Football testing method and system based on AI

Abstract

The invention relates to the technical field of sports science and technology, in particular to an AI-based football testing method and system, which comprises the following steps of: training a data set by constructing a plurality of decision trees by adopting a random forest algorithm on the basis of historical match data, randomly selecting a feature subset, and determining an optimal tactical scheme through a voting mechanism, and analyzing the relationship between the tactical effect and the winning rate of the team, and generating a tactical effect and winning rate correlation analysis result. According to the method, historical competition data are deeply analyzed through the random forest algorithm, the complex relation between the strategy effect and the winning rate is revealed, data support of strategy decision is enhanced, the Bayesian network optimizes role allocation according to part of technical characteristics and historical performance, team performance is improved, and a nonlinear dynamic model theory analyzes option and strategy interaction. The enhanced deep learning method adapts to match changes through a deep neural network, and the strategy and configuration flexibility and the ability of the whole queue to cope with complex match conditions are improved.

Description

Technical Field

[0001] The present invention relates to the field of sports science and technology, and in particular to an AI-based football testing method and system. Background Art

[0002] The field of sports technology is a sub-field of athlete performance evaluation and improvement. This field focuses on using artificial intelligence technologies such as machine learning, computer vision, and data analysis to quantitatively analyze and evaluate athletes' technical, tactical behavior, and physical performance. This method can provide in-depth data support for the coaching team, help develop personalized training plans, improve player performance, and optimize tactical deployment. The introduction of artificial intelligence technology makes the analysis of athlete performance more objective and accurate, and can process and analyze a large amount of game and training data to discover athletes' potential and weaknesses.

[0003] Among them, the AI-based football testing method is a method that uses artificial intelligence technology, especially machine learning algorithms and computer vision, to test and analyze the performance of football players. Its purpose is to evaluate the players' technical level, tactical understanding and physical fitness through in-depth analysis of large amounts of data generated during games and training. This method aims to provide objective and quantitative evaluation criteria to help coaches and managers understand the current performance level of players, identify their strengths and areas that need improvement, so as to develop more effective training plans and game strategies, and achieve the effect of improving the overall performance and competitiveness of the team.

[0004] Traditional AI-based football testing methods have obvious deficiencies in processing complex data and adapting to changes in game strategies in real time. Traditional analysis methods rely on linear models, which make it difficult to capture nonlinear relationships between data and the interaction effects of multiple variables, limiting the depth and accuracy of tactical analysis. The lack of efficient optimization algorithm support makes the tactical plan adjustment process lack dynamic adaptability and makes it difficult to respond quickly and effectively to real-time changes in the game. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an AI-based football testing method and system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a football testing method based on AI, comprising the following steps: S1: Based on historical game data, the random forest algorithm is used to train the data set by building multiple decision trees, randomly selecting feature subsets, and determining the optimal tactical plan through a voting mechanism. The relationship between the team's tactical effect and winning rate is analyzed, and the correlation analysis results between tactical effect and winning rate are generated; S2: Based on the analysis results of the correlation between tactical effect and winning rate, a particle swarm optimization algorithm is used to simulate the social behavior of animals in the hunting process, and the optimal solution is captured by updating the position and speed of particles in the solution space, and the fitness of the tactical solution is evaluated. With reference to the key indicators of ball possession rate and number of shots, a fitness score of the tactical solution is generated; S3: Based on the fitness score of the tactical solution, a simulated annealing algorithm is used to simulate the metal annealing process, gradually reduce the temperature of the environment, reduce the search range of the solution, adjust the tactical parameters, capture the optimal solution, and generate an optimized tactical solution; S4: Based on the optimized tactical plan, a Bayesian network is used to construct a conditional probability distribution according to the technical characteristics and historical performance of the players, and the performance probability of the players in differentiated roles and positions is predicted through the network's reasoning mechanism, and the roles and positions of the players are adjusted to generate a player role and position allocation plan; S5: Based on the player role and position allocation scheme, nonlinear dynamic model theory is adopted, chaos theory and dynamic model are used to analyze the complexity of player and tactical interactions during the game, the performance of differentiated strategies in real-time games is evaluated, and game result predictions are generated by simulating game scenarios; S6: Based on the prediction of the game results, a deep learning method is used to analyze the game data by building a deep neural network model, adjust the tactical layout and player configuration, respond to emergencies in the game and changes in the opponent's strategy, and generate real-time adjusted tactics and configuration.

[0007] As a further solution of the present invention, the analysis results of the association between tactical effects and winning rates include tactics under differentiated defensive intensities, diversity of offensive modes, and player configuration schemes; the fitness score of the tactical scheme includes the contribution of ball possession rate to tactical success, the correlation evaluation between the number of shots and the probability of scoring a goal, and the impact of passing efficiency on the overall performance of the team; the optimized tactical scheme specifically refers to the adjusted offensive speed and rhythm, detailed defensive deployment, and optimization of the cooperation mode between players; the player role and position allocation scheme includes the optimal position matching based on the player's technical characteristics and historical performance, the role allocation enhancement scheme for the team's collaborative combat capability, and the personalized deployment strategy; the game result prediction includes the change in winning rate under differentiated tactical response strategies, the impact evaluation of the complexity of player interactions on the game results, and the diversity of strategies; the real-time adjusted tactics and configurations include improved tactical flexibility for dealing with emergencies, response plans for changes in opponent strategies, and dynamic optimization of player configurations.

[0008] As a further solution of the present invention, based on historical game data, a random forest algorithm is used to train the data set by constructing multiple decision trees, randomly selecting feature subsets, and determining the optimal tactical plan through a voting mechanism, analyzing the relationship between the team's tactical effect and winning rate, and generating the tactical effect and winning rate correlation analysis results in the following steps: S101: Based on historical game data, the random forest algorithm is used to build a decision tree set for data training. A feature subset in the data set is randomly selected as the input of the decision tree to improve the model's generalization ability for data. The prediction result is determined using the majority voting mechanism to generate a historical data training model. S102: Based on the historical data training model, feature criticality assessment is used to identify tactical features that affect the winning rate by analyzing the usage frequency of features in the decision tree and the contribution to the model prediction results, and tactical plans are determined accordingly to generate an optimal tactical plan set; S103: Based on the optimal tactical solution set, model validation technology is used to evaluate the performance of the tactical solution on the differentiated data subsets through a cross-validation method, the relationship between the tactical effect and the team's winning rate is analyzed, and a correlation analysis result between the tactical effect and the winning rate is generated.

[0009] As a further solution of the present invention, based on the analysis results of the association between tactical effect and winning rate, a particle swarm optimization algorithm is used to simulate the social behavior of animals in the hunting process, and the optimal solution is captured by updating the position and speed of particles in the solution space, and the fitness of the tactical solution is evaluated. With reference to the key indicators of ball possession rate and number of shots, the steps of generating the fitness score of the tactical solution are specifically as follows; S201: Based on the analysis results of the association between tactical effect and winning rate, a particle swarm optimization algorithm is used to initialize a particle swarm by simulating hunting behavior. Particles represent tactical plans. The position and speed of particles are updated to simulate the process of capturing the tactical plan with the best fitness. The fitness of the plan is evaluated according to key indicators such as ball possession rate and number of shots, and a particle swarm initialization plan is generated. S202: Based on the particle swarm initialization scheme, a fitness evaluation and update strategy is adopted to compare the fitness scores of particles, update the particle positions and velocities, continuously iterate and optimize to capture the tactical scheme with the best fitness, and generate a strategy scheme optimization index; S203: Based on the strategy optimization index, particle swarm convergence analysis is used to evaluate the optimized tactical plan by analyzing the distribution and convergence speed of the particle swarm in the solution space, and generate a tactical plan fitness score.

[0010] As a further solution of the present invention, based on the fitness score of the tactical solution, a simulated annealing algorithm is used to simulate the metal annealing process, gradually reduce the temperature of the environment, reduce the search range of the solution, adjust the tactical parameters, capture the optimal solution, and generate the optimized tactical solution. Specifically, the steps are as follows: S301: Based on the fitness score of the tactical scheme, a simulated annealing algorithm is used to set an initial temperature and gradually reduce it. During the simulated annealing process, a tactical parameter is randomly selected to perform a small disturbance, the fitness of the scheme after the disturbance is evaluated, the search range of the solution is reduced, and a tactical scheme in the annealing process is generated; S302: Based on the tactical scheme in the annealing process, annealing cooling and refinement search are adopted to gradually reduce the search accuracy of temperature control parameters, refine parameter adjustment of the tactical scheme, capture the optimal solution, and generate a refined tactical parameter adjustment scheme; S303: Based on the detailed tactical parameter adjustment scheme, global optimization confirmation is adopted to obtain the performance of the tactical scheme in the parameter setting through parameter adjustment and optimization confirmation, and generate an optimized tactical scheme.

[0011] As a further solution of the present invention, based on the optimized tactical plan, a Bayesian network is used to construct a conditional probability distribution according to the technical characteristics and historical performance of the players, and the performance probability of the players in differentiated roles and positions is predicted through the reasoning mechanism of the network, and the players' roles and positions are adjusted. The steps of generating a player role and position allocation plan are specifically as follows: S401: Based on the optimized tactical plan, a Bayesian network is used to construct a conditional probability distribution of the player's technical characteristics and historical performance by defining the player's technical characteristics and historical performance as nodes in the network, and the dependency relationship between the nodes is represented by directed edges, so as to analyze the player's performance in the target tactical environment and generate a player's performance probability distribution; S402: Based on the player performance probability distribution, Bayesian reasoning is used to calculate the performance probability of the players in different roles and positions using the conditional probability distribution of the Bayesian network, and the interaction and dependency between the players are integrated through the reasoning mechanism of the network to optimize the player role allocation and position layout, and generate a role position matching plan; S403: Based on the role position matching scheme, a comprehensive analysis is performed, referring to the individual abilities of the players, tactical requirements and opponent characteristics, and the allocation of player roles and positions is refined through an optimization algorithm to improve the overall performance and efficiency of the team, thereby generating a player role and position allocation scheme.

[0012] As a further solution of the present invention, based on the player role and position allocation scheme, nonlinear dynamic model theory is adopted, chaos theory and dynamic model are used to analyze the complexity of player and tactical interaction during the game, and the performance of the differentiation strategy in the real-time game is evaluated. By simulating the game scene, the steps of generating the prediction of the game result are specifically as follows: S501: Based on the player role and position allocation scheme, chaos theory is used to analyze the sensitivity of initial conditions and predict the risk of future states, reveal the nonlinear dynamic behavior during the game, identify the key factors that cause drastic changes in game results, and generate a dynamic analysis of the game; S502: Based on the game dynamics analysis, a dynamics model is used to construct a mathematical model describing the interaction between players and tactics, simulate the scenario in the game, evaluate the application effect of differentiated tactical strategies in the real-time game, and generate a strategy effect evaluation; S503: Based on the strategy effect evaluation, scenario simulation technology is used to simulate the game scenario to examine the performance of differentiated tactical strategies under target game conditions, evaluate the adaptability and effectiveness of differentiated tactics, adjust the tactical strategies to optimally match the game process, and generate a prediction of the game results.

[0013] As a further solution of the present invention, based on the prediction of the game results, a deep learning method is used to analyze the game data by building a deep neural network model, adjust the tactical layout and player configuration, and respond to emergencies in the game and changes in the opponent's strategy. The steps of generating real-time adjusted tactics and configuration are specifically as follows: S601: Based on the prediction of the game result, a deep learning method is used to analyze the game data by constructing a deep neural network, including a convolutional neural network and a recurrent neural network, to identify key variables and patterns, evaluate the effectiveness of the tactical layout, and generate a deep analysis result; S602: Based on the deep analysis results, a strategy adjustment algorithm is used, combined with the analysis results of the deep learning model, to optimize the tactical layout and player configuration through the algorithm, adjust the strategy to deal with emergencies in the game and changes in the opponent's strategy, and generate a tactical adjustment plan; S603: Based on the tactical adjustment plan, make real-time decisions and adjustments, refer to the real-time game data and changes in opponent strategies, refine tactical adjustments and player configurations through a decision support mechanism, and generate real-time adjusted tactics and configurations.

[0014] An AI-based football testing system, the AI-based football testing system is used to execute the above-mentioned AI-based football testing method, the system includes a data integration module, a tactical plan module, a tactical evaluation module, a tactical refinement module, a role matching module, a dynamic prediction module, and a strategy adjustment module.

[0015] As a further solution of the present invention, the data integration module uses natural language processing technology based on historical game data to automatically identify and extract key information of player performance, game results and statistical data, analyzes the structure and content in the text through an algorithm, converts unstructured text data into structured data, merges and standardizes the data, and generates processed game data; The tactical solution module uses a support vector machine model based on the processed game data to classify the data by capturing the hyperplane of the differentiated tactical solution intervals in the multi-dimensional feature space, evaluate and select the tactical solution with the best success probability, and generate a preliminary tactical solution set; The tactical evaluation module uses a genetic algorithm based on a set of preliminary tactical solutions to simulate the selection, crossover and mutation processes in natural evolution, iteratively optimizes the tactical solutions for multiple generations, evaluates the performance of the solutions through a fitness function, cyclically updates the tactical solutions to capture the solutions with the best fitness, and generates a fitness score for the tactical solutions; The tactical refinement module uses a gradient boosting tree algorithm based on the fitness score of the tactical plan to model the prediction error of the previous round, gradually reduce the error, refine and adjust the tactical parameters, improve the performance of the overall tactical plan, and generate an optimized tactical plan; The role matching module adopts a graph matching algorithm based on the optimized tactical plan, regards the players and tactical plans as nodes in the graph, and uses the matching degree between the players' technical characteristics and tactical requirements as the weight of the edge. The optimal role and position of the player is determined through matching, and the player role and position allocation plan is generated; The dynamic prediction module uses dynamic simulation based on the player role and position allocation scheme to construct dynamic equations that describe player interactions and tactical changes, simulates dynamic changes during the game by numerically solving the equations, evaluates the effects of differentiated tactical strategies, and generates predictions of game results; The strategy adjustment module is based on the prediction of game results and adopts reinforcement learning technology. By evaluating the impact of differentiated tactical configurations on game results, it adjusts tactical layout and player configuration, matches changes in the game process and opponent strategies, and generates real-time adjusted tactics and configurations.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by adopting random forest algorithm to analyze historical game data, by constructing multiple decision tree training data sets and randomly selecting feature subsets, the voting mechanism determines the optimal tactical plan, effectively reveals the complex relationship between tactical effect and winning rate, and enhances the data support of tactical decision-making. The particle swarm optimization algorithm simulates social behavior to capture the optimal solution, evaluates the fitness of tactical plans, and optimizes tactical plans with reference to key indicators such as ball possession rate and number of shots to improve tactical adaptability and effect. The simulated annealing algorithm simulates the metal annealing process, gradually reduces the search range, fine-tunes tactical parameters, captures the optimal solution, and realizes effective optimization of tactical plans. The Bayesian network predicts the probability of player performance, adjusts the role and position of players based on technical characteristics and historical performance, and improves the overall performance of the team. The nonlinear dynamic model theory analyzes the complexity of player and tactical interaction, evaluates the real-time performance of strategies, and increases the dynamic adaptability of game strategies. The deep learning method constructs a deep neural network model to respond to sudden situations in the game and changes in opponent strategies, and improves the flexibility and adaptability of tactical layout and player configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 This is a detailed flow chart of S1 of the present invention; Figure 3 This is a detailed flow chart of S2 of the present invention; Figure 4 This is a detailed flow chart of S3 of the present invention; Figure 5 This is a detailed flow chart of S4 of the present invention; Figure 6 This is a detailed flow chart of S5 of the present invention; Figure 7 This is a detailed flow chart of S6 of the present invention; Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0020] Embodiment 1 See also Figure 1 The present invention provides a technical solution: an AI-based football testing method, comprising the following steps: S1: Based on historical game data, the random forest algorithm is used to train the data set by building multiple decision trees, randomly selecting feature subsets, and determining the optimal tactical plan through a voting mechanism. The relationship between the team's tactical effect and winning rate is analyzed, and the correlation analysis results between tactical effect and winning rate are generated; S2: Based on the analysis results of the correlation between tactical effect and winning rate, a particle swarm optimization algorithm is used to simulate the social behavior of animals in the hunting process. The optimal solution is captured by updating the position and speed of particles in the solution space, and the fitness of the tactical solution is evaluated. The fitness score of the tactical solution is generated by referring to the key indicators of ball possession rate and number of shots; S3: Based on the fitness score of the tactical solution, the simulated annealing algorithm is used to simulate the metal annealing process, gradually reduce the temperature of the environment, reduce the search range of the solution, adjust the tactical parameters, capture the optimal solution, and generate the optimized tactical solution; S4: Based on the optimized tactical plan, a Bayesian network is used to construct a conditional probability distribution according to the players’ technical characteristics and historical performance. The network’s reasoning mechanism is used to predict the performance probability of players in differentiated roles and positions, adjust the players’ roles and positions, and generate a player role and position allocation plan; S5: Based on the player role and position allocation scheme, nonlinear dynamic model theory is adopted, chaos theory and dynamic model are used to analyze the complexity of player and tactical interaction during the game, evaluate the performance of differentiated strategies in real-time games, and generate game result predictions by simulating game scenarios; S6: Based on the prediction of game results, a deep learning method is used to analyze game data by building a deep neural network model, adjust tactical layout and player configuration, respond to emergencies in the game and changes in opponent's strategy, and generate real-time adjusted tactics and configuration.

[0021] The results of the analysis on the correlation between tactical effect and winning rate include tactics under differentiated defensive intensities, diversity of offensive modes, and player configuration schemes. The fitness score of tactical schemes includes the contribution of ball possession rate to tactical success, the correlation assessment between the number of shots and the probability of scoring a goal, and the impact of passing efficiency on the overall performance of the team. The optimized tactical scheme specifically refers to the adjusted offensive speed and rhythm, detailed defensive deployment, and optimization of the cooperation mode between players. The player role and position allocation scheme includes the optimal position matching based on the player's technical characteristics and historical performance, the role allocation enhancement scheme for the team's coordinated combat capability, and the personalized deployment strategy. The prediction of game results includes the change in winning rate under differentiated tactical response strategies, the impact assessment of the complexity of player interactions on the game results, and the diversity of strategies. The tactics and configurations adjusted in real time include the improvement of tactical flexibility in dealing with emergencies, the response scheme to the opponent's strategy changes, and the dynamic optimization of player configuration.

[0022] In step S1, the random forest algorithm is used to process historical match data. By constructing multiple decision tree training data sets, a feature subset is randomly selected for each tree, and then the optimal tactical plan is determined through a voting mechanism. The specific execution process involves formatting the match data into a feature matrix, where the features include key statistical indicators such as team possession rate, number of shots, and number of goals. During the training process, each decision tree randomly selects a certain proportion of features from the overall feature set to search for the optimal split point, thereby enhancing the generalization ability of the model and reducing the risk of overfitting. The random forest integrates the prediction results of multiple decision trees and uses a majority voting mechanism to determine the final tactical plan, thereby analyzing the relationship between tactical effect and winning rate. The tactical effect and winning rate correlation analysis results generated by this process are presented in the form of a data report, which lists the winning rate prediction values ​​of each tactical plan in detail, providing a scientific basis for the team's tactical adjustments.

[0023] In step S2, based on the analysis results of the correlation between tactical effect and winning rate, the particle swarm optimization algorithm is used to optimize the tactical plan. The algorithm simulates the hunting behavior of bird flocks in nature. The particle updates its position and speed in the solution space according to individual experience and group experience to capture the optimal solution. The algorithm initializes a group of particles, each particle represents a potential tactical plan, its position represents the tactical parameter configuration, and its speed represents the direction and amplitude of parameter adjustment. The particle updates its speed and position according to the individual historical optimal position and the group historical optimal position, gradually approaching the optimal tactical plan. This process considers key indicators such as ball possession rate and number of shots, and calculates the fitness score of each plan to reflect the actual effect of the tactical plan. Through iterative updates, the particle swarm optimization algorithm finally determines the tactical plan with the highest fitness score. The generated tactical plan fitness score reflects the potential of different tactical plans to improve the team's winning rate.

[0024] In step S3, based on the fitness score of the tactical solution, the simulated annealing algorithm is used to further optimize the tactical solution. The simulated annealing algorithm captures the global optimal solution by simulating the metal annealing process in physics, starting at high temperature and gradually reducing the temperature. The algorithm performs multiple searches at each temperature. Each search randomly selects a new configuration of tactical parameters and decides whether to accept this new configuration based on the energy difference (i.e., fitness score difference) and temperature, allowing the algorithm to explore a wider solution space in the early stage and avoid falling into the local optimum in the early stage. The gradual reduction of temperature reduces the probability of accepting inferior solutions and allows the algorithm to gradually focus on the optimal solution. By gradually adjusting the tactical parameters, the simulated annealing algorithm refines the tactical solution. The optimized tactical solution finally generated not only takes into account the potential for improving the winning rate, but also the feasibility of implementation, providing the team with a finely adjusted tactical deployment plan.

[0025] In step S4, the conditional probability distribution of the players' technical characteristics and historical performance is accurately constructed through the Bayesian network, realizing the prediction of the players' performance in differentiated roles and positions. The network relies on the multi-dimensional data of the players, including but not limited to the scores, assists, defensive data and physical condition reports of past games. The data format is a formatted data table, each column represents a statistical indicator, and each row represents the performance of a game or an option. In the Bayesian network, nodes represent variables (such as the speed and shooting accuracy of members), and edges represent the probability dependency relationship between variables. By defining the prior probability and conditional probability table, the network can infer the true probability distribution of the members' performance given the technical characteristics and historical performance of the members. This process involves complex probability calculations, including Bayesian formulas and Markov chain Monte Carlo methods, etc. During the execution of the algorithm, the probability weights in the network will be adjusted to optimize the accuracy of the prediction. The result of this step is to generate a player role and position allocation plan, which guides the team layout and ensures that all players can maximize their abilities in their most suitable positions.

[0026] In step S5, the complexity of the interaction between players and tactics is deeply analyzed through the theory of nonlinear dynamic models, and the performance of differentiated tactical strategies in actual games is effectively evaluated. Using chaos theory and dynamic models, we focus on the dynamic changes of players' positions, speeds and tactical execution in the game. The data format is time series data, which records key indicators such as the position and speed of players at every moment in the game. The interaction between players and tactics is described by dynamic equations, and the model is simulated in three dimensions using numerical solutions such as the Runge-Kutta method to simulate various estimated scenarios during the game. This simulation takes into account nonlinear factors in the game, such as player fatigue and changes in opponent strategies, and can generate predictions of game results. This step evaluates the effectiveness of tactical strategies by comparing simulation results with actual game results, providing scientific basis guidance for the coaching team, so that they can make quick and accurate tactical adjustments in the game.

[0027] In step S6, a deep learning method is used to build a deep neural network model to conduct in-depth analysis of the data and achieve real-time adjustment of the game strategy layout and option configuration. The data used for model training includes information such as option behavior data, game results, and opponent strategies, with diverse formats, including both reconstructed data such as statistics and non-reconstructed data such as video analysis reports. Deep learning models, especially formal neural networks (CNNs) and neural recurrent networks (RNNs), can extract fine features from these complex data and analyze the key and decision points in the game. During the training of the model, the optimization weights and prediction accuracy are continuously improved through the back propagation algorithm. The model output is a real-time adjusted strategy and decision point player configuration plan, which is directly converted into decision support for the coaching team, helping the team to deal with emergencies in the game and the opponent's strategy adjustments, significantly improving the team's tactical flexibility and adjustment capabilities, and ensuring that the most optimized formation layout and player configuration are fully maintained in various competitions.

[0028] See also Figure 2 ,Based on historical game data, the random forest algorithm is used to train the data set by building multiple decision trees,randomly selecting feature subsets, and determining the optimal tactical plan through a voting mechanism,analyzing the relationship between the team's tactical effect and winning rate. The specific steps for generating the analysis results of the correlation between tactical effect and winning rate are as follows; S101: Based on historical game data, the random forest algorithm is used to build a decision tree set for data training. A feature subset in the data set is randomly selected as the input of the decision tree to improve the model's generalization ability for data. The prediction result is determined using the majority voting mechanism to generate a historical data training model. S102: Based on the historical data training model, feature criticality assessment is used to identify tactical features that affect the winning rate by analyzing the frequency of feature usage in the decision tree and the contribution to the model prediction results, and tactical plans are determined accordingly to generate the optimal tactical plan set; S103: Based on the optimal tactical plan set, model validation technology is used to evaluate the performance of tactical plans on differentiated data subsets through cross-validation methods, analyze the relationship between tactical effects and team winning rates, and generate correlation analysis results between tactical effects and winning rates.

[0029] In sub-step S101, the historical match data is processed by the random forest algorithm, and the data is trained by constructing a set of decision trees. The historical match data is formatted into a feature matrix and a result vector. The feature matrix includes key statistical indicators such as ball possession, number of shots, and number of goals, and the result vector records the outcome of the game. When constructing each decision tree, the random forest randomly selects a certain proportion of features from the overall feature set. The introduction of this randomness is intended to improve the model's generalization ability for unseen data. Each tree is trained independently and gives a prediction result for the same input data. Finally, a majority voting mechanism is used to determine the prediction result of the entire model. The historical data training model generated by this process can capture and understand the complex relationship between the winning rate and various tactical features. The generated model is saved as a serialized file for use in subsequent steps.

[0030] In sub-step S102, the model is trained based on historical data, and the tactical features that affect the winning rate are determined through feature criticality assessment. The frequency of use of each feature in the decision tree of the random forest algorithm and its contribution to the model prediction results are analyzed. The built-in feature importance evaluation mechanism of the random forest is used to measure its contribution to the model prediction ability by calculating the average value of the purity gain brought by each feature split, identify the tactical features that have the greatest impact on the winning rate, such as ball possession rate, shooting quality, etc., and screen and construct the optimal tactical solution set based on this. The generated optimal tactical solution set is saved in the form of a data file, providing the team with a scientific tactical decision-making tool, making the tactical adjustment and decision-making process more accurate and efficient.

[0031] In sub-step S103, based on the optimal tactical solution set, the model validation technology is used to evaluate the effectiveness of the tactical solution. Through the cross-validation method, the method divides the data into multiple subsets, leaving out one subset as the test set each time, and the rest for training, and the cycle is repeated until each subset has been used as a test set. This can evaluate the performance of the tactical solution on different data subsets, ensure the stability and reliability of the evaluation results, and analyze the winning rate correlation of the tactical solution, that is, how different tactical solutions affect the team's winning rate. It can not only verify the effectiveness of each tactical solution, but also deeply understand the relationship between tactical effect and winning rate. The generated tactical effect and winning rate correlation analysis results are presented in the form of a report, which provides an intuitive understanding and evaluation of the impact of tactical adjustments, and provides important data support for the optimization of team tactics and strategy formulation.

[0032] See also Figure 3 ,Based on the analysis results of the correlation between tactical effect and winning rate, ,the particle swarm optimization algorithm is used to simulate the social behavior of ,animals in the hunting process. ,The optimal solution is captured by updating the position and speed of ,particles in the solution space, and the fitness of the tactical scheme is evaluated. ,Referring to the key indicators of ball possession rate and ,number of shots, the steps for generating the fitness score of the tactical ,scheme are as follows; S201: Based on the analysis results of the correlation between tactical effect and winning rate, the particle swarm optimization algorithm is used to initialize the particle swarm by simulating hunting behavior. The particles represent tactical plans. The position and speed of the particles are updated to simulate the process of capturing the tactical plan with the best fitness. The fitness of the plan is evaluated based on the key indicators of ball possession rate and number of shots, and the particle swarm initialization plan is generated. S202: Based on the particle swarm initialization scheme, a fitness evaluation and update strategy is adopted to compare the fitness scores of particles, update the particle positions and velocities, continuously iterate and optimize to capture the tactical scheme with the best fitness, and generate a strategy scheme optimization index; S203: Based on the strategy optimization index, particle swarm convergence analysis is used to evaluate the optimized tactical plan by analyzing the distribution and convergence speed of the particle swarm in the solution space, and generate a tactical plan fitness score.

[0033] In sub-step S201, the search process of the headphone group simulation scheme is initialized through the headphone group optimization (PSO) algorithm, which accurately simulates the pursuit of the optimal solution by individuals in hunting behavior in Hubei Province. The headphones represent different headphone schemes, and their position and speed update mechanism simulates the process of gradually approaching the optimal layout scheme. The position of each particle represents the coordinates of an estimated layout scheme in the multidimensional decision space, and the speed reflects the direction and magnitude of the scheme adjustment. The data format uses a multidimensional queue, where each dimension corresponds to a feature of the scheme, such as ball possession and shooting frequency. During the initialization process, the position and speed of the particles are randomly generated, and the fitness of each particle is calculated according to the key indicators of the scenario scheme, that is, the pros and cons of the scheme are evaluated through the analysis results of the specific local effect and the winning rate. The fitness evaluation adopts a pre-defined scoring mechanism, including the degree of correlation between ball possession and the number of shots and the winning rate. By iteratively updating the particle position and speed, the simulation captures the fitness. The particle swarm optimization algorithm in this process takes into account the individual historical optimal position and the global optimal position, thereby guiding the particle swarm to optimize towards the optimal solution. The application of the particle swarm optimization algorithm in this process in this sub-step generates a particle swarm initialization scheme, which provides a basis for subsequent fitness evaluation and efficient optimization of regional schemes. The optimization effect is reflected in a wider search and determination of regional schemes that can improve the team's winning rate.

[0034] In sub-step S202, based on the consumable group initialization scheme, the fitness evaluation and update strategy in the consumable group optimization algorithm is adopted to achieve continuous iterative optimization of the local scheme. Through detailed operations, including the fitness score of each comparison consumable and the update of the particle position and speed according to the scoring results, the process continuously optimizes and captures the orientation scheme with the best fitness. The update of the particle position reflects the adjustment of the update scheme, and the speed adjusts the indicated direction and step size. These updates follow the historical optimal solutions of individuals and groups to ensure that the search process concentrates on local exploration capabilities and also has global search capabilities. The fitness scoring mechanism takes into account the specific execution effect of the local scheme, such as improving the ball possession rate and the number of shots on the winning rate. By accurately adjusting the algorithm parameters, such as learning gain and inertia weight, the search efficiency and accuracy are optimized. The execution of this sub-step generates a strategy scheme optimization index, which reflects the degree of improvement and fitness of the strategy scheme after continuous iterative optimization. It plays an important role in guiding the selection and adjustment of tactics in actual games, ensuring that the team can maintain competitiveness in a changing game environment.

[0035] In sub-step S203, based on the scheme strategy optimization index, the particle swarm convergence analysis is used to evaluate the effect of the optimized local scheme. The analysis of the distribution and convergence speed of the particle swarm in the solution space reveals the stability of the local scheme optimization process. The convergence analysis depends on the statistical analysis of the position of the sample group after multiple iterations, and selects the optimal solution set of whether the sample group tends to be stable. By calculating the distribution density and coefficient of variation of the sample group, the analysis process of evaluating the optimization process ensures that the final selected local scheme not only performs well in a single iteration, but also shows a stable high fitness in multiple iterations. The core result of this sub-step is to generate a local scheme fitness score, which comprehensively considers the optimization effect and stability of the scheme, and provides a quantitative reference for the final selection of the scheme. Through the particle swarm convergence analysis, it can ensure that the selected scheme has a high success rate and adaptability to different competitive situations, and enhance the scientificity and accuracy of specific decisions.

[0036] See also Figure 4 ,Based on the fitness score of the tactical scheme, the simulated annealing algorithm is used to simulate the metal annealing process, gradually reduce the temperature of the environment, reduce the search range of the solution, adjust the tactical parameters, capture the optimal solution, and generate the optimized tactical scheme. The specific steps are as follows; S301: Based on the fitness score of the tactical solution, a simulated annealing algorithm is used. The initial temperature is set and gradually reduced. During the simulated annealing process, the tactical parameters are randomly selected for small perturbations. The fitness of the solution after the perturbation is evaluated, the search range of the solution is reduced, and the tactical solution during the annealing process is generated. S302: Based on the tactical scheme in the annealing process, annealing cooling and refinement search are adopted to gradually reduce the search accuracy of temperature control parameters, refine parameter adjustment of the tactical scheme, capture the optimal solution, and generate a refined tactical parameter adjustment scheme; S303: Based on the detailed tactical parameter adjustment plan, global optimization confirmation is adopted to obtain the performance of the tactical plan in the parameter setting through parameter adjustment and optimization confirmation, and generate an optimized tactical plan.

[0037] In sub-step S301, the simulated annealing algorithm is used to optimize based on the fitness score of the tactical solution. The process of setting a high temperature initially and then gradually reducing it simulates the physical annealing process to capture the optimal tactical solution. During the annealing process, the algorithm randomly selects tactical parameters, perturbs them slightly, and then evaluates the fitness of the perturbed solutions. The data format based on this operation includes multi-dimensional parameters of the tactical solution, such as ball possession, offensive speed, defensive compactness, etc. The perturbation of each parameter is reflected in the slight changes in these dimensions. When evaluating the fitness, the potential impact of the solution on the team's winning rate is considered, and the pre-trained evaluation model is used to quantify the effect of each perturbation solution. As the temperature decreases, the algorithm reduces the search range of solutions and focuses on more promising solutions. The tactical solution generated by this process records the parameter selection and fitness score of each step in the annealing process and is saved in the form of a file, which effectively narrows the search space and ensures the possibility of finding the optimal solution in a wide range of parameter combinations.

[0038] In sub-step S302, based on the tactical scheme in the annealing process, the annealing cooling and refinement search strategy is further adopted. By gradually lowering the temperature, the algorithm accurately controls the accuracy of the parameter search and refines the parameter adjustment of the tactical scheme. The parameters of the tactical scheme and its corresponding fitness score continue to be used as key data input. The algorithm adjusts the tactical parameters through more refined perturbation and evaluation steps to find a solution with higher fitness. As the temperature decreases, the algorithm's search in the parameter space becomes more local and refined, allowing for a deeper exploration of potential optimal solutions. The generated refined tactical parameter adjustment scheme records the parameter adjustment of each step and the corresponding fitness score in detail, providing a basis for further optimization. This strategy ensures a balance between global search and local search, and increases the probability of finding the optimal tactical solution.

[0039] In sub-step S303, a global optimization confirmation method is adopted based on the refinement of the tactical parameter adjustment plan. In this stage, the performance of the tactical plan under different parameter settings is comprehensively evaluated through further parameter adjustment and optimization confirmation of the tactical plan. The parameter settings and fitness scores of the tactical plan continue to be used as the basis for evaluation, and a comprehensive optimization strategy is adopted to carefully examine the tactical effect under each parameter configuration. This process not only considers the adjustment of a single parameter, but also evaluates the impact of the interaction between parameters on the tactical effect, and determines the final optimal tactical plan through comprehensive analysis. The generated optimized tactical plan lists in detail the selected parameter configuration and its expected impact on the winning rate, providing the team with a set of precisely adjusted tactical strategies. The execution of this step strengthens the scientificity and practicality of tactical decision-making, and ensures the effectiveness and competitiveness of the proposed tactical plan in actual competitions.

[0040] See also Figure 5, based on the optimized tactical plan, the Bayesian network is used to construct the conditional probability distribution according to the technical characteristics and historical performance of the players. The performance probability of the players in differentiated roles and positions is predicted through the network's reasoning mechanism, and the players' roles and positions are adjusted. The specific steps of generating the player role and position allocation plan are as follows; S401: Based on the optimized tactical plan, a Bayesian network is used to construct the conditional probability distribution of the players' technical characteristics and historical performance by defining the players' technical characteristics and historical performance as nodes in the network, and the dependencies between the nodes are represented by directed edges, so as to analyze the players' performance in the target tactical environment and generate the probability distribution of the players' performance; S402: Based on the probability distribution of player performance, Bayesian reasoning is used to calculate the performance probability of players in different roles and positions using the conditional probability distribution of the Bayesian network. The interaction and dependency between players are integrated through the reasoning mechanism of the network to optimize the player role allocation and position layout, and generate a role position matching plan; S403: Based on the role and position matching plan, conduct a comprehensive analysis, refer to the individual abilities of the players, tactical requirements and opponent characteristics, refine the allocation of player roles and positions through an optimization algorithm, improve the overall performance and efficiency of the team, and generate a player role and position allocation plan.

[0041] In sub-step S401, the player's technical characteristics and historical performance are defined as network nodes through the Bayesian network, and the dependencies between nodes are represented by directed edges to construct the conditional probability distribution of the player's performance in the target tactical environment. The data format uses the player's technical attributes (such as passing, shooting, speed, etc.) and historical performance data (such as statistical indicators such as scores, assists, steals, etc. in the game) as input and converted into nodes in the network. The dependencies between nodes reflect the interaction between technical characteristics and historical performance, such as the impact of passing ability on the number of assists. By setting conditional probability distribution, the Bayesian network can describe and calculate the probability of player performance in a given tactical environment. During the construction process, historical data and expert knowledge are used to determine the dependencies and conditional probability values ​​between nodes to complete the construction of the network structure. The player performance probability distribution generated by this process provides a quantitative, probability-based framework for subsequent analysis, so that the performance of players under specific tactics can be predicted and analyzed.

[0042] In sub-step S402, based on the probability distribution of player performance, Bayesian reasoning is used to calculate the performance probability of players in differentiated roles and positions. The conditional probability distribution of the Bayesian network is used as the basis for calculation. The reasoning mechanism comprehensively considers the interaction and dependency between players to optimize the player role allocation and position layout. By setting different positions and roles of players as conditions, the Bayesian network is used for conditional probability query to calculate the performance probability of players in specific roles and positions. This process focuses on how players adapt to different tactical requirements, including the adaptability of technical expertise and historical performance in new environments. The generated role position matching scheme is output in the form of a data file, which lists in detail the fitness and efficiency of each player in different positions, providing a scientific basis for tactical layout and personnel allocation.

[0043] In sub-step S403, a comprehensive analysis is conducted based on the role and position matching scheme, and the allocation of player roles and positions is refined through the optimization algorithm. In this stage, the allocation of player roles and positions is adjusted and optimized by using a multi-objective optimization method with reference to the player's personal ability, tactical requirements and opponent characteristics. The optimization goals include improving the overall performance of the team, enhancing the efficiency of specific tactical execution, and balancing the abilities and characteristics of the players. During the optimization process, the algorithm takes into account a variety of factors, such as the matching degree of the player's technical characteristics, the effect of historical performance in the corresponding position, and the adaptability of the tactics against specific opponents. The generated player role and position allocation scheme is presented in the form of a report, which details the optimal position and role assigned to each player, and how this allocation improves the overall performance and efficiency of the team. This scheme makes tactical arrangements more precise, and can specifically use the strengths of the players to counter the weaknesses of the opponents, thereby providing a clear advantage at the competitive level.

[0044] See also Figure 6 ,Based on the player role and position allocation scheme, the nonlinear dynamic model theory is adopted, and the chaos theory and dynamic model are used to analyze the complexity of the interaction between players and tactics during the game, and the performance of the differentiation strategy in the real-time game is evaluated. By simulating the game scene, the specific steps of generating the prediction of the game results are as follows; S501: Based on the player role and position allocation scheme, chaos theory is used to reveal the nonlinear dynamic behavior during the game by analyzing the sensitivity of initial conditions and predicting the risk of future states, identify the key factors that cause drastic changes in game results, and generate game dynamic analysis; S502: Based on the dynamic analysis of the game, a dynamic model is used to construct a mathematical model describing the interaction between players and tactics, simulate the situation in the game, evaluate the application effect of differentiated tactical strategies in real-time games, and generate a strategy effect evaluation; S503: Based on the strategy effect evaluation, scenario simulation technology is used to simulate the game scenario to examine the performance of differentiated tactical strategies under target game conditions, evaluate the adaptability and effectiveness of differentiated tactics, adjust the tactical strategies to optimally match the game process, and generate a prediction of the game results.

[0045] In sub-step S501, chaos theory is used to analyze the sensitivity of initial conditions and predict the risk of future states, revealing nonlinear dynamic behavior in the game. This process uses player position data, role allocation information, and real-time game status as input and formats them into time series data. Through chaotic system theory, the Lyapunov exponent calculation method is used to evaluate the system sensitivity, identify the impact of small changes in initial conditions on the game results, and apply phase space reconstruction technology and Poincaré cross-section analysis to reveal the nonlinear dynamic characteristics during the game. The detailed operations of this analysis process include time series analysis of player movement trajectories, evaluation of the impact of tactical changes on game dynamics, and identification of key event trigger points. Through these specific operations, a game dynamic analysis report is generated, which records in detail the key factors that are expected to cause drastic changes in results during the game, providing a scientific basis for subsequent tactical adjustments and decisions.

[0046] In sub-step S502, a dynamic model is used to simulate the game scenario by constructing a mathematical model of player and tactical interaction. The input data includes player technical characteristics, position data and tactical arrangement information, which are formatted as structured parameters. The dynamic equations are combined with actual game data and iteratively calculated through numerical solutions, such as the Runge-Kutta method, to simulate game scenarios under different tactical strategies. The operation details include the quantification of the interaction forces between players, the simulation of position changes during tactical execution, and the quantitative evaluation of the strategy effect. This process not only evaluates the actual application effect of differentiated tactical strategies, but also generates a strategy effect evaluation report, which shows in detail the game development trend under different tactical executions, providing an empirical basis for tactical selection and adjustment.

[0047] In sub-step S503, scenario simulation technology is used to examine the performance of differentiated tactical strategies under specific game conditions. The input data covers the evaluated tactical strategies, player ability parameters and predetermined game environment factors, and is processed in a highly structured form. Through a high-precision scenario simulation engine, a large number of simulation experiments are carried out by comprehensively applying the Monte Carlo method and artificial intelligence prediction model to accurately calculate the winning rate and effectiveness under various tactical layouts. The operation process goes deep into simulating every pass, shot and defensive behavior in the game scene, evaluating the adaptability and effectiveness of the tactics, and generating a game result prediction report, which not only accurately predicts the game results under different tactical strategies, but also provides a quantitative basis for the final tactical decision, and optimizes the matching degree between the tactical strategy and the game process.

[0048] See also Figure 7 ,Based on the prediction of game results, a deep learning method is used to analyze game data by building a deep neural network model, adjust the tactical layout and player configuration, respond to emergencies in the game and changes in opponent strategies, and generate real-time adjusted tactics and configurations. The specific steps are as follows; S601: Based on the prediction of game results, deep learning methods are used to analyze game data by building deep neural networks, including convolutional neural networks and recurrent neural networks, to identify key variables and patterns, evaluate the effectiveness of tactical layouts, and generate in-depth analysis results; S602: Based on the deep analysis results, a strategy adjustment algorithm is used, combined with the analysis results of the deep learning model, to optimize the tactical layout and player configuration through the algorithm, adjust the strategy to deal with emergencies in the game and changes in the opponent's strategy, and generate a tactical adjustment plan; S603: Based on the tactical adjustment plan, make real-time decisions and adjustments, refer to the real-time game data and changes in opponent strategies, refine tactical adjustments and player configurations through the decision support mechanism, and generate real-time adjusted tactics and configurations.

[0049] In sub-step S601, the match data is comprehensively analyzed through deep learning methods, including structured neural networks (CNNs) and recurrent neural networks (RNNs), to achieve an in-depth evaluation of the spatial layout structure. First, this analysis process involves the collection and production of match data, including project position data, ball possession changes, and number of shots. The data format is time series data, which is obtained through sensors and game process analysis. The game extracts spatial features in the data, such as multiple distributions and movement patterns, while the recurrent neural network processes time series data to capture dynamic changes during the game, such as patterns of ball possession conversion and offense and defense conversion. The core neural network process includes using a large number of historical game data models and continuously optimizing network weights through the back propagation algorithm to reduce prediction tasks. The identification of key indicators and patterns relies on the multi-layer feature extraction capabilities of the model, which can reveal the deep analysis results generated by the ball under differentiated layouts in the form of reports or data sets, which describe the effectiveness of the layout and potential areas for improvement in detail, providing a scientific basis for coaches to formulate teams or adjust layouts.

[0050] In sub-step S602, based on the results of the deep analysis, a strategy adjustment algorithm is used, combined with advanced deep learning model analysis, to optimize the layout and option configuration. The algorithm dynamically adjusts the formation layout according to the key variables and patterns obtained by the deep learning model, based on the unexpected situations and opponent strategy changes expected in the match. During the strategy adjustment process, the algorithm evaluates the impact of different formation schemes on the team's performance, and determines the formation and configuration that is most likely to improve the team's winning rate by simulating the results of the game under different formation layouts. The parameters involved in the algorithm optimization include good interactions between players, defensive and offensive efficiency, etc., to ensure that the formation adjustment can fully respond to various situations in the game. The generated formation adjustment plan details the specific layout adjustment and option configuration optimization measures, and provides the coaching team with a topological strategy adjustment plan to improve the team's overall performance and ability to adapt to changes in the game.

[0051] In sub-step S603, based on the strategy adjustment scheme, real-time decision-making and adjustment are implemented, with reference to the real-time matching data and strategy change mechanism, and the instant optimization of actual strategies and configurations is supported by decision-making. This process involves real-time collection and analysis of matching data, such as the real-time position of options, the team's ball possession rate and shooting targets, etc. The data format is a real-time updated data stream. The decision support mechanism includes a complex event processing system and a real-time data analysis tool, which can quickly identify key events in the match and based on these real-time information, the decision support system automatically proposes tactical adjustments and player configuration plans, or provides data support for team coaches to make quick decisions. Through this real-time adjustment mechanism, the team can flexibly respond to various situations during the game and enhance the control and foresight of the game. The generated real-time adjusted team and configuration plan provides the team with instant team decision support, ensuring that the team can maintain the best state in the game, counter the opponent's strategy, and improve the winning rate of the game.

[0052] See also Figure 8 , AI-based football testing system,The AI-based football testing system is used to execute the above-mentioned AI-based football testing method. The system includes a data integration module, a tactical plan module, a tactical evaluation module, a tactical refinement module, a role matching module, a dynamic prediction module, and a strategy adjustment module.

[0053] The data integration module uses natural language processing technology based on historical match data to automatically identify and extract key information on player performance, match results and statistics. It uses algorithms to analyze the structure and content in the text, converts unstructured text data into structured data, merges and standardizes the data, and generates processed match data. The tactical solution module uses a support vector machine model based on the processed game data to classify the data, evaluate and select the tactical solution with the best success probability, and generate a preliminary tactical solution set by capturing the hyperplane of the differentiated tactical solution interval in the multi-dimensional feature space; The tactical evaluation module uses a genetic algorithm based on a set of preliminary tactical solutions to simulate the selection, crossover and mutation processes in natural evolution, iteratively optimizes the tactical solutions for multiple generations, evaluates the performance of the solutions through the fitness function, cyclically updates the tactical solutions to capture the solutions with the best fitness, and generates the fitness scores of the tactical solutions. The tactical refinement module uses the gradient boosting tree algorithm based on the fitness score of the tactical plan to model the prediction error of the previous round, gradually reduce the error, refine and adjust the tactical parameters, improve the performance of the overall tactical plan, and generate an optimized tactical plan; The role matching module is based on the optimized tactical plan and adopts a graph matching algorithm. It regards players and tactical plans as nodes in the graph, and the matching degree between players' technical characteristics and tactical requirements is used as the weight of the edge. Through matching, the optimal role and position of the player are determined, and the player role and position allocation plan is generated. The dynamic prediction module uses dynamic simulation based on the player role and position allocation scheme to construct dynamic equations that describe player interactions and tactical changes. It simulates dynamic changes during the game by numerically solving the equations, evaluates the effects of differentiated tactical strategies, and generates predictions of game results. The strategy adjustment module is based on the prediction of game results and uses reinforcement learning technology to evaluate the impact of differentiated tactical configurations on game results, adjust tactical layout and player configuration, match changes in the game process and opponent strategies, and generate real-time adjusted tactics and configurations.

[0054] In the data integration module, natural language processing technology is used to automatically identify and extract key information about player performance, game results and statistics, and unstructured text data is converted into structured data. This process involves technologies such as text mining, semantic analysis and information extraction. The initial data sources include unstructured texts such as match reports, news releases and player evaluations. Through part-of-speech tagging and named entity recognition, the names of players, game data and related performance indicators are accurately identified. Syntactic analysis and dependency analysis techniques are used to understand the sentence structure in the text and extract important information about player performance and game results. Text classification technology is used to classify and map the extracted information into predefined structured formats, such as the number of goals and assists of the players and the team's win-loss situation, etc. The processed game data is generated and formatted into a database-compatible table format, including key-value pairs and data records, for subsequent analysis and retrieval. The completion of this module provides accurate and efficient data support for subsequent tactical plans and evaluations, ensures the accuracy and standardization of data processing, and enhances the reliability of analysis results.

[0055] In the tactical scheme module, the processed game data is classified by the support vector machine model, and the tactical scheme with the best success probability is evaluated and selected. The input data of this module is a structured set of player performance data and game statistics. After feature engineering processing, multi-dimensional feature vectors reflecting the tactical execution capabilities of the team and players are extracted. The support vector machine model finds the optimal segmentation hyperplane in these multi-dimensional feature spaces to distinguish the effects of different tactical schemes. The data is mapped to a higher-dimensional space through kernel techniques to solve nonlinear classification problems and optimize the interval boundaries between tactical schemes. Including model training, cross-validation and parameter tuning to ensure classification accuracy and model generalization ability, the generated preliminary tactical scheme set is presented in the form of scores. Each tactical scheme is assigned a score based on its success probability, which provides a scientific basis for subsequent in-depth analysis and decision-making. The key contribution of this module is that it can accurately evaluate the potential effects of different tactical schemes and provide a quantitative reference standard for formulating tactical strategies.

[0056] In the tactical evaluation module, the preliminary tactical solution set is optimized through multiple generations of iterative optimization through genetic algorithms, imitating the evolutionary process of natural selection, crossover and mutation, and constantly updating the tactical solution to capture the solution with the best fitness. The input data is the preliminary tactical solution set and its related performance indicators. When the genetic algorithm is started, these tactical solutions are regarded as individuals in the population, and the performance of each individual is evaluated based on the fitness function, which is defined according to the success probability of the tactical solution and the matching degree of the actual game data. Through the selection operation, the tactical solution with high fitness is selected, and the crossover and mutation operations introduce new tactical variants to increase the diversity of the solution space. After multiple rounds of iterations, the algorithm tends to be stable and finds the optimal or approximately optimal tactical solution. The generated tactical solution fitness score records the performance evaluation of each solution in detail, providing a clear quantitative basis for the final tactical selection. This module effectively optimizes the tactical solution by simulating the natural evolution mechanism, and improves the scientificity and practicality of tactical decision-making.

[0057] In the tactical refinement module, the prediction error of the previous round is modeled by the gradient boosting tree algorithm, the error is gradually reduced, and the tactical parameters are refined and adjusted to improve the performance of the overall tactical plan. The data format used in this module is a structured data set, which contains the multi-dimensional features of the tactical plan and its corresponding success probability score. The gradient boosting tree algorithm fits the residual by iteratively adding decision trees. Each new tree is established to reduce the residual of the previous round of model prediction. The algorithm first calculates the residual of the current model and then builds a new decision tree to predict these residuals. By adjusting the contribution rate (learning rate) of each tree, the algorithm gradually reduces the prediction error during training. The refinement of tactical parameters includes adjusting the player position, passing strategy, and defensive layout to ensure that each iteration is optimized in the direction of improving the overall tactical success rate. The generated optimized tactical plan is presented in the form of detailed parameter configuration and expected effect score. This process significantly improves the adaptability and effectiveness of the tactical plan, providing a more accurate tactical deployment plan for actual games.

[0058] In the role matching module, players and tactical schemes are regarded as nodes in the graph through the graph matching algorithm, and the matching degree between the player's technical characteristics and tactical requirements is used as the weight of the edge. The optimal role and position of the player is determined through the matching process. The data format is a graph structure, the nodes represent the players and tactical schemes, and the weight of the edge reflects the matching degree between the player's skills and tactical requirements. Use graph algorithms such as maximum weight matching or stable marriage algorithm to evaluate and optimize the matching relationship between players and tactics. This operation process involves building a complete graph, which includes all players and available tactical schemes, and then finding the best match through the algorithm to ensure that each player is assigned to the position and role that best suits his technical characteristics. The generated player role and position allocation plan lists in detail the optimal position of each player under a specific tactical system, optimizes the team's overall tactical execution ability, and enhances the actual application effect of the tactical plan.

[0059] In the dynamic prediction module, dynamic equations describing player interactions and tactical changes are constructed through dynamic simulation, and numerical solution methods are used to simulate dynamic changes during the game to evaluate the effects of differentiated tactical strategies. The data format is a numerical representation of player roles and position allocation schemes, including a parameterized model of player interactions. The dynamic simulation process involves defining dynamic equations for player movements, passing, shooting, and other actions, as well as the impact of tactical changes on these behaviors. The system of equations is solved using numerical solution techniques such as the finite difference method or the Runge-Kutta method to simulate various possibilities in the game scenario. The refinement of this process includes adjusting tactical parameters to observe the impact on the game results, evaluating the team performance under different tactical configurations, and generating a game result prediction report that details the game results predicted by the use of a specific tactical scheme, providing a scientific basis for tactical decision-making and enhancing the predictability and flexibility of tactical strategies.

[0060] In the strategy adjustment module, the impact of differentiated tactical configurations on the game results is evaluated through reinforcement learning technology, and the tactical layout and player configuration are adjusted according to changes in the game process and the opponent's strategy. The data format is a structured collection of game result predictions and real-time game data. During the reinforcement learning process, the system uses tactical plans as environmental states and game results as reward signals, and optimizes tactical decisions by exploring and utilizing strategies. The strategy adjustment operation involves defining a reward function, adjusting tactical parameters based on game results and tactical effect feedback, and finding the optimal tactical configuration through continuous learning iterations. This process not only adjusts the tactical layout, but also considers the optimal match of player configuration. The generated real-time adjusted tactics and configuration plans can dynamically respond to various situations in the game, improving the adaptability and winning rate of tactics.

[0061] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession can estimate that the equivalent embodiments of the above disclosed technical contents can be changed or modified into equivalent changes and applied to other fields. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. The AI-based football testing method is characterized by: The following steps are involved: Based on historical game data, the random forest algorithm is used to train the data set by building multiple decision trees, randomly selecting feature subsets, and determining the optimal tactical plan through a voting mechanism. The relationship between the team's tactical effect and winning rate is analyzed, and the correlation analysis results between tactical effect and winning rate are generated; Based on the analysis results of the correlation between tactical effect and winning rate, a particle swarm optimization algorithm is used to simulate the social behavior of animals in the hunting process, and the optimal solution is captured by updating the position and speed of particles in the solution space, and the fitness of the tactical solution is evaluated. With reference to the key indicators of ball possession rate and number of shots, a fitness score of the tactical solution is generated; Based on the fitness score of the tactical scheme, a simulated annealing algorithm is used to simulate the metal annealing process, gradually reduce the temperature of the environment, reduce the search range of the solution, adjust the tactical parameters, capture the optimal solution, and generate an optimized tactical scheme; Based on the optimized tactical plan, a Bayesian network is used to construct a conditional probability distribution according to the technical characteristics and historical performance of the players, and the performance probability of the players in differentiated roles and positions is predicted through the network's reasoning mechanism, and the roles and positions of the players are adjusted to generate a player role and position allocation plan; Based on the player roles and position allocation scheme, nonlinear dynamic model theory is adopted to analyze the complexity of player and tactical interactions during the game using chaos theory and dynamics models, evaluate the performance of differentiated strategies in real-time games, and generate game result predictions by simulating game scenarios; Based on the prediction of the game results, a deep learning method is adopted to analyze the game data by building a deep neural network model, adjust the tactical layout and player configuration, respond to emergencies in the game and changes in the opponent's strategy, and generate real-time adjusted tactics and configuration.

2. The AI-based football testing method according to claim 1, characterized in that: The analysis results of the association between tactical effects and winning rates include tactics under differentiated defensive intensities, diversity of offensive modes, and player configuration plans. The fitness score of the tactical plan includes the contribution of ball possession rate to tactical success, the correlation evaluation between the number of shots and the probability of scoring a goal, and the impact of passing efficiency on the overall performance of the team. The optimized tactical plan specifically refers to the adjusted offensive speed and rhythm, detailed defensive deployment, and optimization of the cooperation mode between players. The player role and position allocation plan includes the optimal position matching based on the player's technical characteristics and historical performance, the role allocation enhancement plan for the team's collaborative combat capability, and the personalized deployment strategy. The game result prediction includes the change in winning rate under differentiated tactical response strategies, the impact evaluation of the complexity of player interactions on the game results, and the diversity of strategies. The real-time adjusted tactics and configurations include the improvement of tactical flexibility in dealing with emergencies, the response plan for changes in opponent strategies, and the dynamic optimization of player configurations.

3. The AI-based football testing method according to claim 1, characterized in that: Based on historical game data, the random forest algorithm is used to train the data set by building multiple decision trees, randomly selecting feature subsets, and determining the optimal tactical plan through a voting mechanism to analyze the relationship between the team's tactical effect and winning rate. The specific steps for generating the analysis results of the correlation between tactical effect and winning rate are as follows; Based on historical competition data, the random forest algorithm is used to build a decision tree set for data training. A feature subset in the data set is randomly selected as the input of the decision tree to improve the model's generalization ability for data. The prediction result is determined using the majority voting mechanism to generate a historical data training model. Based on the historical data training model, feature criticality assessment is adopted to identify tactical features that affect the winning rate by analyzing the usage frequency of features in the decision tree and the contribution to the model prediction results, and tactical plans are determined accordingly to generate an optimal tactical plan set; Based on the optimal tactical solution set, model validation technology is used to evaluate the performance of tactical solutions on differentiated data subsets through cross-validation method, analyze the relationship between tactical effects and team winning rate, and generate tactical effect and winning rate correlation analysis results.

4. The AI-based football testing method according to claim 1, characterized in that: Based on the analysis results of the correlation between tactical effect and winning rate, a particle swarm optimization algorithm is used to simulate the social behavior of animals in the hunting process, and the optimal solution is captured by updating the position and speed of particles in the solution space, and the fitness of the tactical solution is evaluated. With reference to the key indicators of ball possession rate and number of shots, the steps for generating the fitness score of the tactical solution are as follows: Based on the analysis results of the correlation between tactical effect and winning rate, a particle swarm optimization algorithm is used to initialize the particle swarm by simulating hunting behavior. The particles represent tactical plans. The position and speed of the particles are updated to simulate the process of capturing the tactical plan with the best fitness. The fitness of the plan is evaluated according to the key indicators of ball possession rate and number of shots, and a particle swarm initialization plan is generated. Based on the particle swarm initialization scheme, a fitness evaluation and update strategy is adopted to compare the fitness scores of particles, update the particle positions and velocities, continuously iterate and optimize to capture the tactical scheme with the best fitness, and generate a strategy scheme optimization index; Based on the strategy optimization index, particle swarm convergence analysis is adopted to evaluate the optimized tactical scheme by analyzing the distribution and convergence speed of the particle swarm in the solution space, and generate a tactical scheme fitness score.

5. The AI-based football testing method according to claim 1, characterized in that: Based on the fitness score of the tactical scheme, a simulated annealing algorithm is used to simulate the metal annealing process, gradually reduce the temperature of the environment, reduce the search range of the solution, adjust the tactical parameters, capture the optimal solution, and generate the optimized tactical scheme in the following steps: Based on the fitness score of the tactical scheme, a simulated annealing algorithm is used. By setting the initial temperature and gradually reducing it, a tactical parameter is randomly selected to perform a small disturbance during the simulated annealing process, the fitness of the scheme after the disturbance is evaluated, the search range of the solution is reduced, and the tactical scheme in the annealing process is generated; Based on the tactical scheme in the annealing process, annealing cooling and refinement search are adopted to refine the parameter adjustment of the tactical scheme by gradually reducing the search accuracy of the temperature control parameters, capture the optimal solution, and generate a refined tactical parameter adjustment scheme; Based on the detailed tactical parameter adjustment scheme, global optimization confirmation is adopted, and through parameter adjustment and optimization confirmation, the performance of the tactical scheme in the parameter setting is obtained to generate an optimized tactical scheme.

6. The AI-based football testing method according to claim 1, characterized in that: Based on the optimized tactical plan, a Bayesian network is used to construct a conditional probability distribution according to the technical characteristics and historical performance of the players, and the performance probability of the players in differentiated roles and positions is predicted through the network's reasoning mechanism, and the player roles and positions are adjusted. The specific steps of generating a player role and position allocation plan are as follows; Based on the optimized tactical scheme, a Bayesian network is used to construct a conditional probability distribution of the players' technical characteristics and historical performances by defining the players' technical characteristics and historical performances as nodes in the network, and the dependencies between the nodes are represented by directed edges, so as to analyze the players' performances in the target tactical environment and generate a probability distribution of the players' performances; Based on the player performance probability distribution, Bayesian reasoning is adopted, and the conditional probability distribution of the Bayesian network is used to calculate the performance probability of the players in the differentiated roles and positions. The interaction and dependency between the players are integrated through the reasoning mechanism of the network, the player role allocation and position layout are optimized, and the role position matching scheme is generated; Based on the role position matching scheme, a comprehensive analysis is conducted, referring to the individual abilities of the players, tactical requirements and characteristics of the opponents, and the allocation of player roles and positions is refined through an optimization algorithm to improve the overall performance and efficiency of the team and generate a player role and position allocation scheme.

7. The AI-based football testing method according to claim 1, characterized in that: Based on the player role and position allocation scheme, the nonlinear dynamic model theory is adopted, chaos theory and dynamic model are used to analyze the complexity of player and tactical interaction during the game, and the performance of the differentiation strategy in real-time games is evaluated. By simulating the game scene, the specific steps of generating the prediction of the game results are as follows; Based on the player role and position allocation scheme, chaos theory is used to reveal the nonlinear dynamic behavior during the game by analyzing the sensitivity of initial conditions and predicting the risk of future states, identify the key factors that cause drastic changes in game results, and generate a dynamic analysis of the game; Based on the dynamic analysis of the game, a dynamic model is used to simulate the situation in the game by constructing a mathematical model describing the interaction between players and tactics, evaluate the application effect of differentiated tactical strategies in real-time games, and generate a strategy effect evaluation; Based on the strategy effectiveness evaluation, scenario simulation technology is used to examine the performance of differentiated tactical strategies under target game conditions through game scenario simulation, evaluate the adaptability and effectiveness of differentiated tactics, adjust the tactical strategies to optimally match the game process, and generate game result predictions.

8. The AI-based football testing method according to claim 1, characterized in that: Based on the prediction of the match results, a deep learning method is used to analyze the match data by building a deep neural network model, adjust the tactical layout and player configuration, and respond to emergencies in the match and changes in the opponent's strategy. The specific steps of generating real-time adjusted tactics and configurations are as follows: Based on the prediction of the match results, a deep learning method is used to analyze the match data, identify key variables and patterns, evaluate the effectiveness of tactical layout, and generate in-depth analysis results by constructing deep neural networks, including convolutional neural networks and recurrent neural networks; Based on the in-depth analysis results, a strategy adjustment algorithm is used, combined with the analysis results of the deep learning model, to optimize the tactical layout and player configuration through the algorithm, adjust the strategy to deal with emergencies in the game and changes in the opponent's strategy, and generate a tactical adjustment plan; Based on the tactical adjustment plan, real-time decisions and adjustments are made, and with reference to real-time game data and changes in opponent strategies, tactical adjustments and player configurations are refined through a decision support mechanism to generate real-time adjusted tactics and configurations.

9. AI-based football testing system, characterized in that: According to the AI-based football testing method according to any one of claims 1-8, the system includes a data integration module, a tactical plan module, a tactical evaluation module, a tactical refinement module, a role matching module, a dynamic prediction module, and a strategy adjustment module.

10. The AI-based football testing system according to claim 9, characterized in that: The data integration module uses natural language processing technology based on historical game data to automatically identify and extract key information on player performance, game results and statistical data, analyzes the structure and content in the text through algorithms, converts unstructured text data into structured data, merges and standardizes the data, and generates processed game data; The tactical solution module uses a support vector machine model based on the processed game data to classify the data by capturing the hyperplane of the differentiated tactical solution intervals in the multi-dimensional feature space, evaluate and select the tactical solution with the best success probability, and generate a preliminary tactical solution set; The tactical evaluation module uses a genetic algorithm based on a set of preliminary tactical solutions to simulate the selection, crossover and mutation processes in natural evolution, iteratively optimizes the tactical solutions for multiple generations, evaluates the performance of the solutions through a fitness function, cyclically updates the tactical solutions to capture the solutions with the best fitness, and generates a fitness score for the tactical solutions; The tactical refinement module uses a gradient boosting tree algorithm based on the fitness score of the tactical plan to model the prediction error of the previous round, gradually reduce the error, refine and adjust the tactical parameters, improve the performance of the overall tactical plan, and generate an optimized tactical plan; The role matching module adopts a graph matching algorithm based on the optimized tactical plan, regards the players and tactical plans as nodes in the graph, and uses the matching degree between the players' technical characteristics and tactical requirements as the weight of the edge. The optimal role and position of the player is determined through matching, and the player role and position allocation plan is generated; The dynamic prediction module uses dynamic simulation based on the player role and position allocation scheme to construct dynamic equations that describe player interactions and tactical changes, simulates dynamic changes during the game by numerically solving the equations, evaluates the effects of differentiated tactical strategies, and generates predictions of game results; The strategy adjustment module is based on the prediction of game results and adopts reinforcement learning technology. By evaluating the impact of differentiated tactical configurations on game results, it adjusts tactical layout and player configuration, matches changes in the game process and opponent strategies, and generates real-time adjusted tactics and configurations.

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