respiration change coefficient and the heart rate change coefficient are used for receiving the motion data analysis module, the motion intensity index of each sub-data area is calculated through the sub-data area comprehensive model, and the motion intensity index is transmitted to the comprehensive analysis module.
And the comprehensive analysis module is used for: the motion intensity judgment module is used for receiving the motion intensity indexes of all the sub-data areas transmitted by the motion intensity calculation module, calculating the user comprehensive motion intensity index of the target data area through the target data area comprehensive model and transmitting the user comprehensive motion intensity index to the motion intensity judgment module;
the exercise intensity judging module: the motion intensity judgment unit is used for judging the comprehensive motion intensity index of the user of the target data area to detect the motion intensity and generating a corresponding detection report according to the judgment result.
And the early warning module is used for: and detecting and early warning the exercise intensity of the user through the exercise intensity report data, and when the exercise intensity reaches a preset early warning threshold, automatically sending an early warning signal by the early warning module to remind the user to reduce the exercise intensity or take necessary protective measures.
Preferably, the respiratory variation parameters include respiratory rate, respiratory depth and single respiratory time, the heart rate variation parameters include average heart rate, resting heart rate and maximum heart rate, and the running speed variation parameters include step frequency, stride and movement distance, respectively marked as、/>、/>、/>、/>、/>、/>、/>And->Where i=1, 2 … … n, i denotes the i-th sub-data area and the height, weight, and age of the user are collected, labeled +.>、/>And->。
Preferably, the exercise data acquisition module specifically utilizes big data technology to acquire breathing parameters by placing a breathing sensor on the chest of a user, acquires heart rate parameters by a heart rate belt carried by the user, and acquires running speed variation parameters when the user runs by placing a camera at a position parallel to a running path of the user.
Preferably, the mathematical model used by the respiratory variation data analysis unit is:，/>user respiratory coefficient of variation representing the ith sub-data area,/->User respiratory rate, indicative of the ith sub-data area, ">User respiratory depth, +.>Indicating the user's single breath time of the ith sub-data area,/->Representing the time difference between the ith sub data area and the (i-1) th sub data area, +.>Indicating the weight of the user->Representing the height of the user->Other influencing factors representing the respiratory variation coefficient.
Preferably, the mathematical model used by the heart rate variation data analysis unit is:，/>user heart rate variability factor representing the ith sub-data area,/->User average heart rate, indicative of the ith sub-data area,/-, for example>User resting heart rate, indicative of the ith sub-data area,/->User maximum heart rate, representing the ith sub-data area, < ->Age influence coefficient representing the heart rate of the user, +.>Indicates the age of the user->Other influencing factors representing heart rate variability coefficients.
Preferably, the mathematical model used by the running speed variation data analysis unit is:，/>user running speed variation coefficient representing the ith sub data area,/->User step frequency representing the ith sub data area, < ->User stride indicating i-th sub data area, and +.>User movement distance, < +.>Indicating the weight of the user->Representing the height of the user->Representing the year of the userThe age of the product is that,representing the time difference between the ith sub data area and the (i-1) th sub data area, +.>Other influencing factors representing running speed variation coefficients.
Preferably, the sub data area comprehensive data model is:，/>user exercise intensity index indicating the ith sub data area,/->User respiratory coefficient of variation representing the ith sub-data area,/->User heart rate variability factor representing the ith sub-data area,/->User running speed variation coefficient representing the ith sub data area,/->Other influencing factors representing the exercise intensity index.
Preferably, the target data area comprehensive data model is:，/>user integrated exercise intensity index representing target area, < +.>Representing the user exercise intensity index of the i-th sub-data area.
Preferably, the exercise intensity judging unit compares the exercise intensity index of each sub-data area with a preset different area exercise intensity index threshold value to detect the exercise intensity whenThe motion intensity of the ith sub data area is described as a low intensity motion when +.>The motion intensity of the ith sub data area is described as medium intensity motion when +.>When the motion intensity of the i-th sub data area is described as a high intensity motion.
Preferably, a motion intensity detection method based on big data comprises the following steps:
step S01: monitoring time division: the method comprises the steps of determining motion data of a user in a monitored time process as a target data area, dividing the motion time data of the user in a target time period into sub data areas in an equal time division mode, and marking the sub data areas as 1 and 2 … … n in sequence;
step S02: and (3) motion data acquisition: the method comprises the steps of collecting respiratory variation parameters, heart rate variation parameters and running speed variation parameters of each sub-data area, and transmitting collected data to an exercise data analysis step;
step S03: motion data analysis: the exercise intensity index calculation method comprises a respiration variation data analysis unit, a heart rate variation data analysis unit and a running speed variation data analysis unit, wherein the respiration variation data analysis unit, the heart rate variation data analysis unit and the running speed variation data analysis unit are used for analyzing parameters transmitted in the exercise data acquisition step, analyzing and obtaining respiration variation coefficients, heart rate variation coefficients and running speed variation coefficients of all sub-data areas, and transmitting the respiration variation coefficients, the heart rate variation coefficients and the running speed variation coefficients to the exercise intensity index calculation step;
step S04: motion intensity calculation: the method comprises the steps of receiving a respiratory variation coefficient, a heart rate variation coefficient and a running speed variation coefficient transmitted by a motion data analysis step, calculating a motion intensity index of each sub-data area through a sub-data area comprehensive model, and transmitting the motion intensity index to the comprehensive analysis step;
step S05: comprehensive analysis: the motion intensity index of each sub-data area transmitted in the motion intensity calculating step is received, the user comprehensive motion intensity index of the target data area is calculated through the target data area comprehensive model, and the user comprehensive motion intensity index is transmitted to the motion intensity judging step;
step S06: judging the exercise intensity: the motion intensity judgment unit is used for judging the comprehensive motion intensity index of the user of the target data area to detect the motion intensity and generating a corresponding detection report according to the judgment result;
step S07: early warning: and detecting and early warning the exercise intensity of the user through the exercise intensity report data, and automatically sending an early warning signal in the early warning step when the exercise intensity reaches a preset early warning threshold value, so as to remind the user to reduce the exercise intensity or take necessary protective measures.
The invention has the technical effects and advantages that:
dividing the movement time of a user in a target time period into each sub-data area through a monitoring time dividing module, numbering, acquiring the movement information of the user in each sub-data area through a movement data acquisition module by utilizing a big data technology, analyzing the data transmitted by the movement data acquisition module through a mathematical model corresponding to an analysis unit by utilizing a movement data analysis module, integrating the data transmitted by the movement data acquisition module with the body data of the user, calculating a change coefficient affecting the movement intensity, calculating a comprehensive movement intensity index through a comprehensive analysis module, judging the comprehensive movement intensity index through a movement intensity judging module to detect the movement intensity, generating a corresponding detection report according to a judging result, and detecting and early warning the movement intensity of the user through an early warning module;
according to the invention, the exercise time of the user is divided and detected, so that the repetition of work and the waste of resources are effectively avoided, the exercise data are acquired through a big data technology, the acquired exercise data are more comprehensive and accurate, the characteristics related to the exercise intensity can be extracted from a large amount of data through the analysis of the exercise data, the detection accuracy is improved, the potential exercise risk and physical discomfort can be found in time through generating an exercise intensity report, the user is reminded to take necessary protective measures, the occurrence of exercise injury is effectively prevented, and the exercise machine is beneficial to promoting the development of the body-building industry and improving the public health level.
Drawings
Fig. 1 is a schematic diagram of the overall structure of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a motion intensity detection system and method based on big data includes: the system comprises a monitoring time dividing module, a motion data acquisition module, a motion data analysis module, a motion intensity calculation module, a comprehensive analysis module, a motion intensity judgment module and an early warning module.
The monitoring time dividing module is used for determining motion data of a user in the monitored time process as a target data area, dividing the motion time data of the user in the target time period into all sub-data areas in an equal time dividing mode, and marking the sub-data areas as 1 and 2 … … n in sequence.
The exercise data acquisition module is used for acquiring the respiration variation parameters, the heart rate variation parameters and the running speed variation parameters of each sub-data area and transmitting the acquired data to the exercise data analysis module.
The respiratory variation parameters comprise respiratory frequency, respiratory depth and single respiratory time, the heart rate variation parameters comprise average heart rate, resting heart rate and maximum heart rate, and the running speed variation parameters comprise stepsFrequency, stride, and distance of motion, respectively, marked as、/>、/>、/>、/>、/>、/>、/>And->Where i=1, 2 … … n, i denotes the i-th sub-data area and the height, weight, and age of the user are collected, labeled +.>、/>And->。
The embodiment needs to be specifically explained, the exercise data acquisition module specifically utilizes the big data technology to acquire respiratory parameters by placing a respiratory sensor on the chest of a user, acquires the heart rate parameters by a heart rate belt carried by the user, and acquires running speed variation parameters when the user runs by placing a camera at a position parallel to a running path of the user
The exercise data analysis module comprises a respiration change data analysis unit, a heart rate change data analysis unit and a running speed change data analysis unit, and is used for analyzing parameters transmitted by the exercise data acquisition module, analyzing and obtaining respiration change coefficients, heart rate change coefficients and running speed change coefficients of all sub-data areas, and transmitting the respiration change coefficients, heart rate change coefficients and running speed change coefficients to the exercise intensity calculation module.
The exercise data analysis module utilizes a big data analysis technology, analyzes the data transmitted by the exercise data acquisition module through a mathematical model corresponding to the analysis unit, integrates the data transmitted by the exercise data acquisition module with the body data of the user, and calculates a respiratory change coefficient, a heart rate change coefficient and a running speed change coefficient which affect exercise intensity.
The breath change data analysis unit is used for establishing a breath change mathematical model, and importing breath change parameters into the breath change mathematical model to obtain，/>User respiratory coefficient of variation representing the ith sub-data area,/->User respiratory rate, indicative of the ith sub-data area, ">User respiratory depth, +.>Indicating the user's single breath time of the ith sub-data area,/->Representing the time difference between the ith sub data area and the (i-1) th sub data area, +.>Indicating the weight of the user->Representing the height of the user->Other influencing factors representing the respiratory variation coefficient.
The heart rate variation data analysis unit is used for establishing a heart rate variation mathematical model, and importing heart rate variation parameters into the heart rate variation mathematical model to obtain，/>User heart rate variability factor representing the ith sub-data area,/->User average heart rate, indicative of the ith sub-data area,/-, for example>User resting heart rate, indicative of the ith sub-data area,/->User maximum heart rate, representing the ith sub-data area, < ->Age influence coefficient representing the heart rate of the user, +.>Indicates the age of the user->Other influencing factors representing heart rate variability coefficients.
The running speed change data analysis unit is used for establishing a running speed change mathematical model, and importing running speed change parameters into the running speed change mathematical model to obtain，/>User running speed variation coefficient representing the ith sub data area,/->User step frequency representing the ith sub data area, < ->User stride indicating i-th sub data area, and +.>User movement distance, < +.>Indicating the weight of the user,representing the height of the user->Indicates the age of the user->Representing the time difference between the ith sub data area and the (i-1) th sub data area, +.>Other influencing factors representing running speed variation coefficients.
The exercise intensity calculation module is used for receiving the respiratory variation coefficient, the heart rate variation coefficient and the running speed variation coefficient transmitted by the exercise data analysis module, calculating the exercise intensity index of each sub-data area through the sub-data area comprehensive model and transmitting the exercise intensity index to the comprehensive analysis module.
The sub-data area comprehensive data model is as follows:，/>user exercise intensity index indicating the ith sub data area,/->User respiratory coefficient of variation representing the ith sub-data area,/->User heart rate variability factor representing the ith sub-data area,/->Representing the user running speed change coefficient of the i-th sub data area,other influencing factors representing the exercise intensity index.
The comprehensive analysis module is used for receiving the motion intensity indexes of all the sub-data areas transmitted by the motion intensity calculation module, calculating the comprehensive motion intensity indexes of the users of the target data areas through the comprehensive model of the target data areas, and transmitting the comprehensive motion intensity indexes to the motion intensity judgment module.
The target data area comprehensive data model is as follows:，/>user integrated exercise intensity index representing target area, < +.>A user exercise intensity index representing an i-th sub-data area;
the exercise intensity judging module is used for receiving the exercise intensity index transmitted by the exercise intensity calculating module, detecting the exercise intensity by judging the user comprehensive exercise intensity index of the target data area through the exercise intensity judging unit, and generating a corresponding detection report according to the judging result.
Said movementThe intensity judging unit compares the exercise intensity index of each sub-data area with the preset different area exercise intensity index threshold value to detect the exercise intensity whenThe motion intensity of the ith sub data area is described as a low intensity motion when +.>The motion intensity of the ith sub data area is described as medium intensity motion when +.>When the motion intensity of the i-th sub data area is described as a high intensity motion.
The exercise intensity judging module generates an exercise intensity report according to the detection result of the exercise intensity judging unit on the exercise intensity, the corresponding detection area number and the exercise data, and generates a corresponding exercise optimization suggestion according to the exercise intensity report and the user body data.
The early warning module detects and early warns the exercise intensity of the user through the exercise intensity report data, and when the exercise intensity reaches a preset early warning threshold, the early warning module automatically sends out an early warning signal to remind the user to reduce the exercise intensity or take necessary protective measures.
The early warning module is arranged atThe method has the advantages that the user is warned through sound prompt, lamplight flickering and a mobile phone pushing mode, the user is reminded to reduce the exercise intensity or take necessary protective measures, the method comprises the steps of reminding the user to reduce the exercise speed, avoiding physical discomfort caused by suddenly reducing the exercise intensity of the user, reducing the exercise time, enabling the user to conduct proper amount of exercise in a limited time, avoiding physical fatigue and injury caused by excessive exercise, reminding the user to supplement water in time when the high-intensity exercise is kept for a long time, and avoiding physical fatigue and injury caused by dehydration.
In this embodiment, it should be specifically described that the present invention provides a motion intensity detection method based on big data, including the following steps:
step S01: monitoring time division: the method comprises the steps of determining motion data of a user in a monitored time process as a target data area, dividing the motion time data of the user in a target time period into sub data areas in an equal time division mode, and marking the sub data areas as 1 and 2 … … n in sequence;
step S02: and (3) motion data acquisition: the method comprises the steps of collecting respiratory variation parameters, heart rate variation parameters and running speed variation parameters of each sub-data area, and transmitting collected data to an exercise data analysis step;
step S03: motion data analysis: the exercise intensity index calculation method comprises a respiration variation data analysis unit, a heart rate variation data analysis unit and a running speed variation data analysis unit, wherein the respiration variation data analysis unit, the heart rate variation data analysis unit and the running speed variation data analysis unit are used for analyzing parameters transmitted in the exercise data acquisition step, analyzing and obtaining respiration variation coefficients, heart rate variation coefficients and running speed variation coefficients of all sub-data areas, and transmitting the respiration variation coefficients, the heart rate variation coefficients and the running speed variation coefficients to the exercise intensity index calculation step;
step S04: motion intensity calculation: the method comprises the steps of receiving a respiratory variation coefficient, a heart rate variation coefficient and a running speed variation coefficient transmitted by a motion data analysis step, calculating a motion intensity index of each sub-data area through a sub-data area comprehensive model, and transmitting the motion intensity index to the comprehensive analysis step;
step S05: comprehensive analysis: the motion intensity index of each sub-data area transmitted in the motion intensity calculating step is received, the user comprehensive motion intensity index of the target data area is calculated through the target data area comprehensive model, and the user comprehensive motion intensity index is transmitted to the motion intensity judging step;
step S06: judging the exercise intensity: the motion intensity judgment unit is used for judging the comprehensive motion intensity index of the user of the target data area to detect the motion intensity and generating a corresponding detection report according to the judgment result;
step S07: early warning: and detecting and early warning the exercise intensity of the user through the exercise intensity report data, and automatically sending an early warning signal in the early warning step when the exercise intensity reaches a preset early warning threshold value, so as to remind the user to reduce the exercise intensity or take necessary protective measures.
According to the invention, the motion time of a user in a target time period is divided into each sub-data area and numbered through the monitoring time dividing module, the motion information of the user in each sub-data area is acquired through the motion data acquisition module by utilizing a big data technology, the data transmitted by the motion data acquisition module is analyzed through the motion data analysis module by utilizing a mathematical model corresponding to the analysis unit, the data transmitted by the motion data acquisition module and the body data of the user are integrated, the change coefficient affecting the motion intensity is calculated, the comprehensive motion intensity index is calculated through the comprehensive analysis module, the motion intensity is detected through the comprehensive motion intensity judging module, a corresponding detection report is generated according to the judging result, and the motion intensity of the user is detected and early warned through the early warning module.
According to the invention, the exercise time of the user is divided and detected, so that the repetition of work and the waste of resources are effectively avoided, the exercise data are acquired through a big data technology, the acquired exercise data are more comprehensive and accurate, the characteristics related to the exercise intensity can be extracted from a large amount of data through the analysis of the exercise data, the detection accuracy is improved, the potential exercise risk and physical discomfort can be found in time through generating an exercise intensity report, the user is reminded to take necessary protective measures, the occurrence of exercise injury is effectively prevented, and the exercise machine is beneficial to promoting the development of the body-building industry and improving the public health level.
Secondly: in the drawings of the disclosed embodiments, only the structures related to the embodiments of the present disclosure are referred to, other structures can refer to the common design, and the same embodiment and different embodiments of the present disclosure can be combined with each other under the condition of no conflict;
finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (6)
1. A big data based motion intensity detection system, comprising:
monitoring time dividing module: the method comprises the steps of determining motion data of a user in a monitored time process as a target data area, dividing the motion time data of the user in a target time period into sub data areas in an equal time division mode, and marking the sub data areas as 1 and 2 … … n in sequence;
the motion data acquisition module: the system comprises a data analysis module, a data acquisition module and a data acquisition module, wherein the data acquisition module is used for acquiring respiratory variation parameters, heart rate variation parameters and running speed variation parameters of each sub-data area and transmitting acquired data to the data analysis module;
motion data analysis module: the system comprises a respiration change data analysis unit, a heart rate change data analysis unit and a running speed change data analysis unit, wherein the respiration change data analysis unit, the heart rate change data analysis unit and the running speed change data analysis unit are used for analyzing parameters transmitted by the exercise data acquisition module, analyzing and obtaining respiration change coefficients, heart rate change coefficients and running speed change coefficients of all sub-data areas, and transmitting the respiration change coefficients, the heart rate change coefficients and the running speed change coefficients to the exercise intensity calculation module;
the respiratory variation parameters comprise respiratory frequency, respiratory depth and single respiratory time, the heart rate variation parameters comprise average heart rate, resting heart rate and maximum heart rate, the running speed variation parameters comprise step frequency, stride and movement distance, and the steps are respectively marked as、/>、/>、/>、/>、/>、/>、/>And->Where i=1, 2 … … n, i denotes the i-th sub-data area and the height, weight, and age of the user are collected, labeled +.>、/>And->；
The motion intensity calculation module: the system comprises a respiration change coefficient, a heart rate change coefficient and a running speed change coefficient, wherein the respiration change coefficient and the heart rate change coefficient are used for receiving the motion data analysis module, calculating the motion intensity index of each sub-data area through the sub-data area comprehensive model, and transmitting the motion intensity index to the comprehensive analysis module;
and the comprehensive analysis module is used for: the motion intensity judgment module is used for receiving the motion intensity indexes of all the sub-data areas transmitted by the motion intensity calculation module, calculating the user comprehensive motion intensity index of the target data area through the target data area comprehensive model and transmitting the user comprehensive motion intensity index to the motion intensity judgment module;
the exercise intensity judging module: the motion intensity judgment unit is used for judging the comprehensive motion intensity index of the user of the target data area to detect the motion intensity and generating a corresponding detection report according to the judgment result;
and the early warning module is used for: detecting and early warning the exercise intensity of the user through the exercise intensity report data, and automatically sending an early warning signal by the early warning module when the exercise intensity reaches a preset early warning threshold value, so as to remind the user to reduce the exercise intensity or take necessary protective measures;
the breath change data analysis unit is used for establishing a breath change mathematical model, and importing breath change parameters into the breath change mathematical model to obtain，/>User respiratory coefficient of variation representing the ith sub-data area,/->User respiratory rate, indicative of the ith sub-data area, ">User respiratory depth, +.>Indicating the user's single breath time of the ith sub-data area,/->Representing the time difference between the ith sub data area and the (i-1) th sub data area, +.>Indicating the weight of the user->Representing the height of the user->Other influencing factors representing respiratory variation coefficients;
analysis of the heart rate variability dataThe unit is used for establishing a heart rate variation mathematical model, and leading the heart rate variation parameters into the heart rate variation mathematical model to obtain，/>User heart rate variability factor representing the ith sub-data area,/->User average heart rate, indicative of the ith sub-data area,/-, for example>User resting heart rate, indicative of the ith sub-data area,/->User maximum heart rate, representing the ith sub-data area, < ->Age influence coefficient representing the heart rate of the user, +.>Indicates the age of the user->Other influencing factors representing heart rate variability coefficients;
the running speed change data analysis unit is used for establishing a running speed change mathematical model, and importing running speed change parameters into the running speed change mathematical model to obtain，/>User running speed variation coefficient representing the ith sub data area,/->User step frequency representing the ith sub data area, < ->User stride indicating i-th sub data area, and +.>User movement distance, < +.>Indicating the weight of the user->Representing the height of the user->Indicates the age of the user->Representing the time difference between the ith sub data area and the (i-1) th sub data area, +.>Other influencing factors representing running speed variation coefficients.
2. The big data based exercise intensity detection system of claim 1, wherein: the exercise data acquisition module specifically utilizes big data technology to acquire breathing parameters by placing a breathing sensor on the chest of a user, acquires heart rate parameters by a heart rate belt carried by the user, and acquires running speed variation parameters when the user runs by placing a camera at a position parallel to a running path of the user.
3. Big data based motion intensity detection as claimed in claim 1The system is characterized in that: the sub-data area comprehensive data model is as follows:，/>user exercise intensity index indicating the ith sub data area,/->User respiratory coefficient of variation representing the ith sub-data area,/->User heart rate variability factor representing the ith sub-data area,/->User running speed variation coefficient representing the ith sub data area,/->Other influencing factors representing the exercise intensity index.
4. The big data based exercise intensity detection system of claim 1, wherein: the target data area comprehensive data model is as follows:，/>a user integrated motor strength index representing the target area,representing the user exercise intensity index of the i-th sub-data area.
5. Big data based on the method of claim 1Is characterized in that: the exercise intensity judging unit compares the exercise intensity index of each sub-data area with the preset different area exercise intensity index threshold value to detect the exercise intensity whenThe motion intensity of the ith sub data area is described as a low intensity motion when +.>When the motion intensity of the ith sub-data area is described as medium intensity motionWhen the motion intensity of the i-th sub data area is described as a high intensity motion.
6. A method for detecting exercise intensity based on big data, based on the exercise intensity detection system based on big data of any one of claims 1 to 5, comprising the steps of:
step S01: monitoring time division: the method comprises the steps of determining motion data of a user in a monitored time process as a target data area, dividing the motion time data of the user in a target time period into sub data areas in an equal time division mode, and marking the sub data areas as 1 and 2 … … n in sequence;
step S02: and (3) motion data acquisition: the method comprises the steps of collecting respiratory variation parameters, heart rate variation parameters and running speed variation parameters of each sub-data area, and transmitting collected data to an exercise data analysis step;
step S03: motion data analysis: the exercise intensity index calculation method comprises a respiration variation data analysis unit, a heart rate variation data analysis unit and a running speed variation data analysis unit, wherein the respiration variation data analysis unit, the heart rate variation data analysis unit and the running speed variation data analysis unit are used for analyzing parameters transmitted in the exercise data acquisition step, analyzing and obtaining respiration variation coefficients, heart rate variation coefficients and running speed variation coefficients of all sub-data areas, and transmitting the respiration variation coefficients, the heart rate variation coefficients and the running speed variation coefficients to the exercise intensity index calculation step;
step S04: motion intensity calculation: the method comprises the steps of receiving a respiratory variation coefficient, a heart rate variation coefficient and a running speed variation coefficient transmitted by a motion data analysis step, calculating a motion intensity index of each sub-data area through a sub-data area comprehensive model, and transmitting the motion intensity index to the comprehensive analysis step;
step S05: comprehensive analysis: the motion intensity index of each sub-data area transmitted in the motion intensity calculating step is received, the user comprehensive motion intensity index of the target data area is calculated through the target data area comprehensive model, and the user comprehensive motion intensity index is transmitted to the motion intensity judging step;
step S06: judging the exercise intensity: the motion intensity judgment unit is used for judging the comprehensive motion intensity index of the user of the target data area to detect the motion intensity and generating a corresponding detection report according to the judgment result;
step S07: early warning: and detecting and early warning the exercise intensity of the user through the exercise intensity report data, and automatically sending an early warning signal in the early warning step when the exercise intensity reaches a preset early warning threshold value, so as to remind the user to reduce the exercise intensity or take necessary protective measures.
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