CN114582195B - Intelligent teaching system and teaching method for table tennis - 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 service robot and eagle eye equipment, wherein the service 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 students; the course distribution module is used for sending the corresponding teaching courses to the serving robots corresponding to the students; the service robot is used for performing service according to the service instruction; the eagle eye equipment is used for collecting the movement data of the table tennis ball and feeding back the movement data to the control terminal; the ball returning quality analysis module is used for analyzing the ball speed, the net passing height, the angle, the drop point and the rotation speed of the table tennis ball; 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 service record and the track data of the table tennis. The application can improve the teaching effect.

Description

Intelligent teaching system and teaching method for table tennis

Technical Field

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

Background

The table tennis is known as a national ball in China, is deeply loved by wide sports lovers, and 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, sports teachers in many schools are both life and role-play, and no special table tennis teacher exists; and secondly, sports teachers in many places are not trained by related table tennis balls and are not deeply built in high schools, and the correct steps, the correct shooting postures and other contents are difficult to master without special training, and the table tennis teaching is more important to the use of individual skills of the table tennis teacher and the training experience.

Disclosure of Invention

The application provides an intelligent teaching system and a teaching method for table tennis, which at least solve the problems in the related art to a certain extent.

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 with the control terminal;

the control terminal is provided with a matching module, a course distribution module, a ball returning quality analysis module, a parameter adjustment module and a report generation module; the matching module is used for matching corresponding teaching courses for the students according to the history training records of the students; the course distribution module is used for sending corresponding teaching courses to the service robot corresponding to the trainee;

the service robot is used for performing service according to a service instruction; the eagle eye equipment is used for collecting movement data of the table tennis ball and feeding back the movement data to the control terminal;

The ball returning quality analysis module is used for analyzing the ball speed, the net passing height, the angle, the drop point and the rotation speed of the table tennis ball so as to obtain an effective table tennis ball track;

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

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

In the intelligent table tennis teaching system, the matching module extracts training record portraits of the students from the history training records of the students, and pushes corresponding courses to the students according to the training record portraits;

the training record representation comprises three dimensions; the first dimension comprises the usual handholding, age and sex of the learner, the second dimension comprises the ability growth condition, stability and action consistency of the learner, and the third dimension comprises the weighted average difficulty of the training course of the learner in a period of time.

In the intelligent teaching system for table tennis, the working process of the ball returning quality analysis module is as follows:

Acquiring effective target space data according to the physical law of table tennis flight, and synchronizing the coordinates of a moving target into 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 valid coordinate data from the queue T;

Calculating the acquisition direction of track data Traj according to the acquisition time of track data Traj and the relative coordinates of the track data in a three-dimensional space coordinate system of a table tennis table;

For each set of table tennis track data Traj, the ball speed v_net, the net height h_net, the angle_net and the drop point (x_ rebound, y_ rebound) of the net passing direction at the net passing time of the single set of track are calculated by using a fitting algorithm, and the rotation speed of the table tennis is calculated by using a deep neural network (w _x,w_y,w_z).

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

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

Acquiring single-ball service parameters of a service machine in a current service parameter queue;

sequentially and correspondingly comparing each parameter in the single-serve parameters of the current serving parameter queue with each parameter in the single-serve parameters of the preset serving machine, and updating the single-serve parameters of the serving machine in the current serving parameter queue according to the comparison result;

and acquiring the service state of the service robot, judging whether the service state meets the updated service condition according to the service state, and if so, directly issuing an update instruction to update the service single-ball parameters of the service robot.

Further, the process of comparing the ball return quality analysis result with a preset training feature target and modifying the single-ball service parameters of the preset service robot according to the comparison result is as follows:

Presetting a single-ball service parameter t_p_g of a service robot: linear velocity t_v_s, rotational velocity t_r_s, rotational direction t_r_t, landing position (t_x, t_y), and ball serving frequency t_fr;

Comparing the speed v_net of the net passing direction in the dimension of each group of track data Traj with the speed p_net of the net passing ball of the preset target parameter Sa; if v_net > 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 lifted; if v_net=p_net, the linear speed t_v_s and the service frequency t_fr in the single-shot service parameter t_p_g of the service robot are unchanged; if v_net < p_net, reducing 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;

Comparing the passing height h_net of the passing direction in the dimension of each group of track data Traj with the passing height p_h_net of the preset target parameter Sa; if h_net > p_h_net, decreasing the spin speed t_r_s, the linear speed t_v_s and the serve frequency t_fr in the serve single-serve parameter t_p_g of the serve machine; if h_net=p_h_net, the spin t_r_s, the linear speed t_v_s and the serve frequency t_fr in the serve single-serve parameter t_p_g are unchanged; if h_net < p_h_net, the rotation 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 lifted;

Comparing the angle_net of the passing direction in the dimension of each group of track data Traj with the passing angle p_angle_net of the preset target parameter Sa; if angle_net > p_angle_net, then increasing the spin speed t_r_s, the linear speed t_v_s and the serve frequency t_fr in the single serve parameter t_p_g of the serve machine and decreasing the drop point position coordinate t_y; if angle_net=p_angle_net, the rotation 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 ball serving machine are not changed; if angle_net < p_angle_net, decreasing the rotation speed t_r_s, the linear speed t_v_s, the ball serving frequency t_fr and the lifting drop point position coordinate t_y in the single ball serving parameter t_p_g of the ball dispenser;

Comparing the falling points (x_ rebound, y_ rebound) in the dimension of each set of trajectory data Traj with the falling points (p_t_x, p_t_y) of the preset target parameter Sa; if x_ rebound > p_t_x, decreasing the spin speed t_r_s and the serve frequency t_fr in the serve single-serve parameter t_p_g and increasing the drop point position coordinate t_x; if x_ rebound =p_t_x, then the spin speed t_r_s, the serve frequency t_fr, and the drop point position coordinates t_x in the primary player single serve parameter t_p_g are maintained; if x_ rebound is less than p_t_x, then increasing the spin speed t_r_s and the serve frequency t_fr in the single serve parameter t_p_g of the serve machine and decreasing the drop point position coordinate t_x; if y_ rebound > p_t_x, then decreasing the linear speed t_v_s and the serve frequency t_fr in the single serve parameter t_p_g of the machine and increasing the drop point position coordinate t_y; if y_ rebound =p_t_x, then the linear speed t_v_s, the serve frequency t_fr and the drop point position coordinates t_y in the primary player single serve parameter t_p_g are maintained; if y_ rebound < p_t_x, the linear speed t_v_s and the serve frequency t_fr in the single serve parameter t_p_g of the machine are raised and the drop point position coordinates t_y are increased.

Further, the process of comparing each parameter of the single-serve parameters of the current serve parameter queue with each parameter of the single-serve parameters of the preset serve machine in sequence, and updating the single-serve parameters of the current serve machine in the serve parameter queue according to the comparison result is as follows:

The single-ball serving parameters p_g of the current serving parameter queue comprise a linear speed v_s, a rotating speed r_s, a rotating direction r_t, a drop point position (x, y) and a serving frequency fr;

The preset single-ball serving parameters t_p_g of the ball serving machine comprise a linear speed t_v_s, a rotating speed t_r_s, a rotating direction t_r_t, a drop 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 the parameters different from the preset service parameters t_p_g into the service parameters p_g of the service machine in the current service parameter queue.

In the intelligent teaching system for table tennis, the report generating module generates a data report by the following steps:

Analyzing a single-ball service record queue Q1 of the service robot for service, and acquiring a single-ball service record in the queue;

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

carrying out capacity grading according to the ball return quality result;

And calculating a data report of single training according to weights of different scoring dimensions, wherein the scoring dimensions comprise speed, angle, drop point and upper platform rate.

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

Single sphere height score = corresponding rule height score lower limit + height score difference ++height difference × (net height-corresponding rule height lower limit);

Wherein,

Height score difference = corresponding rule height score upper limit-corresponding rule height score lower limit;

height difference = corresponding rule upper height limit-corresponding rule lower height limit;

Single ball drop score = origin score + X-axis difference in drop area ∈x-axis length in drop area × (X-axis coordinate-drop area corresponds to X-axis origin start coordinate) +y-axis difference in drop area ++y-axis length in drop area× (Y-axis coordinate-drop area corresponds to Y-axis origin start coordinate)

Wherein,

The X/Y axis length difference in the drop point area = the X/Y axis end coordinate point in the drop point area-the X/Y axis start coordinate point in the drop point area;

X/Y axis difference in drop point region = X/Y axis end coordinate point score in drop point region-X/Y axis start coordinate point score in drop point region;

single sphere angle score = corresponding rule angle score lower limit + angle score difference +.o angle difference × (net crossing angle-corresponding rule angle lower limit);

Wherein,

Angle score difference = corresponding rule angle score upper limit-corresponding rule angle score lower limit;

angle difference = corresponding to upper rule angle limit-corresponding to lower rule angle limit;

Single ball speed score = corresponding rule speed score lower limit + speed score difference +.speed difference × (speed net angle-corresponding rule speed lower limit);

Wherein,

Speed score difference = corresponding rule speed score upper limit-corresponding rule speed score lower limit;

speed difference = upper rule-corresponding speed limit-lower rule-corresponding speed limit.

According to a second aspect of the embodiment of the application, the application also provides an intelligent teaching method for table tennis, which comprises the following steps:

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

distributing the teaching courses to the corresponding service robots, and enabling the service robots to serve the ball and start training;

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

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

the service parameters of the service robot are adjusted in real time according to the ball return quality analysis result;

and generating a data report of single training according to the single ball service record and the track data of the table tennis so as to finish objective evaluation of 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 result of the ball return quality analysis is as follows:

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

Acquiring single-ball service parameters of a service machine in a current service parameter queue;

sequentially and correspondingly comparing each parameter in the single-serve parameters of the current serving parameter queue with each parameter in the single-serve parameters of the preset serving machine, and updating the single-serve parameters of the serving machine in the current serving parameter queue according to the comparison result;

and acquiring the service state of the service robot, judging whether the service state meets the updated service condition according to the service state, and if so, directly issuing an update instruction to update the service single-ball parameters of the service robot.

According to the above specific embodiments of the present application, at least the following advantages are achieved: the application provides a table tennis intelligent teaching system, which is characterized in that a control terminal, a service robot and eagle eye equipment are arranged, the eagle eye equipment acquires an effective moving target, the control terminal analyzes according to a history training record of a student and automatically matches teaching courses according to analysis results, and after the courses are distributed to corresponding service robots, table tennis training is started; the service robot performs service according to a course preset instruction, and meanwhile, eagle eye equipment performs data acquisition; the eagle eye equipment feeds back the acquired data to the control terminal, the control terminal performs ball returning quality analysis, the analysis result is compared with a preset training characteristic target, the ball serving parameters of the ball serving robot are adjusted in real time according to the actual comparison condition, the ball serving parameters are changed, and the teaching target of multi-ball training is realized; 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, feed back objective evaluation based on data in the complex teaching process, complete the teaching process of a coach and a plurality of students at the same time, and solve the social problems of shortage and lack 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 application, as claimed.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, 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 of an intelligent teaching method for table tennis, according to an embodiment of the present application, analyzing according to a history training record of a student, and matching teaching courses according to an analysis result.

Fig. 4 is a flowchart of starting a service robot to serve a ball and simultaneously starting eagle eye equipment to collect data in the intelligent teaching method for table tennis provided by the embodiment of the application.

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

Fig. 6 is a flowchart of comparing a ball return quality analysis result with a preset training feature target in the intelligent teaching method for table tennis provided by the embodiment of the application.

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

Fig. 8 is a schematic view of a landing point area in a table tennis intelligent teaching method according to an embodiment of the present application.

Reference numerals illustrate:

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

2. a service robot;

3. Eagle eye equipment.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the spirit of the present disclosure will be clearly described in the following drawings and detailed description, and any person skilled in the art, after having appreciated the embodiments of the present disclosure, may make alterations and modifications by the techniques taught by the present disclosure without departing from the spirit and scope of the present disclosure.

The exemplary embodiments of the present application and the descriptions thereof are intended to illustrate the present application, but not to limit the present application. In addition, the same or similar reference numerals are used for the same or similar parts in the drawings and the embodiments.

The terms "first," "second," …, etc. as used herein do not denote a particular order or sequence, nor are they intended to limit the application, but rather are merely used to distinguish one element or operation from another in the same technical term.

As used herein, the terms "comprising," "including," "having," "containing," and the like are intended to be inclusive and mean an inclusion, but not limited to.

As used herein, "and/or" includes any or all combinations of such things.

Reference herein to "a plurality" includes "two" and "more than two"; the term "plurality of sets" as used herein includes "two sets" and "more than two sets".

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

As shown in fig. 1, the table tennis intelligent teaching system provided by the embodiment of the application comprises a control terminal 1, a service robot 2 and an eagle eye device 3, wherein the service robot 2 and the eagle eye device 3 are connected with the control terminal 1.

Wherein, 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 corresponding teaching courses for the students according to the history training records of the students.

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

The ball returning quality analysis module 13 is used for analyzing the ball speed, the passing height, the angle, the drop point and the rotation speed of the table tennis ball so as to obtain an effective table tennis ball track.

The parameter adjustment module 14 is used for adjusting the service parameters of the service robot 2 in real time according to the analysis result of the ball return quality analysis module 13.

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

Specifically, the history training record of the learner mainly includes service dimension information and device acquisition dimension information. The service dimension information mainly comprises, but is not limited to, the following information: time, training course, comprehensive score, single score, training duration, etc. The device acquisition dimension information mainly comprises, but is not limited to, the following information: ball return track, net passing height, net passing angle, ball return speed, stage rate, ball return drop point, etc. The ball return track is represented by a three-dimensional track point matrix.

The matching module 11 adopts the existing big data model to rapidly extract training record images of the students from the history training records of the students, and pushes corresponding courses to the students according to the training record images so as to help the students to gradually improve the technical level.

Wherein, training record portrait mainly includes three dimensions. The first dimension mainly includes personal information such as usual hand-holding, age, sex, etc. of the learner. The second dimension mainly includes a comprehensive representation of the trainee's ability calculated by accumulating according to the time period, for example, the trainee's ability growth, stability, action consistency, etc. The third dimension primarily includes a weighted average difficulty of the trainee's training course over a period of time.

The service robot 2 stores the received combined single-ball service instruction in the corresponding teaching course temporarily. The temporary storage combined single-ball service instruction specifically comprises a combined ball list combined ball service mode, wherein the combined ball service mode comprises an ordered ball service mode and a random ball service mode, and the ordered ball service mode comprises an ordered ball service mode and a reverse ball service mode with an inverted combined ball list sequence.

The random service mode is to randomly select a single ball to be served from the combined ball list; the service parameter p_g of each single ball comprises a linear speed v_s, a rotation speed r_s, a rotation direction r_t, a drop point position (x, y), a service frequency fr and other equipment attributes, and the service attributes are related to the service number or service duration and the combination mode.

The combined mode is mainly used for initial contact courses and training scenes after familiarity with the courses.

After the service robot 2 finishes temporary storage, the eagle eye equipment 3 performs data acquisition according to the starting 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 caching algorithm, entering a device starting state, and returning a starting mark; and restarting if temporary storage fails.

The control terminal 1 controls the service robot 2 to execute a cache service instruction according to the communication signaling of the eagle eye starting mark; the start-up failure is restarted.

The eagle eye equipment 3 feeds back the acquired movement data of the table tennis ball to the control terminal 1, a ball return quality analysis module 13 is further arranged in the control terminal 1, and the specific working process of the ball return quality analysis module 13 is as follows:

and acquiring effective target space data according to the physical law of the ping-pong ball flight, and synchronizing the coordinates of the moving target into 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, the valid coordinate data is cleared from the queue T.

Calculating the acquisition direction of track data Traj according to the acquisition time of track data Traj and the relative coordinates of the track data in a three-dimensional space coordinate system of a table tennis table; for each set of table tennis track data Traj, the ball speed v_net, the net height h_net, the angle_net and the drop point (x_ rebound, y_ rebound) of the net passing direction at the net passing time of the single set of track are calculated by using a fitting algorithm, and the rotation speed of the table tennis is calculated by using a deep neural network (w _x,w_y,w_z).

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

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

Presetting a single-ball service parameter t_p_g of a service robot: the linear speed t_v_s, the rotating speed t_r_s, the rotating direction t_r_t, the drop point position (t_x, t_y) and the service frequency t_fr, and the parameters are copied according to the next service parameters in the service queue QT of the preset combination; and acquiring a preset target Sa from the combined service queue QT for comparison.

Comparing the track data Traj with a preset target parameter Sa in a training course through a rule engine c_i, and modifying a single-ball service parameter t_p_g of a preset ball service machine according to the comparison result, wherein the specific process is as follows;

And comparing the ball speed v_net of the passing direction in the dimension of each set of track data Traj with the passing ball speed p_net of the preset target parameter Sa.

If v_net > 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 lifted; if v_net=p_net, the linear speed t_v_s and the service frequency t_fr in the single-shot service parameter t_p_g of the service robot are unchanged; if v_net < p_net, the linear speed t_v_s and the serve frequency t_fr in the single serve parameter t_p_g of the machine are reduced.

And comparing the passing height h_net of the passing direction in the dimension of each set of track data Traj with the passing height p_h_net of the preset target parameter Sa.

If h_net > p_h_net, decreasing the spin speed t_r_s, the linear speed t_v_s and the serve frequency t_fr in the serve single-serve parameter t_p_g of the serve machine; if h_net=p_h_net, the spin t_r_s, the linear speed t_v_s and the serve frequency t_fr in the serve single-serve parameter t_p_g are unchanged; if h_net < p_h_net, the spin 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 dispenser are lifted.

The angle_net of the passing direction in the dimension of each set of trajectory data Traj is compared with the passing angle p_angle_net of the preset target parameter Sa.

If angle_net > p_angle_net, then increasing the spin speed t_r_s, the linear speed t_v_s and the serve frequency t_fr in the single serve parameter t_p_g of the serve machine and decreasing the drop point position coordinate t_y; if angle_net=p_angle_net, the rotation 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 ball serving machine are not changed; if angle_net < p_angle_net, the spin speed t_r_s, the linear speed t_v_s, the serve frequency t_fr, and the lift drop point position coordinate t_y in the serve single serve parameter t_p_g are reduced.

The drop points (x_ rebound, y_ rebound) in the dimensions of each set of trajectory data Traj are compared with the drop points (p_t_x, p_t_y) of the preset target parameter Sa.

If x_ rebound > p_t_x, decreasing the spin speed t_r_s and the serve frequency t_fr in the serve single-serve parameter t_p_g and increasing the drop point position coordinate t_x; if x_ rebound =p_t_x, then the spin speed t_r_s, the serve frequency t_fr, and the drop point position coordinates t_x in the primary player single serve parameter t_p_g are maintained; if x_ rebound < p_t_x, the spin speed t_r_s and the serve frequency t_fr in the single serve parameter t_p_g of the machine are raised and the drop point position coordinate t_x is lowered.

If y_ rebound > p_t_x, then decreasing the linear speed t_v_s and the serve frequency t_fr in the single serve parameter t_p_g of the machine and increasing the drop point position coordinate t_y; if y_ rebound =p_t_x, then the linear speed t_v_s, the serve frequency t_fr and the drop point position coordinates t_y in the primary player single serve parameter t_p_g are maintained; if y_ rebound < p_t_x, the linear speed t_v_s and the serve frequency t_fr in the single serve parameter t_p_g of the machine are raised and the drop point position coordinates t_y are increased.

S142, acquiring a single-ball service parameter p_g of the service robot in the current service parameter queue.

S143, comparing each parameter in the single-serve parameter p_g of the current serve parameter queue with each parameter in the single-serve parameter t_p_g of the preset serve machine in sequence, and updating the single-serve parameter p_g of the current serve machine according to the comparison result.

The single-serve parameter p_g of the current serve parameter queue includes a linear velocity v_s, a rotational velocity r_s, a rotational direction r_t, a drop point position (x, y), and a serve frequency fr.

The preset single-serve parameters t_p_g of the ball dispenser comprise a linear speed t_v_s, a rotating speed t_r_s, a rotating direction t_r_t, a drop point position (t_x, t_y) and a serving frequency t_fr.

The specific comparison process comprises the following steps:

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 the parameters different from the preset service parameters t_p_g into the service parameters p_g of the service machine in the current service parameter queue.

S144, acquiring a service state of the service robot, judging whether the service state meets the updated service condition according to the service state, and if so, 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 meeting the condition; if the single-ball service parameter t_p_g of the preset service robot 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 service parameters are being acquired or updated in the service combination, the service combination needs to wait, i.e. the service combination does not meet the updated service condition.

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

Specifically, the report generating module 15 generates a data report by:

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

Combining the track data queue Q2 acquired by the eagle eye equipment 3, acquiring track data Traj in the queue, and sequentially carrying out ball returning quality analysis;

carrying out capacity grading according to the ball return quality result;

And calculating a data report of single training according to weights of different scoring dimensions, wherein the scoring dimensions comprise speed, angle, drop point and upper platform rate.

According to the intelligent table tennis teaching system, the control terminal 1, the service robot 2 and the eagle eye equipment 3 are arranged, so that an effective table tennis track can be obtained through the eagle eye equipment 3 in the continuous playing or training process, track parameters are compared with preset ball return parameters in the service robot 2 in real time, a service strategy is adjusted in real time according to actual conditions, and the teaching effect in the table tennis teaching process is improved; meanwhile, the objective evaluation based on data in the complex teaching process can be fed back, the teaching process of one coach to a plurality of students can be completed, and the social problems of shortage and loss of professional coach resources can be solved.

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

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

S22, distributing the teaching courses to the corresponding service robots 2, and enabling the service robots 2 to serve the ball and start training.

S33, acquiring track data of the table tennis ball in the training process by utilizing the eagle eye equipment 3.

S44, performing ball returning quality analysis according to the acquired track data of the table tennis ball.

S55, the service parameters of the service robot 2 are adjusted in real time according to the analysis result of the ball return quality.

And S66, after training is finished, generating a data report of single training according to the single ball service record and the track data of the table tennis so as to finish objective evaluation of the teaching process.

As shown in fig. 3, in the step S11, the specific process of analyzing the history training record of the learner and matching the corresponding teaching course according to the analysis result is as follows:

and extracting training record portraits of the students from the history training records of the students by adopting the existing big data model.

And pushing corresponding teaching courses to students according to the training record portrait.

The history training record of the student mainly comprises service dimension information and equipment acquisition dimension information. The service dimension information mainly comprises, but is not limited to, the following information: time, training course, comprehensive score, single score, training duration, etc. The device acquisition dimension information mainly comprises, but is not limited to, the following information: ball return track, net passing height, net passing angle, ball return speed, stage rate, ball return drop point, etc. The ball 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 usual hand-holding, age, sex, etc. of the learner. The second dimension mainly includes a comprehensive representation of the trainee's ability calculated by accumulating according to the time period, for example, the trainee's ability growth, stability, action consistency, etc. The third dimension primarily includes a weighted average difficulty of the trainee's training course over a period of time.

As shown in fig. 4, in the above step S22, the service robot 2 stores the received combined single-ball service instruction in the corresponding teaching course temporarily. The temporary storage combined single-ball service instruction specifically comprises a combined ball list combined ball service mode, wherein the combined ball service mode comprises an ordered ball service mode and a random ball service mode, and the ordered ball service mode comprises an ordered ball service mode and a reverse ball service mode with an inverted combined ball list sequence.

The random service mode is to randomly select a single ball to be served from the combined ball list; the service parameter p_g of each single ball comprises a linear speed v_s, a rotation speed r_s, a rotation direction r_t, a drop point position (x, y), a service frequency fr and other equipment attributes, and the service attributes are related to the service number or service duration and the combination mode.

The combined mode is mainly used for initial contact courses and training scenes after familiarity with the courses.

As shown in fig. 5, in the step S44, the specific process of performing the ball returning quality analysis according to the collected track data of the table tennis ball is as follows:

s441, acquiring effective target space data according to a physical rule of table tennis flight, and synchronizing coordinates of a moving target into 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, the valid coordinate data is cleared from the queue T.

S442, calculating the acquisition direction of the track data Traj according to the acquisition time of the track data Traj and the relative coordinates of the track data in the three-dimensional space coordinate system of the table tennis table; for each set of table tennis track data Traj, the ball speed v_net, the net height h_net, the angle_net and the drop point (x_ rebound, y_ rebound) of the net passing direction at the net passing time of the single set of track are calculated by using a fitting algorithm, and the rotation speed of the table tennis is calculated by using a deep neural network (w _x,w_y,w_z).

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

S551, as shown in FIG. 5, comparing the ball return quality analysis result with a preset training characteristic target, wherein the specific process is as follows:

Presetting a single-ball service parameter t_p_g of a service robot: the linear speed t_v_s, the rotating speed t_r_s, the rotating direction t_r_t, the drop point position (t_x, t_y) and the service frequency t_fr, and the parameters are copied according to the next service parameters in the service queue QT of the preset combination; and acquiring a preset target Sa from the combined service queue QT for comparison.

The process of comparing the track data Traj with the preset target parameter Sa in the training course and modifying the single-ball service parameter t_p_g of the preset ball service machine according to the comparison result is as follows:

And comparing the ball speed v_net of the passing direction in the dimension of each set of track data Traj with the passing ball speed p_net of the preset target parameter Sa.

If v_net > 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 lifted; if v_net=p_net, the linear speed t_v_s and the service frequency t_fr in the single-shot service parameter t_p_g of the service robot are unchanged; if v_net < p_net, the linear speed t_v_s and the serve frequency t_fr in the single serve parameter t_p_g of the machine are reduced.

And comparing the passing height h_net of the passing direction in the dimension of each set of track data Traj with the passing height p_h_net of the preset target parameter Sa.

If h_net > p_h_net, decreasing the spin speed t_r_s, the linear speed t_v_s and the serve frequency t_fr in the serve single-serve parameter t_p_g of the serve machine; if h_net=p_h_net, the spin t_r_s, the linear speed t_v_s and the serve frequency t_fr in the serve single-serve parameter t_p_g are unchanged; if h_net < p_h_net, the spin 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 dispenser are lifted.

The angle_net of the passing direction in the dimension of each set of trajectory data Traj is compared with the passing angle p_angle_net of the preset target parameter Sa.

If angle_net > p_angle_net, then increasing the spin speed t_r_s, the linear speed t_v_s and the serve frequency t_fr in the single serve parameter t_p_g of the serve machine and decreasing the drop point position coordinate t_y; if angle_net=p_angle_net, the rotation 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 ball serving machine are not changed; if angle_net < p_angle_net, the spin speed t_r_s, the linear speed t_v_s, the serve frequency t_fr, and the lift drop point position coordinate t_y in the serve single serve parameter t_p_g are reduced.

The drop points (x_ rebound, y_ rebound) in the dimensions of each set of trajectory data Traj are compared with the drop points (p_t_x, p_t_y) of the preset target parameter Sa.

If x_ rebound > p_t_x, decreasing the spin speed t_r_s and the serve frequency t_fr in the serve single-serve parameter t_p_g and increasing the drop point position coordinate t_x; if x_ rebound =p_t_x, then the spin speed t_r_s, the serve frequency t_fr, and the drop point position coordinates t_x in the primary player single serve parameter t_p_g are maintained; if x_ rebound < p_t_x, the spin speed t_r_s and the serve frequency t_fr in the single serve parameter t_p_g of the machine are raised and the drop point position coordinate t_x is lowered.

If y_ rebound > p_t_x, then decreasing the linear speed t_v_s and the serve frequency t_fr in the single serve parameter t_p_g of the machine and increasing the drop point position coordinate t_y; if y_ rebound =p_t_x, then the linear speed t_v_s, the serve frequency t_fr and the drop point position coordinates t_y in the primary player single serve parameter t_p_g are maintained; if y_ rebound < p_t_x, the linear speed t_v_s and the serve frequency t_fr in the single serve parameter t_p_g of the machine are raised and the drop point position coordinates t_y are increased.

S552, obtaining the single-ball service parameter p_g of the service robot in the current service parameter queue.

S553, comparing each parameter in the single-serve parameter p_g of the current serve parameter queue with each parameter in the single-serve parameter t_p_g of the preset serve machine in sequence, and updating the single-serve parameter p_g of the current serve machine according to the comparison result.

The single-serve parameter p_g of the current serve parameter queue includes a linear velocity v_s, a rotational velocity r_s, a rotational direction r_t, a drop point position (x, y), and a serve frequency fr.

The preset single-serve parameters t_p_g of the ball dispenser comprise a linear speed t_v_s, a rotating speed t_r_s, a rotating direction t_r_t, a drop point position (t_x, t_y) and a serving frequency t_fr.

Specifically, the 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 the parameters different from the preset service parameters t_p_g into the service parameters p_g of the service machine in the current service parameter queue.

Acquiring a service state of the service robot, judging whether the service state meets the updated service condition according to the service state, and if so, 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 meeting the condition; if the single-ball service parameter t_p_g of the preset service robot 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 track data of the table tennis, the data report includes the ability score, the ball return speed curve, the quality score and the comprehensive growth report.

Wherein the capability score includes hit rate, altitude score, drop point score, angle score, and speed score.

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

Height score refers to the score of the average internet height per training.

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

single ball height score = corresponding rule height score lower limit + height score difference +.v. height difference × (net height-corresponding rule height lower limit)

Wherein:

Height score difference = corresponding rule height score upper limit-corresponding rule height score lower limit;

height difference = upper rule height limit-lower rule height limit.

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

A passing height of 0-15 and a corresponding score of 100-50;

A passing height of 15-60 and a corresponding score of 50-0;

60< internet height, corresponding to a score of 0.

The drop point score refers to the average drop point region score per training.

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

the falling point area converts the X-axis coordinate and the Y-axis coordinate according to an absolute value mode;

As shown in fig. 8, 8 landing areas are provided in total, and each area corresponds to the upper left corner as the origin of the landing area.

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

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

Single ball drop score = origin score + X-axis difference in drop area ∈x-axis length in drop area × (X-axis coordinate-drop area corresponds to X-axis origin start coordinate) +y-axis difference in drop area ++y-axis length in drop area× (Y-axis coordinate-drop area corresponds to Y-axis origin start coordinate)

Wherein,

The X/Y axis length difference in the drop point area = the X/Y axis end coordinate point in the drop point area-the X/Y axis start coordinate point in the drop point area;

X/Y axis difference in drop point area = X/Y axis ending coordinate point score in drop point area-X/Y axis starting coordinate point score in drop point area.

The angle score refers to the average angle score for each training.

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

Single sphere angle score = lower limit of corresponding rule angle score + angle score difference +.o angle difference × (net crossing angle-lower limit of corresponding rule angle).

Wherein,

Angle score difference = corresponding rule angle score upper limit-corresponding rule angle score lower limit;

angle difference = corresponding to upper rule angle limit-corresponding to lower rule angle limit;

Each training may be obtained by calculating an average score of a net angle in the plurality of track data, and each net angle specific score calculation rule may be:

The corresponding score is 50-100, wherein the passing angle is less than or equal to 30 and is 0;

the corresponding score is 100-80, and the passing angle is 30-90.

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

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

single ball speed score = lower limit of corresponding rule speed score + speed score difference +.speed difference × (speed net angle-lower limit of corresponding rule speed)

Wherein,

Speed score difference = corresponding rule speed score upper limit-corresponding rule speed score lower limit;

speed difference = upper rule-corresponding speed limit-lower rule-corresponding speed limit.

Each training may obtain a single ball speed score by calculating a net speed (net_speed) average score in a plurality of track data, and each net speed specific score calculation rule may be:

the net passing speed is equal to or less than 0 and equal to or less than 10, and the corresponding score is 0-85;

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

15 +..

The return speed profile refers to the return speed/net speed (net_speed) score profile for each training.

The quality score refers to the average ball return quality score per training.

Each training is obtained by calculating a weighted average score of a net speed (net_speed), a net height (net_high) and a drop point coordinate (ball. X; ball. Y) in a plurality of track data, and each net speed score can be calculated according to the following formula:

single ball quality score = single ball speed score x 30% + single ball height score x 30% + single ball drop point score x 40%.

Comprehensive growth reports refer to the average of the five dimensions above the first 3 training capacity scores and the average of the five dimensions above the last 3 training capacity scores. If there are less than 3 training capacity scores, only the average of the five dimensions in the first 3 training capacity scores is displayed. Green representation in integrated growth report: an initial level; red represents: ability to grow.

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

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

The foregoing is merely illustrative of the embodiments of this application and any equivalent and equivalent changes and modifications can be made by those skilled in the art without departing from the spirit and principles of this application.

Claims (7)

1. The intelligent teaching system for the table tennis is characterized by comprising a control terminal, a service robot and eagle eye equipment, wherein the service 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 returning quality analysis module, a parameter adjustment module and a report generation module; the matching module is used for matching corresponding teaching courses for the students according to the history training records of the students; the course distribution module is used for sending corresponding teaching courses to the service robot corresponding to the trainee;

the service robot is used for performing service according to a service instruction; the eagle eye equipment is used for collecting movement data of the table tennis ball and feeding back the movement data to the control terminal;

The ball returning quality analysis module is used for analyzing the ball speed, the net passing height, the angle, the drop point and the rotation speed of the table tennis ball so as to obtain an effective table tennis ball track; the working process of the ball return quality analysis module is as follows:

Comparing the ball returning quality analysis result with a preset training characteristic target, and modifying single ball serving parameters of the preset ball serving machine according to the comparison result, wherein the process comprises the following steps:

Presetting a single-ball service parameter t_p_g of a service robot: linear velocity t_v_s, rotational velocity t_r_s, rotational direction t_r_t, landing position (t_x, t_y), and ball serving frequency t_fr;

Comparing the speed v_net of the net passing direction in the dimension of each group of track data Traj with the speed p_net of the net passing ball of the preset target parameter Sa; if v_net > 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 lifted; if v_net=p_net, the linear speed t_v_s and the service frequency t_fr in the single-shot service parameter t_p_g of the service robot are unchanged; if v_net < p_net, reducing 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;

Comparing the passing height h_net of the passing direction in the dimension of each group of track data Traj with the passing height p_h_net of the preset target parameter Sa; if h_net > p_h_net, decreasing the spin speed t_r_s, the linear speed t_v_s and the serve frequency t_fr in the serve single-serve parameter t_p_g of the serve machine; if h_net=p_h_net, the spin t_r_s, the linear speed t_v_s and the serve frequency t_fr in the serve single-serve parameter t_p_g are unchanged; if h_net < p_h_net, the rotation 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 lifted;

Comparing the angle_net of the passing direction in the dimension of each group of track data Traj with the passing angle p_angle_net of the preset target parameter Sa; if angle_net > p_angle_net, then increasing the spin speed t_r_s, the linear speed t_v_s and the serve frequency t_fr in the single serve parameter t_p_g of the serve machine and decreasing the drop point position coordinate t_y; if angle_net=p_angle_net, the rotation 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 ball serving machine are not changed; if angle_net < p_angle_net, decreasing the rotation speed t_r_s, the linear speed t_v_s, the ball serving frequency t_fr and the lifting drop point position coordinate t_y in the single ball serving parameter t_p_g of the ball dispenser;

Comparing the falling points (x_ rebound, y_ rebound) in the dimension of each set of trajectory data Traj with the falling points (p_t_x, p_t_y) of the preset target parameter Sa; if x_ rebound > p_t_x, decreasing the spin speed t_r_s and the serve frequency t_fr in the serve single-serve parameter t_p_g and increasing the drop point position coordinate t_x; if x_ rebound =p_t_x, then the spin speed t_r_s, the serve frequency t_fr, and the drop point position coordinates t_x in the primary player single serve parameter t_p_g are maintained; if x_ rebound is less than p_t_x, then increasing the spin speed t_r_s and the serve frequency t_fr in the single serve parameter t_p_g of the serve machine and decreasing the drop point position coordinate t_x; if y_ rebound > p_t_y, decreasing the linear speed t_v_s and the serve frequency t_fr in the single serve parameter t_p_g of the serve machine and increasing the drop point position coordinate t_y; if y_ rebound =p_t_y, then the linear speed t_v_s, the serve frequency t_fr and the drop point position coordinates t_y in the primary player single serve parameter t_p_g are maintained; if y_ rebound is less than p_t_y, 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 lifted and the drop point position coordinate t_y is increased;

Acquiring single-ball service parameters of a service machine in a current service parameter queue;

sequentially and correspondingly comparing each parameter in the single-serve parameters of the current serving parameter queue with each parameter in the single-serve parameters of the preset serving machine, and updating the single-serve parameters of the serving machine in the current serving parameter queue according to the comparison result;

acquiring a service state of the service robot, judging whether the service state meets the updated service condition according to the service state, and if so, directly issuing an update instruction to update the service single-ball parameters of the service robot;

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

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

2. The intelligent teaching system of table tennis according to claim 1, wherein the matching module extracts training record portraits of the students from the history training records of the students, and pushes corresponding courses to the students according to the training record portraits;

the training record representation comprises three dimensions; the first dimension comprises the usual handholding, age and sex of the learner, the second dimension comprises the ability growth condition, stability and action consistency of the learner, and the third dimension comprises the weighted average difficulty of the training course of the learner in a period of time.

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

Acquiring effective target space data according to the physical law of table tennis flight, and synchronizing the coordinates of a moving target into 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 valid coordinate data from the queue T;

Calculating the acquisition direction of track data Traj according to the acquisition time of track data Traj and the relative coordinates of the track data in a three-dimensional space coordinate system of a table tennis table;

For each set of table tennis track data Traj, the ball speed v_net, the net height h_net, the angle_net and the drop point (x_ rebound, y_ rebound) of the net passing direction at the net passing time of the single set of track are calculated by using a fitting algorithm, and the rotation speed of the table tennis is calculated by using a deep neural network (w _x,w_y,w_z).

4. The intelligent teaching system for table tennis according to claim 1, wherein the process of sequentially comparing each of the single serving parameters of the current serving parameter queue with each of the single serving parameters of the preset serving machine and updating the single serving parameters of the current serving parameter queue according to the comparison result is as follows:

The single-ball serving parameters p_g of the current serving parameter queue comprise a linear speed v_s, a rotating speed r_s, a rotating direction r_t, a drop point position (x, y) and a serving frequency fr;

The preset single-ball serving parameters t_p_g of the ball serving machine comprise a linear speed t_v_s, a rotating speed t_r_s, a rotating direction t_r_t, a drop 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 the parameters different from the preset service parameters t_p_g into the service parameters p_g of the service machine in the current service parameter queue.

5. The intelligent teaching system of claim 1, wherein the report generating module generates a data report by:

Analyzing a single-ball service record queue Q1 of the service robot for service, and acquiring a single-ball service record in the queue;

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

carrying out capacity grading according to the ball return quality result;

And calculating a data report of single training according to weights of different scoring dimensions, wherein the scoring dimensions comprise speed, angle, drop point and upper platform rate.

6. The intelligent teaching system for table tennis according to claim 5, wherein said ability scoring based on ball return quality results comprises hit rate, altitude score, drop point score, angle score and speed score;

Single sphere height score = corresponding rule height score lower limit + height score difference ++height difference × (net height-corresponding rule height lower limit);

Wherein,

Height score difference = corresponding rule height score upper limit-corresponding rule height score lower limit;

height difference = corresponding rule upper height limit-corresponding rule lower height limit;

Single ball drop score = origin score + X-axis difference in drop area ∈x-axis length in drop area × (X-axis coordinate-drop area corresponds to X-axis origin start coordinate) +y-axis difference in drop area ++y-axis length in drop area× (Y-axis coordinate-drop area corresponds to Y-axis origin start coordinate)

Wherein,

The X/Y axis length difference in the drop point area = the X/Y axis end coordinate point in the drop point area-the X/Y axis start coordinate point in the drop point area;

X/Y axis difference in drop point region = X/Y axis end coordinate point score in drop point region-X/Y axis start coordinate point score in drop point region;

single sphere angle score = corresponding rule angle score lower limit + angle score difference +.o angle difference × (net crossing angle-corresponding rule angle lower limit);

Wherein,

Angle score difference = corresponding rule angle score upper limit-corresponding rule angle score lower limit;

angle difference = corresponding to upper rule angle limit-corresponding to lower rule angle limit;

Single ball speed score = corresponding rule speed score lower limit + speed score difference +.speed difference × (speed net angle-corresponding rule speed lower limit);

Wherein,

Speed score difference = corresponding rule speed score upper limit-corresponding rule speed score lower limit;

speed difference = upper rule-corresponding speed limit-lower rule-corresponding speed limit.

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

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

distributing the teaching courses to the corresponding service robots, and enabling the service robots to serve the ball and start training;

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

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

the ball serving parameters of the ball serving robot are adjusted in real time according to the ball return quality analysis result, and the ball serving robot comprises the following steps:

comparing the ball returning quality analysis result with a preset training characteristic target, and modifying single ball serving parameters of the preset ball serving machine according to the comparison result; the process for comparing the ball returning quality analysis result with a preset training characteristic target comprises the following steps:

Presetting a single-ball service parameter t_p_g of a service robot: the linear speed t_v_s, the rotating speed t_r_s, the rotating direction t_r_t, the drop point position (t_x, t_y) and the service frequency t_fr, and the parameters are copied according to the next service parameters in the service queue QT of the preset combination; the preset target Sa is obtained from the combined service queue QT for comparison;

The process of comparing the track data Traj with the preset target parameter Sa in the training course and modifying the single-ball service parameter t_p_g of the preset ball service machine according to the comparison result is as follows:

Comparing the speed v_net of the net passing direction in the dimension of each group of track data Traj with the speed p_net of the net passing ball of the preset target parameter Sa;

If v_net > 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 lifted; if v_net=p_net, the linear speed t_v_s and the service frequency t_fr in the single-shot service parameter t_p_g of the service robot are unchanged; if v_net < p_net, reducing 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;

Comparing the passing height h_net of the passing direction in the dimension of each group of track data Traj with the passing height p_h_net of the preset target parameter Sa;

If h_net > p_h_net, decreasing the spin speed t_r_s, the linear speed t_v_s and the serve frequency t_fr in the serve single-serve parameter t_p_g of the serve machine; if h_net=p_h_net, the spin t_r_s, the linear speed t_v_s and the serve frequency t_fr in the serve single-serve parameter t_p_g are unchanged; if h_net < p_h_net, the rotation 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 lifted;

comparing the angle_net of the passing direction in the dimension of each group of track data Traj with the passing angle p_angle_net of the preset target parameter Sa;

if angle_net > p_angle_net, then increasing the spin speed t_r_s, the linear speed t_v_s and the serve frequency t_fr in the single serve parameter t_p_g of the serve machine and decreasing the drop point position coordinate t_y; if angle_net=p_angle_net, the rotation 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 ball serving machine are not changed; if angle_net < p_angle_net, decreasing the rotation speed t_r_s, the linear speed t_v_s, the ball serving frequency t_fr and the lifting drop point position coordinate t_y in the single ball serving parameter t_p_g of the ball dispenser;

comparing the falling points (x_ rebound, y_ rebound) in the dimension of each set of trajectory data Traj with the falling points (p_t_x, p_t_y) of the preset target parameter Sa;

If x_ rebound > p_t_x, decreasing the spin speed t_r_s and the serve frequency t_fr in the serve single-serve parameter t_p_g and increasing the drop point position coordinate t_x; if x_ rebound =p_t_x, then the spin speed t_r_s, the serve frequency t_fr, and the drop point position coordinates t_x in the primary player single serve parameter t_p_g are maintained; if x_ rebound is less than p_t_x, then increasing the spin speed t_r_s and the serve frequency t_fr in the single serve parameter t_p_g of the serve machine and decreasing the drop point position coordinate t_x;

if y_ rebound > p_t_y, decreasing the linear speed t_v_s and the serve frequency t_fr in the single serve parameter t_p_g of the serve machine and increasing the drop point position coordinate t_y; if y_ rebound =p_t_y, then the linear speed t_v_s, the serve frequency t_fr and the drop point position coordinates t_y in the primary player single serve parameter t_p_g are maintained; if y_ rebound is less than p_t_y, 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 lifted and the drop point position coordinate t_y is increased;

Acquiring single-ball service parameters of a service machine in a current service parameter queue;

sequentially and correspondingly comparing each parameter in the single-serve parameters of the current serving parameter queue with each parameter in the single-serve parameters of the preset serving machine, and updating the single-serve parameters of the serving machine in the current serving parameter queue according to the comparison result;

acquiring a service state of the service robot, judging whether the service state meets the updated service condition according to the service state, and if so, directly issuing an update instruction to update the service single-ball parameters of the service robot;

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