Running machine and display system-based referee anaerobic judgment training method

Abstract

The invention relates to the technical field of judgment penalty, in particular to a judgment oxygen-free judgment training method based on a treadmill and a display system, and the method comprises the steps: collecting the physiological state of a user as a reference, inducing and generating a dynamic scene through an oxygen-free state, completing the penalty interaction of the user, feeding back the penalty interaction to a display screen in time, and evaluating based on a penalty interaction result. According to the method, the individual resting heart rate and age characteristics are calibrated to generate the personalized anaerobic threshold interval, so that the limitation of traditional unified intensity training is eliminated, the training starting point and load regulation and control better fit the individual physiological characteristics, the training safety and pertinence are ensured, and adaptive anaerobic state induction schemes are provided for referees with different physical ability levels.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of referee penalty, in particular to a referee anaerobic penalty training method based on a treadmill and a display system. BACKGROUND

[0002] In sports events, the accuracy and timeliness of the referee's penalty directly affect the fairness and smoothness of the game. In the actual game environment, the referee needs to quickly handle complex game scenes in high-intensity exercise. The decision-making ability not only depends on professional knowledge reserves, but also is closely related to physiological regulation, attention allocation and stress response in the anaerobic state. However, the existing referee training methods have obvious limitations:

[0003] Traditional penalty training is mainly based on static scene playback or low-intensity simulation, ignoring the influence of high-intensity exercise on physiological state in the real game, resulting in a gap between the training scene and the actual enforcement environment, and it is difficult to effectively improve the decision stability of the referee under anaerobic load. At the same time, the existing training lacks targeted consideration of individual physiological characteristics, and uses uniform training intensity and scene difficulty, which cannot adapt to the physical level and cognitive characteristics of different referees, and is prone to problems such as poor training effect or excessive load.

[0004] In addition, the traditional evaluation method mainly depends on subjective scoring, lacks quantitative analysis of the correlation between physiological state, decision efficiency and scene difficulty, and is difficult to accurately locate the ability short board of the referee under different physiological load, resulting in a lack of scientific basis for training scheme optimization. These problems make it difficult for the existing method to meet the requirements of modern sports events for high-intensity enforcement ability of referees. SUMMARY

[0005] The present application provides a referee anaerobic penalty training method based on a treadmill and a display system to solve the technical problems in the prior art.

[0006] The technical solution of the present application to solve the above technical problems is as follows: a referee anaerobic penalty training method based on a treadmill and a display system, comprising the following steps: the method comprises: collecting the physiological state of the user as a reference, inducing the anaerobic state and generating dynamic scenes, completing the penalty interaction of the user, and feeding back to the display screen in time, and evaluating based on the penalty interaction result, further comprising the following substeps:

[0007] S101, after the initialization speed of the treadmill, the heart rate data in the resting state is obtained based on the blood oxygen sensor worn by the user, and the anaerobic threshold reference interval is generated;

[0008] S102, the speed of the treadmill is gradually increased, the heart rate exceeding the preset threshold is taken as the trigger signal, the game scene matched with the current physiological state is generated, and the speed of the treadmill is adjusted to maintain the heart rate in the anaerobic threshold reference interval;

[0009] S103, the display screen displays content according to a preset match scene, and a user completes a penalty decision through a voice instruction, and the preset standard answer and the penalty decision are compared;

[0010] S104, based on the accuracy and physiological state obtained by comparing the penalty delay, the preset standard answer and the penalty decision, a quantitative report is generated, and a heart rate threshold- treadmill speed mapping curve is used to reversely deduce a penalty ability critical point under different physiological loads.

[0011] In a preferred embodiment, the physiological state of the user is collected as a reference, including:

[0012] The user wears a blood oxygen sensor to collect heart rate data HR in a resting state in real time through wireless transmission raw Calibration is performed to exclude sensor differentiation, and calibrated heart rate data HR is obtained rest According to the age of the user, a reference anaerobic threshold heart rate AnT_HR based on the user is obtained through an anaerobic threshold prediction model, including:

[0013] AnT_HR = 0.85 × (220-Age) - 0.3 × HR rest ;

[0014] Where Age is the age of the user, (220-Age) is the maximum heart rate of the individual, and the coefficient 0.85 is derived from research results in the field of exercise physiology. In numerous experiments on anaerobic exercise heart rate monitoring for people of different ages and different physical qualities, it is found that when an individual performs high-intensity anaerobic exercise, the heart rate will usually reach about 85% of the maximum heart rate reserve. HR rest is the calibrated heart rate data;

[0015] After obtaining the reference anaerobic threshold heart rate AnT_HR of the user, a reference interval of the anaerobic threshold is determined based on the standard deviation SD of three repeated HR rest measurements, that is:

[0016] AnT_range = [AnT_HR-2×SD, AnT_HR+2×SD];

[0017] Where AnT_HR is the reference anaerobic threshold heart rate, and SD is the standard deviation of the HR rest measurement.

[0018] In a preferred embodiment, the generation of the dynamic scene through the induction of the anaerobic state includes:

[0019] After obtaining the anaerobic threshold reference interval AnT_range and the reference anaerobic threshold heart rate AnT_HR, the speed of the treadmill is gradually increased as the induction of the anaerobic state, and the real-time heart rate HRact , the heart rate-based treadmill speed increasing formula is established:

[0020] V t+1 = V t + a x (HR act -AnT_HR);

[0021] wherein V t is the current treadmill speed, V t+1 is the next second treadmill speed, a is the speed adjustment coefficient, which is set according to the age, 0.8 for 20-30 years old and 0.6 for 30-40 years old to ensure safe speed increase, HR act is the real-time collected real-time heart rate, and AnT_HR is the reference anaerobic threshold heart rate;

[0022] When the treadmill gradually increases the speed, and the user's real-time heart rate HR act exceeds the reference anaerobic threshold heart rate AnT_HR, the anaerobic interval locking is triggered to maintain the user's physiological state at this time.

[0023] In a preferred embodiment, the treadmill speed during the anaerobic interval locking has the following settings:

[0024] When HR act < AnT_range, a is positive, and the treadmill speed increases; and when HR act > AnT_range, a is negative, and the treadmill speed decreases to maintain the user's heart rate within the anaerobic threshold reference interval AnT_range.

[0025] In a preferred embodiment, it further comprises:

[0026] The generation of the dynamic scene takes the duration of the heart rate maintained within AnT_range and the real-time heart rate HR act as variables, generates a scene complexity coefficient, generates the number of players, the action speed coefficient, and the penalty interference term based on the scene complexity coefficient;

[0027] The generation of the scene complexity coefficient comprises:

[0028]

[0029] wherein C is the scene complexity coefficient, HR act is the real-time heart rate, AnT_HR is the reference anaerobic threshold heart rate, and T is the duration of the heart rate maintained within AnT_range;

[0030] Further, the generation of the number of players comprises N = 6 + |2C|, the action speed coefficient is S = 1 + 0.3(C-1), and the penalty interference term is D = |C x 1.5|.

[0031] Wherein the number of players is a dynamic number of players, the action speed coefficient is embodied by the scene playing speed ratio, and the penalty interference term is embodied by the number of hidden foul details.

[0032] In a preferred embodiment, the complete user penalty interaction comprises:

[0033] The microphone array collects user voice instructions, synchronously starts a noise reduction algorithm, eliminates treadmill noise interference, and the user voice instructions are converted into recognition pokes after being converted into text. The noise reduction algorithm started is proportional to the scene complexity coefficient, specifically: noise reduction coefficient = 0.5 + 0.1(C-1), the answer of 1 and / or 0 is generated according to the difference between the recognition pokes and the preset standard answer, and the comprehensive efficiency score is calculated according to the weighted scoring model;

[0034] Wherein in the weighted scoring model, the difference between the time when the user voice instruction is issued and the time when the environment is generated is taken as the penalty delay, and the length of time when the microphone collects the user voice instruction is taken as the response delay, including:

[0035]

[0036] Wherein, E is the decision efficiency score, A is the penalty accuracy, A = 1 and / or A = 0, T1 is the response delay, and R is the penalty delay;

[0037] After the decision efficiency score E obtained by the generated quantitative report is marked, the user is maintained in the duration of AnT_range.

[0038] In a preferred embodiment, after obtaining the user decision efficiency score E, according to the positive and negative difference between the decision efficiency score E and the highest value and the lowest value, the following specific settings are also included:

[0039] When the user decision efficiency score E is greater than the highest value, the scene complexity coefficient C is increased;

[0040] When the user decision efficiency score E is less than the lowest value, the scene complexity coefficient C is decreased;

[0041] When the user decision efficiency score E is located in the highest value and the lowest value interval, the scene complexity coefficient C is unchanged.

[0042] In a preferred embodiment, the generation of the quantitative report includes the following settings:

[0043] When the user decision score E is maintained in the highest value and the lowest value interval, the penalty delay-user decision score mapping curve is generated in the quantitative report, and the number of times of the first slope k<0 in the delay-user decision score mapping curve is taken as the critical point of the user penalty ability;

[0044] When the user decision efficiency score E is greater than the maximum value, the scene complexity coefficient C is increased, a penalty delay-user decision score mapping curve is formed, and the number of times of the first slope k=0 in the delay-user decision score mapping curve is taken as a user penalty ability critical point.

[0045] The beneficial effects of the present application are: the personalized anaerobic threshold interval is generated by calibrating the individual resting heart rate and age characteristics, the limitations of traditional uniform intensity training are broken, the training starting point and load control are more in line with individual physiological characteristics, the training safety and pertinence are ensured, and adaptive anaerobic state induction schemes are provided for referees with different physical abilities.

[0046] Based on real-time heart rate and anaerobic duration, the scene complexity is dynamically adjusted to realize accurate matching of physiological load and training difficulty, simulate the linkage environment of "high-intensity exercise-complex scene-rapid decision" in real competition, and make the training effect more easily transferred to the actual law enforcement scene, and strengthen the judgment stability of referees in the stress state.

[0047] Through the heart rate-speed dynamic feedback model, the anaerobic state is maintained in real time, and the scene difficulty is adaptively adjusted to form the control of physiological state monitoring-training intensity adjustment-scene difficulty adaptation, avoid the deviation of training effect caused by physiological fluctuations, and ensure that each training can focus on the improvement of decision-making ability in the anaerobic state. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 The flowchart of the present application is shown. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0050] In the description of the present application, the terms "first", "second" are only used for description purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features limited by "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0051] In the description of the present application, the term "for example" is used to mean "serving as an example, instance, or illustration." Any embodiment described as "for example" in this application is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the application. In the following description, for purposes of explanation, specific details are set forth. It will be apparent to those skilled in the art that the present application can be practiced without the specific details. In other instances, well-known structures and processes are not shown in detail to avoid obscuring the application. Thus, the present application is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0052] The embodiment provides a referee anaerobic judgment method based on a treadmill and a display system, including the following steps: the method includes: collecting physiological state of a user as a reference, inducing an anaerobic state and generating a dynamic scene, completing user judgment interaction, and feeding back to a display screen in a timely manner, and evaluating based on the judgment interaction result, and further including the following sub-steps:

[0053] S101, after the initialization speed of the treadmill, heart rate data in a resting state is acquired based on a blood oxygen sensor worn by the user, and an anaerobic threshold reference interval is generated;

[0054] In order to effectively exclude sensor individual differences in the heart rate calibration step and improve the accuracy of the original heart rate data, the user physiological state is collected as a reference, including:

[0055] The user wears a blood oxygen sensor, and heart rate data HR in a resting state is collected in real time through wireless transmission raw Calibration is performed to exclude sensor differences, and calibrated heart rate data HR is obtained rest According to the age of the user, a reference anaerobic threshold heart rate AnT_HR based on the user is obtained through an anaerobic threshold prediction model, including:

[0056] AnT_HR = 0.85 * (220-Age) - 0.3 * HR rest ;

[0057] Where Age is the age of the user, (220-Age) is the maximum heart rate of the individual, and the coefficient 0.85 is derived from research results in the field of exercise physiology. In numerous anaerobic exercise heart rate monitoring experiments on different age groups and different physical fitness populations, it is found that when an individual performs high-intensity anaerobic exercise, the heart rate will usually reach about 85% of the maximum heart rate reserve, HR rest is the calibrated heart rate data;

[0058] After obtaining the user's individual baseline anaerobic threshold heart rate AnT_HR, based on 3 repeated HR... rest The standard deviation (SD) of the measurements is used to determine the reference interval for the anaerobic threshold, i.e.:

[0059] AnT_range=[AnT_HR-2×SD, AnT_HR+2×SD];

[0060] Where AnT_HR is the baseline anaerobic threshold heart rate, and SD is the heart rate (HR). rest Standard deviation of the measurement.

[0061] Furthermore, the personalized anaerobic threshold calculation model based on age and resting heart rate fully considers individual physiological differences, making the benchmark threshold more closely match the user's actual physiological characteristics. The reference interval is determined by the standard deviation, providing a flexible and scientific range of physiological indicators for the subsequent induction and maintenance of anaerobic state, reducing the limitations of a single threshold.

[0062] S102: The treadmill gradually increases its speed, using the heart rate exceeding a preset threshold as a trigger signal to generate a competition scenario that matches the current physiological state, and adjusts the treadmill speed to maintain the heart rate within the anaerobic threshold reference range.

[0063] A dynamic speed regulation mechanism based on real-time heart rate is used to scientifically induce anaerobic state, avoiding physiological shock to users from sudden speed changes. This includes anaerobic state induction and dynamic scenario generation, comprising:

[0064] After obtaining the anaerobic threshold reference range AnT_range and the baseline anaerobic threshold heart rate AnT_HR, the anaerobic state was induced by gradually increasing speed on the treadmill, and the real-time heart rate HR was used as the data. act Establish a formula for increasing treadmill speed based on heart rate:

[0065] V t+1 =V t +α×(HR act -AnT_HR);

[0066] Among them, V t V represents the current treadmill speed. t+1 The treadmill speed for the next second is set by α, which is the speed adjustment coefficient. This coefficient is set based on age: 0.8 for 20-30 years old and 0.6 for 30-40 years old, ensuring safe acceleration. HR act AnT_HR is the real-time heart rate collected in real time, and AnT_HR is the baseline anaerobic threshold heart rate.

[0067] As the treadmill speed is gradually increased, the user's real-time heart rate (HR) is monitored. act Once the heart rate exceeds the baseline anaerobic threshold AnT_HR, the anaerobic zone is locked to maintain the user's current physiological state.

[0068] To adjust the treadmill speed in real time to stabilize the anaerobic state and prevent excessive fatigue due to an overly high heart rate or negative impact on training effectiveness due to an overly low heart rate, the treadmill speed has the following settings when the anaerobic zone is locked:

[0069] HR act When <AnT_range, α is a positive number, the treadmill speed increases, and at HR act When α is greater than AnT_range, the treadmill speed slows down to maintain the user's heart rate within the anaerobic threshold reference range AnT_range.

[0070] To ensure users remain within the effective anaerobic zone throughout the training process, guarantee the consistency and effectiveness of training intensity, and improve the quality of anaerobic judgment training, the following measures are also included:

[0071] The generation of dynamic scenes is based on the duration of heart rate maintenance within AnT_range and the real-time heart rate HR. act As variables, generate scene complexity coefficients, and based on the scene complexity coefficients, generate the number of players, action speed coefficients, and penalty interference terms;

[0072] The generation of scene complexity coefficients includes:

[0073]

[0074] Where C is the scenario complexity coefficient, HR act AnT_HR is the real-time heart rate, AnT_HR is the baseline anaerobic threshold heart rate, and T is the duration of heart rate maintained within AnT_range.

[0075] Furthermore, the number of players is generated as follows: N = 6 + |2C|, where N is the number of players in the scene, the action speed coefficient is S = 1 + 0.3(C-1), and the penalty interference term is D = |C×1.5|.

[0076] The number of players refers to the number of dynamic players, the action speed coefficient is reflected by the scene playback speed multiplier, and the penalty interference item is reflected by the number of hidden foul details.

[0077] S103: The display screen shows content according to the preset competition scenario. Users can make judgment decisions through voice commands and compare the preset standard answer with the judgment decision.

[0078] The process of completing the user's judgment interaction includes:

[0079] The microphone array collects user voice commands and simultaneously activates a noise reduction algorithm to eliminate treadmill noise interference. The user's voice commands are converted into text to generate a recognition stamp. The noise reduction algorithm activated is proportional to the scene complexity coefficient, specifically the noise reduction coefficient = 0.5 + 0.1(C-1). Based on the difference between the recognition stamp and the preset standard answer, an answer of 1 and / or 0 is generated. The comprehensive efficiency score is calculated based on the weighted scoring model.

[0080] The weighted scoring model uses accuracy and latency as the quantification basis. It introduces the difference between the user's voice command issuance time and the environment's generation time as the penalty latency, and the microphone's recording time for the user's voice command as the response latency, including:

[0081]

[0082] Where E is the decision efficiency score, A is the judgment accuracy, A=1 and / or A=0, T1 is the response delay, and R is the judgment delay;

[0083] After obtaining the decision efficiency score E from the generated quantitative report, the duration for which the user remains in AnT_range is marked.

[0084] S104. Based on the accuracy and physiological state obtained from the comparison of penalty delay, preset standard answer and penalty decision, generate a quantitative report, and deduce the critical point of penalty ability under different physiological loads through the heart rate threshold-treadmill speed mapping curve.

[0085] After obtaining the user's decision efficiency score E, based on the sign of the difference between the decision efficiency score E and the highest and lowest values, the following specific settings are also included:

[0086] When the user's decision-making efficiency score E is greater than the highest value, the scenario complexity coefficient C increases;

[0087] When the user's decision-making efficiency score E is less than the minimum value, the scenario complexity coefficient C decreases;

[0088] When the user's decision-making efficiency score E is between the highest and lowest values, the scenario complexity coefficient C remains unchanged.

[0089] The generation of quantitative reports includes the following settings:

[0090] When the user decision score E remains within the range of the highest and lowest values, a penalty delay-user decision score mapping curve is generated in the quantitative report. The number of times the first slope k < 0 in the delay-user decision score mapping curve is taken as the critical point of the user's penalty ability.

[0091] When the user decision efficiency score E is greater than the maximum value, the scenario complexity coefficient C increases, forming a penalty delay-user decision score mapping curve. The number of times the first slope k=0 in the delay-user decision score mapping curve is taken as the critical point of user penalty ability.

[0092] Furthermore, this application forms an assessment-adjustment-retraining system that specifically addresses users' weaknesses, effectively improves judgment capabilities under anaerobic conditions, provides data support for optimizing personalized training programs, and helps develop targeted reinforcement strategies.

[0093] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0094] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0096] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0098] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0099] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A referee-based anaerobic training assessment method using a treadmill and display system, characterized in that: The method includes: collecting the user's physiological state as a baseline, inducing anaerobic state and generating dynamic scenarios to complete the user's judgment interaction, and providing timely feedback to the display screen; evaluating based on the judgment interaction results; and further includes the following sub-steps: S101. After the treadmill initializes its speed, it acquires resting heart rate data based on the blood oxygen sensor worn by the user and generates an anaerobic threshold reference range. S102: The treadmill gradually increases its speed, using the heart rate exceeding a preset threshold as a trigger signal to generate a competition scenario that matches the current physiological state, and adjusts the treadmill speed to maintain the heart rate within the anaerobic threshold reference range. S103: The display screen shows content according to the preset competition scenario. Users can make judgment decisions through voice commands and compare the preset standard answer with the judgment decision. S104. Based on the accuracy and physiological state obtained from the comparison of penalty delay, preset standard answer and penalty decision, generate a quantitative report, and deduce the critical point of penalty ability under different physiological loads through the heart rate threshold-treadmill speed mapping curve.

2. The referee's anaerobic training judgment method based on a treadmill and display system according to claim 1, characterized in that, The collection of user physiological states as a benchmark includes: Users wear a blood oxygen sensor to wirelessly collect resting heart rate data (HR) in real time. raw Perform calibration to eliminate sensor differences and obtain calibrated heart rate data (HR). rest Based on the user's age, an anaerobic threshold heart rate (AnT_HR) is obtained using an anaerobic threshold prediction model, including: AnT_HR=0.85×(220-Age)-0.3×HR rest ; Where Age is the user's age, (220-Age) is the individual's maximum heart rate, and HR is... rest For calibrated heart rate data; After obtaining the user's individual baseline anaerobic threshold heart rate AnT_HR, based on 3 repeated HR... rest The standard deviation (SD) of the measurements is used to determine the reference interval for the anaerobic threshold, i.e.: AnT_range=[AnT_HR-2×SD, AnT_HR+2×SD]; Where AnT_HR is the baseline anaerobic threshold heart rate, and SD is the heart rate (HR). rest Standard deviation of the measurement.

3. The referee's anaerobic training judgment method based on a treadmill and display system according to claim 2, characterized in that, The process of inducing an anaerobic state and generating dynamic scenarios includes: After obtaining the anaerobic threshold reference range AnT_range and the baseline anaerobic threshold heart rate AnT_HR, the anaerobic state was induced by gradually increasing speed on the treadmill, and the real-time heart rate HR was used as the data. act Establish a formula for increasing treadmill speed based on heart rate: V t+1 =V t +α×(HR act -AnT_HR); Among them, V t V represents the current treadmill speed. t+1 The treadmill speed for the next second, α is the speed adjustment coefficient, set according to age, HR act AnT_HR is the real-time heart rate collected in real time, and AnT_HR is the baseline anaerobic threshold heart rate. As the treadmill speed is gradually increased, the user's real-time heart rate (HR) is monitored. act Once the heart rate exceeds the baseline anaerobic threshold AnT_HR, the anaerobic zone is locked to maintain the user's current physiological state.

4. The referee's anaerobic training judgment method based on a treadmill and display system according to claim 3, characterized in that, When the anaerobic zone is locked, the treadmill speed is set as follows: HR act When <AnT_range, α is a positive number, the treadmill speed increases, and at HR act When α is greater than AnT_range, the treadmill speed slows down to maintain the user's heart rate within the anaerobic threshold reference range AnT_range.

5. The referee's anaerobic training judgment method based on a treadmill and display system according to claim 4, characterized in that, Also includes: The generation of dynamic scenes is based on the duration of heart rate maintenance within AnT_range and the real-time heart rate HR. act As variables, generate scene complexity coefficients, and based on the scene complexity coefficients, generate the number of players, action speed coefficients, and penalty interference terms; The generation of scene complexity coefficients includes: Where C is the scenario complexity coefficient, HR act AnT_HR is the real-time heart rate, AnT_HR is the baseline anaerobic threshold heart rate, and T is the duration of heart rate maintained within AnT_range. The number of players refers to the number of dynamic players, the action speed coefficient is reflected by the scene playback speed multiplier, and the penalty interference item is reflected by the number of hidden foul details.

6. The referee's anaerobic training judgment method based on a treadmill and display system according to claim 1, characterized in that, The process of completing the user's judgment interaction includes: The microphone array collects user voice commands and simultaneously activates a noise reduction algorithm to eliminate treadmill noise interference. The user's voice commands are converted into text to generate a recognition stamp. The noise reduction algorithm activated is proportional to the scene complexity coefficient. Based on the difference between the recognition stamp and the preset standard answer, an answer of 1 and / or 0 is generated. The comprehensive efficiency score is calculated based on the weighted scoring model. The weighted scoring model uses accuracy and latency as the quantification basis. It introduces the difference between the user's voice command issuance time and the environment's generation time as the penalty latency, and the microphone's recording time for the user's voice command as the response latency, including: Where E is the decision efficiency score, A is the judgment accuracy, A=1 and / or A=0, T1 is the response delay, and R is the judgment delay; After obtaining the decision efficiency score E from the generated quantitative report, the duration for which the user remains in AnT_range is marked.

7. The referee's anaerobic training judgment method based on a treadmill and display system according to claim 6, characterized in that, After obtaining the user's decision efficiency score E, based on the sign of the difference between the decision efficiency score E and the highest and lowest values, the following specific settings are also included: When the user's decision-making efficiency score E is greater than the highest value, the scenario complexity coefficient C increases; When the user's decision-making efficiency score E is less than the minimum value, the scenario complexity coefficient C decreases; When the user's decision-making efficiency score E is between the highest and lowest values, the scenario complexity coefficient C remains unchanged.

8. The referee's anaerobic training judgment method based on a treadmill and display system according to claim 7, characterized in that, The generation of quantitative reports includes the following settings: When the user decision score E remains within the range of the highest and lowest values, a penalty delay-user decision score mapping curve is generated in the quantitative report. The number of times the first slope k < 0 in the delay-user decision score mapping curve is taken as the critical point of the user's penalty ability. When the user decision efficiency score E is greater than the maximum value, the scenario complexity coefficient C increases, forming a penalty delay-user decision score mapping curve. The number of times the first slope k=0 in the delay-user decision score mapping curve is taken as the critical point of user penalty ability.

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