Intelligent training method and system for virtual dance training

Abstract

The application discloses an intelligent training method and system for virtual dance training, and relates to the technical field of intelligent training.The application comprises the following steps: step 1, pairing analysis of dance students and instructors; step 2, dance student action analysis; and step 3, dance training optimization suggestion processing.The application matches the dance students with suitable instructors, thereby reducing the influence of the excessively large body size between the dance students and the dance instructors, reducing the action indicators that the dance students force to reach in order to meet the dance instructors, and thereby reducing the incidence of problems caused by injuries to the dance students.The application specifically analyzes the problem types of the dance students at each problem monitoring time point, divides the action deviation problems of the dance students into rhythm error problems and action error problems, facilitates the adjustment of the dance students according to the actual error problems, and thereby improves the learning efficiency of the dance students.

Description

Technical Field

[0001] This invention relates to the field of intelligent training technology, specifically to an intelligent training method and system for virtual dance training. Background Technology

[0002] In traditional dance teaching, the main reliance is on on-site guidance from instructors and repeated practice by students. Students not only cannot receive real-time suggestions for correcting their movements, resulting in low learning efficiency, but also face limited resources of professional dance instructors and expensive one-on-one training. Therefore, it is necessary to research an intelligent training method for virtual dance training to save on training costs and improve learning efficiency.

[0003] Existing technologies, such as the invention patent application with publication number CN114463148A, disclose a method and system for evaluating staggered tutoring in dance and physical training. This method includes a device, a data acquisition module, a transmission module, a cloud platform, a control terminal, and a user terminal. The technical solution of this invention solves the problems of untimely teaching and low teaching quality and efficiency caused by teaching environment, human factors, and teacher shortages in existing teaching models. Another example is the invention patent application with publication number CN113521711A, which discloses a dance training auxiliary system and method. This system includes: receiving editing operations on dance movement segments to obtain new dance movement combinations; matching and analyzing the new dance movement combinations with the music to determine if errors exist. This invention allows students to recombine individual movements from the dance they are currently learning, conveniently obtaining dance movements suitable for training, helping students improve their proficiency in each individual movement, and increasing the enjoyment of training.

[0004] As can be seen from the above solutions, current intelligent training methods for virtual dance training lack sufficient attention to the intelligent selection of suitable dance instructors. The body types and other data of dance students and instructors often do not match. When there is a significant difference in body type, dance students not only struggle to achieve the movement indicators set by the instructor, but forcing them to achieve these indicators can easily cause injury. Furthermore, when analyzing the movement deviations between dance students and instructors, there are often instances where the dance student's movements are correct but are frequently misjudged as incorrect due to the large difference in body type, thus discouraging the dance student's enthusiasm. Additionally, there is a lack of emphasis on intelligently judging whether the dance student's problem is a movement error or a rhythmic error. Traditional virtual dance training methods often only identify movement errors but lack the ability to judge whether the dance student has rhythmic errors, thereby reducing the learning efficiency of the dance student. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent training method and system for virtual dance training, which solves the problems existing in the background art.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The first aspect of the present invention provides an intelligent training method for virtual dance training, including: Step 1. Pairing analysis between dance students and instructors: After the dance students put on the corresponding positions of each training equipment on their bodies, they perform each movement according to the voice prompts, obtain the spatial relative coordinates of each training equipment belonging to the dance students in each movement, and analyze the suitable instructors for the dance students accordingly.

[0007] Step 2. Dance student movement analysis: When dance students are training with their assigned instructors, the relative coordinates of the dance space of each training device are obtained at each monitoring time point, and the monitoring time points of each problem of the dance students are analyzed.

[0008] Step 3. Processing Dance Training Optimization Suggestions: Analyze the problem types at each monitoring time point for dance students, thereby filtering out monitoring time points for each beat problem and each movement problem, and reminding dance students to adjust their movements.

[0009] A second aspect of the present invention provides an intelligent training system for performing the intelligent training method for virtual dance training, comprising: a dance student and instructor pairing analysis module, used to obtain the spatial relative coordinates of each training device belonging to the dance student in each movement after the dance student wears each training device on the corresponding position on the body and performs each movement according to voice prompts, and analyze the suitable instructor for the dance student based on this.

[0010] The dance student movement analysis module is used to obtain the relative coordinates of the dance space of each training device at each monitoring time point when the dance student is training with the corresponding suitable instructor, and to analyze the monitoring time points of various problems of the dance student.

[0011] The dance training optimization suggestion processing module is used to analyze the problem types at various monitoring time points of dance students, thereby filtering the monitoring time points of each beat problem and each movement problem of dance students, and reminding dance students to adjust their movements.

[0012] The beneficial effects of the present invention are as follows: (1) Step 1 of the present invention: dance student and instructor pairing analysis, after the dance student puts each training equipment on the corresponding position on the body and performs each movement according to the voice prompt, the dance student is intelligently matched with the appropriate instructor based on the X-axis, Y-axis and Z-axis movement coordinates when the dance student performs each movement, thereby reducing the influence of the large body size between the dance student and the dance instructor, reducing the incidence of dance student injury caused by the dance student forcibly reaching the movement indicators performed by the dance instructor, and reducing the incidence of the problem of the dance student frequently misjudging the movement error due to the large body size difference when analyzing the movement deviation between the dance student and the dance instructor, thereby improving the accuracy of the judgment.

[0013] (2) Step 2 of the present invention: dance student movement analysis. By analyzing whether the dance student has made excessive deviations in the dance movements, it is convenient to make suggestions to the dance student in the future.

[0014] (3) Step 3 of the present invention: Dance training optimization suggestion processing. By specifically analyzing the problem types at each problem monitoring time point of the dance students, the movement deviation problems of the dance students are divided into rhythm error problems and movement error problems, so that the dance students can make adjustments according to the actual error problems, thereby improving the learning efficiency of the dance students. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0017] Figure 2 This is a schematic diagram of the system modules of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Reference Figure 1As shown, the first aspect of the present invention provides an intelligent training method for virtual dance training, including: Step 1. Pairing analysis between dance students and instructors: After the dance student puts each training equipment on the corresponding position on their body, they perform each movement according to the voice prompts, obtain the spatial relative coordinates of each training equipment belonging to the dance student in each movement, and analyze the suitable instructor for the dance student based on this.

[0020] It should be noted that the various training devices, such as various knee pads, arm pads, headbands, etc., have built-in sensors and are worn on the joints and forehead.

[0021] It should also be noted that the aforementioned movements include: standing normally, raising both arms vertically, and raising both arms parallel to each other.

[0022] In a specific embodiment of the present invention, the method for obtaining the spatial relative coordinates of each training device belonging to the dance student in each movement is as follows: The target training device and all other training devices are obtained from a local database. The spatial geographic coordinates of the target training device belonging to the dance student in each movement are obtained. A spatial rectangular coordinate system is constructed with the spatial geographic coordinates of the target training device belonging to the dance student as the origin. The spatial relative coordinates of the target training device belonging to the dance student in each movement are marked as (0,0,0). The spatial coordinates of the other training devices belonging to the dance student in each movement relative to the target training device are obtained in the spatial rectangular coordinate system using a three-dimensional spatial sensor, and these coordinates are marked as the spatial relative coordinates of the other training devices belonging to the dance student in each movement.

[0023] It should be noted that the local database is used to store the target training device and all other training devices, the spatial relative coordinates of each training instructor and each training device in each action, the action deviation coefficient threshold, the comparison spatial relative coordinates of each training device of the matching instructor at each monitoring time point, the allowable deviation value of the X-axis action corresponding to each action similarity coefficient interval at each monitoring time point, and the time period for rhythm problem analysis at each problem monitoring time point.

[0024] It should also be noted that the target training device is the one worn on the forehead, while the other training devices are those worn on the joints.

[0025] Summarize the spatial relative coordinates of each training device belonging to a dance student for each movement.

[0026] In one specific embodiment, the method for obtaining the spatial geographic coordinates of the target training device to which the dance student belongs in each movement is as follows: the spatial geographic coordinates of the target training device to which the dance student belongs in each movement are obtained through a spatial positioning sensor.

[0027] In one specific embodiment, the method for establishing the spatial rectangular coordinate system is as follows: the target training device has a built-in micro gyroscope, and obtains the north direction in the vertical direction, the north direction in the horizontal direction, and the east direction from the gyroscope. The north direction in the vertical direction is taken as the positive Z-axis direction of the spatial rectangular coordinate system, the north direction in the horizontal direction is taken as the positive X-axis direction of the spatial rectangular coordinate system, and the east direction in the horizontal direction is taken as the positive Y-axis direction of the spatial rectangular coordinate system.

[0028] In a specific embodiment of the present invention, the method for analyzing the matching instructors for dance students is as follows: obtain the spatial relative coordinates of each training instructor and their training equipment in each movement from the local database, and analyze the movement similarity coefficient between the dance student and each training instructor based on the spatial relative coordinates of each training equipment to which the dance student belongs in each movement.

[0029] The instructor with the highest similarity coefficient in movement will be the appropriate instructor for the dance student.

[0030] In a specific embodiment of the present invention, the method for analyzing the similarity coefficient of movements between dance students and their instructors is as follows: based on the spatial relative coordinates of the training equipment to which the dance student belongs in each movement, the X-axis relative coordinate values ​​of the spatial positions of the training equipment to which the dance student belongs in each movement are extracted. Y-axis relative coordinate value Relative coordinates of the Z-axis , where n represents the number of each training device. m is a positive integer greater than 2, and i represents the number of each action. j is a positive integer greater than 2.

[0031] Based on the spatial relative coordinates of each training device used by each instructor during each movement, extract the X-axis relative coordinate values ​​of each training device used by each instructor during each movement. Y-axis relative coordinate value Relative coordinates of the Z-axis Where p represents the number of each training instructor. q is a positive integer greater than 2.

[0032] Calculate the similarity coefficient between the dance students and each training instructor. , where e represents the natural constant.

[0033] Step 1 of this invention involves pairing and analyzing dance students with their instructors. After the dance student wears the training equipment to the corresponding positions on their body and performs each movement according to voice prompts, the system intelligently matches the dance student with a suitable instructor based on the X-axis, Y-axis, and Z-axis coordinates of the movement. This reduces the impact of large body size differences between the dance student and the instructor, lowers the incidence of injuries caused by the dance student forcibly trying to meet the instructor's movement targets, and reduces the occurrence of frequent misjudgments of correct movements due to large body size differences when analyzing movement deviations between the dance student and the instructor, thereby improving the accuracy of the judgment.

[0034] Step 2. Dance student movement analysis: When dance students are training with their assigned instructors, the relative coordinates of the dance space of each training device are obtained at each monitoring time point, and the monitoring time points of each problem of the dance students are analyzed.

[0035] In one specific embodiment, the method for obtaining the relative coordinates of the dance space of each training device at each monitoring time point of the dance student is as follows: based on the method for obtaining the relative coordinates of the space of each training device to which the dance student belongs at each movement, the relative coordinates of the dance space of each training device at each monitoring time point of the dance student are obtained in the same way.

[0036] In a specific embodiment of the present invention, the specific analysis method for analyzing the monitoring time points of various problems of dance students is as follows: based on the relative coordinates of the dance space of each training device at each monitoring time point, the movement deviation coefficient of the dance students at each monitoring time point is analyzed.

[0037] The motion deviation coefficient threshold is obtained from the local database. If the motion deviation coefficient of a dance student at a certain monitoring time point is greater than the motion deviation coefficient threshold, the monitoring time point is marked as a problem monitoring time point, thereby filtering the problem monitoring time points of the dance students.

[0038] In a specific embodiment of the present invention, the method for analyzing the movement deviation coefficients of dance students at each monitoring time point is as follows: The comparative spatial relative coordinates of each training device at each monitoring time point are obtained from the local database, and the X-axis comparative spatial relative coordinates of each training device at each monitoring time point are extracted. , where t represents the number of each monitoring time point. s is a positive integer greater than 2.

[0039] Based on the dance space relative coordinates of the dance students at each monitoring time point, the X-axis dance space relative coordinates of the dance students at each monitoring time point of the training equipment are extracted. .

[0040] Based on the similarity coefficient between the dance students and each training instructor, the similarity coefficient between the dance students and the suitable instructor is extracted.

[0041] The X-axis movement allowable deviation values ​​corresponding to each movement similarity coefficient interval at each monitoring time point are obtained from the local database, and mapped to obtain the X-axis movement allowable deviation values ​​between the dance student and the assigned instructor at each monitoring time point. .

[0042] It should be noted that the larger the similarity coefficient of the movements, the smaller the allowable deviation value of the X-axis movements at each monitoring time point. The larger the similarity coefficient of the movements, the more similar the body shape and other data between the dance student and the suitable instructor are. When the body shape and other data are more similar, the movements should also be more similar, and the allowable deviation value of the movement data will be smaller.

[0043] Calculate the X-axis motion deviation coefficient of dance students at each monitoring time point. .

[0044] Similarly, calculate the Y-axis motion deviation coefficient of the dance student at each monitoring time point. and Z-axis motion deviation coefficient Calculate the movement deviation coefficient of dance students at each monitoring time point. .

[0045] Step 2 of this invention: Dance student movement analysis. By analyzing whether the dance students have made excessive deviations in their dance movements, it is easier to make suggestions to the dance students in the future.

[0046] Step 3. Processing Dance Training Optimization Suggestions: Analyze the problem types at each monitoring time point for dance students, thereby filtering out monitoring time points for each beat problem and each movement problem, and reminding dance students to adjust their movements.

[0047] In one specific embodiment, the specific screening method for the monitoring time points of each beat problem and each movement problem of the dance student is as follows: if the problem type of a certain monitoring time point of the dance student is a beat error, then the monitoring time point of the problem is marked as a beat problem monitoring time point; otherwise, the monitoring time point of the problem is marked as a movement problem monitoring time point, thereby screening the monitoring time points of each beat problem and each movement problem of the dance student.

[0048] In a specific embodiment of the present invention, the specific analysis method for analyzing the problem types of dance students at each problem monitoring time point is as follows: based on the relative coordinates of the dance space of each training device at each monitoring time point, the relative coordinates of the dance space of each training device at each problem monitoring time point are extracted, and it is determined whether the dance student has a rhythm error at each problem monitoring time point. If the dance student has a rhythm error at a certain problem monitoring time point, then the rhythm error is taken as the problem type of that problem monitoring time point; otherwise, the movement error is taken as the problem type of that problem monitoring time point, and the problem types of the dance student at each problem monitoring time point are summarized.

[0049] In a specific embodiment of the present invention, the method for determining whether a dance student has a beat error at each problem monitoring time point is as follows: obtain the beat problem analysis time period for each problem monitoring time point from the local database.

[0050] Based on the comparative spatial relative coordinates of each training device at each monitoring time point, the comparative spatial relative coordinates of each training device at each monitoring time point are extracted within the beat problem analysis time period of each problem monitoring time point.

[0051] It should be noted that the time period for rhythm problem analysis at each monitoring time point is as follows: for example, if a monitoring time point is 1:00 in a dance song, the corresponding time period for rhythm problem analysis is 0:58 to 1:02; if a monitoring time point is 2:00 in a dance song, the corresponding time period for rhythm problem analysis is 1:59 to 2:01. Since there are fast and slow movements in the dance, the duration of the time period for rhythm problem analysis at different monitoring time points is not consistent and is set by the R&D personnel according to the actual situation of each dance song.

[0052] It should also be noted that if the target movement deviation coefficient of a dance student at a certain problem monitoring time point is less than the movement deviation coefficient threshold, it means that the dance student's movement at that problem monitoring time point is more similar to the monitoring time point of the maximum movement deviation coefficient of the matching instructor within the beat problem analysis period, indicating that the dance student has problems with rushing or slowing the beat.

[0053] Based on the relative coordinates of the dance space of each training device at each problem monitoring time point, and based on the method of analyzing the movement deviation coefficient of the dance student at each monitoring time point, the movement deviation coefficient between the dance student and the matching instructor at each problem monitoring time point and at each monitoring time point within the beat problem analysis period is calculated similarly. The maximum movement deviation coefficient between the dance student and the matching instructor at each problem monitoring time point and within the beat problem analysis period is extracted and used as the target movement deviation coefficient of the dance student at each problem monitoring time point.

[0054] If the target movement deviation coefficient of a dance student is less than the movement deviation coefficient threshold at a certain problem monitoring time point, it is determined that the dance student has a rhythm error at that problem monitoring time point.

[0055] In a specific embodiment of the present invention, the method for reminding dance students to adjust their movements is as follows: the problem types of each problem monitoring time point of the dance student are sent to the dance student's display screen; based on the dance space relative coordinates of each training device at each monitoring time point, the dance space relative coordinates of each training device at each beat problem monitoring time point and each movement problem monitoring time point of the dance student are extracted and visualized on the dance student's display screen.

[0056] Based on the comparative spatial relative coordinates of each training device at each monitoring time point, the comparative spatial relative coordinates of each training device at each movement problem monitoring time point are extracted and visualized on the dance student's display screen, recommending that the dance student perform fixed movement practice according to the corresponding movement.

[0057] Based on the movement deviation coefficients between the dance students and their assigned instructors at each beat problem monitoring time point within the beat problem analysis period, the monitoring time point with the maximum movement deviation coefficient between the dance students and their assigned instructors at each beat problem monitoring time point within the beat problem analysis period is extracted. This time point is then used as the reminder monitoring time point for the dance instructor at each beat problem monitoring time point, reminding the dance students that the movements performed at each beat problem monitoring time point are the same movements performed by the dance instructor at the reminder monitoring time point.

[0058] Step 3 of this invention, "Dance Training Optimization Suggestion Processing," involves specifically analyzing the problem types at various monitoring time points for dance learners. This categorizes dance learners' movement deviations into rhythm errors and movement errors, allowing learners to adjust based on the actual errors and thus improve their learning efficiency.

[0059] Reference Figure 2 As shown, a second aspect of the present invention provides an intelligent training system for implementing the intelligent training method for virtual dance training, comprising: a dance student and instructor pairing analysis module, a dance student movement analysis module, a dance training optimization suggestion processing module, and a local database.

[0060] It should be noted that the dance student and tutor pairing analysis module is connected to the dance student movement analysis module, the dance student movement analysis module is connected to the dance training optimization suggestion processing module, and the local database is connected to the dance student and tutor pairing analysis module, the dance student movement analysis module, and the dance training optimization suggestion processing module.

[0061] The dance student and instructor pairing analysis module is used to obtain the spatial relative coordinates of each training device belonging to the dance student in each movement after the dance student puts on the corresponding position on the body and performs each movement according to the voice prompts, and to analyze the suitable instructor for the dance student based on this.

[0062] The dance student movement analysis module is used to obtain the relative coordinates of the dance space of each training device at each monitoring time point when the dance student is training with the corresponding matching instructor, and to analyze the monitoring time points of each problem of the dance student.

[0063] The dance training optimization suggestion processing module is used to analyze the problem types at each problem monitoring time point of the dance students, thereby filtering the problem monitoring time points of each beat and each movement of the dance students, and reminding the dance students to adjust their movements.

[0064] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. An intelligent training method for virtual dance training, characterized in that, The method comprises the following steps: Step 1: Analysis of pairing of dance students and instructors: after the dance students wear each training equipment to the corresponding position on the body, the dance students perform each action according to the voice prompt, the spatial relative coordinates of each training equipment of the dance students in each action are obtained, and the adaptation instructor of the dance student is analyzed according to the spatial relative coordinates of each training equipment of the dance students in each action; The specific analysis method for analyzing the adaptation instructor of the dance student is: Obtain the spatial relative coordinates of each training instructor and each training equipment in each action from the local database, analyze the action similarity coefficient of the dance student and each training instructor according to the spatial relative coordinates of each training equipment of the dance student in each action; The training instructor with the largest action similarity coefficient is taken as the adaptation instructor of the dance student; The specific analysis method for analyzing the action similarity coefficient of the dance student and each training instructor is: extracting X-axis relative coordinate values of the space positions of the training equipment to which the dance trainees belong in each motion , Y-axis relative coordinate values and Z-axis relative coordinate values , wherein n represents the number of the training equipment, m is a positive integer greater than 2, i represents the number of each motion, and j is a positive integer greater than 2; Based on the spatial relative coordinates of each training device used by each instructor during each movement, extract the X-axis relative coordinate values ​​of each training device used by each instructor during each movement. Y-axis relative coordinate value Relative coordinates of the Z-axis Where p represents the number of each training instructor. q is a positive integer greater than 2; Computing motion similarity coefficients of dance students with each training instructor where e is expressed as a natural constant; Step 2: Analysis of dance student action: when the dance student follows the corresponding adaptation instructor to perform dance training, the dance spatial relative coordinates of each training equipment of the dance student at each monitoring time point are obtained, and each problem monitoring time point of the dance student is analyzed; The specific analysis method for analyzing each problem monitoring time point of the dance student is: According to the dance spatial relative coordinates of each training equipment of the dance student at each monitoring time point, the action deviation coefficient of the dance student at each monitoring time point is analyzed; Obtain the action deviation coefficient threshold from the local database, if the action deviation coefficient of the dance student at a monitoring time point is greater than the action deviation coefficient threshold, mark the monitoring time point as a problem monitoring time point, thereby screening each problem monitoring time point of the dance student; Step 3: Processing of dance training optimization suggestion: analyze the problem type of each problem monitoring time point of the dance student, thereby screening each rhythm problem monitoring time point and each action problem monitoring time point of the dance student, and reminding the dance student to adjust the action. 2.The intelligent training method for virtual dance training according to claim 1, wherein, The specific analysis method for obtaining the spatial relative coordinates of each training equipment of the dance student in each action is: Obtain the target training equipment and each remaining training equipment from the local database, obtain the spatial geographic coordinates of the target training equipment of the dance student in each action, and take the spatial geographic coordinates of the target training equipment of the dance student as the origin to form a spatial rectangular coordinate system, mark the spatial relative coordinates of the target training equipment of the dance student in each action as (0, 0, 0), obtain the spatial coordinates of each remaining training equipment of the dance student in each action relative to the target training equipment in the spatial rectangular coordinate system through a three-dimensional space sensor, and mark them as the spatial relative coordinates of each remaining training equipment of the dance student in each action; Sum up the spatial relative coordinates of each training equipment of the dance student in each action. 3.The intelligent training method for virtual dance training according to claim 1, wherein, The specific analysis method for analyzing the action deviation coefficient of the dance student at each monitoring time point is: Obtain the comparison spatial relative coordinates of each training device at each monitoring time point from the local database, and extract the X-axis comparison spatial relative coordinates of each training device at each monitoring time point , wherein t represents the number of each monitoring time point, s is a positive integer greater than 2. According to the relative coordinates of the dance space of the dance trainee at each training device at each monitoring time point, the relative coordinates of the X-axis dance space of the dance trainee at each training device at each monitoring time point are extracted ; According to the action similarity coefficient of the dance student and each training instructor, the action similarity coefficient of the dance student and the adaptation instructor is extracted; Obtain the X-axis action allowable deviation value of the dance student and the adaptive instructor at each monitoring time point by mapping the action similarity coefficient interval corresponding to each monitoring time point from the local database ; calculating the x-axis movement deviation coefficient of the dance student at each monitoring time point ; The Y-axis motion deviation coefficient of the dance student at each monitoring time point is calculated in the same way and the Z-axis motion deviation coefficient The motion deviation coefficient of the dance student at each monitoring time point is calculated . 4.The intelligent training method for virtual dance training according to claim 1, wherein, The specific analysis method for analyzing the problem type of each problem monitoring time point of the dance student is: According to the dance space relative coordinates of the dance trainees at each monitoring time point, the dance space relative coordinates of the dance trainees at each monitoring time point are extracted, and whether the dance trainees have the problem of rhythm error at each monitoring time point is judged. If the dance trainees have the problem of rhythm error at a certain monitoring time point, the rhythm error is taken as the problem type of the monitoring time point, otherwise, the action error is taken as the problem type of the monitoring time point, and the problem types of the dance trainees at each monitoring time point are summarized.

5. The intelligent training method for virtual dance training according to claim 4, wherein, The specific judgment method of whether the dance trainees have the problem of rhythm error at each monitoring time point is: Obtaining the rhythm problem analysis time period of each monitoring time point from the local database; According to the comparison space relative coordinates of the adaptive instructors at each monitoring time point, the comparison space relative coordinates of the adaptive instructors at each monitoring time point in the rhythm problem analysis time period are extracted; According to the dance space relative coordinates of the dance trainees at each monitoring time point, and according to the method of analyzing the action deviation coefficient of the dance trainees at each monitoring time point, the action deviation coefficient of the dance trainees at each monitoring time point from the adaptive instructors at each monitoring time point in the rhythm problem analysis time period is calculated, the maximum action deviation coefficient of the dance trainees at each monitoring time point from the adaptive instructors in the rhythm problem analysis time period is extracted, and it is taken as the target action deviation coefficient of the dance trainees at each monitoring time point; If the target action deviation coefficient of the dance trainees at a certain monitoring time point is less than the action deviation coefficient threshold, it is judged that the dance trainees have the problem of rhythm error at the monitoring time point.

6. The intelligent training method for virtual dance training according to claim 5, wherein, The specific reminding method of the action adjustment reminder for the dance trainees is: The problem types of the dance trainees at each monitoring time point are sent to the display screen of the dance trainees, according to the dance space relative coordinates of the dance trainees at each monitoring time point, the dance space relative coordinates of the dance trainees at each rhythm problem monitoring time point and each action problem monitoring time point are extracted, and they are visualized and displayed on the display screen of the dance trainees; According to the comparison space relative coordinates of the adaptive instructors at each monitoring time point, the comparison space relative coordinates of the adaptive instructors at each action problem monitoring time point are extracted, and they are visualized and displayed on the display screen of the dance trainees, and the dance trainees are recommended to perform action fixed practice according to the corresponding action; According to the action deviation coefficient of the dance trainees at each rhythm problem monitoring time point from the adaptive instructors at each monitoring time point in the rhythm problem analysis time period, the monitoring time point of the maximum action deviation coefficient of the dance trainees at each rhythm problem monitoring time point from the adaptive instructors in the rhythm problem analysis time period is extracted, and it is taken as the reminding monitoring time point of the dance instructor of the dance trainees at each rhythm problem monitoring time point, and the dance trainees are reminded that the action they do at each rhythm problem monitoring time point is the action done by the dance instructor at the reminding monitoring time point.

7. An intelligent training system for performing the intelligent training method for virtual dance training according to any one of claims 1-6, characterized by, It includes: The dance student and the mentor pairing analysis module is used for the dance student to wear each training equipment to the corresponding position on the body, to perform each action according to the voice prompt, to obtain the spatial relative coordinates of each training equipment of the dance student in each action, and to analyze the adaptive mentor of the dance student according to the spatial relative coordinates; The dance student action analysis module is used for obtaining the dance spatial relative coordinates of each training equipment of the dance student at each monitoring time point when the dance student follows the corresponding adaptive mentor to perform the dance training, and analyzing each problem monitoring time point of the dance student; The dance training optimization suggestion processing module is used for analyzing the problem type of each problem monitoring time point of the dance student, screening each rhythm problem monitoring time point and each action problem monitoring time point of the dance student, and reminding the dance student to adjust the action.

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