Basketball tactical demonstration method and system

Abstract

The embodiment of the invention provides a basketball tactical demonstration method and system, and belongs to the technical field of intelligent sports. The method comprises the following steps: in response to a tactical demonstration request signal of a user, identifying a corresponding tactical serial number and information of a player executing a tactical; calling an on-site moving model of a corresponding player based on the information of the player executing the tactics to serve as a first model; calling a defense model of the corresponding team based on the opponent information on the current field as a second model; executing tactical process model training under the corresponding tactical serial number based on the first model and the second model to obtain a corresponding tactical simulation result; and performing visualization processing on the tactical simulation result, and pushing a visualization result to a user side.

Description

Technical Field

[0001] The present invention relates to the field of intelligent sports technology, and in particular to a basketball tactic demonstration method and a basketball tactic demonstration system. Background Art

[0002] In basketball games and training, the formulation and execution of tactics have a decisive impact on the outcome of the game. The traditional tactical demonstration method mainly relies on the coach's experience, drawing the player's running and passing lines through the tactical board, and analyzing it in combination with the game video. However, this method is difficult to quantify the success rate of tactical execution, and it is also impossible to accurately simulate the performance of players in different tactical environments. With the advancement of data analysis technology, some basketball tactical analysis systems have begun to use 2D or 3D animations for tactical demonstrations, trying to improve the intuitiveness of tactical understanding, but these systems still have many technical limitations. For example, most of the existing tactical demonstrations are preset fixed paths, which cannot be personalized in combination with the actual movement data of the players. At the same time, these systems usually only simulate the running and passing of the offensive side, but fail to consider the dynamic changes of the defensive side, resulting in a lack of real confrontation environment for tactical analysis and an inability to accurately evaluate the success rate of tactical execution.

[0003] In actual games, the effects of the same tactic when executed by different players often vary significantly. For example, in an undefended state, some players move faster, but they may be greatly affected under high-pressure defense, resulting in reduced tactical execution efficiency. In addition, the player's shooting percentage at different positions is also closely related to his historical performance and defensive pressure. The existing tactical demonstration system cannot provide personalized tactical success rate predictions, resulting in a lack of accurate data support for coaches when formulating tactics. Similarly, factors such as the defender's position, steals, and blocking capabilities have an important impact on the results of tactical execution, but existing tactical simulation methods often lack a defensive model and can only present fixed tactical demonstrations, and cannot dynamically simulate the interaction between the offensive and defensive sides in the game. Due to the lack of accurate modeling of individual player characteristics and defensive environments, it is difficult for existing systems to provide scientific tactical optimization solutions, so that tactical demonstrations and analysis still rely on subjective experience judgment.

[0004] In summary, the main problems of existing basketball tactical demonstration methods are the lack of tactical success rate prediction based on personalized data and the failure to dynamically simulate the impact of defense on tactical execution. In view of these problems, a new basketball tactical demonstration scheme needs to be proposed. Summary of the invention

[0005] The purpose of the embodiments of the present invention is to provide a basketball tactical demonstration method and system to at least solve the problems of existing basketball tactical demonstration methods lacking tactical success rate prediction based on personalized data and failing to dynamically simulate the impact of defense on tactical execution.

[0006] In order to achieve the above-mentioned purpose, the first aspect of the present invention provides a basketball tactical demonstration method, which includes: responding to a user's tactical demonstration request signal, identifying a corresponding tactical serial number and player information executing the tactic; calling the corresponding player's on-court movement model as a first model based on the player information executing the tactic; calling the corresponding team's defensive model as a second model based on the opponent information on the current court; executing tactical process model training under the corresponding tactical serial number based on the first model and the second model to obtain a corresponding tactical simulation result; performing visualization processing on the tactical simulation result, and pushing the visualization result to the user end.

[0007] Optionally, the on-court movement model is obtained by training a training sample based on the on-court movement history data of the corresponding player; wherein the on-court movement history data includes: the movement speed of the corresponding player in an unguarded state, the movement speed bypassing the pick-and-roll, the actual position on the court at each moment, and the shooting percentage data of each point under different defensive pressure levels.

[0008] Optionally, the training rules of the on-court movement model include: performing data preprocessing on the on-court movement history data of the target player; based on the on-court movement history data after data preprocessing, using the corresponding player's movement speed in an undefended state and the movement speed bypassing a pick-and-roll as the first training sample; using the corresponding player's shooting hit rate data at each point under different defensive pressure levels as the second training sample; performing model training based on the hidden Markov model algorithm and the first training sample to obtain an on-court position prediction model for the corresponding player; performing model training based on the logistic regression algorithm and the second training sample to obtain a shooting hit rate prediction model for the corresponding player; and coupling the on-court position prediction model with the shooting hit rate prediction model to obtain an on-court movement model for the corresponding player.

[0009] Optionally, the model training is performed based on the hidden Markov model algorithm and the first training sample to obtain the on-field position prediction model of the corresponding player, including: taking the tactical status under different tactical numbers as the hidden variables of the hidden Markov model algorithm, and taking the actual on-field position of the corresponding player at each moment as the observation data of the hidden Markov model algorithm to obtain the corresponding first training sample; setting the state transfer matrix, observation probability matrix and initial state distribution of the hidden Markov model based on the first training sample; in the iterative process, calculating the expected value of each round of iteration based on the forward-backward algorithm, and adjusting the state transfer matrix, the observation probability matrix and the initial state distribution by maximizing the likelihood function until the convergence conditions are met to obtain the optimal state transfer matrix and observation probability matrix; constructing the corresponding hidden Markov model based on the optimal state transfer matrix and observation probability matrix as the on-field position prediction model of the corresponding player.

[0010] Optionally, the model training is performed based on the logistic regression algorithm and the second training sample to obtain a shooting percentage prediction model for the corresponding player, including: annotating the shooting position, defensive pressure level, player shooting type and game background information of each data in the second training sample to obtain a labeled second training sample; wherein the defensive pressure level is determined based on the distance between the defensive player and the current player and the maximum defensive height of the defensive player; the game background information is the total playing time of the current player in this game; the labeled second training sample is used as an input feature of the logistic regression model to perform model training; during the model training process, the loss function is calculated based on maximum likelihood estimation, and the parameters are adjusted by gradient descent until the convergence conditions are met to obtain a shooting percentage prediction model for the corresponding player.

[0011] Optionally, the training rules of the defense model of the corresponding team include: predicting the future position distribution of defensive players based on the hidden Markov model, and calculating the state transition probability of the defense position based on the individual defense ability of the defensive players, the defense tactics and strategies, and the behavior of the offensive players; calculating the defense influence factor based on the state transition probability of the defense position; wherein the defense influence factor includes the interference degree of the defense distance on the shooting accuracy, the probability of the defensive player blocking, and the influence degree of the steal on the passing success rate; using the defense influence factor as the influencing factor of the tactical execution process, simulating the decision changes of the offensive players under different defense modes, and obtaining the initial model; adjusting the position model and defense influence factor of each defensive player of the corresponding team in the initial model based on the historical game data, and adjusting the model parameters in combination with the actual game situation under the corresponding historical data, so as to obtain the defense model of the corresponding team.

[0012] Optionally, the tactical simulation results include: the position change process of the tactical execution player, the position change process of the defensive player, the simulation process of the basketball transfer path, and the final shooting accuracy prediction value.

[0013] Optionally, visualization processing is performed on the tactical simulation results, and the visualization results are pushed to the user end, including: generating a dynamic tactical demonstration screen based on the position change process of the tactical execution player and the position change process of the defensive player; real-time annotation of tactical data in the dynamic tactical demonstration screen; wherein the tactical data includes: any one or more of the player's running route, passing trajectory and shooting accuracy at the real-time position; and pushing the annotated dynamic tactical demonstration screen to the user end.

[0014] The second aspect of the present invention provides a basketball tactical demonstration system, which includes: a parsing unit, which is used to respond to a user's tactical demonstration request signal to identify a corresponding tactical serial number and player information executing the tactic; a first model calling unit, which is used to call the on-field movement model of the corresponding player based on the player information executing the tactic as a first model; a second model calling unit, which is used to call the corresponding team's defensive model based on the opponent information on the current field as a second model; a simulation unit, which is used to perform tactical process model training under the corresponding tactical serial number based on the first model and the second model to obtain a corresponding tactical simulation result; and a visualization unit, which is used to perform visualization processing on the tactical simulation result and push the visualization result to the user end.

[0015] On the other hand, the present invention provides a computer-readable storage medium, on which instructions are stored, which, when executed on a computer, enable the computer to execute the above-mentioned basketball tactical demonstration method.

[0016] Through the above technical scheme, the scheme of the present invention automatically identifies the target tactics and related player information based on the user's tactical demonstration request, and calls the corresponding player's on-field movement model and defense model to build a tactical simulation system that is more in line with the actual game environment. By combining the personalized motion data of the offensive player and the dynamic changes of the defender, the method can perform tactical process model training and generate more accurate tactical simulation results. Compared with the traditional tactical demonstration that only relies on fixed paths and static tactical diagrams, the present invention can dynamically adjust the player's running position, passing path and shooting selection to make the tactical demonstration more realistic. At the same time, the tactical simulation results are pushed to the user end after visualization processing, providing an intuitive tactical demonstration effect, so that coaches and players can clearly understand the tactical execution process and possible game impact. This method not only improves the accuracy and adaptability of tactical demonstrations, but also enables tactical analysis to develop from empirical judgment to data-driven optimization, providing more scientific support for tactical formulation and player training.

[0017] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings: Figure 1 is a flowchart of the steps of a basketball tactical demonstration method provided by one embodiment of the present invention; Figure 2 It is a system structure diagram of a basketball tactic demonstration system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0019] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0020] Figure 1 1 is a flowchart of a basketball tactical demonstration method provided by an embodiment of the present invention. Figure 1 As shown, an embodiment of the present invention provides a basketball tactic demonstration method, the method comprising: Step S10: In response to the user's tactical demonstration request signal, identify the corresponding tactical serial number and the player information executing the tactic.

[0021] Specifically, the identification of tactical numbers not only includes tactical types, but may also involve specific execution plans. For example, when the same tactic is executed by different players, their running patterns, passing strategies and shooting methods may be significantly different. Therefore, accurate identification of tactical numbers and player information is the basis of tactical simulation. The determination of tactical numbers ensures that the tactical model called subsequently matches the user's needs, making the tactical demonstration process more accurate and realistic. When identifying the player information of the tactical execution, it is necessary to comprehensively consider the player's historical data, including the moving speed in an unguarded state, the movement method bypassing the pick-and-roll, and the shooting percentage under different defensive pressures, so as to accurately call the tactical execution method that meets the player's sports characteristics. In addition, real-time game data can also be combined to ensure that the player's current state (such as physical energy consumption, game performance) matches the tactical execution requirements. Compared with traditional static tactical demonstrations, this method can intelligently match player data according to the input tactical information to achieve personalized tactical simulation.

[0022] Step S20: Based on the information of the player executing the tactics, the on-field movement model of the corresponding player is called as the first model.

[0023] Specifically, the on-court movement model is obtained by training a training sample composed of the on-court movement history data of the corresponding player; wherein the on-court movement history data includes: the movement speed of the corresponding player in an unguarded state, the movement speed bypassing the pick-and-roll, the actual position on the court at each moment, and the shooting percentage data of each point under different defensive pressure levels.

[0024] In an embodiment of the present invention, the on-court movement model is obtained by training a training sample constructed based on the on-court movement history data of the corresponding player. The training sample covers the movement characteristics of the player under different tactical execution situations to ensure that the model can accurately simulate the movement pattern of the player in a real game environment. The on-court movement history data contains multiple key variables, including the player's movement speed in an unguarded state, the movement speed bypassing the pick-and-roll, the actual position on the court at each moment, and any one or more of the shooting percentage data of each point under different defensive pressure levels. These data together constitute the basis of the player's movement behavior in different scenarios, so that the trained model can not only reflect the player's natural movement pattern in an open state, but also reflect its dynamic changes during the tactical execution process.

[0025] Furthermore, the movement speed in an unguarded state reflects the player's ability to run autonomously when there is no defensive pressure, while the movement speed around the pick-and-roll is a key factor in the execution of offensive tactics, which determines whether the player can quickly get rid of the defender and enter the best offensive position. The actual position data on the court at each moment is used to record the player's running pattern in the game, providing an accurate movement trajectory reference for modeling. The shooting percentage data under different levels of defensive pressure further supplements the individual differences in the tactical execution process, enabling the model to dynamically adjust the player's shooting behavior prediction in combination with defensive pressure, so as to be closer to the real game situation.

[0026] Preferably, the training rules of the on-court movement model include: performing data preprocessing on the on-court movement history data of the target player; based on the on-court movement history data after data preprocessing, using the movement speed of the corresponding player in an undefended state and the movement speed bypassing the pick-and-roll as the first training sample; using the shooting hit rate data of each point of the corresponding player under different defensive pressure levels as the second training sample; performing model training based on the hidden Markov model algorithm and the first training sample to obtain the on-court position prediction model of the corresponding player; performing model training based on the logistic regression algorithm and the second training sample to obtain the shooting hit rate prediction model of the corresponding player; and coupling the on-court position prediction model with the shooting hit rate prediction model to obtain the on-court movement model of the corresponding player.

[0027] In an embodiment of the present invention, the training rules of the on-court movement model include a series of data processing and modeling steps to ensure that the model can accurately simulate the player's movement pattern and shooting performance in different game situations. First, the on-court movement history data of the target player is preprocessed to remove abnormal data and normalize the data format to improve the quality and consistency of the training samples. After the data preprocessing is completed, the player's movement speed in an unguarded state and the movement speed bypassing the pick-and-roll are used as the first training samples, and combined with the player's actual position on the court at each moment to construct the player's basic movement pattern in offensive tactics. At the same time, the player's shooting hit rate data at each point under different defensive pressure levels is used as the second training sample to train the shooting decision model so that it can accurately predict the player's shooting success rate under different defensive intensities.

[0028] During the model training process, the first training sample is trained based on the hidden Markov model algorithm to establish a player's on-court position prediction model. This model can learn the movement patterns of players in different tactical situations and predict their possible positions in future time steps, thereby providing dynamic running data for tactical simulation. At the same time, the second training sample is trained based on the logistic regression algorithm to establish a shooting percentage prediction model, which can comprehensively consider factors such as shooting position and defensive pressure to calculate the player's shooting success probability in different areas. Finally, the on-court position prediction model is coupled with the shooting percentage prediction model to form a complete on-court movement model, which can not only predict the player's running trajectory, but also provide accurate shooting success rate estimation based on position information.

[0029] Specifically, the model training is performed based on the hidden Markov model algorithm and the first training sample to obtain the on-field position prediction model of the corresponding player, including: using the tactical status under different tactical numbers as the hidden variables of the hidden Markov model algorithm, and using the actual on-field position of the corresponding player at each moment as the observation data of the hidden Markov model algorithm to obtain the corresponding first training sample; setting the state transfer matrix, observation probability matrix and initial state distribution of the hidden Markov model based on the first training sample; in the iterative process, calculating the expected value of each round of iteration based on the forward-backward algorithm, and adjusting the state transfer matrix, the observation probability matrix and the initial state distribution by maximizing the likelihood function until the convergence conditions are met to obtain the optimal state transfer matrix and observation probability matrix; constructing the corresponding hidden Markov model based on the optimal state transfer matrix and observation probability matrix as the on-field position prediction model of the corresponding player.

[0030] In the embodiment of the present invention, the tactical states under different tactical numbers are used as hidden variables of the hidden Markov model, and the actual positions of the players at each moment are used as observation data to construct the first training sample. The introduction of tactical states ensures that the model can distinguish the player movement patterns under different tactical situations, and the selection of observation data provides real motion trajectory information for training, so that the model can learn the possible position distribution of players in the process of executing different tactics.

[0031] After obtaining the first training sample, the core parameters of the hidden Markov model are set based on the data, including the state transfer matrix, observation probability matrix and initial state distribution. The state transfer matrix is ​​used to describe the probability of a player moving from one position to another between different time steps and is defined as: ; in, Indicates that the player is from the state Transfer to state probability; is the player position state at time step t. The observation probability matrix is ​​used to measure the probability of a player appearing in a specific position under a specific tactical state and is defined as: ; in, is the observed position of the player; Reflects the status Observed below The initial state distribution determines the position tendency of the players in the initial stage of tactical execution, which is defined as: ; in, Indicates that the player is initially in state probability.

[0032] During the model training process, the forward-backward algorithm is used for expectation calculation, and the state transfer matrix, observation probability matrix and initial state distribution are adjusted in combination with the method of maximizing the likelihood function. The forward-backward algorithm optimizes the prediction of the player's movement trend by calculating the probability distribution of the current state, combining historical data and possible future states. The application of the maximization likelihood function enables the training process to gradually converge, and finally obtains the state transfer matrix and observation probability matrix that can best describe the player's movement pattern. Iterative training continuously optimizes these parameters until the convergence conditions are met, that is, the prediction ability of the model reaches a stable and accurate state.

[0033] Finally, based on the optimal state transfer matrix and observation probability matrix obtained through training, a hidden Markov model is constructed and used as a player's on-field position prediction model. The model can predict the possible position of the player in the future time step given the tactical sequence number and the game environment, thereby providing dynamic motion trajectory data for tactical simulation. Compared with the traditional fixed-path tactical demonstration, the present invention uses a hidden Markov model to enable tactical simulation to be dynamically adjusted according to tactical changes and player habits, thereby improving the accuracy and personalization of tactical demonstration.

[0034] Based on the scheme of the present invention, by introducing the hidden Markov model to predict the position of players, the tactical simulation is evolved from a static, preset running trajectory to an intelligent prediction model based on historical data and tactical background. The model can adapt to the movement patterns of different players in different tactical environments, making the tactical simulation closer to the real game situation. In addition, through the maximum likelihood optimization algorithm, the model can adaptively adjust parameters to ensure the stability and accuracy of the prediction results, providing scientific data support for tactical formulation, player training and game analysis.

[0035] Preferably, the model training is performed based on the logistic regression algorithm and the second training sample to obtain the shooting hit rate prediction model of the corresponding player, including: labeling the shooting position, defensive pressure level, player shooting type and game background information of each data in the second training sample to obtain the labeled second training sample; wherein the defensive pressure level is determined based on the distance between the defensive player and the current player and the maximum defensive height of the defensive player; the game background information is the total playing time of the current player in this game; the labeled second training sample is used as the input feature of the logistic regression model to perform model training; during the model training process, the loss function is calculated based on the maximum likelihood estimation, and the parameters are adjusted by gradient descent until the convergence conditions are met to obtain the shooting hit rate prediction model of the corresponding player.

[0036] In an embodiment of the present invention, the second training sample is data labeled to ensure that the model can accurately capture the key factors that affect the shooting accuracy. In the data labeling process, the shooting position is first determined, and the player's shooting point (x, y) is used as an input variable to establish the relationship between the shooting area and the shooting accuracy. Next, the defensive pressure level is calculated. This indicator is defined based on the distance d between the defensive player and the current shooter and the maximum defensive height h of the defensive player, indicating the degree of interference exerted by the defensive player on the shooter. The calculation formula for the defensive pressure level can be expressed as: ; Among them, d represents the Euclidean distance between the defender and the shooter, h represents the maximum defensive height of the defender, and the denominator is increased by 1 to avoid zero division in the calculation. When the defender is close and tall, the value is larger, indicating that the defensive interference is stronger, which may reduce the shooting accuracy.

[0037] Furthermore, the type of shot is an important factor affecting the success rate of shots, including catch-and-shoot, dribble-shoot, fade-away jump shot, etc. The success rate of each shooting method is different, so the type of shot is coded as a categorical variable. Finally, consider the background information of the game, especially the total playing time of the player. To measure the impact of players' physical condition on shooting performance. As the game time increases, players may become tired and their hit rate decreases, so As one of the input features. After completing the data annotation, it is used as the input feature of the logistic regression model to establish a shooting accuracy prediction model. The basic mathematical model of logistic regression is expressed as: ; in, It represents the probability of making a shot given the input features X (including shooting position, defensive pressure, game time, and shooting type); is the weight parameter to be trained. During the model training process, the maximum likelihood estimation algorithm is used to calculate the loss function to ensure that the model parameters can maximize the fitting degree of the shooting success rate. The loss function of logistic regression is defined as: ; Where N is the number of training samples, Indicates whether the shot of the i-th sample hits or not (1 for hit, 0 for miss). The weight parameter w is updated by the gradient descent algorithm, and the loss function is iteratively optimized until the convergence condition is met, that is, the prediction error of the model is minimized and remains stable.

[0038] Furthermore, after training, the logistic regression model can output an estimated success rate of the shot given the player's shooting position, defensive pressure level, shot type and game background information. The technical effect of this technical solution is that by constructing a data-driven shooting accuracy prediction model, the tactical simulation can not only present the player's running position and shooting situation, but also predict the shooting success rate based on data analysis, thereby improving the accuracy of tactical evaluation. Compared with traditional tactical demonstration methods, this method can dynamically adjust shooting decisions based on individual player data, making tactical formulation more scientific and providing coaches and players with more targeted tactical optimization suggestions.

[0039] Step S30: Based on the opponent information on the current field, the defense model of the corresponding team is called as the second model.

[0040] Specifically, the training rules of the defense model of the corresponding team include: predicting the future position distribution of the defensive players based on the hidden Markov model, and calculating the state transition probability of the defense position based on the individual defense ability of the defensive players, the defense tactical strategy and the behavior of the offensive players; calculating the defense influence factor based on the state transition probability of the defense position; wherein the defense influence factor includes the interference degree of the defense distance on the shooting accuracy, the probability of the defensive player blocking and the influence degree of the steal on the passing success rate; using the defense influence factor as the influencing factor of the tactical execution process, simulating the decision changes of the offensive players under different defense modes, and obtaining the initial model; adjusting the position model and defense influence factor of each defensive player of the corresponding team in the initial model based on the historical game data, and adjusting the model parameters in combination with the actual game situation under the corresponding historical data, so as to obtain the defense model of the corresponding team.

[0041] In the embodiment of the present invention, the future position distribution of the defensive player is predicted based on the hidden Markov model to calculate the state transition probability of the defensive position. In this process, the individual defensive ability of the defensive player, the defensive tactical strategy and the behavior of the offensive player jointly determine the movement pattern of the defensive player. The state transition probability matrix is ​​used to describe the probability of the defensive player moving from one position to another between different time steps, which can be expressed as: ; in, represents the position of the defensive player at the current time step, is the possible position in the next time step, Represents the current offensive player's position, The tactical status is the corresponding element in the state transition probability matrix, indicating the transition probability from state i to state j. Based on the state transition probability of the defensive position, the defensive influence factor is further calculated to quantify the degree of interference of the defensive player on the shooting and passing success rates. The defensive influence factor mainly includes three aspects: the impact of the defensive distance on the shooting accuracy, the probability of the defensive player blocking, and the impact of the steal on the passing success rate. Among them, the interference degree of the defensive distance on the shooting accuracy can be calculated by the following formula: ; Among them, d represents the distance between the defender and the offensive player, and λ is the attenuation coefficient set empirically. When the defender is closer to the offensive player, the interference is greater and the hit rate is reduced; on the contrary, when the defensive distance is farther, the interference effect gradually decreases. The block probability calculation is based on the defender's height, block tendency, and opponent's shooting method. The steal probability calculation is based on the intersection area of ​​the defender's position, reaction speed, and pass trajectory.

[0042] Furthermore, when constructing the initial model, the calculated defensive influence factors are introduced into the tactical execution process to simulate the decision-making changes of offensive players under different defensive modes. The model can simulate the impact of man-to-man defense, zone defense, defensive rotation, and double-teaming strategies on the selection of offensive players. For example, when the system detects that an offensive player is facing high defensive pressure, it may predict that he is more inclined to pass the ball rather than shoot directly. In order to improve the accuracy of the defensive model, the initial model needs to be optimized in combination with historical game data. By analyzing the actual positions, blocks, and steals of defensive players in historical games, the state transition probability and defensive influence factors of the model are corrected to make it more in line with the actual game situation.

[0043] Based on the scheme of the present invention, the positions of defensive players are predicted through hidden Markov models, and the changes in shooting and passing success rates are calculated in combination with defensive influencing factors, so that the tactical simulation not only focuses on the tactical execution of the offensive side, but also can truly reproduce the tactical adjustments of the defensive side. Compared with the traditional static tactical demonstration, this method improves the authenticity of tactical simulation, enabling it to dynamically calculate the tactical execution success rate of the offensive side under different defensive tactical configurations, thereby providing more accurate data support for tactical optimization.

[0044] Step S40: Execute tactical process model training under the corresponding tactical sequence number based on the first model and the second model to obtain corresponding tactical simulation results.

[0045] Specifically, the tactical simulation results include: the position change process of the tactical execution player, the position change process of the defensive player, the simulation process of the basketball transfer path, and the final shooting accuracy prediction value.

[0046] In an embodiment of the present invention, the tactical process model training under the corresponding tactical sequence number is performed based on the first model and the second model to obtain the corresponding tactical simulation results. In this process, the first model, i.e., the player's on-court movement model, predicts the player's movement pattern under specific tactics by learning the player's historical movement trajectory, speed in an unguarded state, way of bypassing pick-and-roll, and shooting percentage. The second model, i.e., the team's defensive model, combines the defensive player's position, steal tendency, blocking ability and defensive strategy to simulate the defensive side's countermeasures during the tactical execution process. The combination of the two can construct a complete tactical execution simulation system, so that the tactical demonstration is not only limited to the offensive side's tactical execution, but can also dynamically adapt to the changes of the defensive side, thereby improving the authenticity of the tactical simulation.

[0047] When training the tactical process model, the tactical simulation results mainly include four aspects: the position change process of the tactical execution player, the position change process of the defensive player, the simulation process of the basketball transfer path, and the final shooting percentage prediction value. First, the position change process of the tactical execution player is used to track the running trajectory of the offensive player during the tactical execution. For different tactics, such as pick-and-roll, cut, and screen shooting, the player's movement pattern is different, and this process can accurately simulate the player's real running trajectory in different tactical backgrounds. The probability distribution of the player's future position is calculated through the hidden Markov model.

[0048] The position change process of the defensive player is used to simulate the tactical adjustment strategy of the defense. The movement of the defensive player is not only affected by his own tactical arrangement, but also closely related to the behavior of the offensive player. The model predicts the position adjustment of the defense by calculating the state transition probability of the defensive player. For example, when defending the pick-and-roll tactic, the defensive player may choose to switch defense, go around the screen or delay the offense, and these changes directly affect the effectiveness of the tactical execution. The defensive influencing factor has a key impact on the player's shooting decision and passing path.

[0049] The simulation process of basketball transfer paths is used to track the way the ball is passed during tactical execution. Under different tactics, the basketball passing mode may include ordinary passes, ground passes, high throws, etc. The choice of passing method directly affects the success rate of tactical execution. The model dynamically calculates the best passing path by analyzing the player's position, the blocking situation of the defensive player, and the passing success rate, thereby improving the practical value of tactical simulation. The shooting hit rate prediction value is used to evaluate the final scoring possibility of the tactic. The prediction is based on multiple factors such as the player's historical shooting data, defensive pressure, and game rhythm, and the probability of shooting success is calculated through logistic regression.

[0050] Based on the scheme of the present invention, by integrating the movement model of the offensive player and the interference model of the defensive player, the tactical simulation system has higher dynamic adaptability and realism. Compared with the traditional static tactical demonstration method, this method can not only track the movement path of the players, but also simulate the confrontation process between offense and defense, and finally calculate the shooting success rate of tactical execution, providing accurate data support for tactical optimization and game strategy.

[0051] Step S50: Perform visualization processing on the tactical simulation results, and push the visualization results to the user end.

[0052] Specifically, a dynamic tactical demonstration screen is generated based on the position change process of the tactical execution player and the position change process of the defensive player; tactical data is annotated in real time in the dynamic tactical demonstration screen; wherein the tactical data includes: any one or more of the player's running route, passing trajectory and shooting accuracy at the real-time position; and the annotated dynamic tactical demonstration screen is pushed to the user end.

[0053] In the embodiment of the present invention, in the visualization process, a dynamic tactical demonstration screen is generated based on the position change process of the tactical execution player and the position change process of the defensive player to show the player's movement trajectory, defensive reaction and basketball passing path during the tactical execution process. Compared with the traditional static tactical diagram, the dynamic demonstration screen can more clearly present the real-time position changes of the players during the tactical execution process, so that coaches and players can understand the tactical operation logic more intuitively.

[0054] After generating a dynamic tactical demonstration screen, in order to further enhance the accuracy of tactical analysis, real-time annotation of tactical data is performed in the demonstration screen, so that users can not only observe the overall process of tactical execution, but also obtain data information of key tactical indicators. The tactical data annotation content includes but is not limited to the player's running route, passing trajectory, and shooting accuracy at the real-time position. Among them, the annotation of the player's running route can use curves of different colors to represent the player's movement trajectory, and dynamically update the player's position change in combination with the timeline. The passing trajectory can use arrows or animation effects to show the way the basketball is passed between different players, making the tactical passing selection clearer. For the shooting accuracy at the real-time position, the player's shooting success rate at that position can be calculated and displayed based on the player's current position, and the value can be intuitively presented in the form of floating labels or heat maps.

[0055] Furthermore, after the annotation is completed, the generated dynamic tactical demonstration screen will be pushed to the user end, supporting interactive display on tactical analysis terminals, mobile devices or tactical coach boards. Users can customize tactical analysis methods by adjusting the viewing angle, replaying key tactical clips, and viewing different tactical data layers (such as only showing running routes or only viewing shooting heat maps), etc., to improve the efficiency of tactical teaching and training. In addition, the visualization interface can also be combined with 3D modeling technology to make player movement, shooting and defensive actions more realistic, thereby improving the immersion and intuitiveness of tactical analysis.

[0056] Based on the scheme of the present invention, by combining dynamic tactical demonstration and data annotation, tactical visualization not only stays at the level of player's movement trajectory, but also can combine key tactical indicators such as shooting accuracy and pass selection to achieve more refined tactical analysis. Compared with traditional tactical boards or static tactical diagrams, this method can display the dynamic changes in the tactical execution process in real time, and optimize tactical demonstration in a data-driven way, providing coaches and players with more scientific tactical understanding and decision-making support.

[0057] Figure 2 FIG. 1 is a system structure diagram of a basketball tactical demonstration system provided by an embodiment of the present invention. Figure 2As shown, an embodiment of the present invention provides a basketball tactic demonstration system, which includes: a parsing unit, which is used to respond to a user's tactical demonstration request signal, identify a corresponding tactical serial number and player information executing the tactic; a first model calling unit, which is used to call the on-field movement model of the corresponding player based on the player information executing the tactic, as a first model; a second model calling unit, which is used to call the corresponding team's defense model based on the opponent information on the current field, as a second model; a simulation unit, which is used to perform tactical process model training under the corresponding tactical serial number based on the first model and the second model to obtain a corresponding tactical simulation result; a visualization unit, which is used to perform visualization processing on the tactical simulation result, and push the visualization result to the user end.

[0058] The embodiment of the present invention further provides a computer-readable storage medium, on which instructions are stored, which, when executed on a computer, enable the computer to execute the above-mentioned basketball tactics demonstration method.

[0059] Those skilled in the art will understand that all or part of the steps in the method for implementing the above-mentioned embodiments can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including several instructions for making a single-chip microcomputer, a chip or a processor execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0060] The optional embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above embodiments. Within the technical concept of the embodiments of the present invention, the technical scheme of the embodiments of the present invention can be subjected to a variety of simple modifications, and these simple modifications all belong to the protection scope of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the embodiments of the present invention will not further describe various possible combinations.

[0061] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the embodiments of the present invention, they should also be regarded as the contents disclosed in the embodiments of the present invention.

Claims

1. A basketball tactical demonstration method, characterized in that: The method comprises: In response to a tactical demonstration request signal from a user, identifying a corresponding tactical sequence number and information of a player executing the tactic; Based on the player information of the executed tactics, the on-field movement model of the corresponding player is called as the first model; Based on the opponent information on the current field, the defense model of the corresponding team is called as the second model; Performing tactical process model training under corresponding tactical sequence numbers based on the first model and the second model to obtain corresponding tactical simulation results; Visualization processing is performed on the tactical simulation results, and the visualization results are pushed to the user end.

2. The basketball tactics demonstration method according to claim 1, characterized in that: The on-field movement model is obtained by training a training sample consisting of on-field movement history data of the corresponding player; wherein, The on-field movement history data includes: The corresponding player's movement speed in an unguarded state, the movement speed bypassing the pick-and-roll, the actual position on the court at each moment, and the shooting percentage data of each point under different defensive pressure levels.

3. The basketball tactics demonstration method according to claim 2, characterized in that: The training rules of the on-field movement model include: Performing data preprocessing on the target player's on-field movement history data; Based on the on-court movement history data after data preprocessing, the movement speed of the corresponding player in an unguarded state and the movement speed bypassing the pick-and-roll are used as the first training samples; The shooting percentage data of each point of the corresponding player under different defensive pressure levels is used as the second training sample; Performing model training based on the hidden Markov model algorithm and the first training sample to obtain a prediction model of the on-field position of the corresponding player; Perform model training based on the logistic regression algorithm and the second training sample to obtain a shooting percentage prediction model for the corresponding player; The on-court position prediction model and the shooting accuracy prediction model are coupled to obtain an on-court movement model of the corresponding player.

4. The basketball tactics demonstration method according to claim 3, characterized in that: The performing of model training based on the hidden Markov model algorithm and the first training sample to obtain a corresponding player's on-field position prediction model includes: The tactical states under different tactical numbers are used as hidden variables of the hidden Markov model algorithm, and the actual positions of the corresponding players at each moment are used as observation data of the hidden Markov model algorithm to obtain the corresponding first training samples; Setting a state transfer matrix, an observation probability matrix and an initial state distribution of a hidden Markov model based on the first training sample; In the iterative process, the expected value of each round of iteration is calculated based on the forward-backward algorithm, and the state transfer matrix, the observation probability matrix and the initial state distribution are adjusted by maximizing the likelihood function until the convergence condition is met to obtain the optimal state transfer matrix and observation probability matrix; Based on the optimal state transfer matrix and observation probability matrix, a corresponding hidden Markov model is constructed as the on-field position prediction model of the corresponding player.

5. The basketball tactics demonstration method according to claim 3, characterized in that: The method of performing model training based on the logistic regression algorithm and the second training sample to obtain a shooting percentage prediction model for the corresponding player includes: The shooting position, defensive pressure level, player shooting type and game background information of each data in the second training sample are annotated to obtain the annotated second training sample; wherein, The defensive pressure level is determined based on the distance between the defensive player and the current player and the maximum defensive height of the defensive player; The game background information is the total playing time of the current player in this game; Using the annotated second training sample as an input feature of the logistic regression model to perform model training; During the model training process, the loss function is calculated based on the maximum likelihood estimation, and the parameters are adjusted by gradient descent until the convergence conditions are met to obtain the corresponding player's shooting percentage prediction model.

6. The basketball tactics demonstration method according to claim 1, characterized in that: The training rules corresponding to the team's defensive model include: Predict the future position distribution of defensive players based on the hidden Markov model to calculate the state transition probability of the defensive position based on the individual defensive ability, defensive tactical strategy and offensive player behavior of the defensive players; The defensive influence factor is calculated based on the state transition probability of the defensive position; among them, The defensive influencing factors include the interference degree of the defensive distance on the shooting accuracy, the probability of the defensive player blocking the shot, and the influence degree of the steal on the passing success rate; Taking the defensive influencing factor as the influencing factor of the tactical execution process, simulating the decision-making changes of offensive players under different defensive modes, and obtaining the initial model; Based on the historical game data, the position model and defensive influencing factors of the defensive players of the corresponding team in the initial model are adjusted, and the model parameters are adjusted in combination with the actual game situation under the corresponding historical data to obtain the defensive model of the corresponding team.

7. The basketball tactics demonstration method according to claim 1, characterized in that: The tactical simulation results include: The position change process of the tactical execution players, the position change process of the defensive players, the simulation process of the basketball transfer path, and the final shooting percentage prediction value.

8. The basketball tactics demonstration method according to claim 7, characterized in that: Performing visualization processing on the tactical simulation results and pushing the visualization results to the user end includes: Generate dynamic tactical demonstration screens based on the position change process of tactical execution players and defensive players; Perform real-time annotation of tactical data in the dynamic tactical demonstration screen; wherein, The tactical data includes: Any one or more of the player's running route, passing trajectory and shooting percentage at real-time position; Push the annotated dynamic tactical demonstration screen to the user end.

9. A basketball tactical demonstration system, characterized in that: The system comprises: A parsing unit, for responding to a tactical demonstration request signal from a user, identifying a corresponding tactical sequence number and information about players executing the tactic; A first model calling unit, configured to call the on-field movement model of the corresponding player based on the player information executing the tactics as the first model; A second model calling unit, used for calling the corresponding team's defense model as the second model based on the opponent information on the current field; A simulation unit, configured to perform tactical process model training under a corresponding tactical sequence number based on the first model and the second model to obtain a corresponding tactical simulation result; The visualization unit is used to perform visualization processing on the tactical simulation results and push the visualization results to the user end.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the basketball tactical demonstration method described in any one of claims 1-8.

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