Auxiliary training system for basketball sports

Abstract

The invention discloses a basketball auxiliary training system, which relates to the technical field of basketball auxiliary training, and comprises a position detection module, a height detection module, a sound wave detection module, a camera module, a framing sensor, a video analysis module and a data processing module, the position detection module is used for detecting the position of a player on a court to obtain first player position data and generating player movement track data based on the player position data; according to the auxiliary training system for basketball sports, through the arrangement of the position detection module, the height detection module, the sound wave detection module, the camera module, the entry sensor, the video analysis module and the data processing module, the expressions of players at different positions on a court can be calculated respectively, and the direction positions where the players are good at and the direction positions where the players are short are found; and players can conveniently select directions and positions for training according to needs so as to improve the upper limit or reinforce the short board.

Description

Technical Field

[0001] The present invention relates to the technical field of basketball auxiliary training, and in particular to a basketball auxiliary training system. Background Art

[0002] Basketball is a ball game played by two teams on a rectangular basketball court. Each team has five players who can pass, throw, shoot, roll or carry the ball in any direction. The goal is to score by throwing the basketball into the opponent's basket and prevent the opponent from getting possession of the ball or scoring. With the development of intelligent technology, some basketball stands now also have intelligent structures. Some of them can play music to create entertainment and fun, and some can display score data and the impact points of the basketball on the stand to assist players in shooting training.

[0003] For example, the invention patent with publication number CN113727761B discloses a system and method for monitoring player performance and events in sports, and describes a system and method related to evaluating the performance of people participating in sports (such as basketball). The system and method can be used to provide an evaluation sequence that can determine and evaluate the performance level of a person in one or more sports skills. The system and method can also be used to monitor and control one or more clocks in a sports event (such as a basketball game).

[0004] The invention patent with publication number CN109862949B discloses a system for evaluating basketball shooting performance, which can be used to determine and evaluate the shot landing point of a basketball at a basket ring. The shot landing point includes a lateral position and a depth position, and is determined based on a base point on the basket ring. The base point can correspond to the portion of the basket ring closest to the position of the person on the basketball court when the shot is made. A landing point map can be provided to a person, which provides information about the person's shot landing point so that the person can adjust his / her shot landing point and increase the probability of scoring a shot.

[0005] The above-mentioned prior art analyzes the shooting actions and results of basketball players during the game to determine the deficiencies in the player's shooting process, so as to remind the player to exercise the deficiencies and reduce the shortcomings. However, the players have different levels of mastery of a skill at different positions on the court, and different players are good at different areas. The prior art is not convenient for discovering the players' strong and weak positions, so as to conduct position training as needed to improve the players' ability ceiling or make up for the shortcomings. Summary of the invention

[0006] The purpose of the present invention is to provide a basketball sports auxiliary training system to solve the above-mentioned deficiencies in the prior art.

[0007] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a basketball sports auxiliary training system, including a position detection module, a height detection module, a sound wave detection module, a camera module, a frame entry sensor, a video analysis module, and a data processing module;

[0008] The position detection module is used to detect the position of the player on the court, obtain the first player position data, and generate the player activity trajectory data based on the player position data, wherein the position detection module can use a millimeter wave radar, and the millimeter wave radar can be set to accurately detect the position of the player on the court, so as to facilitate the data collected from different positions of the player on the court, respectively, to determine whether there is an area where the player has better performance;

[0009] The height detection module is used to detect the height of the player, and determine whether the player jumps and the jump height based on the player's height, and obtain the player's jump data. The height detection module can use a laser radar module. In one embodiment, the laser radar detects lasers toward the court, and detects whether the player's feet are off the ground by laser to determine whether the player jumps, and then detects the height of the player's feet off the ground by laser to determine the player's jump height;

[0010] The sound wave detection module is used to detect the footsteps of players on the court and the sound of basketballs falling to the ground, and obtain the dribbling data of the players. The sound wave detection module can use a sound sensor to collect the sounds on the court, remove the noise data therein, and classify the footsteps and the sound of basketballs falling to the ground;

[0011] The camera module is used to monitor the court when the players are training on the court, and obtain the player training video, wherein the camera module can use a high-definition camera to improve the clarity of the shooting, and can clearly shoot the player's movements; in one embodiment, the camera module can be equipped with a pan-tilt base, and the pan-tilt base can adjust the horizontal shooting angle and the pitch shooting angle of the camera module, and at the same time, the target detection algorithm is used to detect and mark the players (human bodies) in the player training video, and the pan-tilt is controlled to drive the camera module to follow the players to shoot, and then the identity of the players is identified by the face recognition algorithm, wherein the target detection algorithm can select a pre-trained target detection algorithm, identify and mark the players in the player training video, and then use the pre-trained face recognition algorithm to identify the face of the player, and compare the facial features of the players with the pre-stored facial features of the players, so as to determine the identity of the players;

[0012] The frame entry sensor is used to sense whether the basketball enters the frame and count when the basketball enters the frame. The frame entry sensor can be a photoelectric sensor, which is installed near the basket and uses the interruption of the light beam to detect whether the ball passes through the basket.

[0013] The video analysis module is used to detect the position of the players in the player training video based on the target detection algorithm, and is also used to identify the identity of the players in the player training video based on the face recognition algorithm;

[0014] The data processing module is used to classify the player activity trajectory data, the player take-off data, the player dribbling data and the player training video according to the different player identities to obtain multiple player training data sets;

[0015] The data processing module is also used to classify the data set according to the different player activity trajectory data to obtain multiple player training data subsets, and score each player training data subset according to the set scoring rules to obtain the score of the corresponding position of each player's different activity trajectory.

[0016] Furthermore, the system also includes an identity authentication module, which is used to communicate with the player's mobile terminal, verify and confirm the player's identity, and transmit the player's activity trajectory data, player's take-off data, player's dribbling data, player's training video and player's score to the corresponding player's mobile terminal after the player's identity authentication and confirmation are successful; wherein, when performing identity authentication, the identity authentication module can establish a communication connection with the player's mobile terminal such as a mobile phone, tablet, etc. through Bluetooth, WiFi, etc., and realize identity authentication and confirmation by verifying the identification code of the mobile terminal, and then transmit data through Bluetooth or WiFi.

[0017] Furthermore, the camera module is also used to identify the position of the player in the player training video to obtain the second player position data, including the following steps:

[0018] A1. Identify and mark the players in the player training video through the target detection algorithm, and identify the identity of the players through the face recognition algorithm;

[0019] A2. Divide the court evenly into several different areas and make different identification marks for each area;

[0020] A3. Outputting the area corresponding to the identification mark closest to the marked player as the second player position data of the player, so that the player position data corresponds to the identity of the player.

[0021] Furthermore, the A3 further comprises the following steps:

[0022] A3.1. Determine whether there are multiple players in the same area at the same time;

[0023] A3.2. If yes, mark the relative position relationship between each player and the adjacent player, and add the relative position relationship to the second player position data of the corresponding player, where "up", "down", "left", "right", "upper left", "upper right", "lower left", and "lower right" can be used to represent it, where the direction from the center of the court to the basket in the player's half is the "up" direction, where the player changes the "up" direction when crossing the center line of the court, and continues to use the previous "up" direction when standing on the center line.

[0024] Furthermore, the data processing module is further used to determine the precise positions of different players on the court based on the first player position data and the second player position data, including the following steps:

[0025] Acquire first player position data of each player on the court detected by the position detection module;

[0026] generating a first player position relationship graph based on the acquired first player position data;

[0027] Obtain second player position data for each player on the court and the identity of the corresponding player;

[0028] generating a corresponding second player position relationship graph according to the acquired second player position data;

[0029] Based on the relative positions of the players in the first player position relationship graph and the second player position relationship graph, each first player position data is associated and bound with the identity of the player corresponding to the second player position relationship corresponding to the relative position.

[0030] Furthermore, the acoustic wave detection module is used to detect the footsteps of players on the court and the sound of basketball landing, and obtain the dribbling data of players, including the following steps:

[0031] Record the player's footsteps and the sound of the basketball landing separately in a quiet environment;

[0032] Analyze the frequency range of footsteps and basketball landing sounds to obtain the frequency interval of footsteps and the frequency interval of basketball landing sounds;

[0033] Acquire the player's dribbling data to obtain a corresponding sound time domain graph, and convert the sound time domain graph into a sound frequency domain graph based on Fourier transform;

[0034] The part of the sound frequency range graph that exceeds the frequency range of footsteps and the frequency range of basketball landing sound is filtered out to obtain the expected sound frequency range graph;

[0035] Based on the inverse Fourier transform, the frequency domain of the expected sound frequency domain map is converted back to the time domain, and the noise-removed player dribbling data is obtained, thereby removing a part of the noise data.

[0036] Furthermore, the data processing module is also used to identify the footsteps and basketball landing sounds in the player's dribbling data, including the following steps:

[0037] Set the footsteps tag and basketball sound tag to be associated with the player's footsteps and basketball landing sounds recorded in a quiet environment respectively;

[0038] The input sample sound is obtained by mixing the footsteps of the players and the sound of the basketball landing recorded in a quiet environment;

[0039] Based on the footsteps label and basketball sound label, the footsteps of the players and the sound of the basketball falling to the ground are recorded in a quiet environment, and the sample sound is input to train the deep learning model to obtain the sound separation model. The sound separation model is used to identify the footsteps and the sound of the basketball falling to the ground from the input player dribbling data and output them separately.

[0040] The player's dribbling data is input into the sound separation model, and the footsteps and basketball landing sounds are identified and output separately, wherein the footsteps are associated with the footsteps label when output, and the basketball landing sound is associated with the basketball sound label when output, thereby obtaining two sets, realizing the separate output of footsteps and basketball landing sounds.

[0041] 1. Compared with the prior art, the basketball auxiliary training system provided by the present invention can calculate the performance of players in different positions on the court separately by setting a position detection module, a height detection module, a sound wave detection module, a camera module, an in-frame sensor, a video analysis module, and a data processing module, and find out the direction positions where the players are good at and the direction positions where they have shortcomings, so that the players can choose the direction positions for training according to their needs to improve their upper limit or strengthen their shortcomings.

[0042] 2. Compared with the prior art, a basketball auxiliary training system provided by the present invention, by setting a position detection module, a height detection module, a sound wave detection module, a camera module, an in-frame sensor, a video analysis module, and a data processing module, can be combined with the position detection module and the camera module to realize the identification of each player during multi-person training, and bind the collected training data to the corresponding player. It can accurately identify the training data of each player during multi-person training, and facilitate the analysis of the players' actual combat capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0044] Figure 1 A schematic diagram of the system structure provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0046] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0047] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0048] Example embodiments will be described more fully below with reference to the accompanying drawings, but the example embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. On the contrary, the purpose of providing these embodiments is to make the present disclosure thorough and complete and to enable those skilled in the art to fully understand the scope of the present disclosure.

[0049] In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.

[0050] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0051] The terms used herein are only used to describe specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a", "an" and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "comprising" and / or "made of" are used in this specification, the presence of the features, wholes, steps, operations, elements and / or components is specified, but the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof is not excluded.

[0052] The embodiments described herein may be described with reference to plan views and / or cross-sectional views by means of idealized schematic diagrams of the present disclosure. Therefore, the example illustrations may be modified according to manufacturing techniques and / or tolerances. Therefore, the embodiments are not limited to the embodiments shown in the accompanying drawings, but include modifications of the configurations formed based on the manufacturing process. Therefore, the regions illustrated in the accompanying drawings have schematic properties, and the shapes of the regions shown in the figures illustrate the specific shapes of the regions of the elements, but are not intended to be limiting.

[0053] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted as having an idealized or overly formal meaning unless explicitly defined as such herein.

[0054] See also Figure 1 , a basketball sports auxiliary training system, including a position detection module, a height detection module, a sound wave detection module, a camera module, a frame entry sensor, a video analysis module, and a data processing module;

[0055] The position detection module is used to detect the position of the player on the court, obtain the first player position data, and generate the player activity trajectory data based on the player position data. The position detection module can use a millimeter wave radar, and the millimeter wave radar can be set to accurately detect the position of the player on the court, so as to facilitate the data collected from different positions of the player on the court to be analyzed separately, so as to determine whether there is an area where the player has better performance;

[0056] The height detection module is used to detect the height of the player, and determine whether the player jumps and the jump height based on the player's height, and obtain the player's jump data. The height detection module can use a laser radar module. In one embodiment, the laser radar detects lasers toward the court, and detects whether the player's feet are off the ground by laser to determine whether the player jumps, and then detects the height of the player's feet off the ground by laser to determine the player's jump height;

[0057] The sound wave detection module is used to detect the footsteps of players on the court and the sound of basketballs falling to the ground, and obtain the dribbling data of the players. The sound wave detection module can use a sound sensor to collect the sounds on the court, remove the noise data therein, and classify the footsteps and basketballs falling to the ground, including the following steps:

[0058] Record the player's footsteps and the sound of the basketball landing separately in a quiet environment;

[0059] Analyze the frequency range of footsteps and basketball landing sounds to obtain the frequency interval of footsteps and the frequency interval of basketball landing sounds;

[0060] The player's dribbling data is obtained to obtain a corresponding sound time domain graph, and the sound time domain graph is converted into a sound frequency domain graph based on Fourier transform;

[0061] The part of the sound frequency spectrum that exceeds the frequency interval of footsteps and the frequency interval of basketball landing sound is filtered out to obtain the expected sound frequency spectrum;

[0062] Based on the inverse Fourier transform, the frequency domain of the expected sound frequency domain map is converted back to the time domain, and the noise-removed player dribbling data is obtained, thereby removing a part of the noise data.

[0063] The camera module is used to monitor the court when the players are training on the court, and obtain the players' training video, wherein the camera module can use a high-definition camera to improve the clarity of the shooting, and can clearly shoot the players' movements; in one embodiment, the camera module can be equipped with a pan-tilt base, and the pan-tilt base can adjust the horizontal shooting angle and the pitch shooting angle of the camera module, and at the same time, the target detection algorithm is used to detect and mark the players (human bodies) in the players' training video, and the pan-tilt is controlled to drive the camera module to follow the players to shoot, and then the identity of the players is identified by the face recognition algorithm, wherein the target detection algorithm can select a pre-trained target detection algorithm to identify and mark the players in the players' training video, and then the faces of the players identified by the pre-trained face recognition algorithm are compared with the facial features of the players stored in advance, so as to determine the identity of the players; in one embodiment, the marking and positioning of the players can be achieved by framing the players with a box, and the identities of the players identified by the face recognition algorithm are marked on one side of the box, such as the upper side, to distinguish different players;

[0064] The camera module is also used to identify the position of the player in the player training video to obtain the second player position data, including the following steps:

[0065] A1. Identify and mark the players in the player training video through the target detection algorithm, and identify the identity of the players through the face recognition algorithm;

[0066] A2. Divide the court evenly into several different areas and make different identification marks for each area;

[0067] A3. Outputting the area corresponding to the identification mark closest to the marked player as the second player position data of the player, so that the player position data corresponds to the identity of the player.

[0068] Furthermore, A3 also includes the following steps:

[0069] A3.1. Determine whether there are multiple players in the same area at the same time;

[0070] A3.2. If so, mark the relative position relationship between each player and the adjacent player, and add the relative position relationship to the second player position data of the corresponding player, where "up", "down", "left", "right", "upper left", "upper right", "lower left", and "lower right" can be used to represent it, where the direction from the center of the court to the basket in the player's half of the court can be the "up" direction, where the player changes the "up" direction when crossing the center line of the court, and continues to use the previous "up" direction when standing on the center line.

[0071] Because of occlusion and other problems of the markers, the accuracy of the second player position data may be insufficient. Therefore, the approximate position of each player can be located through the second player position data. Based on the approximate position and its positional relationship, the first player position data can be obtained in combination with the position detection module. The first player position data can be matched with each player to improve the accuracy of player positioning.

[0072] The frame entry sensor is used to sense whether the basketball enters the frame and count the number of times the basketball enters the frame. The frame entry sensor can be a photoelectric sensor, which is installed near the basket and uses the interruption of the light beam to detect whether the ball passes through the basket.

[0073] The video analysis module is used to detect the position of the players in the player training video based on the target detection algorithm, and is also used to identify the identity of the players in the player training video based on the face recognition algorithm;

[0074] The data processing module is used to classify the player activity trajectory data, the player take-off data, the player dribbling data and the player training video according to the different player identities to obtain multiple player training data sets;

[0075] The data processing module is also used to classify the data set according to the different player activity trajectory data, obtain multiple player training data subsets, and score each player training data subset according to the set scoring rules to obtain the scores of the corresponding positions of different activity trajectories of each player; for example, the number of jumps in the jump data can be used to confirm whether the player has violated the travel violation, whether to jump and shoot or three-step layup. Among them, for a three-step layup, after the basketball stops slapping and making a sound, it jumps three times and only shoots once (the jump amplitude is relatively low); for a jump shot, the basketball slaps and makes a sound many times, and only jumps once after the slapping stops, and the jump height is recorded (higher).

[0076] In one embodiment, the player training data set of player A includes player training data subset 1: 20 shots on the left side of the court, 15 misses; player training data subset 2: 20 shots on the right side of the court, 10 misses; player training data subset 3: 20 shots in the middle area of ​​the court, 5 misses; by setting scoring rules to score player training data subsets 1-3, the scores of player A on the left side of the court, the right side of the court, and the middle of the court can be obtained, so as to determine the position where the player is best at jump shots, as well as the weak position, and choose to further improve the ability of the position that is good at, or to make up for the weak position as needed. Similarly, the number of three-step layups is also counted, and the shooting hit rate of each position and the position that is prone to violations are calculated. At the same time, the shooting distance is recorded, whether the shooting hit rate is high at the three-point line, inside or outside.

[0077] The data processing module is also used to determine the precise positions of different players on the court based on the first player position data and the second player position data, including the following steps:

[0078] Acquire first player position data of each player on the court detected by the position detection module;

[0079] generating a first player position relationship graph based on the acquired first player position data;

[0080] Obtain second player position data for each player on the court and the identity of the corresponding player;

[0081] generating a corresponding second player position relationship graph according to the acquired second player position data;

[0082] Based on the relative positions of the players in the first player position relationship graph and the second player position relationship graph, each first player position data is associated and bound with the identity of the player corresponding to the second player position relationship corresponding to the relative position.

[0083] In one embodiment, there are player 1 and player 2. The first player position data 1 corresponding to player 1 is at the lower left of the first player position data 2 corresponding to player 2. The position detection module detects the positions of two players: second player position data 1 and second player position data 2. The second player position data 1 is at the lower left of the second player position data 2. Then the second player position data 1 is associated and bound with player 1, and the second player position data 2 is associated and bound with player 2.

[0084] The data processing module is also used to identify the footsteps and basketball landing sounds in the player's dribbling data, including the following steps:

[0085] Set the footsteps tag and basketball sound tag to be associated with the player's footsteps and basketball landing sounds recorded in a quiet environment respectively;

[0086] The input sample sound is obtained by mixing the footsteps of the players and the sound of the basketball landing recorded in a quiet environment;

[0087] Based on the footsteps label and basketball sound label, the footsteps of the players and the sound of the basketball falling to the ground are recorded in a quiet environment, and the sample sound is input to train the deep learning model to obtain the sound separation model. The sound separation model is used to identify the footsteps and the sound of the basketball falling to the ground from the input player dribbling data and output them separately.

[0088] The player's dribbling data is input into the sound separation model, and the footsteps and basketball landing sounds are identified and output separately. The footsteps are associated with the footsteps label when output, and the basketball landing sound is associated with the basketball sound label when output, thereby obtaining two sets, realizing the separate output of footsteps and basketball landing sounds.

[0089] The system also includes an identity authentication module, which is used to communicate with the player's mobile terminal, verify and confirm the player's identity, and transmit the player's activity trajectory data, player's take-off data, player's dribbling data, player's training video and player's score to the corresponding player's mobile terminal after the player's identity authentication and confirmation are successful; wherein, when performing identity authentication, the identity authentication module can establish a communication connection with the player's mobile terminal such as a mobile phone, tablet, etc. through Bluetooth, WiFi, etc., and realize identity authentication and confirmation by verifying the identification code of the mobile terminal, and then transmit data through Bluetooth or WiFi.

[0090] The above only describes some exemplary embodiments of the present invention by way of illustration. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the present invention.

Claims

1. A basketball sports auxiliary training system, characterized in that: It includes a position detection module, a height detection module, a sound wave detection module, a camera module, a frame entry sensor, a video analysis module, and a data processing module; The position detection module is used to detect the position of the player on the court, obtain first player position data, and generate player activity trajectory data based on the player position data; The height detection module is used to detect the height of the player, and determine whether the player jumps and the jump height based on the player's height, and obtain the player's jump data; The acoustic wave detection module is used to detect the footsteps of players on the court and the sound of basketball landing, and obtain the dribbling data of players, including the following steps: Record the player's footsteps and the sound of the basketball landing separately in a quiet environment; Analyze the frequency range of footsteps and basketball landing sounds to obtain the frequency interval of footsteps and the frequency interval of basketball landing sounds; Acquire the player's dribbling data to obtain a corresponding sound time domain graph, and convert the sound time domain graph into a sound frequency domain graph based on Fourier transform; The part of the sound frequency range graph that exceeds the frequency range of footsteps and the frequency range of basketball landing sound is filtered out to obtain the expected sound frequency range graph; The frequency domain of the expected sound frequency domain map is converted back to the time domain based on the inverse Fourier transform, and the player dribbling data with noise removed is obtained; The camera module is used to monitor the court when the players are training on the court, obtain the players' training video, detect and mark the players in the players' training video through the target detection algorithm, and then identify the identity of the players through the face recognition algorithm; The frame entry sensor is used to sense whether the basketball has entered the frame and count when the basketball has entered the frame; The video analysis module is used to detect the position of the players in the player training video based on the target detection algorithm, and is also used to identify the identity of the players in the player training video based on the face recognition algorithm; The data processing module is used to classify the player activity trajectory data, the player take-off data, the player dribbling data and the player training video according to the different player identities to obtain multiple player training data sets; The data processing module is also used to classify the data set according to the different player activity trajectory data to obtain multiple player training data subsets, and score each player training data subset according to the set scoring rules to obtain the score of the corresponding position of each player's different activity trajectory.

2. A basketball sports auxiliary training system according to claim 1, characterized in that: The system also includes an identity authentication module, which is used to communicate with the player's mobile terminal, verify and confirm the player's identity, and transmit the player's activity trajectory data, player's take-off data, player's dribbling data, player's training video and player's score to the corresponding player's mobile terminal after the player's identity authentication and confirmation are successful.

3. A basketball sports auxiliary training system according to claim 2, characterized in that: The camera module is also used to identify the position of the player in the player training video to obtain the second player position data, including the following steps: A1. Identify and mark the players in the player training video through the target detection algorithm, and identify the identity of the players through the face recognition algorithm; A2. Divide the court evenly into several different areas and make different identification marks for each area; A3. Outputting the area corresponding to the identification mark closest to the marked player as the second player position data of the player.

4. A basketball sports auxiliary training system according to claim 3, characterized in that: The A3 further comprises the following steps: A3.

1. Determine whether there are multiple players in the same area at the same time; A3.

2. If yes, mark the relative position relationship between each player and the adjacent player, and add the relative position relationship to the second player position data of the corresponding player.

5. The basketball sports auxiliary training system according to claim 3, characterized in that: The data processing module is also used to determine the precise positions of different players on the court based on the first player position data and the second player position data, including the following steps: Acquire first player position data of each player on the court detected by the position detection module; generating a first player position relationship graph based on the acquired first player position data; Obtain second player position data for each player on the court and the identity of the corresponding player; generating a corresponding second player position relationship graph according to the acquired second player position data; Based on the relative positions of the players in the first player position relationship graph and the second player position relationship graph, each first player position data is associated and bound with the identity of the player corresponding to the second player position relationship corresponding to the relative position.

6. The basketball sports auxiliary training system according to claim 1, characterized in that: The data processing module is also used to identify the footsteps and basketball landing sounds in the player's dribbling data, including the following steps: Set the footsteps tag and basketball sound tag to be associated with the player's footsteps and basketball landing sounds recorded in a quiet environment respectively; The input sample sound is obtained by mixing the footsteps of the players and the sound of the basketball landing recorded in a quiet environment; Based on the footsteps label and basketball sound label, the footsteps of the players and the sound of the basketball landing are recorded in a quiet environment, and the sample sound is input to train the deep learning model to obtain the sound separation model. The sound separation model is used to identify the footsteps and basketball landing sounds in the input player dribbling data and output them separately. The player's dribbling data is input into the sound separation model, and the footsteps and basketball landing sounds are identified and output respectively.

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