A physical training virtual simulation training system based on machine learning

Abstract

The present invention relates to the field of artificial intelligence technology, and is used to solve the problem that traditional training methods lack scientificity and data support and mainly rely on personal experience and intuition to formulate training plans. In particular, a physical training virtual simulation training system based on machine learning is disclosed, comprising a data acquisition module, a server, a training state analysis module, a physical state analysis module, a simulation training planning module, a display terminal and a simulation cloud database; the present invention monitors the training state information and physical state information of physical trainees, and analyzes the information to obtain a training state evaluation coefficient and a physical state evaluation coefficient, performs simulation training simulation planning analysis based on the evaluation coefficients, and finally generates a personalized training plan for the physical trainees, thereby providing the physical trainees with a scientific and targeted training plan, which is helpful to improve their physical fitness level and competitive ability.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a physical training virtual simulation training system based on machine learning. Background Art

[0002] In today's society, physical training is crucial for athletes, military personnel and even the general public. However, traditional training methods often lack scientific and data support. Coaches and physical trainers mainly rely on personal experience and intuition to formulate training plans, lacking in-depth evaluation and analysis of individual differences and dynamic changes of physical trainers.

[0003] Since each trainee has different physical conditions, skill levels, training goals, etc., and traditional training methods often ignore these differences, the training plan cannot be accurately and scientifically planned, and it is difficult to accurately assess the athlete's physical condition, skill level and training needs, which leads to the training plan being too general and unable to fully meet the athlete's personalized needs, which not only affects the athlete's training results, but also may increase the risk of injury;

[0004] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0005] The purpose of this invention is to solve the problem that traditional training methods often lack scientificity and data support, mainly rely on personal experience and intuition to formulate training plans, and lack in-depth evaluation and analysis of individual differences and dynamic changes of trainees. A physical training virtual simulation training system based on machine learning is proposed.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A physical training virtual simulation training system based on machine learning, comprising a data acquisition module, a server, a training state analysis module, a body state analysis module, a simulation training planning module, a display terminal and a simulation cloud database;

[0008] The data acquisition module is used to collect the training status information and physical status information of the target trainee in each monitoring time period within the corresponding monitoring period, and send the information to the training status analysis module and the physical status analysis module respectively through the server;

[0009] The training status analysis module is used to receive the training status information of the target trainee in each monitoring time period in the corresponding monitoring period, and analyze it to obtain the total strength value, standard rate value and sports fatigue value of the target trainee in each monitoring time period in the corresponding monitoring period, thereby evaluating and analyzing the training status of the target trainee in the corresponding monitoring period, obtaining the training status evaluation coefficient of the target trainee in the corresponding monitoring period, and sending it to the simulation training planning module;

[0010] The physical state analysis module is used to receive the physical state information of the target trainee in each monitoring time period in the corresponding monitoring period, and analyze it to obtain the heart rate change value, body temperature change value and respiratory rate change value of each training item in each monitoring time period in the corresponding monitoring period of the target trainee, thereby evaluating and analyzing the physical state of the target trainee in the corresponding monitoring period, obtaining the physical state evaluation coefficient of the target trainee in the corresponding monitoring period, and sending it to the simulation training planning module;

[0011] The simulation training planning module is used to receive the training state evaluation coefficient and the physical state evaluation coefficient of the target trainee in the corresponding monitoring period, thereby performing simulation training simulation planning analysis to obtain a personalized plan for the target trainee.

[0012] As a further improvement of the present invention, the process of analyzing the training state information is as follows:

[0013] By obtaining the number of training items and the duration of each training item in each monitoring period of the target training personnel, setting the intensity conversion factor corresponding to each training item, and multiplying the duration of each training item by the intensity conversion factor corresponding to each training item, the training intensity value of each training item is obtained. After extracting the number of training items and the training intensity value of each training item, the total intensity value of the target training personnel in each monitoring period of the monitoring period is obtained, which is recorded as Y i teity ;

[0014] By acquiring the video of each training item in each monitoring time period of the target physical trainee corresponding to the monitoring period, and parsing the video of each training item in each monitoring time period of the target physical trainee corresponding to the monitoring period, the image of each monitoring time point in each training item in each monitoring time period of the target physical trainee corresponding to the monitoring period is obtained, and the human body contour of each monitoring time point in each training item in each monitoring time period of the target physical trainee corresponding to the monitoring period is extracted;

[0015] The human body contour of the target trainee at each monitoring time point in each training item in each monitoring time period within the corresponding monitoring period is overlapped and compared with the human body contour of the corresponding previous monitoring time point, thereby obtaining the overlapping area of ​​the human body contour of the target trainee in each training item in each monitoring time period within the corresponding monitoring period, and marking it as the body contour weight face value, setting the reference body contour weight face value, and comparing and analyzing the body contour weight face value with the reference body contour weight face value. When the body contour weight face value is greater than or equal to the reference body contour weight face value, it is judged as standard. When the body contour weight face value is less than the reference body contour weight face value, it is judged as non-standard. The number of times the target trainee is judged as standard and the total number of times he is judged as standard in each training item in each monitoring time period within the corresponding monitoring period are counted, and they are marked as gf respectively. x i and zp x i , and take its value according to the formula: , get the standard rate value Y of the target training personnel in each monitoring period within the corresponding monitoring period i nrvue , where x represents the number of each training item, x=1, 2, 3…n, n represents the total number of training item numbers, i represents the number of each monitoring time period in the monitoring cycle, i=1, 2, 3…m, m represents the total number of each monitoring time period in the monitoring cycle, and a1 and a2 represent the set weight coefficients;

[0016] Obtain the duration from the human body contour of each monitoring time point in each training item in each monitoring time period of the target physical trainer to the next monitoring time point of the same human body contour, and calculate the average value thereof, thereby obtaining the reaction value of each training item in each monitoring time period of the target physical trainer in the corresponding monitoring period, and calculate the average value thereof again to obtain the total reaction value of each monitoring time period of the target physical trainer in the corresponding monitoring period, extract the standard rate value and the total reaction value of each monitoring time period of the target physical trainer in the corresponding monitoring period, multiply them by the corresponding weight coefficient, and then add them to obtain the sports fatigue value of each monitoring time period of the target physical trainer in the corresponding monitoring period, recorded as Y i efvue .

[0017] As a further improvement of the present invention, the process of evaluating and analyzing the training status is as follows:

[0018] Extract the total intensity value, standard rate value and sports fatigue value of the target physical trainer in each monitoring period within the corresponding monitoring period and normalize them according to the formula: , the training status evaluation coefficient XLP of the target training personnel in the corresponding monitoring period is obtained, where β1, β2 and β3 represent the weight coefficients of the total intensity value, the standard rate value and the sports fatigue value, respectively, and β1>β2>β3.

[0019] As a further improvement of the present invention, the process of analyzing the body status information is as follows:

[0020] By obtaining the heart rate of the target trainee at each monitoring time point in each training item in each monitoring time period during the corresponding monitoring period, and extracting the heart rate at the initial monitoring time point, the initial heart rate of the target trainee at each monitoring time period during the corresponding monitoring period is taken as xh ix ; According to the formula: , and obtain the heart rate change value R1 of each training item in each monitoring period of the target trainee corresponding to the monitoring period ix , where R1 ix f It is represented as the heart rate of the target trainee at the fth monitoring time point in the xth training item during the i-th monitoring period in the corresponding monitoring cycle, xh * It represents the normal heart rate of the target trainee, η1 and η2 represent the correction factors corresponding to the initial heart rate change value and the normal heart rate change value respectively;

[0021] According to the processing steps of obtaining the heart rate change value of each training item in each monitoring time period of the target training personnel corresponding to the monitoring period, the temperature change value and respiratory rate change value of each training item in each monitoring time period of the target training personnel corresponding to the monitoring period can be obtained in the same way, and they are recorded as R2 ix and R3 ix .

[0022] As a further improvement of the present invention, the process of evaluating and analyzing the physical state is as follows:

[0023] Extract the heart rate change value R1 of each training item in each monitoring period of the target training personnel corresponding to the monitoring period ix , body temperature change value R2 ix And respiratory rate change value R3 ix The value of is normalized according to the formula: , get the physical condition evaluation coefficient STP of the target training personnel in the corresponding monitoring period, where R1 * , R2 * and R3 * They respectively represent the reference heart rate change value, reference body temperature change value and reference respiratory rate change value, β4, β5 and β6 respectively represent the weight coefficients of the heart rate change deviation value, body temperature change deviation value and respiratory rate change deviation value, and β4>β5>β6.

[0024] As a further improvement of the present invention, the process of performing simulation training simulation planning analysis is as follows:

[0025] Extract the training status evaluation coefficient XLP and the physical status evaluation coefficient STP of the target training personnel in the corresponding monitoring period and normalize them according to the formula: , the comprehensive evaluation value TNZ of the target physical training personnel is obtained, where λ1 and λ2 represent the proportional coefficients of the training state evaluation coefficient XLP and the physical state evaluation coefficient, respectively, and λ1>λ2;

[0026] The comprehensive evaluation value of the target physical trainee is matched and analyzed with the comprehensive evaluation status table stored in the simulation cloud database to obtain the comprehensive evaluation grade of the target physical trainee, and each comprehensive evaluation value of the target physical trainee corresponds to a comprehensive evaluation grade, and at the same time, it is matched with the physical fitness state corresponding to the comprehensive evaluation grade to obtain the physical fitness state of the target physical trainee, wherein the physical fitness state includes an excellent state, a good state and a poor state, and the physical fitness state of the target physical trainee is quantified based on the machine learning algorithm to obtain the quantified physical fitness value of the target physical trainee;

[0027] By obtaining the competition items of the target trainees, using simulation technology to construct virtual competition item scenes of the target trainees, and setting the impact values ​​of different levels of difficulty of the virtual competition item scenes, the scene impact value is obtained, and the numerical values ​​of the quantified physical fitness value and the scene impact value are extracted, multiplied by the corresponding weight coefficients and then added to obtain the actual physical fitness value of the target trainees. At the same time, the required physical fitness value corresponding to the competition items is extracted from the simulation cloud database, and the actual physical fitness value of the target trainees and the required physical fitness value are calculated to obtain the physical fitness difference of the target trainees. The physical fitness difference of the target trainees is matched and analyzed with the physical fitness difference matching table stored in the simulation cloud database to obtain the physical fitness difference level of the target trainees, and the physical fitness difference value of each target trainee corresponds to a physical fitness difference level, and it is matched with the personalized plan corresponding to the physical fitness difference level to obtain the personalized plan of the target trainees.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] The present invention arranges trainees in a special training room, collects and analyzes the training status information and physical status information of the trainees in a specific monitoring period in real time, ensures the timeliness and accuracy of the data, and extracts key indicators such as total strength value, standard rate value and sports fatigue value through in-depth analysis of the training status information to obtain the training status evaluation coefficient, thereby achieving a comprehensive evaluation of the training status of the trainees. At the same time, the physical status information is refined, and the dynamic changes of physiological indicators such as heart rate, body temperature and respiratory rate are analyzed to obtain the physical status evaluation coefficient, thereby achieving accurate reflection of the physical status of the trainees.

[0030] Based on the obtained training status assessment coefficient and physical status assessment coefficient, and further combined with the rich historical data and advanced algorithm models stored in the simulation cloud database, simulation training simulation planning analysis is carried out. By matching the comprehensive evaluation status table and physical status, the physical condition of the trainees can be quickly and accurately assessed, and then analyzed with the preset impact value corresponding to the virtual competition project scene constructed by the simulation technology, so as to obtain the actual physical fitness value of the trainees, and analyze and process it with the required physical fitness value corresponding to the competition project of the trainees, so as to realize the accurate matching of personalized training plans according to the physical fitness difference. These plans are in line with the current physical fitness level of the trainees, and can effectively promote their physical fitness improvement, ensure the scientificity and effectiveness of the training, and provide a more scientific and targeted training plan for the trainees, which not only helps to improve the physical fitness level and competitive ability of the trainees, but also protects the physical health of the trainees and reduces injuries caused by improper training. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0032] Figure 1 This is the overall system block diagram of the present invention. DETAILED DESCRIPTION

[0033] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] like Figure 1 As shown, a physical training virtual simulation training system based on machine learning includes: a data acquisition module, a server, a training state analysis module, a body state analysis module, a simulation training planning module, a display terminal and a simulation cloud database;

[0035] The data collection module is used to collect the training status information and physical status information of the target trainee in each monitoring period within the corresponding monitoring period, and send it to the training status analysis module and the physical status analysis module respectively through the server;

[0036] It should be noted that the selection of monitoring cycle is flexible and can be set to one day, one week or one month according to the specific needs of the trainees. Generally speaking, the longer the monitoring cycle, the more comprehensive and accurate the data obtained, which helps to more accurately evaluate the training status and physical reactions of the trainees. At the same time, the monitoring time period refers to the specific time period when the trainees are performing the training program, and the rest time is not included. The trainees are arranged in a special training room, which is equipped with advanced sensors and cameras and other equipment. These devices can capture the status information of the trainees during the training process in real time. The trainees need to exercise according to the pre-set training programs. These programs cover a variety of physical training contents such as strength training, aerobic exercise, flexibility exercises, etc.

[0037] The simulation cloud database is used to store the comprehensive evaluation status table, physical fitness difference matching table and the required physical fitness values ​​corresponding to the participating events;

[0038] The training status analysis module is used to receive the training status information of the target trainee in each monitoring period within the corresponding monitoring period, thereby evaluating and analyzing the training status of the target trainee within the corresponding monitoring period. The specific analysis process is as follows:

[0039] By obtaining the number of training items and the duration of each training item in each monitoring period of the target training personnel, setting the intensity conversion factor corresponding to each training item, and multiplying the duration of each training item by the intensity conversion factor corresponding to each training item, the training intensity value of each training item is obtained, and then the number of training items and the training intensity value of each training item are extracted and summed to obtain the total intensity value of the target training personnel in each monitoring period of the monitoring period, recorded as Y i teity , wherein the intensity conversion factor corresponding to each training item indicates that the duration of each training item can be converted into the training intensity value of each training item. The specific intensity conversion factor is reasonably set by those skilled in the art according to the actual situation;

[0040] By acquiring the video of each training item in each monitoring time period of the target physical trainee corresponding to the monitoring period, and parsing the video of each training item in each monitoring time period of the target physical trainee corresponding to the monitoring period, the image of each monitoring time point in each training item in each monitoring time period of the target physical trainee corresponding to the monitoring period is obtained, and the human body contour of each monitoring time point in each training item in each monitoring time period of the target physical trainee corresponding to the monitoring period is extracted;

[0041] The human body contour of the target trainee at each monitoring time point in each training item in each monitoring time period within the corresponding monitoring period is overlapped and compared with the human body contour of the corresponding previous monitoring time point, thereby obtaining the overlapping area of ​​the human body contour of the target trainee in each training item in each monitoring time period within the corresponding monitoring period, and marking it as the body contour weight face value, setting the reference body contour weight face value, and comparing and analyzing the body contour weight face value with the reference body contour weight face value. When the body contour weight face value is greater than or equal to the reference body contour weight face value, it is judged as standard. When the body contour weight face value is less than the reference body contour weight face value, it is judged as non-standard. The number of times the target trainee is judged as standard and the total number of times he is judged as standard in each training item in each monitoring time period within the corresponding monitoring period are counted, and they are marked as gf respectively. x i and zp x i , and take its value according to the formula: , get the standard rate value Y of the target training personnel in each monitoring period within the corresponding monitoring period i nrvue , where x represents the number of each training item, x=1, 2, 3…n, n represents the total number of training item numbers, i represents the number of each monitoring time period in the monitoring cycle, i=1, 2, 3…m, m represents the total number of each monitoring time period in the monitoring cycle, and a1 and a2 represent the set weight coefficients;

[0042] Obtain the duration between the human body contour of each monitoring time point in each training item in each monitoring time period in the corresponding monitoring period of the target physical trainee and the next monitoring time point of the same human body contour, and calculate the average thereof, thereby obtaining the reaction value of each training item in each monitoring time period in the corresponding monitoring period of the target physical trainee, and calculate the average thereof again to obtain the total reaction value of each monitoring time period in the corresponding monitoring period of the target physical trainee;

[0043] Extract the standard rate value and total reaction value of the target physical trainer in each monitoring period of the corresponding monitoring period, multiply them by the corresponding weight coefficient, and then add them to get the sports fatigue value of the target physical trainer in each monitoring period of the corresponding monitoring period, recorded as Y i efvue ;

[0044] Extract the total intensity value, standard rate value and sports fatigue value of the target physical trainer in each monitoring period within the corresponding monitoring period and normalize them according to the formula: , the training status evaluation coefficient XLP of the target training personnel in the corresponding monitoring period is obtained, where β1, β2 and β3 represent the weight coefficients of the total strength value, the standard rate value and the sports fatigue value respectively, and β1>β2>β3. The weight coefficient is used to balance the weight of each data in the formula calculation, thereby promoting the accuracy of the calculation result;

[0045] The obtained training status evaluation coefficient of the target physical trainee in the corresponding monitoring period is sent to the simulation training planning module;

[0046] The physical condition analysis module is used to receive the physical condition information of the target trainee in each monitoring period within the corresponding monitoring period, thereby evaluating and analyzing the physical condition of the target trainee within the corresponding monitoring period. The specific analysis process is as follows:

[0047] By obtaining the heart rate of the target trainee at each monitoring time point in each training item in each monitoring time period during the corresponding monitoring period, and extracting the heart rate at the initial monitoring time point, the initial heart rate of the target trainee at each monitoring time period during the corresponding monitoring period is taken as xh ix ; According to the formula: , and obtain the heart rate change value R1 of each training item in each monitoring period of the target trainee corresponding to the monitoring period ix , where R1 ix f It is represented as the heart rate of the target trainee at the fth monitoring time point in the xth training item during the i-th monitoring period in the corresponding monitoring cycle, xh * It represents the normal heart rate of the target trainee, η1 and η2 represent the correction factors corresponding to the initial heart rate change value and the normal heart rate change value respectively;

[0048] It should be noted that the initial heart rate change value is the heart rate change value of the heart rate at each monitoring time point compared with the initial heart rate; the normal heart rate change value is the heart rate change value of the heart rate at each monitoring time point compared with its corresponding normal heart rate;

[0049] According to the processing steps of obtaining the heart rate change value of each training item in each monitoring time period of the target training personnel corresponding to the monitoring period, the temperature change value and respiratory rate change value of each training item in each monitoring time period of the target training personnel corresponding to the monitoring period can be obtained in the same way, and they are recorded as R2 ix and R3 ix ;

[0050] Extract the heart rate change value R1 of each training item in each monitoring period of the target training personnel corresponding to the monitoring period ix , body temperature change value R2 ix And respiratory rate change value R3 ix The value of is normalized according to the formula: , get the physical condition evaluation coefficient STP of the target training personnel in the corresponding monitoring period, where R1 * , R2 * and R3 *They represent reference heart rate change value, reference body temperature change value and reference respiratory rate change value respectively, β4, β5 and β6 represent weight coefficients of heart rate change deviation value, body temperature change deviation value and respiratory rate change deviation value respectively, and β4>β5>β6. The weight coefficient is used to balance the weight of each data in the formula calculation, so as to promote the accuracy of the calculation result;

[0051] The obtained physical condition evaluation coefficient of the target training personnel in the corresponding monitoring period is sent to the simulation training planning module;

[0052] The simulation training planning module is used to receive the training status evaluation coefficient and the physical status evaluation coefficient of the target training personnel in the corresponding monitoring period, and thus conduct simulation training simulation planning analysis. The specific analysis process is as follows:

[0053] Extract the training status evaluation coefficient XLP and the physical status evaluation coefficient STP of the target training personnel in the corresponding monitoring period and normalize them according to the formula: , the comprehensive evaluation value TNZ of the target physical training personnel is obtained, where λ1 and λ2 represent the proportional coefficients of the training state evaluation coefficient XLP and the physical state evaluation coefficient respectively, and λ1>λ2. The proportional coefficient is used to balance the weight of each data in the formula calculation, thereby promoting the accuracy of the calculation result;

[0054] The comprehensive evaluation value of the target physical trainee is matched and analyzed with the comprehensive evaluation status table stored in the simulation cloud database to obtain the comprehensive evaluation grade of the target physical trainee, and each comprehensive evaluation value of the target physical trainee corresponds to a comprehensive evaluation grade, and at the same time, it is matched with the physical fitness state corresponding to the comprehensive evaluation grade to obtain the physical fitness state of the target physical trainee, wherein the physical fitness state includes an excellent state, a good state and a poor state, and the physical fitness state of the target physical trainee is quantified based on the machine learning algorithm to obtain the quantified physical fitness value of the target physical trainee;

[0055] It should be noted that the comprehensive evaluation status table is a two-dimensional table, one column of which is the range of the comprehensive evaluation value, and the other column is the corresponding comprehensive evaluation level. For example, the comprehensive evaluation value can be divided into several intervals, and each interval corresponds to a comprehensive evaluation level. In this way, when the comprehensive evaluation value of the target trainee falls into a certain interval, the comprehensive evaluation level can be quickly determined. The construction of the comprehensive evaluation status table is based on the processing of a large amount of historical training data, and the specific settings are set by technicians in this field according to actual conditions;

[0056] By obtaining the competition items of the target trainee, using simulation technology to construct a virtual competition item scene of the competition item of the target trainee, and setting the impact value of different levels of difficulty of the virtual competition item scene, thereby obtaining the scene impact value, extracting the numerical values ​​of the quantified physical fitness value and the scene impact value, multiplying them by the corresponding weight coefficients and then adding them to obtain the actual physical fitness value of the target trainee, and at the same time extracting the required physical fitness value corresponding to the competition item from the simulation cloud database, and performing a difference calculation between the actual physical fitness value of the target trainee and the required physical fitness value to obtain the physical fitness difference value of the target trainee, matching and analyzing the physical fitness difference value of the target trainee with the physical fitness difference matching table stored in the simulation cloud database to obtain the physical fitness difference level of the target trainee, and each target trainee's physical fitness difference value corresponds to a physical fitness difference level, and at the same time matching it with the personalized plan corresponding to the physical fitness difference level to obtain the personalized plan of the target trainee, and sending it to the display terminal for display notification;

[0057] It should be noted that the personalized plan includes specific training content based on the current physical condition of the target trainees, training intensity, training frequency, diet combination and recovery and rest. The specific training content includes strength training, endurance training, flexibility training, and coordination training. The training intensity includes weight, repetitions, number of sets, and speed. The training frequency includes the number of training sessions per week and the duration of each training session. Diet combination includes staple food combination and meat and vegetable combination. Recovery and rest includes recovery and rest time.

[0058] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A physical training virtual simulation training system based on machine learning, including a data acquisition module, a server, a display terminal and a simulation cloud database, characterized in that: It also includes a training status analysis module, a physical status analysis module and a simulation training planning module; The data acquisition module is used to collect the training status information and physical status information of the target trainee in each monitoring time period within the corresponding monitoring period, and send the information to the training status analysis module and the physical status analysis module respectively through the server; The training status analysis module is used to receive the training status information of the target trainee in each monitoring time period in the corresponding monitoring period, and analyze it to obtain the total strength value, standard rate value and sports fatigue value of the target trainee in each monitoring time period in the corresponding monitoring period, thereby evaluating and analyzing the training status of the target trainee in the corresponding monitoring period, obtaining the training status evaluation coefficient of the target trainee in the corresponding monitoring period, and sending it to the simulation training planning module; The process of analyzing the training status information is as follows: By obtaining the number of training items and the duration of each training item in each monitoring time period of the target physical training personnel in the corresponding monitoring period, setting the intensity conversion factor corresponding to each training item, and multiplying the duration of each training item by the intensity conversion factor corresponding to each training item, the training intensity value of each training item is obtained, and the numerical values ​​of the number of training items and the training intensity value of each training item are extracted and summed to obtain the total intensity value of the exercise in each monitoring time period in the corresponding monitoring period of the target physical training personnel; By acquiring the video of each training item in each monitoring time period of the target physical trainee corresponding to the monitoring period, and parsing the video of each training item in each monitoring time period of the target physical trainee corresponding to the monitoring period, the image of each monitoring time point in each training item in each monitoring time period of the target physical trainee corresponding to the monitoring period is obtained, and the human body contour of each monitoring time point in each training item in each monitoring time period of the target physical trainee corresponding to the monitoring period is extracted; The human body contours of the target physical trainees at each monitoring time point in each training item in each monitoring time period within the corresponding monitoring period are overlapped and compared with the human body contours of the corresponding previous monitoring time point, thereby obtaining the overlapped area of ​​the human body contours in each training item in each monitoring time period within the corresponding monitoring period of the target physical trainees, and marking it as the body contour weight face value, setting the reference body contour weight face value, and comparing and analyzing the body contour weight face value with the reference body contour weight face value, when the body contour weight face value is greater than or equal to the reference body contour weight face value, it is determined to be standard, and when the body contour weight face value is less than the reference body contour weight face value, it is determined to be non-standard, and the number of times the target physical trainees are determined to be standard in each training item in each monitoring time period within the corresponding monitoring period and the total number of times they are determined are counted, and according to calculation and processing, the standardization rate value of each monitoring time period within the corresponding monitoring period of the target physical trainees is obtained; Obtain the duration from the human body contour of each monitoring time point in each training item in each monitoring time period within the corresponding monitoring period of the target physical trainee to the next monitoring time point of the same human body contour, and perform mean calculation on the duration, thereby obtaining the reaction value of each training item in each monitoring time period within the corresponding monitoring period of the target physical trainee, and perform mean calculation on the duration again, to obtain the total reaction value of each monitoring time period within the corresponding monitoring period of the target physical trainee, extract the standard rate value and the total reaction value of each monitoring time period within the corresponding monitoring period of the target physical trainee, multiply them by the corresponding weight coefficient respectively, and then add them to obtain the sports fatigue value of each monitoring time period within the corresponding monitoring period of the target physical trainee; The physical state analysis module is used to receive the physical state information of the target trainee in each monitoring time period in the corresponding monitoring period, and analyze it to obtain the heart rate change value, body temperature change value and respiratory rate change value of each training item in each monitoring time period in the corresponding monitoring period of the target trainee, thereby evaluating and analyzing the physical state of the target trainee in the corresponding monitoring period, obtaining the physical state evaluation coefficient of the target trainee in the corresponding monitoring period, and sending it to the simulation training planning module; The simulation training planning module is used to receive the training state evaluation coefficient and the physical state evaluation coefficient of the target trainee in the corresponding monitoring period, thereby performing simulation training simulation planning analysis to obtain a personalized plan for the target trainee.

2. According to the physical training virtual simulation training system based on machine learning in claim 1, it is characterized in that: The process of evaluating and analyzing the training status is as follows: The total intensity value, standard rate value and sports fatigue value of the target training personnel in each monitoring period within the corresponding monitoring period are extracted and normalized and calculated to obtain the training status evaluation coefficient of the target training personnel in the corresponding monitoring period.

3. The physical training virtual simulation training system based on machine learning according to claim 1 is characterized in that: The process of analyzing the body status information is as follows: By obtaining the heart rate of each monitoring time point in each training item in each monitoring time period within the corresponding monitoring cycle of the target physical trainee, and extracting the heart rate at the initial monitoring time point as the initial heart rate of each training item in each monitoring time period within the corresponding monitoring cycle of the target physical trainee, the heart rate change value of each training item in each monitoring time period within the corresponding monitoring cycle of the target physical trainee is obtained according to calculation and processing; According to the processing steps of obtaining the heart rate change value of each training item in each monitoring time period of the target trainee corresponding to the monitoring cycle, the body temperature change value and respiratory rate change value of each training item in each monitoring time period of the target trainee corresponding to the monitoring cycle can be obtained similarly.

4. The physical training virtual simulation training system based on machine learning according to claim 1 is characterized in that: The process of evaluating and analyzing the physical condition is as follows: The heart rate change value, body temperature change value and respiratory rate change value of each training item in each monitoring time period of the target trainee in the corresponding monitoring period are extracted and normalized and calculated to obtain the physical condition assessment coefficient of the target trainee in the corresponding monitoring period.

5. The physical training virtual simulation training system based on machine learning according to claim 1 is characterized in that: The process of simulation training and planning analysis is as follows: Extract the values ​​of the training state evaluation coefficient and the physical state evaluation coefficient of the target physical trainee in the corresponding monitoring period, perform normalization calculation and processing, and obtain the comprehensive evaluation value of the target physical trainee; The comprehensive evaluation value of the target physical trainee is matched and analyzed with the comprehensive evaluation status table stored in the simulation cloud database to obtain the comprehensive evaluation grade of the target physical trainee, and each comprehensive evaluation value of the target physical trainee corresponds to a comprehensive evaluation grade, and at the same time, it is matched with the physical fitness state corresponding to the comprehensive evaluation grade to obtain the physical fitness state of the target physical trainee, wherein the physical fitness state includes an excellent state, a good state and a poor state, and the physical fitness state of the target physical trainee is quantified based on the machine learning algorithm to obtain the quantified physical fitness value of the target physical trainee; By obtaining the competition items of the target trainees, using simulation technology to construct virtual competition item scenes of the target trainees, and setting the impact values ​​of different levels of difficulty of the virtual competition item scenes, the scene impact value is obtained, and the numerical values ​​of the quantified physical fitness value and the scene impact value are extracted, multiplied by the corresponding weight coefficients and then added to obtain the actual physical fitness value of the target trainees. At the same time, the required physical fitness value corresponding to the competition items is extracted from the simulation cloud database, and the actual physical fitness value of the target trainees and the required physical fitness value are calculated to obtain the physical fitness difference of the target trainees. The physical fitness difference of the target trainees is matched and analyzed with the physical fitness difference matching table stored in the simulation cloud database to obtain the physical fitness difference level of the target trainees, and the physical fitness difference value of each target trainee corresponds to a physical fitness difference level, and it is matched with the personalized plan corresponding to the physical fitness difference level to obtain the personalized plan of the target trainees.

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