Artificial intelligence data acquisition device
By designing an artificial intelligence data acquisition device, collecting and analyzing tennis players’ hitting data, the error and omission problems of athletes’ hitting status analysis in the existing technology are solved, and real-time and accurate sports data analysis and strategy provision are achieved.
Patent Information
- Application Number
- CN202510139440.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-08
- Publication Date
- 2025-04-29
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing technology lacks flexibility in the technical statistics process and after the game, resulting in errors and omissions in the analysis of athletes' hitting status and movement status.
Design an artificial intelligence data acquisition device, including a motion acquisition module, a motion analysis module, a storage module, an intelligent question-and-answer module and a motion strategy module. The device collects athlete's motion image data, performs clustering and statistical analysis of hitting movements, receives and answers hitting movement questions, and constructs a hitting strategy based on the answers.
Real-time automatic collection and analysis of tennis players' hitting data is realized, accurately judges the hitting status and provides effective response strategies, which significantly improves the effectiveness of tennis court sports data.
Smart Images
Figure CN119904915A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of artificial intelligence technology, and in particular to an artificial intelligence data acquisition device. Background Art
[0002] Artificial intelligence is currently widely used in tennis, such as real-time judgment of whether the hitting point is in bounds / out of bounds, bounce analysis at the moment of hitting, etc., which greatly avoids the occurrence of misjudgments during the game.
[0003] However, there is still a lack of flexibility in the technical statistics during and after tennis games. The analysis of athletes' hitting and moving states is often done manually in the background while watching the game, which is prone to errors and omissions. Therefore, how to intelligently analyze the movement state of tennis games in real time and provide corresponding strategies is a technical problem that needs to be solved urgently. Summary of the invention
[0004] To achieve the above objectives, this application provides the following technical solutions: According to a first aspect of the present invention, the present invention claims protection for an artificial intelligence data acquisition device, comprising: a motion acquisition module, a motion analysis module, a storage module, an intelligent question-and-answer module, and a motion strategy module; The motion acquisition module acquires motion image data of athletes on the tennis court; The motion analysis module clusters and divides the player's hitting movements based on the motion image data; The storage module stores the clustering information of the batting action and performs statistical analysis; The intelligent question-answering module receives questions about the player's batting motion and searches for answers; The motion strategy module constructs a hitting strategy for the player based on the answers.
[0005] Furthermore, the motion acquisition module acquires motion image data of athletes on the tennis court, and further includes: Collecting the player's round hitting action image and end hitting action image; The player's round batting action image and end batting action image at least include: an image of the player's hitting arm; An image of the player's batting racket face; The player's hitting footwork information; The athlete's location information.
[0006] Furthermore, the motion analysis module clusters the player's hitting movements based on the motion image data, and further includes: Clustering and dividing the player's hitting actions into forehand front court hitting, forehand baseline hitting, backhand front court hitting, backhand baseline hitting, and serving based on the motion image data; When the motion image data clusters the player's hitting action into forehand frontcourt hitting or forehand baseline hitting, the player's hitting action is secondary clustered and divided into forehand winning points, forehand unforced errors, and forehand forced errors; The shots of the forehand winning points, forehand unforced errors, and forehand forced errors are clustered three times into high pressure balls, flat shots, topspin balls, and volleys; When the motion image data clusters the player's hitting action into backhand frontcourt hitting or backhand baseline hitting, the player's hitting action is secondary clustered and divided into backhand winning points, backhand unforced errors, and backhand forced errors; The backhand winning points, backhand unforced errors, and backhand forced errors are clustered three times into high pressure balls, flat balls, topspin balls, and volley balls; When the motion image data clusters the player's hitting action into serving, the player's hitting action is secondary clustered into serving into the zone and serving errors; When the player's hitting action is serving into the zone, three clustering divisions are performed into serving value and serving round; When the player's hitting action is a serving error, three clustering divisions are performed into serving out of bounds and serving into the net.
[0007] Furthermore, the storage module stores the clustering information of the hitting action and performs statistical analysis, and also includes: According to the primary clustering information of the hitting action, statistically analyzing the number of forehand hits, the number of backhand hits and the number of serves of the hitting action; According to the secondary clustering information of the hitting action, statistically analyzing the number of winning points, the number of unforced errors, the number of forced errors, the number of serve-in zones, and the number of serve errors of the hitting action; According to the three-level clustering information of the hitting action, the number of high-pressure balls, the number of flat balls, the number of topspin balls, the number of volleys, the number of serve values, the number of serve rounds, the number of serve outs and the number of serve into the net of the hitting action are statistically analyzed.
[0008] Furthermore, the intelligent question-answering module receives the batting motion questions for the athlete and searches for answers, and further includes: receiving a question statement regarding a ball-battling motion problem of the player; Performing semantic analysis on the question statement to obtain the hitting target intention of the question statement; Based on the hitting target intention, a corresponding answer is searched from the storage module.
[0009] Furthermore, the intelligent question-answering module receives the batting motion questions for the athlete and searches for answers, and further includes: When the keywords of the question sentence include the target intention keywords, confirming the hitting target intention of the question sentence; The target range of the hitting target intention is analyzed, and according to the target range, an answer to the hitting target intention corresponding to the target range is searched from the storage module.
[0010] Furthermore, the sports strategy module constructs a batting strategy for the player based on the answer, and further includes: Performing a shot quality analysis on the corresponding answer obtained by searching in the storage module to obtain a shot quality result of the player; A hitting strategy for the player is constructed based on the player's hitting quality results.
[0011] Furthermore, the sports strategy module constructs a batting strategy for the player based on the answer, and further includes: Performing forehand shot quality analysis, backhand shot quality analysis or serve quality analysis on the corresponding answers searched and obtained from the storage module to obtain the forehand shot quality result, backhand shot quality result or serve quality result of the athlete; A hitting strategy for the athlete is constructed according to the forehand hitting quality result, the backhand hitting quality result or the serve quality result of the athlete.
[0012] Furthermore, the constructing of a hitting strategy for the athlete based on the athlete's forehand hitting quality result, backhand hitting quality result or serve quality result also includes: When the player's forehand shot quality result or backhand shot quality result is the most unforced errors, the shot strategy for the player is a forehand low-speed ball or a backhand low-speed ball; When the forehand shot quality result or the backhand shot quality result of the athlete is the most forced errors, the shot strategy for the athlete is a forehand fast ball or a backhand fast ball; When the serve quality result of the athlete is that the most serve errors are made, the hitting strategy for the athlete is to receive the serve inside the baseline; When the player's serve quality result is that the most serves enter the zone and the number of serve wins is greater than the number of serve rounds, the batting strategy for the player is baseline outside serve.
[0013] The present application relates to the field of artificial intelligence technology, and in particular to an artificial intelligence data acquisition device, wherein a motion acquisition module acquires motion image data of athletes on a tennis court; a motion analysis module clusters and divides the athletes' batting actions based on the motion image data; a storage module stores cluster information of batting actions and performs statistical analysis; an intelligent question-and-answer module receives batting motion questions for athletes and searches for answers; and a motion strategy module constructs a batting strategy for athletes based on the answers. The present invention can automatically acquire the batting data of tennis players in real time, make judgments on the batting status, and provide effective response strategies for related issues, greatly improving the statistical analysis effect of the sports data on the tennis court. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] Figure 1 A structural module diagram of an artificial intelligence data acquisition device claimed for protection in an embodiment of the present application; Figure 2 This is a workflow diagram of an artificial intelligence data acquisition device claimed for protection in an embodiment of the present application. DETAILED DESCRIPTION
[0015] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0016] The terms "first", "second" and "third" in this application are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of this application are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.
[0017] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.
[0018] According to the first embodiment of the present invention, referring to Figure 1 , the present invention claims protection for an artificial intelligence data acquisition device, comprising: a motion acquisition module, a motion analysis module, a storage module, an intelligent question and answer module, and a motion strategy module; The motion acquisition module acquires motion image data of athletes on the tennis court; The motion analysis module clusters and divides the player's hitting movements based on the motion image data; The storage module stores the clustering information of the batting action and performs statistical analysis; The intelligent question-answering module receives questions about the player's batting motion and searches for answers; The motion strategy module constructs a hitting strategy for the player based on the answers.
[0019] In this embodiment, the batting action images of two players, ie, a first player and a second player, who are playing tennis in a tennis court are collected.
[0020] Furthermore, the motion acquisition module acquires motion image data of athletes on the tennis court, and further includes: Collecting the player's round hitting action image and end hitting action image; The player's round batting action image and end batting action image at least include: an image of the player's hitting arm; An image of the player's batting racket face; The player's hitting footwork information; The athlete's location information.
[0021] In this embodiment, the player's batting arm image includes at least: the player's batting arm, the batting arm elbow angle, the batting arm body distance information, and the batting arm's batting gripping method.
[0022] The player's hitting racket face image at least includes: the contact angle between the player's hitting racket face and the horizontal plane and the ball.
[0023] The player's hitting footstep information at least includes: the player's feet position information, leg angle information, and leg center of gravity information.
[0024] The position information of the athlete at least includes: baseline position and midfield position.
[0025] Specifically, the athlete's batting grip arm indicates whether the athlete's current batting arm is located on the left or right side of the body. If it is on the left side of the body, it is the backhand for right-handed athletes and the forehand for left-handed athletes. If it is on the right side of the body, it is the forehand for right-handed athletes and the backhand for left-handed athletes.
[0026] The angle of the elbow of the batting arm indicates whether the arm is straight or bent. A straight arm can exert more force when hitting the ball, while a bent arm can limit the force when hitting the ball.
[0027] The hitting arm body distance information indicates the distance between the front side of the hitting arm and the torso. If the distance is too close or too far, it means that the hitting point is not in place.
[0028] The way a batsman holds the racket is the contact edge between the base of the palm of the hand holding the racket and the handle of the racket. According to the different contact edges, it is divided into continental grip and western grip.
[0029] The size of the angle between the player's hitting racket face and the horizontal plane indicates that the flight trajectory of the ball after hitting the ball is a high-pressure ball, a flat ball, a topspin ball, and a volley ball.
[0030] The information on the positions of the athlete's feet includes standing side by side or standing one behind the other.
[0031] If the angle between the legs is too small or too large, it means that the athlete is not running properly when hitting the ball.
[0032] If the center of gravity in the leg center of gravity information is lowered, it means that the athlete is not running well at the baseline when hitting the ball or is volleying at the net.
[0033] The bottom line position is the distance area from the bottom line to the serving line.
[0034] The mid-front court position is the distance area from the serving line to the net belt.
[0035] Furthermore, the motion analysis module clusters the player's hitting movements based on the motion image data, and further includes: Clustering and dividing the player's hitting actions into forehand front court hitting, forehand baseline hitting, backhand front court hitting, backhand baseline hitting, and serving based on the motion image data; When the motion image data clusters the player's hitting action into forehand frontcourt hitting or forehand baseline hitting, the player's hitting action is secondary clustered and divided into forehand winning points, forehand unforced errors, and forehand forced errors; The shots of the forehand winning points, forehand unforced errors, and forehand forced errors are clustered three times into high pressure balls, flat shots, topspin balls, and volleys; When the motion image data clusters the player's hitting action into backhand frontcourt hitting or backhand baseline hitting, the player's hitting action is secondary clustered and divided into backhand winning points, backhand unforced errors, and backhand forced errors; The backhand winning points, backhand unforced errors, and backhand forced errors are clustered three times into high pressure balls, flat balls, topspin balls, and volley balls; When the motion image data clusters the player's hitting action into serving, the player's hitting action is secondary clustered into serving into the zone and serving errors; When the player's hitting action is serving into the zone, three clustering divisions are performed into serving value and serving round; When the player's hitting action is a serving error, three clustering divisions are performed into serving out of bounds and serving into the net.
[0036] Among them, in this embodiment: Forehand midcourt shot: The current striking arm is on the dominant side of the athlete's body and the athlete is standing in the midcourt position.
[0037] Forehand baseline shot: The current striking arm is on the dominant side of the player's body and the player is standing in the middle front court position.
[0038] Backhand mid-court shot: The current striking arm is on the opposite side of the athlete's body from the dominant hand and the athlete is standing in the mid-court position.
[0039] Backhand baseline shot: The current striking arm is on the opposite side of the player's body from the dominant hand and the player is standing at the baseline.
[0040] Serve: The first or second serve (first error or net hit) of a player in each point.
[0041] Forehand winning point: The point scored by the current athlete after hitting the ball with the forehand into the opponent's boundary without the opponent touching the ball.
[0042] Forehand unforced error: The athlete's footsteps are in place when hitting the ball and the ball goes out of bounds for the opponent or into the net on his own side.
[0043] Forehand forced error: The athlete's footsteps are not in place when hitting the ball and the ball goes out of the opponent's court or into the net on his own side.
[0044] Establish a coordinate system based on the racket face: High pressure ball: When the athlete hits the ball, the angle between the racket face and the horizontal plane is less than 45 degrees; Flat shot: When the athlete hits the ball, the angle between the racket face and the horizontal plane is less than 90 degrees and greater than 45 degrees; Topspin: When the athlete hits the ball, the angle between the racket face and the horizontal plane is greater than 90 degrees; Volley: When the athlete hits the ball, the angle between the racket face and the horizontal plane is greater than 45 degrees and less than 90 degrees, and the ball has not landed before hitting the ball; Serve into the area: The athlete serves the ball into the area surrounded by the serving line and the net; Serving error: The athlete serves the ball but it does not enter the area surrounded by the serving line and the net; Serve worth it: After the athlete serves, the opponent does not hit the ball back to the athlete's side of the court; Serving Round: After the athlete serves, the opponent hits the ball back to the athlete's side.
[0045] Furthermore, the storage module stores the clustering information of the hitting action and performs statistical analysis, and also includes: According to the primary clustering information of the hitting action, statistically analyzing the number of forehand hits, the number of backhand hits and the number of serves of the hitting action; According to the secondary clustering information of the hitting action, statistically analyzing the number of winning points, the number of unforced errors, the number of forced errors, the number of serve-in zones, and the number of serve errors of the hitting action; According to the three-level clustering information of the hitting action, the number of high-pressure balls, the number of flat balls, the number of topspin balls, the number of volleys, the number of serve values, the number of serve rounds, the number of serve outs and the number of serve into the net of the hitting action are statistically analyzed.
[0046] In this embodiment, the number of forehand shots in the shot action indicates the frequency of forehand use by the athlete during the game; The number of backhand shots indicates how often the player uses the backhand during the game; The number of serves indicates the number of times the player served during this match; The stated high number of serves indicates that the player’s serve games have more rallies; The number of winning points indicates the effectiveness of the athlete's frontal attack in this game; The number of unforced errors indicates the ineffectiveness of the player's attack in this game; The number of forced errors indicates the effectiveness of the opponent's players' offense in this game; The number of serves into the zone indicates the player's success rate in serving in this game; The number of serve errors indicates the player's serve error rate in this match.
[0047] Furthermore, the intelligent question-answering module receives the batting motion questions for the athlete and searches for answers, and further includes: receiving a question statement regarding a ball-battling motion problem of the player; Performing semantic analysis on the question statement to obtain the hitting target intention of the question statement; Based on the hitting target intention, a corresponding answer is searched from the storage module.
[0048] Among them, in this embodiment, the question statement for the athlete's batting motion problem is: What is Athlete S's forehand baseline error rate in this game? Performing semantic analysis on the question sentence to obtain that the hitting target intention of the question sentence is a forehand hit to the S player and the player is at the baseline and makes a hitting error; Based on the hitting target intention, a corresponding answer is searched from the storage module.
[0049] Furthermore, the intelligent question-answering module receives the batting motion questions for the athlete and searches for answers, and further includes: When the keywords of the question sentence include the target intention keywords, confirming the hitting target intention of the question sentence; The target range of the hitting target intention is analyzed, and according to the target range, an answer to the hitting target intention corresponding to the target range is searched from the storage module.
[0050] In this embodiment, a semantic analysis is performed on "What is the forehand baseline error rate of player S in this game?" to obtain the hitting target intention as a forehand hit to player S and the player is at the baseline and hits the ball wrong, and its specific keywords include: forehand, baseline, error; The target range of the hitting target intention is analyzed, and the hitting target intention corresponding to the target range is searched from the storage module according to the target range. The answer is the forehand unforced error of the athlete in the forehand baseline shot.
[0051] Furthermore, the sports strategy module constructs a batting strategy for the player based on the answer, and further includes: Performing a shot quality analysis on the corresponding answer obtained by searching in the storage module to obtain a shot quality result of the player; A hitting strategy for the player is constructed based on the player's hitting quality results.
[0052] Furthermore, the sports strategy module constructs a batting strategy for the player based on the answer, and further includes: Performing forehand shot quality analysis, backhand shot quality analysis or serve quality analysis on the corresponding answers searched and obtained from the storage module to obtain the forehand shot quality result, backhand shot quality result or serve quality result of the athlete; A hitting strategy for the athlete is constructed according to the forehand hitting quality result, the backhand hitting quality result or the serve quality result of the athlete.
[0053] Furthermore, the constructing of a hitting strategy for the athlete based on the athlete's forehand hitting quality result, backhand hitting quality result or serve quality result also includes: When the player's forehand shot quality result or backhand shot quality result is the most unforced errors, the shot strategy for the player is a forehand low-speed ball or a backhand low-speed ball; When the forehand shot quality result or the backhand shot quality result of the athlete is the most forced errors, the shot strategy for the athlete is a forehand fast ball or a backhand fast ball; When the serve quality result of the athlete is that the most serve errors are made, the hitting strategy for the athlete is to receive the serve inside the baseline; When the player's serve quality result is that the most serves enter the zone and the number of serve wins is greater than the number of serve rounds, the batting strategy for the player is baseline outside serve.
[0054] In this embodiment, when the player's forehand shot quality result or backhand shot quality result shows that the player has the most unforced errors, it indicates that the player is very likely to make mistakes when hitting the ball, so there is no need to take a high risk to hit the ball. The hitting strategy for the player is to hit the ball to the forehand at a medium or low speed or to the backhand at a medium or low speed. When the forehand shot quality result or the backhand shot quality result of the athlete is the most forced errors, it indicates that the athlete is very likely to make errors when the ball is not in position, and therefore, it is appropriate to take a high risk to hit the ball. Then, the hitting strategy for the athlete is to hit the ball to the forehand quickly or the backhand quickly; When the serve quality result of the athlete is that the most serve errors are made, it indicates that the serve of the athlete is very prone to errors, and therefore, pressure can be applied to the serve of the athlete, and the batting strategy for the athlete is to receive the serve from the baseline; When the serve quality result of the athlete is that the most serves enter the zone and the number of direct serves is greater than the number of serving rounds, it indicates that the serve threat of the athlete is relatively large. Therefore, a safe strategy can be adopted for the serve and receive of the athlete, and the batting strategy for the athlete is to receive the serve from the baseline.
[0055] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.
[0056] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the specification and drawings of this application, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present application.
[0057] The specific implementation methods of the invention are described in detail above, but they are only examples, and the present application is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present application, and therefore, the equalization, modification, and improvement made without departing from the spirit and principle of the present application should be included in the scope of the present application.
Claims
1. An artificial intelligence data collection device, characterized in that: include: Motion acquisition module, motion analysis module, storage module, intelligent question and answer module, motion strategy module; The motion acquisition module acquires motion image data of athletes on the tennis court; The motion analysis module clusters and divides the player's hitting movements based on the motion image data; The storage module stores the clustering information of the batting action and performs statistical analysis; The intelligent question-answering module receives questions about the player's batting motion and searches for answers; The motion strategy module constructs a hitting strategy for the player based on the answers.
2. The artificial intelligence data acquisition device according to claim 1, characterized in that: The motion acquisition module acquires motion image data of athletes on the tennis court, and further includes: Collecting the player's round hitting action image and end hitting action image; The player's round batting action image and end batting action image at least include: an image of the player's hitting arm; An image of the player's batting racket face; The player's hitting footwork information; The athlete's location information.
3. The artificial intelligence data acquisition device according to claim 2, characterized in that: The motion analysis module clusters and divides the player's hitting movements based on the motion image data, and further includes: Clustering and dividing the player's hitting actions into forehand front court hitting, forehand baseline hitting, backhand front court hitting, backhand baseline hitting, and serving based on the motion image data; When the motion image data clusters the player's hitting action into forehand frontcourt hitting or forehand baseline hitting, the player's hitting action is secondary clustered and divided into forehand winning points, forehand unforced errors, and forehand forced errors; The shots of the forehand winning points, forehand unforced errors, and forehand forced errors are clustered three times into high pressure shots, flat shots, topspin shots, and volleys; When the motion image data clusters the player's hitting action into backhand frontcourt hitting or backhand baseline hitting, the player's hitting action is secondary clustered and divided into backhand winning points, backhand unforced errors, and backhand forced errors; The backhand winning points, backhand unforced errors, and backhand forced errors are clustered three times into high pressure balls, flat balls, topspin balls, and volley balls; When the motion image data clusters the player's hitting action into serving, the player's hitting action is secondary clustered into serving into the zone and serving errors; When the player's hitting action is serving into the zone, three clustering divisions are performed into serving value and serving round; When the player's hitting action is a serving error, three clustering divisions are performed into serving out of bounds and serving into the net.
4. The artificial intelligence data acquisition device according to claim 3, characterized in that: The storage module stores the clustering information of the batting action and performs statistical analysis, and also includes: According to the primary clustering information of the hitting action, statistically analyzing the number of forehand hits, the number of backhand hits and the number of serves of the hitting action; According to the secondary clustering information of the hitting action, statistically analyzing the number of winning points, the number of unforced errors, the number of forced errors, the number of serve-in zones, and the number of serve errors of the hitting action; According to the three-level clustering information of the hitting action, the number of high-pressure balls, the number of flat balls, the number of topspin balls, the number of volleys, the number of serve values, the number of serve rounds, the number of serve outs and the number of serve into the net of the hitting action are statistically analyzed.
5. The artificial intelligence data acquisition device according to claim 4, characterized in that: The intelligent question-answering module receives questions about the player's ball-striking motion and searches for answers, and further includes: receiving a question statement regarding a ball-battling motion problem of the player; Performing semantic analysis on the question statement to obtain the hitting target intention of the question statement; Based on the hitting target intention, a corresponding answer is searched from the storage module.
6. The artificial intelligence data acquisition device according to claim 5, characterized in that: The intelligent question-answering module receives questions about the player's ball-striking motion and searches for answers, and further includes: When the keywords of the question sentence include the target intention keywords, confirming the hitting target intention of the question sentence; The target range of the hitting target intention is analyzed, and according to the target range, an answer to the hitting target intention corresponding to the target range is searched from the storage module.
7. The artificial intelligence data acquisition device according to claim 6, characterized in that: The sports strategy module constructs a hitting strategy for the player based on the answer, and further includes: Performing a shot quality analysis on the corresponding answer obtained by searching in the storage module to obtain a shot quality result of the player; A hitting strategy for the player is constructed based on the player's hitting quality results.
8. The artificial intelligence data acquisition device according to claim 7, characterized in that: The sports strategy module constructs a hitting strategy for the player based on the answer, and further includes: Performing forehand shot quality analysis, backhand shot quality analysis or serve quality analysis on the corresponding answers searched and obtained from the storage module to obtain the forehand shot quality result, backhand shot quality result or serve quality result of the athlete; A hitting strategy for the athlete is constructed according to the forehand hitting quality result, the backhand hitting quality result or the serve quality result of the athlete.
9. The artificial intelligence data acquisition device according to claim 8, characterized in that: The step of constructing a hitting strategy for the athlete based on the forehand hitting quality result, the backhand hitting quality result or the serving quality result of the athlete further includes: When the player's forehand shot quality result or backhand shot quality result is the most unforced errors, the shot strategy for the player is a forehand low-speed ball or a backhand low-speed ball; When the forehand shot quality result or the backhand shot quality result of the athlete is the most forced errors, the shot strategy for the athlete is a forehand fast ball or a backhand fast ball; When the serve quality result of the athlete is that the most serve errors are made, the hitting strategy for the athlete is to receive the serve inside the baseline; When the player's serve quality result is that the most serves enter the zone and the number of serve wins is greater than the number of serve rounds, the batting strategy for the player is baseline outside serve.