CN114582195A - Intelligent table tennis teaching system and teaching method - Google Patents

Abstract

The application provides a table tennis intelligent teaching system and a teaching method, wherein the table tennis intelligent teaching system comprises a control terminal, a serving robot and eagle eye equipment, wherein the serving robot and the eagle eye equipment are connected with the control terminal; the control terminal is provided with a matching module, a course distribution module, a ball return quality analysis module, a parameter adjustment module and a report generation module; the matching module is used for matching corresponding teaching courses for the trainees; the course distribution module is used for sending the corresponding teaching courses to the service robots corresponding to the students; the service robot is used for serving according to the service instruction; the eagle eye equipment is used for collecting the motion data of the table tennis and feeding back the motion data to the control terminal; the ball return quality analysis module is used for analyzing the ball speed, the net passing height, the angle, the falling point and the rotating speed of the table tennis; the parameter adjusting module is used for adjusting the service parameters of the service robot in real time; the report generation module is used for generating a data report of single training according to the single ball serving record and the table tennis track data. This application can promote the teaching effect.

Description

Intelligent table tennis teaching system and teaching method

Technical Field

The application belongs to the technical field of data analysis, and particularly relates to a table tennis intelligent teaching system and a table tennis intelligent teaching method.

Background

The table tennis is praised as the Chinese 'national ball', is deeply loved by the majority of sports enthusiasts, and meanwhile, the table tennis sport technology has the advantages of fine and smooth requirements, high speed, various changes and the like, so that the table tennis is popular in schools and is also an important content of school sports teaching. At present, many sports teachers in schools are both full-time and full-time, and no special table tennis teachers exist; secondly, sports teachers in many places do not have related table tennis training or have not been deeply built in colleges and universities, and generally do not have special training to master the contents such as correct pace and shooting posture, and the use of personal skills and training experience of table tennis teachers are emphasized by table tennis teaching.

Disclosure of Invention

In order to overcome the problems in the related art at least to a certain extent, the application provides a table tennis intelligent teaching system and a table tennis intelligent teaching method.

According to a first aspect of an embodiment of the present application, the present application provides a table tennis intelligent teaching system, which includes a control terminal, and a service robot and an eagle eye device connected to the control terminal;

the control terminal is provided with a matching module, a course distribution module, a ball return quality analysis module, a parameter adjusting module and a report generating module; the matching module is used for matching corresponding teaching courses for the student according to the historical training records of the student; the course distribution module is used for sending corresponding teaching courses to the service robots corresponding to the students;

the service robot is used for serving according to the service instruction; the eagle eye equipment is used for collecting the motion data of the table tennis and feeding back the motion data to the control terminal;

the ball return quality analysis module is used for analyzing the ball speed, the net passing height, the angle, the falling point and the rotating speed of the table tennis to obtain an effective table tennis track;

the parameter adjusting module is used for adjusting the service parameters of the service robot in real time according to the analysis result of the return ball quality analysis module;

the report generation module is used for generating a single training data report according to the single ball serving record and the table tennis track data so as to complete the objective evaluation of the teaching process.

In the table tennis intelligent teaching system, the matching module extracts a training record portrait of a student from a historical training record of the student and pushes a corresponding course to the student according to the training record portrait;

the training record portrait comprises three dimensions; the first dimension comprises the common handedness, age and gender of the student, the second dimension comprises the ability growth condition, stability and action consistency of the student, and the third dimension comprises the weighted average difficulty of the student training course in a period of time.

In the above-mentioned table tennis intelligent teaching system, the working process of the ball return quality analysis module is:

obtaining effective target space data according to the physical law of table tennis flight, and synchronizing the coordinates of the moving target to a motion state analysis cache queue T;

judging whether a plurality of effective coordinate data cached in the queue T form an effective track, if the effective coordinate data in the cache queue T meet the acquisition condition, forming the effective track by the effective coordinate data, and outputting track data Traj in the queue T; otherwise, clearing the effective coordinate data from the queue T;

calculating the acquisition direction of the trajectory data Traj according to the acquisition time of the trajectory data Traj and the relative coordinates of the trajectory data in a three-dimensional space coordinate system of the ping-pong table;

for each group of ping-pong ball trajectory data Traj, calculating ball speed v _ net, net passing height h _ net, angle _ net and drop point (x _ rebound, y _ rebound) of the net passing direction at the net passing moment of the single group of trajectories by using a fitting algorithm, and calculating the rotation speed (w _ rebound) of the ping-pong ball by using a deep neural network_x,w_y,w_z)。

Further, the working process of the parameter adjusting module is as follows:

comparing the ball return quality analysis result with a preset training characteristic target, and modifying the single ball serving parameters of the preset serving machine according to the comparison result;

acquiring the single-ball serving parameters of the serving machine in the current serving parameter queue;

sequentially and correspondingly comparing each parameter in the single-ball service parameters of the service robot in the current service parameter queue with each parameter in the preset single-ball service parameters of the service robot, and updating the single-ball service parameters of the service robot in the current service parameter queue according to the comparison result;

and obtaining the service state of the service robot, judging whether the service state meets the updating service condition or not according to the service state, and directly issuing an updating instruction to update the service single-ball parameters of the service robot if the service state meets the updating service condition.

Furthermore, the process of comparing the ball return quality analysis result with the preset training characteristic target and modifying the preset pitching parameters of the pitching machine according to the comparison result comprises the following steps:

presetting a single ball serving parameter t _ p _ g of the pitching machine: linear velocity t _ v _ s, rotation speed t _ r _ s, rotation direction t _ r _ t, landing position (t _ x, t _ y) and serving frequency t _ fr;

comparing the ball speed v _ net in the net passing direction in the dimensionality of each group of track data Traj with the net passing ball speed p _ net of a preset target parameter Sa; if v _ net is larger than p _ net, the linear speed t _ v _ s and the ball serving frequency t _ fr in the single ball serving parameter t _ p _ g of the ball serving machine are improved; if v _ net is p _ net, the linear speed t _ v _ s and the service frequency t _ fr in the single ball service parameter t _ p _ g of the service robot are not changed; if v _ net is less than p _ net, reducing the linear velocity t _ v _ s and the ball serving frequency t _ fr in the single ball serving parameter t _ p _ g of the ball serving machine;

comparing the internetwork height h _ net in the internetwork direction in the dimensionality of each group of track data Traj with the internetwork height p _ h _ net of a preset target parameter Sa; if h _ net is larger than p _ h _ net, reducing the rotating speed t _ r _ s, the linear speed t _ v _ s and the ball serving frequency t _ fr in the serving machine single ball serving parameter t _ p _ g; if h _ net is p _ h _ net, the rotating speed t _ r _ s, the linear speed t _ v _ s and the ball serving frequency t _ fr in the serving machine single ball serving parameter t _ p _ g are not changed; if h _ net is less than p _ h _ net, the rotating speed t _ r _ s, the linear speed t _ v _ s and the ball serving frequency t _ fr in the single ball serving parameter t _ p _ g of the serving machine are improved;

comparing the angle _ net of the networking direction in the dimension of each group of track data Traj with the networking angle p _ angle _ net of a preset target parameter Sa; if angle _ net is larger than p _ angle _ net, the rotating speed t _ r _ s, the linear speed t _ v _ s and the ball serving frequency t _ fr in the single ball serving parameter t _ p _ g of the ball serving machine are improved, and the position coordinate t _ y of the drop point is reduced; if the angle _ net is equal to p _ angle _ net, the rotating speed t _ r _ s, the linear speed t _ v _ s, the ball serving frequency t _ fr and the falling point position coordinate t _ y in the single ball serving parameter t _ p _ g of the serving machine are not changed; if angle _ net is less than p _ angle _ net, reducing the rotating speed t _ r _ s, the linear speed t _ v _ s, the serving frequency t _ fr and the lifting and dropping point position coordinate t _ y in the serving machine single ball serving parameter t _ p _ g;

comparing the falling point (x _ rebound, y _ rebound) in the dimension of each group of track data Traj with the falling point (p _ t _ x, p _ t _ y) of the preset target parameter Sa; if x _ rebound > p _ t _ x, reducing the rotating speed t _ r _ s and the serving frequency t _ fr in the serving machine single ball serving parameter t _ p _ g and increasing the position coordinate t _ x of the falling point; if x _ rebound is p _ t _ x, maintaining the rotating speed t _ r _ s, the serving frequency t _ fr and the falling point position coordinate t _ x in the single ball serving parameter t _ p _ g of the original serving machine; if x _ rebound is less than p _ t _ x, the rotating speed t _ r _ s and the serving frequency t _ fr in the serving machine single ball serving parameter t _ p _ g are increased, and the falling point position coordinate t _ x is reduced; if y _ rebound is larger than p _ t _ x, reducing the linear speed t _ v _ s and the serving frequency t _ fr in the serving machine single ball serving parameter t _ p _ g and increasing the position coordinate t _ y of the falling point; if y _ rebound is p _ t _ x, keeping the linear speed t _ v _ s, the serve frequency t _ fr and the falling point position coordinate t _ y in the single ball serving parameter t _ p _ g of the original serving machine; if y _ bound < p _ t _ x, the linear speed t _ v _ s and the serve frequency t _ fr in the serve parameter t _ p _ g of the serve machine are increased, and the position coordinate t _ y of the drop point is increased.

Furthermore, the process of sequentially and correspondingly comparing each parameter in the single-ball service parameters of the service robot in the current service parameter queue with each parameter in the single-ball service parameters of the preset service robot, and updating the single-ball service parameters of the service robot in the current service parameter queue according to the comparison result is as follows:

the single-ball serving parameters p _ g of the serving machine in the current serving parameter queue comprise a linear velocity v _ s, a rotating speed r _ s, a rotating direction r _ t, a drop point position (x, y) and a serving frequency fr;

presetting a single ball serving parameter t _ p _ g of the ball serving machine, wherein the single ball serving parameter t _ p _ g comprises a linear speed t _ v _ s, a rotating speed t _ r _ s, a rotating direction t _ r _ t, a falling point position (t _ x, t _ y) and a ball serving frequency t _ fr;

comparing v _ s with t _ v _ s, comparing r _ s with t _ r _ s, comparing r _ t with t _ r _ t, comparing (x, y) with (t _ x, t _ y), comparing fr with t _ fr, and updating different parameters in the preset serving parameter t _ p _ g to the serving parameter p _ g of the serving machine in the current serving parameter queue.

In the above table tennis intelligent teaching system, the process of the report generation module generating the data report is as follows:

analyzing a single-serve record queue Q1 of serve of the serve robot to obtain single-serve records in the queue;

acquiring trajectory data in a queue by combining a trajectory data queue Q2 acquired by eagle eye equipment, and sequentially carrying out ball return quality analysis;

performing ability scoring according to the ball return quality result;

and calculating a data report of single training according to the weights of different scoring dimensions, wherein the scoring dimensions comprise speed, angle, falling point and station-climbing rate.

Further, the scoring the ability according to the return ball quality result comprises a hit rate, a height score, a drop point score, an angle score and a speed score;

single ball height score is equal to corresponding rule height score lower limit + height score difference ÷ height difference × (cross-web height-corresponding rule height lower limit);

wherein the content of the first and second substances,

the height score difference is the corresponding rule height score upper limit-the corresponding rule height score lower limit;

the height difference is the upper limit of the corresponding regular height-the lower limit of the corresponding regular height;

the single ball drop point score is the origin score plus the X-axis differential in the drop point region divided by the X-axis length differential in the drop point region (X-axis coordinate-drop point region corresponding to X-axis origin start coordinate) + the Y-axis differential in the drop point region divided by the Y-axis length differential in the drop point region (Y-axis coordinate-drop point region corresponding to Y-axis origin start coordinate)

Wherein the content of the first and second substances,

the length difference of an X/Y axis in the drop point area is equal to an X/Y axis termination coordinate point in the drop point area-an X/Y axis starting coordinate point in the drop point area;

the X/Y axis difference in the drop point region is the X/Y axis termination coordinate point score in the drop point region-the X/Y axis starting coordinate point score in the drop point region;

the single-ball angle score is equal to the corresponding regular angle score lower limit + the angle score difference ÷ the angle difference x (the net passing angle-the corresponding regular angle lower limit);

wherein the content of the first and second substances,

the angle score difference is the corresponding rule angle score upper limit-the corresponding rule angle score lower limit;

the angle difference is the corresponding regular angle upper limit-the corresponding regular angle lower limit;

a single-ball speed score is equal to the corresponding regular speed score lower limit + speed score difference ÷ speed difference × (speed net angle-corresponding regular speed lower limit);

wherein the content of the first and second substances,

the speed score difference is the corresponding rule speed score upper limit-the corresponding rule speed score lower limit;

the speed difference corresponds to the upper regular speed limit-the lower regular speed limit.

According to a second aspect of the embodiments of the present application, the present application further provides a table tennis intelligent teaching method, which includes the following steps:

analyzing the historical training records of the trainees, and matching corresponding teaching courses according to the analysis results;

distributing the teaching courses to corresponding service robots, serving by the service robots, and starting training;

acquiring track data of a table tennis ball in a training process by utilizing eagle eye equipment;

performing ball return quality analysis according to the collected track data of the table tennis;

adjusting the serve parameters of the serve robot in real time according to the return ball quality analysis result;

and generating a data report of single training according to the single ball serving record and the track data of the table tennis to finish objective evaluation on the teaching process.

In the above table tennis intelligent teaching method, the process of adjusting the service parameters of the service robot in real time according to the return quality analysis result is as follows:

comparing the return ball quality analysis result with a preset training characteristic target, and modifying the single ball serving parameters of the preset serving machine according to the comparison result;

acquiring the single-ball serving parameters of the serving machine in the current serving parameter queue;

sequentially and correspondingly comparing each parameter in the single-ball service parameters of the service robot in the current service parameter queue with each parameter in the preset single-ball service parameters of the service robot, and updating the single-ball service parameters of the service robot in the current service parameter queue according to the comparison result;

and obtaining the service state of the service robot, judging whether the service state meets the updating service condition or not according to the service state, and directly issuing an updating instruction to update the service single-ball parameters of the service robot if the service state meets the updating service condition.

According to the above embodiments of the present application, at least the following advantages are obtained: the intelligent table tennis teaching system is provided with a control terminal, a serving robot and eagle eye equipment, wherein the eagle eye equipment acquires an effective moving target, the control terminal analyzes according to a historical training record of a student and automatically matches teaching courses according to an analysis result, and after the courses are distributed to corresponding serving robots, table tennis training is started; the serve robot serves according to a course preset instruction, and meanwhile, the eagle eye device collects data; the eagle eye equipment feeds acquired data back to the control terminal, the control terminal performs ball return quality analysis, an analysis result is compared with a preset training characteristic target, ball serving parameters of the ball serving robot are adjusted in real time according to an actual comparison condition, ball serving parameter change is performed, and a teaching target of multi-ball training is achieved; after training, the control terminal can provide a real and effective data report. The application can improve the teaching effect in the table tennis teaching process, and can also feed back objective evaluation based on data in the complex teaching process, and can complete the teaching process of a coach for a plurality of students at the same time, thereby solving the social problem of shortage and loss of professional coach resources.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the scope of the invention, as claimed.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of the specification of the application, illustrate embodiments of the application and together with the description, serve to explain the principles of the application.

Fig. 1 is a block diagram of a table tennis intelligent teaching system according to an embodiment of the present application.

Fig. 2 is a flowchart of a table tennis intelligent teaching method according to an embodiment of the present application.

Fig. 3 is a flowchart illustrating a table tennis intelligent teaching method according to an embodiment of the present application, wherein the table tennis intelligent teaching method is performed according to a historical training record of a student, and a teaching course is matched according to an analysis result.

Fig. 4 is a flowchart illustrating that a service robot is started to serve balls and an eagle eye device is started to acquire data in the table tennis intelligent teaching method according to the embodiment of the present application.

Fig. 5 is a flowchart of ball return quality analysis performed according to collected track data of table tennis balls in the table tennis intelligent teaching method provided in the embodiment of the present application.

Fig. 6 is a flowchart illustrating comparison between a ball return quality analysis result and a preset training characteristic target in the table tennis intelligent teaching method according to the embodiment of the present application.

Fig. 7 is a flowchart of adjusting the service parameters of the service robot in real time according to the comparison result in the table tennis intelligent teaching method provided by the embodiment of the application.

Fig. 8 is a schematic view of a drop point area in the table tennis intelligent teaching method provided in the embodiment of the present application.

Description of reference numerals:

1. a control terminal; 11. a matching module; 12. a course distribution module; 13. a ball return quality analysis module; 14. a parameter adjusting module; 15. a report generation module;

2. a service robot;

3. an eagle eye device.

Detailed Description

For the purpose of promoting a clear understanding of the objects, aspects and advantages of the embodiments of the present application, reference will now be made to the accompanying drawings and detailed description, wherein like reference numerals refer to like elements throughout.

The exemplary embodiments and descriptions of the present application are provided to explain the present application and should not be taken as limiting the present application. In addition, the same or similar reference numbers used in the drawings and the embodiments are used to denote the same or similar parts.

As used herein, "first," "second," …, etc., are not specifically intended to mean in a sequential or chronological order, nor are they intended to limit the application, but merely to distinguish between elements or operations described in the same technical language.

As used herein, the terms "comprising," "including," "having," "containing," and the like are open-ended terms that mean including, but not limited to.

As used herein, "and/or" includes any and all combinations of the described items.

References to "plurality" herein include "two" and "more than two"; reference to "multiple sets" herein includes "two sets" and "more than two sets".

Certain words used to describe the present application are discussed below or elsewhere in this specification to provide additional guidance to those skilled in the art in describing the present application.

As shown in fig. 1, the table tennis intelligent teaching system provided in the embodiment of the present application includes a control terminal 1, and a service robot 2 and an eagle eye device 3 connected to the control terminal 1.

The control terminal 1 is provided with a matching module 11, a course distribution module 12, a ball return quality analysis module 13, a parameter adjustment module 14 and a report generation module 15.

The matching module 11 is used for matching the corresponding teaching courses for the trainees according to the historical training records of the trainees.

The course distribution module 12 is configured to send the corresponding teaching course to the service robot 2 corresponding to the student.

The service robot 2 is used for performing service according to a service instruction. The eagle eye device 3 is used for collecting the motion data of the table tennis and feeding back the motion data to the control terminal 1.

The ball return quality analysis module 13 is used for analyzing the ball speed, the net passing height, the angle, the drop point and the rotating speed of the ping-pong ball so as to obtain an effective ping-pong ball track.

The parameter adjusting module 14 is configured to adjust the serve parameters of the serve robot 2 in real time according to the analysis result of the return ball quality analyzing module 13.

The report generating module 15 is used for generating a data report of single training according to the single ball serving record and the table tennis track data so as to complete the objective evaluation of the teaching process.

Specifically, the historical training records of the trainees mainly comprise business dimension information and equipment acquisition dimension information. The service dimension information mainly includes, but is not limited to, the following information: time, training course, composite score, individual score, training duration, and the like. The device acquisition dimension information mainly includes, but is not limited to, the following information: the ball return track, the net passing height, the net passing angle, the ball return speed, the landing rate, the ball return falling point and the like. The return track is represented by a three-dimensional track point matrix.

The matching module 11 adopts the existing big data model to quickly extract the training record portrait of the student from the historical training record of the student, and pushes the corresponding course to the student according to the training record portrait, so as to help the student gradually improve the technical level.

Wherein, the training record portrait mainly comprises three dimensions. The first dimension mainly includes personal information such as the student's usual handedness, age, gender, etc. The second dimension mainly comprises comprehensive representation of student ability calculated according to the time period accumulation, such as ability growth condition, stability, action consistency and the like of the student. The third dimension primarily includes the weighted average difficulty of the student's workout over a period of time.

The serve robot 2 temporarily stores the received combined single serve instruction in the corresponding teaching course. The temporarily stored combined single-ball serving instruction specifically comprises a combined ball list combined serving mode, wherein the combined serving mode comprises an ordered serving mode and a random serving mode, and the ordered serving mode comprises a sequential serving mode and a reverse serving mode for inverting the sequence of the combined ball list.

The random serving mode is to randomly select a single ball to be served from the combined ball list; the serve parameters p _ g of each single ball comprise equipment attributes such as linear velocity v _ s, rotating speed r _ s, rotating direction r _ t, landing point position (x, y), serve frequency fr and the like, and service attributes related to the number of serve balls or serve duration and combination mode.

The combination mode is mainly used for initial contact courses and training scenes after the courses are familiar.

After the temporary storage of the serve robot 2 is completed, the eagle eye device 3 performs data acquisition according to the opening instruction sent by the control terminal 1.

The starting process of the eagle eye device 3 is as follows: automatically calibrating the ball table and focusing; creating a three-dimensional space coordinate system through the binocular image; running a cache algorithm, entering a device starting state, and returning a starting mark; and restarting if the temporary storage fails.

The control terminal 1 controls the serve robot 2 to execute a cache serve instruction according to the communication signaling of the eagle eye starting identifier; the start is resumed if the start fails.

The eagle eye device 3 feeds back the collected motion data of the table tennis to the control terminal 1, the control terminal 1 is also provided with a ball return quality analysis module 13, and the specific working process of the ball return quality analysis module 13 is as follows:

and obtaining effective target space data according to the physical law of table tennis flight, and synchronizing the coordinates of the moving target to a motion state analysis buffer queue T. Judging whether a plurality of effective coordinate data cached in the queue T form an effective track, if the effective coordinate data in the cache queue T meet the acquisition condition, forming the effective track by the effective coordinate data, and outputting track data Traj in the queue T; otherwise, the valid coordinate data is cleared from the queue T.

Calculating the acquisition direction of the trajectory data Traj according to the acquisition time of the trajectory data Traj and the relative coordinates of the trajectory data in a three-dimensional space coordinate system of the ping-pong table; for each group of ping-pong ball trajectory data Traj, calculating ball speed v _ net, net passing height h _ net, angle _ net and drop point (x _ rebound, y _ rebound) of the net passing direction at the net passing moment of the single group of trajectories by using a fitting algorithm, and calculating the rotation speed (w _ rebound) of the ping-pong ball by using a deep neural network_x,w_y,w_z)。

The control terminal 1 is further provided with a parameter adjusting module 14, the parameter adjusting module 14 is used for adjusting the service parameters of the service robot 2 in real time, and the adjusting process is as follows:

s141, as shown in fig. 5, comparing the ball return quality analysis result with the preset training characteristic target, and modifying the preset pitching parameters of the pitching machine according to the comparison result, wherein the specific process is as follows:

presetting a single ball serving parameter t _ p _ g of the pitching machine: the parameters of the linear velocity t _ v _ s, the rotation speed t _ r _ s, the rotation direction t _ r _ t, the landing point position (t _ x, t _ y) and the serve frequency t _ fr are copied according to the next serve parameter in a serve queue QT of a preset combination; and acquiring a preset target Sa from the combined service queue QT for comparison.

Comparing the trajectory data Traj with a preset target parameter Sa in a training course through a rule engine c _ i, and modifying a preset pitching parameter t _ p _ g of the pitching machine according to a comparison result, wherein the specific process is as follows;

and comparing the ball speed v _ net in the net passing direction in the dimensionality of each group of track data Traj with the net passing ball speed p _ net of a preset target parameter Sa.

If v _ net is larger than p _ net, the linear speed t _ v _ s and the ball serving frequency t _ fr in the single ball serving parameter t _ p _ g of the ball serving machine are improved; if the v _ net is p _ net, the linear speed t _ v _ s and the ball serving frequency t _ fr in the single ball serving parameter t _ p _ g of the serving machine are not changed; if v _ net is less than p _ net, the linear velocity t _ v _ s and the ball serving frequency t _ fr in the single ball serving parameter t _ p _ g of the ball serving machine are reduced.

And comparing the net passing height h _ net in the net passing direction in the dimension of each group of track data Traj with the net passing height p _ h _ net of the preset target parameter Sa.

If h _ net is larger than p _ h _ net, reducing the rotating speed t _ r _ s, the linear speed t _ v _ s and the ball serving frequency t _ fr in the serving machine single ball serving parameter t _ p _ g; if h _ net is p _ h _ net, the rotating speed t _ r _ s, the linear speed t _ v _ s and the ball serving frequency t _ fr in the serving machine single ball serving parameter t _ p _ g are not changed; if h _ net is less than p _ h _ net, the rotating speed t _ r _ s, the linear speed t _ v _ s and the ball serving frequency t _ fr in the single ball serving parameter t _ p _ g of the serving machine are improved.

And comparing the angle _ net of the networking direction in the dimension of each group of track data Traj with the networking angle p _ angle _ net of the preset target parameter Sa.

If angle _ net is larger than p _ angle _ net, the rotating speed t _ r _ s, the linear speed t _ v _ s and the ball serving frequency t _ fr in the single ball serving parameter t _ p _ g of the ball serving machine are improved, and the position coordinate t _ y of the drop point is reduced; if the angle _ net is equal to p _ angle _ net, the rotating speed t _ r _ s, the linear speed t _ v _ s, the ball serving frequency t _ fr and the falling point position coordinate t _ y in the single ball serving parameter t _ p _ g of the serving machine are not changed; if angle _ net is less than p _ angle _ net, the rotating speed t _ r _ s, the linear speed t _ v _ s, the serving frequency t _ fr and the lifting and dropping point position coordinate t _ y in the serving machine single ball serving parameter t _ p _ g are reduced.

Comparing the falling points (x _ rebound, y _ rebound) in the dimension of each set of track data Traj with the falling points (p _ t _ x, p _ t _ y) of the preset target parameter Sa.

If x _ rebound > p _ t _ x, reducing the rotating speed t _ r _ s and the serving frequency t _ fr in the serving machine single ball serving parameter t _ p _ g and increasing the position coordinate t _ x of the falling point; if x _ rebound is p _ t _ x, keeping the rotating speed t _ r _ s, the serving frequency t _ fr and the falling point position coordinate t _ x in the single ball serving parameter t _ p _ g of the original serving machine; if x _ rebound < p _ t _ x, the rotating speed t _ r _ s and the serving frequency t _ fr in the serving machine single ball serving parameter t _ p _ g are increased and the falling point position coordinate t _ x is reduced.

If y _ rebound is larger than p _ t _ x, reducing the linear speed t _ v _ s and the serving frequency t _ fr in the serving machine single ball serving parameter t _ p _ g and increasing the position coordinate t _ y of the falling point; if y _ rebound is p _ t _ x, keeping the linear speed t _ v _ s, the serve frequency t _ fr and the falling point position coordinate t _ y in the single ball serving parameter t _ p _ g of the original serving machine; if y _ bound < p _ t _ x, the linear speed t _ v _ s and the serve frequency t _ fr in the serve parameter t _ p _ g of the serve machine are increased, and the position coordinate t _ y of the drop point is increased.

And S142, obtaining the single-ball serving parameter p _ g of the serving machine in the current serving parameter queue.

S143, sequentially and correspondingly comparing each parameter in the single ball serving parameters p _ g of the pitching machine in the current pitching parameter queue with each parameter in the preset pitching parameters t _ p _ g of the pitching machine, and updating the single ball serving parameters p _ g of the pitching machine in the current pitching parameter queue according to the comparison result.

The single-ball service parameters p _ g of the service machine in the current service parameter queue comprise a linear velocity v _ s, a rotating speed r _ s, a rotating direction r _ t, a falling point position (x, y) and a service frequency fr.

The preset serving parameters t _ p _ g of the single ball of the serving machine comprise a linear speed t _ v _ s, a rotating speed t _ r _ s, a rotating direction t _ r _ t, a falling point position (t _ x, t _ y) and a serving frequency t _ fr.

The specific comparison process is as follows:

comparing v _ s with t _ v _ s, comparing r _ s with t _ r _ s, comparing r _ t with t _ r _ t, comparing (x, y) with (t _ x, t _ y), comparing fr with t _ fr, and updating different parameters in the preset serving parameter t _ p _ g to the serving parameter p _ g of the serving machine in the current serving parameter queue.

S144, obtaining the service state of the service robot, judging whether the service condition is met or not according to the service state, and if yes, directly issuing an update instruction to update the service single-ball parameters of the service robot 2; if not, waiting for the next time window until the condition is met; and if the preset serve parameter t _ p _ g of the serving machine is updated again in the waiting period, comparing again, and repeating the process until the updating is successful.

It should be noted that if the serve parameters are being acquired or updated in the serve combination, the wait is needed, that is, the update serve condition is not met.

After training, the control terminal 1 can provide a real and effective data report. The control terminal 1 is also provided with a report generation module 15.

Specifically, the process of generating the data report by the report generating module 15 is as follows:

analyzing a single-ball service record queue Q1 of the service robot 2 to obtain the single-ball service records in the queue;

acquiring trajectory data Traj in a queue by combining a trajectory data queue Q2 acquired by the eagle eye equipment 3, and sequentially carrying out ball return quality analysis;

performing ability scoring according to the ball return quality result;

and calculating a data report of single training according to the weights of different scoring dimensions, wherein the scoring dimensions comprise speed, angle, falling point and station-climbing rate.

According to the intelligent table tennis teaching system, the control terminal 1, the serving robot 2 and the eagle eye device 3 are arranged, so that an effective table tennis track can be obtained through the eagle eye device 3 in the continuous batting or training process, and the track parameter is compared with the preset return parameter in the serving robot 2 in real time, so that the serving strategy is adjusted in real time according to the actual situation, and the teaching effect in the table tennis teaching process is improved; meanwhile, objective evaluation based on data in a complex teaching process can be fed back, the teaching process of a coach for multiple students at the same time is completed, and the social problem that professional coaches are short in resources and lack is solved.

In an exemplary embodiment, as shown in fig. 2, an embodiment of the present application further provides a table tennis intelligent teaching method, which includes the following steps:

and S11, analyzing the historical training records of the trainees, and matching corresponding teaching courses according to the analysis results.

And S22, distributing the teaching courses to the corresponding service robots 2, and starting training by the service robots 2.

And S33, acquiring the track data of the table tennis ball in the training process by using the eagle eye device 3.

And S44, performing ball return quality analysis according to the collected track data of the table tennis.

And S55, adjusting the serve parameters of the serve robot 2 in real time according to the return ball quality analysis result.

And S66, after training, generating a data report of single training according to the single ball serving record and the track data of the table tennis to finish objective evaluation on the teaching process.

As shown in fig. 3, in the step S11, the specific process of analyzing the trainee' S historical training record and matching the corresponding teaching lesson according to the analysis result includes:

the existing big data model is adopted to extract the training record portrait of the student from the historical training record of the student.

And pushing corresponding teaching courses to the trainees according to the training record pictures.

The historical training records of the trainees mainly comprise service dimension information and equipment acquisition dimension information. The service dimension information mainly includes, but is not limited to, the following information: time, training course, composite score, individual score, training duration, and the like. The device acquisition dimension information mainly includes, but is not limited to, the following information: ball return tracks, net passing height, net passing angle, ball return speed, table-putting rate, ball return drop points and the like. The return track is represented by a three-dimensional track point matrix.

The training record representation mainly comprises three dimensions. The first dimension mainly includes personal information such as the student's usual handedness, age, gender, etc. The second dimension mainly comprises comprehensive representation of student ability calculated according to the time period accumulation, such as ability growth condition, stability, action consistency and the like of the student. The third dimension primarily includes the weighted average difficulty of the student's workout over a period of time.

As shown in fig. 4, in step S22, the service robot 2 temporarily stores the received combined single serve instruction in the corresponding teaching lesson. The temporarily stored combined single-ball serving instruction specifically comprises a combined ball list combined serving mode, wherein the combined serving mode comprises an ordered serving mode and a random serving mode, and the ordered serving mode comprises a sequential serving mode and a reverse serving mode for inverting the sequence of the combined ball list.

The random serving mode is to randomly select a single ball to be served from the combined ball list; the serve parameters p _ g of each single ball comprise equipment attributes such as linear velocity v _ s, rotating speed r _ s, rotating direction r _ t, landing point position (x, y), serve frequency fr and the like, and service attributes related to the number of serve balls or serve duration and combination mode.

The combination mode is mainly used for initial contact courses and training scenes after the courses are familiar.

As shown in fig. 5, in the step S44, the specific process of performing ball return quality analysis according to the collected trajectory data of the table tennis ball includes:

and S441, obtaining effective target space data according to the physical law of table tennis flying, and synchronizing the coordinates of the moving target to a motion state analysis buffer queue T.

Judging whether a plurality of effective coordinate data cached in the queue T form an effective track, if the effective coordinate data in the cache queue T meet the acquisition condition, forming the effective track by the effective coordinate data, and outputting track data Traj in the queue T; otherwise, the valid coordinate data is cleared from the queue T.

S442, calculating the acquisition direction of the trajectory data Traj according to the acquisition time of the trajectory data Traj and the relative coordinates of the trajectory data in the three-dimensional space coordinate system of the ping-pong table; for each group of ping-pong ball trajectory data Traj, calculating ball speed v _ net, net passing height h _ net, angle _ net and drop point (x _ rebound, y _ rebound) of the net passing direction at the net passing moment of the single group of trajectories by using a fitting algorithm, and calculating the rotation speed (w _ rebound) of the ping-pong ball by using a deep neural network_x,w_y,w_z)。

As shown in fig. 6 and 7, in the step S55, the specific process of adjusting the serve parameters of the serve robot 2 in real time according to the return ball quality analysis result includes:

s551, as shown in fig. 5, comparing the result of the ball return quality analysis with a preset training feature target, the specific process is as follows:

presetting a single ball serving parameter t _ p _ g of the pitching machine: the parameters of the linear velocity t _ v _ s, the rotation speed t _ r _ s, the rotation direction t _ r _ t, the landing point position (t _ x, t _ y) and the serve frequency t _ fr are copied according to the next serve parameter in a serve queue QT of a preset combination; and acquiring a preset target Sa from the combined service queue QT for comparison.

The track data Traj and a preset target parameter Sa in the training course are compared, and the process of modifying the preset pitching parameter t _ p _ g of the pitching machine according to the comparison result is as follows:

and comparing the ball speed v _ net in the net passing direction in the dimensionality of each group of track data Traj with the net passing ball speed p _ net of a preset target parameter Sa.

If v _ net is larger than p _ net, the linear speed t _ v _ s and the ball serving frequency t _ fr in the single ball serving parameter t _ p _ g of the ball serving machine are improved; if the v _ net is p _ net, the linear speed t _ v _ s and the ball serving frequency t _ fr in the single ball serving parameter t _ p _ g of the serving machine are not changed; if v _ net is less than p _ net, the linear velocity t _ v _ s and the ball serving frequency t _ fr in the single ball serving parameter t _ p _ g of the serving machine are reduced.

And comparing the networking height h _ net in the networking direction in the dimensionality of each group of track data Traj with the networking height p _ h _ net of a preset target parameter Sa.

If h _ net is larger than p _ h _ net, reducing the rotating speed t _ r _ s, the linear speed t _ v _ s and the ball serving frequency t _ fr in the serving machine single ball serving parameter t _ p _ g; if h _ net is p _ h _ net, the rotating speed t _ r _ s, the linear speed t _ v _ s and the ball serving frequency t _ fr in the serving machine single ball serving parameter t _ p _ g are not changed; if h _ net is less than p _ h _ net, the rotating speed t _ r _ s, the linear speed t _ v _ s and the ball serving frequency t _ fr in the single ball serving parameter t _ p _ g of the serving machine are improved.

And comparing the angle _ net of the networking direction in the dimension of each group of track data Traj with the networking angle p _ angle _ net of the preset target parameter Sa.

If angle _ net is larger than p _ angle _ net, the rotating speed t _ r _ s, the linear speed t _ v _ s and the ball serving frequency t _ fr in the single ball serving parameter t _ p _ g of the ball serving machine are improved, and the position coordinate t _ y of the drop point is reduced; if the angle _ net is equal to p _ angle _ net, the rotating speed t _ r _ s, the linear speed t _ v _ s, the ball serving frequency t _ fr and the falling point position coordinate t _ y in the single ball serving parameter t _ p _ g of the serving machine are not changed; if angle _ net is less than p _ angle _ net, the rotating speed t _ r _ s, the linear speed t _ v _ s, the serving frequency t _ fr and the lifting and dropping point position coordinate t _ y in the serving machine single ball serving parameter t _ p _ g are reduced.

Comparing the falling points (x _ rebound, y _ rebound) in the dimension of each set of track data Traj with the falling points (p _ t _ x, p _ t _ y) of the preset target parameter Sa.

If x _ rebound > p _ t _ x, reducing the rotating speed t _ r _ s and the serving frequency t _ fr in the serving machine single ball serving parameter t _ p _ g and increasing the position coordinate t _ x of the falling point; if x _ rebound is p _ t _ x, keeping the rotating speed t _ r _ s, the serving frequency t _ fr and the falling point position coordinate t _ x in the single ball serving parameter t _ p _ g of the original serving machine; if x _ rebound < p _ t _ x, the rotating speed t _ r _ s and the serving frequency t _ fr in the serving machine single ball serving parameter t _ p _ g are increased and the falling point position coordinate t _ x is reduced.

If y _ rebound is larger than p _ t _ x, reducing the linear speed t _ v _ s and the serving frequency t _ fr in the serving machine single ball serving parameter t _ p _ g and increasing the position coordinate t _ y of the falling point; if y _ rebound is p _ t _ x, keeping the linear speed t _ v _ s, the serve frequency t _ fr and the falling point position coordinate t _ y in the single ball serving parameter t _ p _ g of the original serving machine; if y _ bound < p _ t _ x, the linear speed t _ v _ s and the serve frequency t _ fr in the serve parameter t _ p _ g of the serve machine are increased, and the position coordinate t _ y of the drop point is increased.

S552, obtaining the single-ball serving parameter p _ g of the serving machine in the current serving parameter queue.

And S553, sequentially and correspondingly comparing each parameter in the single ball serving parameter p _ g of the pitching machine in the current pitching parameter queue with each parameter in the preset pitching parameter t _ p _ g of the pitching machine, and updating the single ball serving parameter p _ g of the pitching machine in the current pitching parameter queue according to the comparison result.

The single-ball service parameters p _ g of the service machine in the current service parameter queue comprise a linear velocity v _ s, a rotating speed r _ s, a rotating direction r _ t, a falling point position (x, y) and a service frequency fr.

The preset serving parameters t _ p _ g of the single ball of the serving machine comprise a linear speed t _ v _ s, a rotating speed t _ r _ s, a rotating direction t _ r _ t, a falling point position (t _ x, t _ y) and a serving frequency t _ fr.

Specifically, the alignment process is as follows:

comparing v _ s with t _ v _ s, comparing r _ s with t _ r _ s, comparing r _ t with t _ r _ t, comparing (x, y) with (t _ x, t _ y), comparing fr with t _ fr, and updating different parameters in the preset serving parameter t _ p _ g to the serving parameter p _ g of the serving machine in the current serving parameter queue.

The service state of the service robot is obtained, whether the service condition is met or not is judged according to the service state, and if the service condition is met, an updating instruction is directly issued to update the service single-shot parameters of the service robot 2; if not, waiting for the next time window until the condition is met; and if the preset serve parameter t _ p _ g of the serving machine is updated again in the waiting period, comparing again, and repeating the process until the updating is successful.

In the step S66, in the process of generating the data report of the single training according to the single serve record and the table tennis trajectory data, the data report includes the ability score, the return speed curve, the quality score and the comprehensive growth report.

Wherein the ability score comprises a hit rate, a height score, a drop point score, an angle score, and a speed score.

The hit rate mainly refers to the table-tennis putting rate of each training, and comprises the hit rate of the track in a single training and the single-ball hit rate. The hit rate of the track in a single training can be changed in real time according to specific conditions until the training is finished, and the hit rate of the training is obtained; and the single ball hit rate in a single training represents the hit rate accumulated to the current trajectory number in the single training.

The height score refers to the average height of the trawl for each training.

The height score for a single track may be calculated according to the following equation:

height of single ball is equal to height difference of corresponding rule, lower limit of height score, height difference x (height of passing through net-corresponding rule, lower limit of height)

Wherein:

the height score difference is the corresponding rule height score upper limit-the corresponding rule height score lower limit;

the height difference is the corresponding rule height upper limit-the corresponding rule height lower limit.

Specifically, each training session will obtain a single-ball height score by calculating an average score of the height of the trawl in the plurality of trajectory data. Wherein, the specific score calculation rule of each net passing height may be:

0< net height < 15, corresponding to a score of 100 to 50;

15 < net height < 60, corresponding to 50-0;

60< height of cross web, corresponding to a score of 0.

The drop point score refers to the average drop point region score for each training.

Each training will obtain a single ball drop point score by calculating an average score of drop point coordinates (ball.x; ball.y) in a plurality of trajectory data, and the specific score calculation rule of each drop point coordinate may be:

converting the X-axis and Y-axis coordinates of the drop point area according to an absolute value mode;

as shown in fig. 8, a total of 8 dot regions are set, and the upper left corner of each region is the origin of the dot region.

The range of the falling point score value-taking domain in each falling point region cannot exceed the score calculation domain corresponding to the falling point region;

the drop point region score for a single trajectory may be calculated according to the following equation:

the single ball drop point score is the origin score plus the X-axis differential in the drop point region divided by the X-axis length differential in the drop point region (X-axis coordinate-drop point region corresponding to X-axis origin start coordinate) + the Y-axis differential in the drop point region divided by the Y-axis length differential in the drop point region (Y-axis coordinate-drop point region corresponding to Y-axis origin start coordinate)

Wherein the content of the first and second substances,

the length difference of an X/Y axis in the drop point area is equal to an X/Y axis termination coordinate point in the drop point area-an X/Y axis starting coordinate point in the drop point area;

and the X/Y axis difference in the drop point region is the X/Y axis ending coordinate point score in the drop point region-the X/Y axis starting coordinate point score in the drop point region.

The angle score refers to the average angle score per training.

The angle score for a single trajectory may be calculated according to the following equation:

the single-ball angle score is equal to the corresponding regular angle score lower limit + the angle score difference ÷ the angle difference × (passing angle-corresponding regular angle lower limit).

Wherein the content of the first and second substances,

the angle score difference is the corresponding rule angle score upper limit-the corresponding rule angle score lower limit;

the angle difference is the corresponding regular angle upper limit-the corresponding regular angle lower limit;

each training is obtained by calculating an average score of the net angle (net _ angle) in the plurality of trajectory data, and the specific score calculation rule of each net angle may be:

0 ≦ screen angle <30, corresponding score 50-100;

the screen angle is less than or equal to 30 and less than or equal to 90, and the corresponding score is 100-80.

The velocity score refers to the average velocity score per training.

The velocity score for a single trajectory may be calculated according to the following equation:

single ball velocity score is corresponding to regular velocity score lower limit + velocity score difference ÷ velocity difference × (net angle-corresponding regular velocity lower limit)

Wherein the content of the first and second substances,

the speed score difference is the upper limit of the corresponding rule speed score-the lower limit of the corresponding rule speed score;

the speed difference corresponds to the upper regular speed limit-the lower regular speed limit.

Each training will obtain a single-ball speed score by calculating the average score of the net speed (net _ speed) in a plurality of trajectory data, and each net speed specific score calculation rule can be as follows:

0 & lt, screen speed & lt, 10, corresponding score is 0-85;

the screen passing speed is less than or equal to 10 and less than or equal to 15, and the corresponding score is 85-99;

15 ≦ speed to cross web, corresponding to a score of 100.

The ball return speed curve refers to a ball return speed/net speed (net _ speed) score curve of each training.

The quality score refers to the average return quality score of each training.

Each training is obtained by calculating weighted average scores of the net passing speed (net _ speed), the net passing height (net _ high) and the drop point coordinate (ball.x; ball.y) in a plurality of track data, and each net passing speed score can be calculated according to the following formula:

single ball quality score-single ball speed score × 30% + single ball height score × 30% + single ball drop point score × 40%.

The overall growth report refers to the average of the five dimensions in the first 3 training ability scores and the average of the five dimensions in the last 3 training ability scores. If there are less than 3 training ability scores, only the average of five dimensions in the previous 3 training ability scores is displayed. Green in the integrated growth report represents: an initial level; red stands for: the ability grows.

It should be noted that: the table tennis intelligent teaching system provided by the embodiment and the table tennis intelligent teaching method belong to the same concept.

The embodiments of the present application described above may be implemented in various hardware, software code, or a combination of both. For example, the embodiments of the present application may also be program code for executing the above-described method in a data signal processor. The present application may also relate to various functions performed by a computer processor, digital signal processor, microprocessor, or field programmable gate array. The processor described above may be configured in accordance with the present application to perform certain tasks by executing machine-readable software code or firmware code that defines certain methods disclosed herein. Software code or firmware code may be developed in different programming languages and in different formats or forms. Software code may also be compiled for different target platforms. However, different code styles, types, and languages of software code and other types of configuration code for performing tasks according to the present application do not depart from the spirit and scope of the present application.

The foregoing is merely an illustrative embodiment of the present application, and any equivalent changes and modifications made by those skilled in the art without departing from the spirit and principles of the present application shall fall within the protection scope of the present application.

Claims (10)

1. The table tennis intelligent teaching system is characterized by comprising a control terminal, a serving robot and eagle eye equipment, wherein the serving robot and the eagle eye equipment are connected with the control terminal;

the control terminal is provided with a matching module, a course distribution module, a ball return quality analysis module, a parameter adjusting module and a report generating module; the matching module is used for matching corresponding teaching courses for the student according to the historical training records of the student; the course distribution module is used for sending corresponding teaching courses to the service robots corresponding to the students;

the service robot is used for serving according to the service instruction; the eagle eye equipment is used for collecting the motion data of the table tennis and feeding back the motion data to the control terminal;

the ball return quality analysis module is used for analyzing the ball speed, the net passing height, the angle, the falling point and the rotating speed of the table tennis to obtain an effective table tennis track;

the parameter adjusting module is used for adjusting the service parameters of the service robot in real time according to the analysis result of the return ball quality analysis module;

the report generation module is used for generating a single training data report according to the single ball serving record and the table tennis track data so as to complete the objective evaluation of the teaching process.

2. The intelligent table tennis teaching system of claim 1, wherein the matching module extracts a training record portrait of a student from a historical training record of the student and pushes a corresponding course to the student according to the training record portrait;

the training record representation comprises three dimensions; the first dimension comprises the common handedness, age and gender of the student, the second dimension comprises the ability growth condition, stability and action consistency of the student, and the third dimension comprises the weighted average difficulty of the student training course in a period of time.

3. The table tennis intelligent teaching system of claim 1, wherein the working process of the ball return quality analysis module is as follows:

obtaining effective target space data according to the physical law of table tennis flight, and synchronizing the coordinates of the moving target to a motion state analysis cache queue T;

judging whether a plurality of effective coordinate data cached in the queue T form an effective track, if the effective coordinate data in the cache queue T meet the acquisition condition, forming the effective track by the effective coordinate data, and outputting track data Traj in the queue T; otherwise, clearing the effective coordinate data from the queue T;

calculating the acquisition direction of the trajectory data Traj according to the acquisition time of the trajectory data Traj and the relative coordinates of the trajectory data in a three-dimensional space coordinate system of the ping-pong table;

for each group of ping-pong ball trajectory data Traj, calculating ball speed v _ net, net passing height h _ net, angle _ net and drop point (x _ rebound, y _ rebound) of the net passing direction at the net passing moment of the single group of trajectories by using a fitting algorithm, and calculating the rotation speed (w _ rebound) of the ping-pong ball by using a deep neural network_x,w_y,w_z)。

4. The table tennis intelligent teaching system of claim 3, wherein the parameter adjusting module works in the following process:

comparing the ball return quality analysis result with a preset training characteristic target, and modifying the single ball serving parameters of the preset serving machine according to the comparison result;

acquiring single-ball serving parameters of a serving machine in a current serving parameter queue;

sequentially and correspondingly comparing each parameter in the single-ball service parameters of the service robot in the current service parameter queue with each parameter in the preset single-ball service parameters of the service robot, and updating the single-ball service parameters of the service robot in the current service parameter queue according to the comparison result;

and obtaining the service state of the service robot, judging whether the service state meets the updating service condition or not according to the service state, and directly issuing an updating instruction to update the service single-ball parameters of the service robot if the service state meets the updating service condition.

5. The intelligent table tennis teaching system according to claim 4, wherein the process of comparing the ball return quality analysis result with the preset training characteristic target and modifying the single ball serving parameters of the preset serving machine according to the comparison result comprises:

presetting a single ball serving parameter t _ p _ g of a serving machine: linear velocity t _ v _ s, rotation speed t _ r _ s, rotation direction t _ r _ t, landing position (t _ x, t _ y) and serving frequency t _ fr;

comparing the ball speed v _ net in the net passing direction in the dimensionality of each group of track data Traj with the net passing ball speed p _ net of a preset target parameter Sa; if v _ net is larger than p _ net, the linear speed t _ v _ s and the service frequency t _ fr in the single ball service parameter t _ p _ g of the service robot are improved; if the v _ net is p _ net, the linear speed t _ v _ s and the ball serving frequency t _ fr in the single ball serving parameter t _ p _ g of the serving machine are not changed; if v _ net is less than p _ net, reducing the linear velocity t _ v _ s and the ball serving frequency t _ fr in the single ball serving parameter t _ p _ g of the ball serving machine;

comparing the net passing height h _ net in the net passing direction in the dimension of each group of track data Traj with the net passing height p _ h _ net of a preset target parameter Sa; if h _ net is larger than p _ h _ net, reducing the rotating speed t _ r _ s, the linear speed t _ v _ s and the ball serving frequency t _ fr in the single ball serving parameter t _ p _ g of the serving machine; if h _ net is p _ h _ net, the rotating speed t _ r _ s, the linear speed t _ v _ s and the ball serving frequency t _ fr in the serving machine single ball serving parameter t _ p _ g are not changed; if h _ net is less than p _ h _ net, the rotating speed t _ r _ s, the linear speed t _ v _ s and the ball serving frequency t _ fr in the single ball serving parameter t _ p _ g of the serving machine are improved;

comparing the angle _ net of the networking direction in the dimension of each group of track data Traj with the networking angle p _ angle _ net of a preset target parameter Sa; if angle _ net is larger than p _ angle _ net, the rotating speed t _ r _ s, the linear speed t _ v _ s and the ball serving frequency t _ fr in the single ball serving parameter t _ p _ g of the ball serving machine are improved, and the position coordinate t _ y of the drop point is reduced; if the angle _ net is equal to p _ angle _ net, the rotating speed t _ r _ s, the linear speed t _ v _ s, the ball serving frequency t _ fr and the falling point position coordinate t _ y in the single ball serving parameter t _ p _ g of the serving machine are not changed; if angle _ net is less than p _ angle _ net, reducing the rotating speed t _ r _ s, linear speed t _ v _ s, service frequency t _ fr and lifting falling point position coordinate t _ y in the service robot single ball service parameter t _ p _ g;

comparing the falling point (x _ rebound, y _ rebound) in the dimension of each group of track data Traj with the falling point (p _ t _ x, p _ t _ y) of the preset target parameter Sa; if x _ rebound > p _ t _ x, reducing the rotating speed t _ r _ s and the serving frequency t _ fr in the serving machine single ball serving parameter t _ p _ g and increasing the position coordinate t _ x of the falling point; if x _ rebound is p _ t _ x, keeping the rotating speed t _ r _ s, the serving frequency t _ fr and the falling point position coordinate t _ x in the single ball serving parameter t _ p _ g of the original serving machine; if x _ rebound is less than p _ t _ x, the rotating speed t _ r _ s and the serving frequency t _ fr in the serving machine single ball serving parameter t _ p _ g are increased, and the falling point position coordinate t _ x is reduced; if y _ rebound is larger than p _ t _ x, reducing the linear speed t _ v _ s and the serving frequency t _ fr in the serving machine single ball serving parameter t _ p _ g and increasing the position coordinate t _ y of the falling point; if y _ rebound is p _ t _ x, keeping the linear speed t _ v _ s, the serve frequency t _ fr and the falling point position coordinate t _ y in the single ball serving parameter t _ p _ g of the original serving machine; if y _ bound < p _ t _ x, the linear speed t _ v _ s and the serve frequency t _ fr in the serve parameter t _ p _ g of the serve machine are increased, and the position coordinate t _ y of the drop point is increased.

6. The intelligent table tennis teaching system according to claim 4, wherein the process of sequentially and correspondingly comparing each of the individual service parameters of the serving machine in the current service parameter queue with each of the individual service parameters of the preset serving machine, and updating the individual service parameters of the serving machine in the current service parameter queue according to the comparison result is as follows:

the single-ball serving parameters p _ g of the serving machine in the current serving parameter queue comprise a linear velocity v _ s, a rotating speed r _ s, a rotating direction r _ t, a drop point position (x, y) and a serving frequency fr;

presetting a single ball serving parameter t _ p _ g of the ball serving machine, wherein the single ball serving parameter t _ p _ g comprises a linear speed t _ v _ s, a rotating speed t _ r _ s, a rotating direction t _ r _ t, a falling point position (t _ x, t _ y) and a ball serving frequency t _ fr;

comparing v _ s with t _ v _ s, comparing r _ s with t _ r _ s, comparing r _ t with t _ r _ t, comparing (x, y) with (t _ x, t _ y), comparing fr with t _ fr, and updating different parameters in the preset serving parameter t _ p _ g to the serving parameter p _ g of the serving machine in the current serving parameter queue.

7. The table tennis intelligent teaching system of claim 1, wherein the report generation module generates the data report by:

analyzing a single-serve record queue Q1 of serve of the serve robot to obtain single-serve records in the queue;

acquiring trajectory data in a queue by combining a trajectory data queue Q2 acquired by eagle eye equipment, and sequentially carrying out ball return quality analysis;

performing ability scoring according to the ball return quality result;

and calculating a data report of single training according to the weights of different scoring dimensions, wherein the scoring dimensions comprise speed, angle, falling point and station-climbing rate.

8. The intelligent table tennis teaching system of claim 7, wherein said scoring abilities based on return quality results comprises hit rate, height score, drop point score, angle score, and speed score;

single ball height score is equal to corresponding rule height score lower limit + height score difference ÷ height difference × (cross-web height-corresponding rule height lower limit);

wherein the content of the first and second substances,

the height score difference is the corresponding rule height score upper limit-the corresponding rule height score lower limit;

the height difference is the upper limit of the corresponding regular height-the lower limit of the corresponding regular height;

the single ball drop point score is the origin score plus the X-axis differential in the drop point region divided by the X-axis length differential in the drop point region (X-axis coordinate-drop point region corresponding to X-axis origin start coordinate) + the Y-axis differential in the drop point region divided by the Y-axis length differential in the drop point region (Y-axis coordinate-drop point region corresponding to Y-axis origin start coordinate)

Wherein the content of the first and second substances,

the length difference of an X/Y axis in the drop point area is equal to an X/Y axis termination coordinate point in the drop point area-an X/Y axis starting coordinate point in the drop point area;

the X/Y axis difference in the drop point region is the X/Y axis termination coordinate point score in the drop point region-the X/Y axis starting coordinate point score in the drop point region;

the single-ball angle score is equal to the corresponding regular angle score lower limit + the angle score difference ÷ the angle difference x (the net passing angle-the corresponding regular angle lower limit);

wherein the content of the first and second substances,

the angle score difference is the corresponding rule angle score upper limit-the corresponding rule angle score lower limit;

the angle difference is the corresponding regular angle upper limit-the corresponding regular angle lower limit;

a single-ball speed score is equal to the corresponding regular speed score lower limit + speed score difference ÷ speed difference × (speed net angle-corresponding regular speed lower limit);

wherein the content of the first and second substances,

the speed score difference is the corresponding rule speed score upper limit-the corresponding rule speed score lower limit;

the speed difference corresponds to the upper regular speed limit-the lower regular speed limit.

9. An intelligent table tennis teaching method is characterized by comprising the following steps:

analyzing the historical training records of the trainees, and matching corresponding teaching courses according to the analysis results;

distributing the teaching courses to corresponding service robots, serving the balls by the service robots, and starting training;

acquiring track data of a table tennis ball in a training process by utilizing eagle eye equipment;

performing ball returning quality analysis according to the acquired track data of the table tennis;

adjusting the serve parameters of the serve robot in real time according to the return ball quality analysis result;

and generating a data report of single training according to the single ball serving record and the track data of the table tennis to finish objective evaluation on the teaching process.

10. The intelligent table tennis teaching method according to claim 9, wherein the process of adjusting the serving parameters of the serving robot in real time according to the return quality analysis result is as follows:

comparing the return ball quality analysis result with a preset training characteristic target, and modifying the single ball serving parameters of the preset serving machine according to the comparison result;

acquiring the single-ball serving parameters of the serving machine in the current serving parameter queue;

sequentially and correspondingly comparing each parameter in the single ball service parameters of the service robot in the current service parameter queue with each parameter in the single ball service parameters of the preset service robot, and updating the single ball service parameters of the service robot in the current service parameter queue according to the comparison result;

and obtaining the service state of the service robot, judging whether the service state meets the updating service condition or not according to the service state, and directly issuing an updating instruction to update the service single-ball parameters of the service robot if the service state meets the updating service condition.

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