Intelligent physical training planning system based on big data

Abstract

The invention discloses an intelligent physical training planning system based on big data, which relates to the technical field of intelligent physical training and comprises a personal information management module based on system architecture, a training data analysis module and a training planning optimization module. The system is characterized in that the personal information management module adopts a layered architecture design of a data input layer, a data storage layer, a data processing layer, a data access layer and a user interaction layer to ensure safe and efficient processing of data; the training data analysis module adopts an efficient data processing algorithm and an engine to quickly analyze and process training data; the training planning optimization module generates a personalized training plan by adopting a machine learning algorithm, and divides a training planning function into a personalized training planning module and a training effect evaluation and feedback module. Compared with the prior art, the method has the advantages that the data processing capability is efficient; personalized training planning is carried out; the system integration degree is high; and carrying out data visualization.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent sports training, and in particular to an intelligent sports training planning system based on big data. Background Art

[0002] With the continuous development of science and technology, people's demand for healthy life is increasing, and sports training has become an important means to improve health. However, traditional sports training methods often lack scientificity and personalization, and it is difficult to meet the training needs of different users. In recent years, with the rise of big data and artificial intelligence technology, more and more intelligent sports training systems have emerged.

[0003] In the existing technology, some intelligent sports training systems are already able to collect and analyze user training data, but most of them have the following problems: First, the data processing capabilities are insufficient, making it difficult to efficiently process and analyze massive amounts of data; second, the training plans lack personalization, and often adopt a general training plan that cannot be dynamically adjusted according to the user's actual situation; third, the system integration is low, making it difficult to seamlessly connect with other devices and systems, resulting in untimely data synchronization and updates.

[0004] For example, the invention of patent number CN114797034A proposes an intelligent sports training system that can collect and analyze users' training data, but the data processing algorithm is relatively simple and cannot cope with complex and changeable data scenarios, and the training plan lacks a personalized adjustment mechanism. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent sports training planning system based on big data to solve the problems existing in the prior art, such as insufficient data processing capability, lack of personalization in training planning, and low system integration.

[0006] Specifically, the technical solution provided by the present invention is: an intelligent sports training planning system based on big data, including a personal information management module, a training data analysis module, and a training planning optimization module based on the system architecture, characterized in that: the personal information management module adopts a layered architecture design of a data input layer, a data storage layer, a data processing layer, a data access layer, and a user interaction layer to ensure the security and efficient processing of data, and the personal information management function is split into a basic information input module and a real-time parameter collection module in the architecture, which is convenient for system maintenance and expansion, and supports integration with other modules and systems, such as connection with wearable devices, to ensure real-time synchronization and updating of data;

[0007] The training data analysis module uses efficient data processing algorithms and engines to quickly analyze and process training data, splitting the data analysis function into single-factor data analysis modules and multi-factor optimization combination modules to facilitate system maintenance and expansion; it provides data visualization functions to present analysis results to users in the form of charts and reports;

[0008] The training planning optimization module uses machine learning algorithms to generate personalized training plans, splitting the training planning function into a personalized training planning module and a training effect evaluation and feedback module to facilitate system maintenance and expansion. It dynamically adjusts the training plan based on users' real-time data and feedback to ensure maximum training effect.

[0009] Furthermore, the functions of the personal information management module include: (1) personal information entry: supporting users to manually input basic information, including name, age, gender, height, and weight information; (2) real-time parameter collection: collecting the user's movement trajectory, heart rate, and training duration parameters in real time through wearable devices, and automatically synchronizing them to the system; (3) data query and display: providing user personal information query function, supporting filtering and display according to time range and data type conditions; (4) data update and maintenance: allowing users to modify personal information at any time, and the system will also automatically clean up invalid or outdated data regularly.

[0010] Furthermore, the personal information management module receives information input by the user through the user interface, and displays the query results and prompt information; provides a unified interface through the data access layer for other modules to access personal information data; and synchronizes and transmits data with wearable devices through Bluetooth and Wi-Fi communication methods.

[0011] Furthermore, the functions of the training data analysis module include: (1) single factor data analysis: screening and sorting the various parameters of the trainees to obtain the optimal single factor parameter value that affects the training results; (2) multi-factor optimization combination: using genetic algorithms to optimize the multi-factor parameter combination of the trainees to obtain the optimal training plan; (3) data visualization: presenting the analysis results to the user in the form of charts, reports, etc. to help the user better understand the training data.

[0012] Furthermore, the training data analysis module obtains the user's personal information and real-time parameters from the personal information management module as input for data analysis, and transmits the analysis results to the training planning optimization module as a basis for generating a personalized training plan; the analysis results and visual reports are displayed through the user interface.

[0013] Furthermore, the functions of the training planning optimization module include: (1) Personalized training planning: Automatically generate personalized training plans based on the user's personal information, real-time parameters and data analysis results; (2) Training effect evaluation: Real-time monitoring of the user's training status during training and evaluation of the training effect; (3) Feedback and adjustment: Based on the evaluation results, provide real-time training guidance and feedback, and dynamically adjust the training plan.

[0014] Furthermore, the training planning optimization module obtains the user's personal information and real-time parameters from the personal information management module as a basis for generating a training plan; receives the analysis results of the training data analysis module as a reference for optimizing the training plan; displays the training plan, evaluation results and feedback information through the user interface, and receives the user's modification and adjustment suggestions.

[0015] Furthermore, the basic information input module is used to receive and store the personal information of the sports trainees, including age, gender, height, weight, place of origin, hobbies, diet structure, training time period, and work and rest time; the real-time parameter collection module collects the real-time parameters of the trainees' heart rate, movement trajectory, and training time through a high-precision positioning bracelet or other wearable devices, and transmits the data to the system for analysis.

[0016] Furthermore, the single-factor data analysis module is used to screen and sort the basic parameters and real-time parameters of the trainees, and obtain the optimal single-factor parameter values ​​that affect the training results, including the optimal training time period and the optimal dietary structure; the multi-factor optimization combination module uses a genetic algorithm to optimize the multi-factor parameter combination of the trainees, and obtain the optimal parameter combination under different training items and different training stages to improve the training effect.

[0017] Furthermore, the personalized training planning module automatically generates a personalized training plan based on the trainee's personal information, real-time parameters and data analysis results, including daily training items, training intensity, and training duration; the training effect evaluation and feedback module is used to monitor the trainee's training status in real time during the training process, evaluate the training effect, and adjust the training plan based on the evaluation results, providing the trainee with real-time training guidance and feedback.

[0018] Compared with the prior art, the advantages of the present invention are: (1) efficient data processing capability: by adopting a layered architecture design and an efficient data processing algorithm, the system can realize rapid processing and analysis of massive data, thereby improving data processing efficiency; (2) personalized training planning: based on the user's personal information and real-time data, the system can automatically generate a personalized training plan, and dynamically adjust it according to the user's feedback to ensure the maximization of the training effect; (3) high system integration: the system supports integration with other modules and systems, such as connection with wearable devices, to ensure real-time synchronization and updating of data, and improve the system's integration and data synchronization capabilities; (4) data visualization: providing data visualization function, and displaying the analysis results to the user in the form of charts, reports, etc., to help the user better understand the training data and improve the scientificity and effectiveness of the training. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the patent of the present invention.

[0020] Figure 2 It is a schematic diagram of the workflow of the present invention. DETAILED DESCRIPTION

[0021] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0022] Embodiment 1

[0023] like Figure 1 and Figure 2 As shown, this scheme proposes an intelligent sports training planning system based on big data, including a personal information management module, a training data analysis module, and a training planning optimization module based on the system architecture, characterized in that: the personal information management module adopts a layered architecture design of data input layer, data storage layer, data processing layer, data access layer and user interaction layer to ensure the security and efficient processing of data, and splits the personal information management function into a basic information input module and a real-time parameter acquisition module in the architecture, which is convenient for system maintenance and expansion, and supports integration with other modules and systems, such as connection with wearable devices, to ensure real-time synchronization and update of data; the training data analysis module adopts an efficient data processing algorithm and engine to quickly analyze and process the training data, and splits the data analysis function into a single-factor data analysis module and a multi-factor optimization combination module, which is convenient for system maintenance and expansion; provides data visualization function, and displays the analysis results to users in the form of charts and reports; the training planning optimization module adopts a machine learning algorithm to generate a personalized training plan, and splits the training planning function into a personalized training planning module and a training effect evaluation and feedback module, which is convenient for system maintenance and expansion, and dynamically adjusts the training plan according to the user's real-time data and feedback to ensure the maximization of the training effect.

[0024] The functions of the personal information management module include: (1) Personal information entry: supports users to manually enter basic information, including name, age, gender, height, and weight information; (2) Real-time parameter collection: collects the user's movement trajectory, heart rate, and training duration parameters in real time through wearable devices, and automatically synchronizes them to the system; (3) Data query and display: provides the query function of user personal information, supports filtering and displaying according to time range and data type conditions; (4) Data update and maintenance: allows users to modify personal information at any time, and the system will automatically clean up invalid or outdated data regularly. The personal information management module receives user input information through the user interface, displays query results and prompt information; provides a unified interface through the data access layer for other modules to access personal information data; and synchronizes and transmits data with wearable devices through Bluetooth and Wi-Fi communication methods.

[0025] The functions of the training data analysis module include: (1) Single factor data analysis: screening and sorting the parameters of the trainees to obtain the optimal single factor parameter value that affects the training results; (2) Multi-factor optimization combination: using genetic algorithms to optimize the multi-factor parameter combination of the trainees to obtain the optimal training plan; (3) Data visualization: presenting the analysis results to the user in the form of charts, reports, etc. to help the user better understand the training data. The training data analysis module obtains the user's personal information and real-time parameters from the personal information management module as input for data analysis, and transmits the analysis results to the training planning optimization module as the basis for generating personalized training plans; the analysis results and visualization reports are displayed through the user interface.

[0026] The functions of the training plan optimization module include: (1) Personalized training plan: Automatically generate personalized training plans based on the user's personal information, real-time parameters and data analysis results; (2) Training effect evaluation: Real-time monitoring of the user's training status during training and evaluation of the training effect; (3) Feedback and adjustment: Provide real-time training guidance and feedback based on the evaluation results, and dynamically adjust the training plan. The training plan optimization module obtains the user's personal information and real-time parameters from the personal information management module as the basis for generating a training plan; receives the analysis results of the training data analysis module as a reference for optimizing the training plan; displays the training plan, evaluation results and feedback information through the user interface, and receives the user's modification and adjustment suggestions.

[0027] The basic information input module is used to receive and store the personal information of sports trainees, including age, gender, height, weight, place of origin, hobbies, diet structure, training time period, and work and rest time; the real-time parameter collection module uses a high-precision positioning bracelet or other wearable devices to collect the real-time parameters of the trainees' heart rate, movement trajectory, and training duration, and transmits the data to the system for analysis.

[0028] The single-factor data analysis module is used to screen and sort the basic parameters and real-time parameters of the trainees, and obtain the optimal single-factor parameter values ​​that affect the training results, including the optimal training time period and the optimal dietary structure; the multi-factor optimization combination module uses genetic algorithms to optimize the multi-factor parameter combination of the trainees, and obtain the optimal parameter combination under different training items and different training stages to improve the training effect.

[0029] The personalized training planning module automatically generates a personalized training plan based on the trainee's personal information, real-time parameters and data analysis results, including daily training items, training intensity, and training duration; the training effect evaluation and feedback module is used to monitor the trainee's training status in real time during the training process, evaluate the training effect, and adjust the training plan based on the evaluation results, providing the trainee with real-time training guidance and feedback.

[0030] Embodiment 2

[0031] Based on the first embodiment, Figure 1 and Figure 2 As shown, the workflow of this system is:

[0032] S1. System initialization and configuration

[0033] S1.1. System deployment and module integration: Install the hardware and software components of the intelligent sports training planning system, including data collection equipment (such as wearable devices, sensors, etc.), cloud computing servers, data analysis tools, machine learning model libraries, etc. Integrate various modules, including user information management, data collection and processing, data analysis and prediction, training plan generation and optimization, community interaction and knowledge sharing, etc.

[0034] S1.2. User registration and information entry: Users complete registration through the system interface and enter basic personal information, such as age, gender, height, weight, sports preferences, training goals, etc.

[0035] S2. Data Collection and Processing

[0036] S2.1. Real-time data collection: The system collects the user's data during training and competition in real time through integrated data acquisition equipment, such as heart rate, cadence, speed, strength, sports posture, etc. The data is transmitted to the system background in real time through wireless transmission technology and stored in the cloud computing server.

[0037] S2.2, Data preprocessing: The collected data is cleaned, denoised, and standardized to improve the quality and usability of the data. The preprocessed data is stored in the data warehouse for subsequent analysis.

[0038] S3. Data analysis and prediction

[0039] S3.1. Big data analysis: Use big data analysis tools to conduct in-depth analysis of pre-processed data to explore potential patterns and trends in the data. The analysis results include the user's exercise habits, physical condition, training effects, etc.

[0040] S3.2, Machine learning model training and prediction: Based on the results of big data analysis, the machine learning model is trained to achieve intelligent analysis and prediction of user training data. The model can predict the user's future sports performance, potential health risks, training effects, etc.

[0041] S4. Training plan generation and optimization

[0042] S4.1. Personalized training plan formulation: The training planning module formulates a personalized training plan for the user based on the user's personal information, training goals, and the prediction results of the machine learning model. The training plan includes training items, training intensity, training frequency, rest time, etc.

[0043] S4.2, Real-time feedback and adjustment: When users execute training plans, the system provides real-time training data and suggestions through real-time monitoring and feedback mechanisms. Based on user feedback and real-time data, the training planning module can dynamically adjust and optimize the training plan.

[0044] Embodiment 3

[0045] Based on the first embodiment, Figure 1 and Figure 2 As shown, the single factor data analysis module plays an important role in training data analysis. It screens and sorts the basic parameters and real-time parameters of the trainees to find the optimal single factor parameter value that affects the training results.

[0046] Algorithm steps:

[0047] (1) Data preparation: Collect basic information of trainees (such as age, gender, height, weight, etc.) and real-time parameters (such as heart rate, training duration, training intensity, diet records, etc.). Group the data by time period (such as different hours of the day) and diet structure (such as the ratio of protein, carbohydrates, and fat).

[0048] (2) Data cleaning: Remove invalid or abnormal data, such as data points with abnormally high or low heart rates. Interpolate or delete missing data.

[0049] (3) Single factor screening: Analyze each time period or dietary structure separately and calculate its impact on training performance (such as recovery time after training, training efficiency, etc.).

[0050] (4) Sorting analysis: Based on the analysis results, sort each time period or dietary structure to find the optimal parameter value.

[0051] (5) Result output: Output the optimal training time period and optimal diet structure.

[0052] Set the data set data, which contains the following fields:

[0053] timestamp: training timestamp

[0054] training_score: training performance (can be a quantitative indicator of recovery time, training efficiency, etc.)

[0055] protein_ratio: The ratio of protein in the diet

[0056] carb_ratio: The ratio of carbohydrates in the diet

[0057] fat_ratio: The proportion of fat in the diet

[0058] Optimal training time period analysis: Group the data by timestamp and calculate the average training_score for each time period. Find the time period with the highest average training_score. Formula:

[0059]

[0060] Among them, t represents a time period, and i represents the data points in the time period.

[0061] Optimal diet structure analysis: Group the data by protein_ratio, carb_ratio and fat_ratio, and calculate the average training_score of each group. Find the diet structure combination with the highest average training_score. Formula:

[0062]

[0063] Among them, diet represents a dietary structure combination, and i represents a data point in the combination.

[0064] The following is part of the execution code:

[0065] import pandas as pd

[0066] import numpy as np

[0067] #data is the loaded DataFrame

[0068] #data=pd.read_csv('training_data.csv')#:Load data from CSV file

[0069] #Data cleaning (here is just a simple example)

[0070] data = data.dropna() # delete missing values

[0071] #Optimal training time period analysis

[0072] data['hour']=pd.to_datetime(data['timestamp']).dt.hour

[0073] time_groups=data.groupby('hour')['training_score'].mean().reset_index()

[0074] time_groups=time_groups.sort_values(by='training_score', ascending=False)

[0075] optimal_time_period=time_groups.iloc[0]['hour']#optimal time period

[0076] #Optimal Diet Analysis

[0077] diet_groups=data.groupby(['protein_ratio','carb_ratio',

[0078] 'fat_ratio'])['training_score'].mean().reset_index()

[0079] diet_groups=diet_groups.sort_values(by='training_score', ascending=False)

[0080] optimal_diet=diet_groups.iloc[0][['protein_ratio','carb_ratio','fat_ratio']].values[0]#Optimal diet structure

[0081] # Output results

[0082] print(f"Optimal training time period: {optimal_time_period} hours")

[0083] print(f"Optimal diet structure: protein ratio = {optimal_diet[0]}, carbohydrate ratio = {optimal_diet[1]}, fat ratio = {optimal_diet[2]}")

[0084] Embodiment 4

[0085] Based on the first embodiment, Figure 1 and Figure 2 As shown, the factor optimization combination module aims to optimize the multi-factor parameter combination of the trainee by adopting genetic algorithm (GA) or other optimization algorithms in training data analysis, so as to find the optimal parameter combination under different training items and different training stages, thereby improving the training effect.

[0086] Algorithm steps:

[0087] (1) Initialization of the population: Define the multi-factor parameter space of the trainee, such as training intensity, training frequency, dietary composition ratio, etc. Randomly generate an initial population in which each individual represents a parameter combination.

[0088] (2) Fitness evaluation: For each individual in the population, the fitness value is calculated based on its performance in the training project (such as training results, recovery time, etc.). The higher the fitness value, the better the parameter combination.

[0089] (3) Selection operation: Select excellent individuals as parents based on their fitness values ​​to generate the next generation. Roulette selection, tournament selection, and other methods can be used.

[0090] (4) Crossover operation: Crossover (hybridization) of selected parents to generate new offspring individuals. The crossover operation can simulate gene recombination and increase the diversity of the population.

[0091] (5) Mutation operation: Randomly mutate the offspring individuals to introduce new genetic information. The mutation operation can increase the coverage of the search space and avoid premature convergence.

[0092] (6) Generate a new generation of population: Replace the parent individuals with the offspring individuals to generate a new generation of population.

[0093] (7) Termination condition judgment: Check whether the termination condition is met, such as reaching the maximum number of iterations, the fitness value reaching the preset threshold, etc. If the termination condition is met, the optimal parameter combination is output; otherwise, return to step 2 to continue iteration.

[0094] Set up a multi-factor parameter space, where each factor can be represented as a gene, and an individual is composed of multiple genes, represented as a chromosome. The fitness function f(individual) represents the fitness value of the individual, which is calculated based on the training results. Selection operation (roulette selection): The probability P(i) of each individual being selected is proportional to its fitness value:

[0095]

[0096] Where N is the population size.

[0097] Crossover operation (single-point crossover): randomly select a crossover point, exchange the genes of the two parent individuals at the crossover point, and generate two offspring individuals.

[0098] Mutation operation: For each gene of each offspring individual, with a certain probability P m Perform random mutation.

[0099] The following is part of the execution code:

[0100] import random

[0101] import numpy as np

[0102] from deap import base,creator,tools,algorithms

[0103] #Define the fitness function

[0104] def fitness_function(individual):

[0105] #Assume that individual is a list containing multiple parameters, such as [training intensity, training frequency, protein ratio,...]

[0106] #This is just an example. In practice, the fitness value needs to be calculated based on specific data.

[0107] #Return a floating point number to represent the fitness value

[0108] #For example:

[0109] #return sum(individual)#Assuming that the larger the sum of parameters, the higher the fitness

[0110] #In practice, complex models may be needed to calculate fitness

[0111] #...

[0112] #Return the fitness value

[0113] return np.sum(individual)#

[0114] # Create the basic components needed for the genetic algorithm

[0115] creator.create("FitnessMax",base.Fitness,weights=(1.0,))#Maximize fitness

[0116] creator.create("Individual",list,fitness=creator.FitnessMax)

[0117] toolbox = base.Toolbox()

[0118] #Define parameter range and encoding method

[0119] param_bounds = [(0,10),(0,5),(0.1,1.0),...] # Example parameter range and bounds

[0120] toolbox.register("attr_float",random.uniform,*param_bounds[0])#Register a single parameter generation function

[0121] toolbox.register("individual",tools.initRepeat,creator.Individual,toolbox.attr_float,len(param_bounds))#Register individual generation function

[0122] toolbox.register("population",tools.initRepeat,list,toolbox.individual)#Register population generation function

[0123] #Register genetic algorithm operations

[0124] toolbox.register("mate",tools.cxBlend,alpha=0.5)#Cross operation

[0125] toolbox.register("mutate",tools.mutGaussian,mu=0,sigma=1,indpb=0.2)#Mutation operation

[0126] toolbox.register("select",tools.selTournament,tournsize=3)#Select operation

[0127] toolbox.register("evaluate",fitness_function)#Fitness evaluation function

[0128] #Set the parameters of the genetic algorithm

[0129] population_size=50#population size

[0130] num_generations = 100 #Number of iterations

[0131] crossover_probability=0.7#crossover probability

[0132] mutation_probability=0.2#Mutation probability

[0133] # Run the genetic algorithm

[0134] population=toolbox.population(n=population_size)

[0135] algorithms.eaSimple(population,toolbox,cxpb=crossover_probability,mutpb=mutation_probability,ngen=num_generations,verbose=True)

[0136] # Output the optimal parameter combination

[0137] best_individual=tools.selBest(population,k=1)[0]

[0138] print("Optimal parameter combination:",best_individual)

[0139] print("Best fitness value:", best_individual.fitness.values[0])

[0140] This solution uses genetic algorithms to transform the problem-solving process into a process similar to the crossover and mutation of chromosome genes in biological evolution through mathematical methods and computer simulation operations. When solving more complex combinatorial optimization problems, it can obtain better optimization results and the required feature data more quickly than some conventional optimization algorithms.

[0141] The above shows and describes the basic principles and main features of the present invention and the advantages of the invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An intelligent sports training planning system based on big data, including a personal information management module, a training data analysis module, and a training planning optimization module based on the system architecture, characterized in that: The personal information management module adopts a layered architecture design of data input layer, data storage layer, data processing layer, data access layer and user interaction layer to ensure the security and efficient processing of data. The personal information management function is divided into a basic information input module and a real-time parameter collection module in the architecture to facilitate the maintenance and expansion of the system and support integration with other modules and systems, such as connection with wearable devices, to ensure real-time synchronization and update of data; The training data analysis module uses efficient data processing algorithms and engines to quickly analyze and process training data, splitting the data analysis function into a single-factor data analysis module and a multi-factor optimization combination module to facilitate system maintenance and expansion; Provide data visualization function to display analysis results to users in the form of charts and reports; The training planning optimization module uses a machine learning algorithm to generate a personalized training plan, splitting the training planning function into a personalized training planning module and a training effect evaluation and feedback module, which facilitates system maintenance and expansion, and dynamically adjusts the training plan based on the user's real-time data and feedback to ensure maximum training effect.

2. According to claim 1, an intelligent sports training planning system based on big data is characterized in that The functions of the personal information management module include: (1) personal information entry: supporting users to manually input basic information, including name, age, gender, height, and weight information; (2) real-time parameter collection: collecting the user's movement trajectory, heart rate, and training duration parameters in real time through wearable devices, and automatically synchronizing them to the system; (3) data query and display: providing a query function for user personal information, supporting filtering and display based on time range and data type conditions; (4) data update and maintenance: allowing users to modify their personal information at any time, and the system will also automatically clean up invalid or outdated data on a regular basis.

3. The intelligent sports training planning system based on big data according to claim 1, characterized in that: The personal information management module receives information input by the user through the user interface, displays the query results and prompt information; provides a unified interface through the data access layer for other modules to access personal information data; and synchronizes and transmits data with the wearable device through Bluetooth and Wi-Fi communication methods.

4. The intelligent sports training planning system based on big data according to claim 1 is characterized in that The functions of the training data analysis module include: (1) single factor data analysis: screening and sorting the parameters of the trainees to obtain the single factor optimal parameter value that affects the training results; (2) multi-factor optimization combination: using genetic algorithm to optimize the multi-factor parameter combination of the trainees to obtain the optimal training plan; (3) data visualization: presenting the analysis results to the user in the form of charts, reports, etc. to help the user better understand the training data.

5. The intelligent sports training planning system based on big data according to claim 1, characterized in that: The training data analysis module obtains the user's personal information and real-time parameters from the personal information management module as input for data analysis, and transmits the analysis results to the training planning optimization module as a basis for generating a personalized training plan; The analysis results and visual reports are presented through the user interface.

6. The intelligent sports training planning system based on big data according to claim 1 is characterized in that The functions of the training plan optimization module include: (1) Personalized training planning: automatically generating a personalized training plan based on the user's personal information, real-time parameters and data analysis results; (2) Training effect evaluation: real-time monitoring of the user's training status during training and evaluation of the training effect; (3) Feedback and adjustment: providing real-time training guidance and feedback based on the evaluation results, and dynamically adjusting the training plan.

7. The intelligent sports training planning system based on big data according to claim 1, characterized in that: The training planning optimization module obtains the user's personal information and real-time parameters from the personal information management module as a basis for generating a training plan; Receive the analysis results of the training data analysis module as a reference for optimizing the training plan; The training plan, evaluation results and feedback information are displayed through the user interface, while receiving users' modification and adjustment suggestions.

8. The intelligent sports training planning system based on big data according to claim 1, characterized in that: The basic information input module is used to receive and store the personal information of the sports trainer, including age, gender, height, weight, place of origin, hobbies, diet structure, training time period, and work and rest time; The real-time parameter acquisition module collects the real-time parameters of the trainee's heart rate, movement trajectory, and training duration in real time through a high-precision positioning bracelet or other wearable devices, and transmits the data to the system for analysis.

9. The intelligent sports training planning system based on big data according to claim 1, characterized in that: The single factor data analysis module is used to screen and sort the basic parameters and real-time parameters of the trainees to obtain the single factor optimal parameter value affecting the training performance, including the optimal training time period and the optimal diet structure; The multi-factor optimization combination module uses a genetic algorithm to optimize the multi-factor parameter combination of the trainee, and obtains the optimal parameter combination under different training items and different training stages to improve the training effect.

10. The intelligent sports training planning system based on big data according to claim 1, characterized in that: The personalized training planning module automatically generates a personalized training plan based on the trainee's personal information, real-time parameters and data analysis results, including daily training items, training intensity and training duration; The training effect evaluation and feedback module is used to monitor the training status of the trainee in real time during the training process, evaluate the training effect, and adjust the training plan according to the evaluation results, thereby providing the trainee with real-time training guidance and feedback.

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