Intelligent sports service management system based on big data

Abstract

The invention relates to the technical field of smart sports, and discloses a smart sports service management system based on big data, comprising a data acquisition module used for acquiring motion data of a user through a motion device, a sensor and a wearable device, and transmitting the motion data to a data processing module; the data processing module is connected to the data acquisition module and is used for cleaning, standardizing and storing the acquired motion data; and the user management module is connected to the data processing module and is used for managing user information, analyzing motion behaviors and setting personalized preferences. By introducing big data analysis, a machine learning algorithm and a deep learning technology, accurate modeling can be performed on exercise data of a user, and personalized exercise suggestions, health trend prediction and exercise effect evaluation are provided. The intelligent analysis module automatically generates personalized suggestions such as exercise items, exercise intensity and exercise frequency suitable for the user in combination with exercise data and health conditions of the user.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent sports, and specifically provides an intelligent sports service management system based on big data. Background Art

[0002] With the rapid development of information technology and big data technology, the sports and health field is undergoing an unprecedented transformation. The traditional sports management mode mainly relies on manual guidance, experience judgment, and simple data recording, and cannot provide precise health management services for individuals. Existing sports health management systems mostly rely on basic physiological data such as step counting and heart rate monitoring for analysis, and often cannot comprehensively reflect the user's health status and exercise effect, nor can they provide personalized and scientific exercise suggestions. There are many deficiencies in this traditional management method, especially in aspects such as the arrangement of exercise volume, the regulation of exercise intensity, and the evaluation of exercise effect, lacking refined and intelligent guidance, which is likely to lead to excessive exercise or insufficient exercise, affecting the health management effect.

[0003] To solve these problems, in recent years, intelligent sports service systems based on big data and artificial intelligence technologies have gradually become a research hotspot. By introducing big data analysis, machine learning, and deep learning technologies, new sports health management systems can collect a large amount of user data in real time, including exercise behavior, physical condition, environmental factors, etc., and conduct data mining and in-depth analysis to provide personalized exercise suggestions and health management plans. However, although there are currently some systems attempting to combine big data technology for personalized exercise management, there is still a large room for improvement in the comprehensiveness and accuracy of data collection, processing, and analysis.

[0004] In existing systems, the collection method of exercise data is single, mostly relying on wearable devices or intelligent sensors, often ignoring multi-dimensional information such as the environmental factors, living habits, and exercise history of different users, resulting in insufficient accuracy of personalized recommendations; at the same time, the existing technology is also relatively limited in data processing and algorithm application, and fails to fully utilize the potential of advanced algorithms such as machine learning and deep learning, making it difficult to achieve high-precision health prediction and exercise effect evaluation. Therefore, those skilled in the art have proposed an intelligent sports service management system based on big data to solve the above problems. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides an intelligent sports service management system based on big data, which solves the problems proposed in the above background art.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent sports service management system based on big data, comprising:

[0007] A data acquisition module, which is used to collect the user's motion data through motion devices, sensors and wearable devices, and transmit the motion data to the data processing module;

[0008] A data processing module, connected to the data acquisition module, which is used to clean, standardize and store the collected motion data;

[0009] A user management module, connected to the data processing module, which is used to manage user information, analyze motion behavior and set personalized preferences;

[0010] An intelligent analysis module, connected to the data processing module, which is used to model the user's motion data based on big data analysis and machine learning algorithms, and provide health trend prediction, motion effect evaluation and personalized motion suggestions;

[0011] A service recommendation module, connected to the intelligent analysis module, which is used to intelligently recommend suitable sports items, venues and coaches according to the user's motion data, preferences and analysis results;

[0012] A data display module, connected to the intelligent analysis module, which is used to display the user's motion data and health status in the form of graphs, charts and reports;

[0013] A user feedback module, connected to the service recommendation module, which is used to collect the user's feedback on the system service and support the continuous optimization of the system function and service content.

[0014] Preferably, the data acquisition module includes:

[0015] A motion sensor unit, which is used to monitor and collect the user's motion data in real time, including information such as steps, motion intensity, heart rate, etc.;

[0016] A wearable device interface unit, which is used to collect the user's real-time motion data through wearable devices such as smart watches and smart bracelets, and transmit it to the data processing module;

[0017] A positioning system unit, which is used to collect the user's motion trajectory data and analyze the motion path and motion environment in combination with GPS positioning information.

[0018] Preferably, the data processing module includes:

[0019] A data cleaning unit, which is used to filter noise, fill in missing values and standardize the collected original motion data;

[0020] A data storage unit, which is used to store the processed data in a database and ensure the security and integrity of the data;

[0021] A data preprocessing unit for performing feature engineering on data to extract key information, such as the user's exercise frequency, exercise duration, exercise intensity, etc.

[0022] Preferably, the user management module includes:

[0023] A user information management unit for storing and managing the user's basic information, fitness history, and exercise preferences;

[0024] A user behavior analysis unit for analyzing the user's exercise behavior patterns and identifying the user's exercise habits, strengths, and weaknesses;

[0025] A personalized preference setting unit for adjusting personalized exercise plans, recommendations, and reminder settings according to the user's needs and feedback.

[0026] Preferably, the intelligent analysis module includes:

[0027] An exercise effect evaluation unit for evaluating the user's exercise effect, including exercise intensity, calories burned, recovery status, etc.;

[0028] A health trend prediction unit for predicting the user's future health status based on historical data and trend analysis and issuing early warning prompts;

[0029] A personalized recommendation generation unit for generating personalized exercise recommendations based on the user's exercise data and health status and recommending appropriate exercise items and exercise intensities.

[0030] Preferably, the service recommendation module includes:

[0031] An exercise item recommendation unit for recommending suitable exercise items according to the user's exercise preferences and health goals, such as running, yoga, swimming, etc.;

[0032] A venue recommendation unit for recommending suitable exercise venues and times based on the user's location, exercise needs, and venue availability;

[0033] A coach matching unit for recommending suitable personal trainers or group courses according to the user's exercise goals and needs.

[0034] Preferably, the data display module includes:

[0035] A real-time data monitoring unit for real-time displaying of the user's exercise data, such as steps, heart rate, exercise trajectory, etc.;

[0036] An exercise analysis report unit for generating reports on the user's exercise progress and health status and presenting them in the form of charts, such as exercise volume trends, health status changes, etc.;

[0037] A health goal tracking unit for displaying the achievement status of the user's exercise goals and providing regular updates and adjustment suggestions.

[0038] Preferably, the user feedback module includes:

[0039] A service evaluation unit for collecting the user's satisfaction evaluations of services such as exercise programs, courses, and venues provided by the system;

[0040] An improvement suggestion collection unit for collecting the user's improvement suggestions on system functions, personalized recommendations, and service content;

[0041] A data analysis unit for analyzing the user feedback information and optimizing the service recommendation algorithm and user experience of the system according to the feedback results.

[0042] Preferably, the intelligent analysis module uses a deep learning algorithm to model the user's exercise data and uses a convolutional neural network to perform image recognition on the exercise trajectory to more accurately analyze the user's exercise pattern and health status.

[0043] Preferably, the system realizes multi-terminal synchronization of data through a cloud platform, supports the user to access personal exercise data, health reports, and system recommendations on different devices such as smartphones, tablets, and smart watches, and enables the user to manage exercise health anytime and anywhere.

[0044] The present invention provides a big data-based intelligent sports service management system. It has the following beneficial effects:

[0045] 1. By introducing big data analysis, machine learning algorithms, and deep learning technologies, the present invention can accurately model the user's exercise data, provide personalized exercise suggestions, health trend predictions, and exercise effect evaluations. The intelligent analysis module of the present invention combines the user's exercise data and health status to automatically generate personalized suggestions such as exercise programs, exercise intensities, and exercise frequencies suitable for the user, thereby helping the user scientifically arrange the exercise plan, avoid over-exercise or under-exercise, and effectively improve the fitness effect. The introduction of the deep learning algorithm can also perform high-precision analysis on the user's exercise trajectory, accurately identify the changes in the user's exercise pattern and health status, and provide a more scientific and personalized exercise management plan than traditional methods.

[0046] 2. The present invention employs a variety of data collection means, including sports equipment, sensors, wearable devices, and positioning systems, to obtain the user's motion data (such as steps, heart rate, exercise intensity, exercise trajectory, etc.) in real time through a data collection module, and transmit this data to a data processing module for cleaning, standardization, and storage. Through multi-dimensional data fusion and in-depth analysis, the system can provide a more comprehensive health assessment, not limited to single motion data, and can comprehensively consider the user's exercise behavior, health trends, environmental factors, etc., to provide a more scientific and comprehensive health management solution for the user.

[0047] 3. The present invention is provided with a user feedback module. Through a service evaluation unit and an improvement suggestion collection unit, it can collect the user's feedback on the system service in real time, and conduct data analysis based on the feedback information, thereby optimizing the system function and service recommendation algorithm. This continuous user feedback mechanism can not only improve the adaptability of the system to user needs, but also enhance the personalization and intelligence level of the system through continuous iteration and optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is the overall flowchart of the present invention;

[0049] Figure 2 is the flowchart of the data collection module of the present invention;

[0050] Figure 3 is the flowchart of the data processing module of the present invention;

[0051] Figure 4 is the flowchart of the user management module of the present invention;

[0052] Figure 5 is the flowchart of the intelligent analysis module of the present invention;

[0053] Figure 6 is the flowchart of the service recommendation module of the present invention;

[0054] Figure 7 is the flowchart of the data display module of the present invention;

[0055] Figure 8 is the flowchart of the user feedback module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0056] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0057] Please refer to the appendix Figure 1 - Appendix Figure 8 Embodiments of the present invention provide a big data-based intelligent sports service management system, including:

[0058] A data acquisition module, configured to collect users' exercise data through exercise devices, sensors, and wearable devices, and transmit the exercise data to a data processing module;

[0059] The data acquisition module includes:

[0060] An exercise sensor unit, configured to monitor and collect users' exercise data in real time, including information such as steps, exercise intensity, and heart rate;

[0061] Specifically, the main function of the exercise sensor unit is to monitor and collect users' exercise status and physiological data. Exercise sensors include acceleration sensors, gyroscopes, heart rate sensors, etc., and can provide the following information:

[0062] Steps: Calculate the number of steps of the user through the acceleration sensor to help the user understand their daily exercise volume.

[0063] Exercise intensity: Analyze the exercise intensity by combining data such as the user's exercise speed and step frequency to help the user judge the current exercise effect.

[0064] Heart rate: Real-time monitor the user's heart rate through the heart rate sensor, provide information on the heart load during the user's exercise, and help evaluate the impact of exercise on health.

[0065] This information can provide real-time feedback on the exercise status for users, helping to adjust the exercise plan and intensity.

[0066] A wearable device interface unit, configured to collect users' real-time exercise data through wearable devices such as smart watches and smart bracelets, and transmit the data to a data processing module;

[0067] Specifically, the wearable device interface unit is used to connect to the system through wearable devices such as smart watches and smart bracelets to obtain exercise data in real time. Its main functions include:

[0068] Data transmission: Transmit the exercise data collected by the device to the system through a wireless communication protocol (such as Bluetooth, Wi-Fi, etc.).

[0069] Real-time monitoring: Obtain information such as the user's exercise status and health data by continuously tracking the wearable device worn by the user.

[0070] A positioning system unit, configured to collect users' exercise trajectory data, and analyze the exercise path and exercise environment in combination with GPS positioning information.

[0071] Specifically, the positioning system unit mainly collects the user's movement trajectory through the GPS module or other positioning technologies. It can provide the following functions for the movement data:

[0072] Movement trajectory data: Through GPS positioning, the system can track the user's specific location and draw a route map of the movement.

[0073] Movement path analysis: By combining the collected location information with the user's step data, movement speed, etc., analyze the user's movement route, movement pattern, etc.

[0074] Movement environment analysis: Through comprehensive analysis by combining environmental information (such as sports venues, weather conditions, etc.), provide feedback on the movement environment to help users optimize their movement methods and locations.

[0075] The data processing module, connected to the data acquisition module, is used to clean, standardize, and store the collected movement data;

[0076] The user management module, connected to the data processing module, is used to manage user information, analyze movement behaviors, and set personalized preferences;

[0077] The user management module includes:

[0078] The user information management unit, used to store and manage the user's basic information, fitness history, and movement preferences;

[0079] Specifically, the user information management unit is responsible for storing and managing the user's basic information and historical data related to sports health. Its main functions include:

[0080] Basic information storage: This unit first records the user's basic information, such as name, gender, age, weight, height, etc. These basic information help provide a preliminary reference for subsequent personalized sports recommendations and health assessments.

[0081] Fitness history management: The system will track the user's movement records, including past exercise amounts, training plans, completion status, etc. These historical data help analyze the user's movement progress and effects, thus providing a basis for formulating new movement plans.

[0082] Movement preference recording: According to the user's participated activities, sports items, preference types (such as aerobic, strength training, yoga, etc.), the system will record the user's movement preferences. These preferences help the system consider individual interests and needs when recommending sports items to the user in the future, increasing the personalization and pertinence of the recommendations.

[0083] By detailedly recording and managing these data, the user information management unit not only provides personalized services for users but also provides data support for subsequent behavior analysis, movement effect evaluation, etc.

[0084] A user behavior analysis unit, which is used to analyze the user's exercise behavior patterns and identify the user's exercise habits, strengths, and weaknesses;

[0085] Specifically, the core task of the user behavior analysis unit is to identify the user's exercise habits, strengths, and weaknesses through in-depth analysis of the user's exercise behavior patterns, so as to provide accurate basis for personalized exercise guidance. Its functions mainly include:

[0086] Exercise habit analysis: This module identifies the user's exercise frequency, time, type and other behavior patterns by collecting the user's exercise data for a long time, and analyzes the user's exercise habits. For example, whether to exercise regularly, the choice of exercise time period, the duration of each exercise, etc.

[0087] Strength and weakness identification: The system can identify the advantages and disadvantages shown by the user during exercise based on the user's exercise history and physiological data (such as exercise intensity, heart rate changes, recovery after exercise, etc.). For example, some users perform excellently in endurance, but are at a disadvantage in strength training. Through this kind of behavior analysis, the system can help users understand their exercise advantages and weaknesses, so as to provide suggestions for adjusting the exercise plan.

[0088] Trend analysis: The user behavior analysis unit can also analyze the user's exercise trends and predict the user's future exercise needs and health changes. For example, some users gradually increase the exercise intensity within a certain period of time, while some users show a trend of decreasing exercise enthusiasm. The system can give appropriate reminders and incentives based on such trends.

[0089] A personalized preference setting unit, which is used to adjust personalized exercise plans, recommendations, and reminder settings according to the user's needs and feedback.

[0090] Specifically, the personalized preference setting unit aims to automatically adjust and optimize exercise plans, recommended content, and reminder settings according to the user's needs and feedback, so as to achieve highly personalized exercise management. Its specific functions include:

[0091] Personalized adjustment of exercise plans: Based on the user's exercise history, behavior analysis results, and health status, this unit can intelligently adjust the exercise plan. For example, if the system identifies that the user is weak in a certain aspect (such as strength training), it can recommend more corresponding training plans and gradually increase the training intensity according to the user's physical fitness level.

[0092] Optimization of exercise recommendations: According to the user's exercise preferences and behavior analysis, the system will recommend suitable exercise items and activity types for the user. For example, if the user is inclined to high-intensity interval training (HIIT), the system will recommend more such training to avoid overly single exercise recommendations.

[0093] Reminder settings: The personalized preference setting unit also allows users to set personalized reminders, such as exercise time, goal achievement reminders, training plan change reminders, etc. These reminders can not only help users adhere to the exercise plan, but also be adjusted in a timely manner according to the user's needs to maintain the achievability of the exercise goal and the balance of the health status.

[0094] The data processing module includes:

[0095] The data cleaning unit is used to perform noise filtering, missing value filling, and data standardization processing on the collected original exercise data;

[0096] The data storage unit is used to store the processed data in the database and ensure the security and integrity of the data;

[0097] The data preprocessing unit is used to perform feature engineering processing on the data, extract key information, such as the user's exercise frequency, exercise duration, exercise intensity, etc.

[0098] The intelligent analysis module is connected to the data processing module and is used to model the user's exercise data based on big data analysis and machine learning algorithms, providing health trend prediction, exercise effect evaluation, and personalized exercise suggestions;

[0099] The intelligent analysis module includes:

[0100] The exercise effect evaluation unit is used to evaluate the user's exercise effect, including exercise intensity, calorie consumption, recovery status, etc.;

[0101] Specifically, the exercise effect evaluation unit

[0102] The main task of this unit is to analyze the user's exercise performance and evaluate the actual effect of exercise on the user's health. It mainly includes the following aspects:

[0103] Exercise intensity: By monitoring data such as heart rate, exercise speed, exercise time, acceleration, etc., evaluate the intensity of exercise. For example, indicators such as running speed, cadence, and heart rate changes can help determine whether the exercise has reached the expected intensity.

[0104] Calorie consumption: Calculate the calorie consumption during exercise based on data such as exercise type, exercise time, exercise intensity, user's weight, age, etc. This is very important for users with goals such as weight loss and body shaping.

[0105] Recovery status: By monitoring data such as heart rate recovery, muscle fatigue, sleep quality, etc., evaluate the user's recovery situation. For example, the speed of heart rate recovery after exercise can reflect the user's physical condition and recovery ability

[0106] A health trend prediction unit, which is used to predict the user's future health status based on historical data and trend analysis and issue early warning prompts;

[0107] Specifically, this unit analyzes the user's historical health data (such as weight, exercise volume, sleep, heart rate, etc.) to predict the user's health trend in the future. It includes:

[0108] Health trend analysis: Based on the user's historical data, analyze whether there is a trend of improvement or deterioration in their health status. For example, if the user has continuously gained weight in the past three months, the system predicts that the user's weight will continue to increase in the future.

[0109] Early warning prompt: If the system detects some abnormal health trends, it will issue early warning prompts. For example, if the user's exercise volume drops significantly or the heart rate is abnormal, the system will remind the user to pay attention to rest and seek professional advice, etc.

[0110] Health risk prediction: Through long-term data analysis, the system can predict the health problems the user will face in the future, such as cardiovascular diseases, diabetes risks, etc., so as to remind the user to take preventive measures in advance.

[0111] A personalized recommendation generation unit, which is used to generate personalized exercise recommendations based on the user's exercise data and health status, and recommend appropriate exercise items and exercise intensities.

[0112] A service recommendation module, connected to the intelligent analysis module, which is used to intelligently recommend suitable exercise items, venues and coaches according to the user's exercise data, preferences and analysis results;

[0113] The service recommendation module includes:

[0114] An exercise item recommendation unit, which is used to recommend suitable exercise items according to the user's exercise preferences and health goals, such as running, yoga, swimming, etc.;

[0115] A venue recommendation unit, which is used to recommend suitable exercise venues and times based on the user's location, exercise needs and venue availability;

[0116] Specifically, this unit will recommend suitable exercise venues based on the user's geographical location, exercise needs and venue availability. The specific recommendation basis includes:

[0117] User location: Through the GPS positioning function, the system can understand the user's location and recommend nearby exercise venues. For example, recommend the nearest running route, park, gym, etc.

[0118] Exercise requirements: Recommend suitable venues according to the user's exercise type requirements. For example, if the user wants to do high-intensity aerobic exercise, recommend a running track or a swimming pool; if the user wants to do strength training, recommend a gym with equipment.

[0119] Venue availability: Provide real-time venue availability information by connecting to the venue management system. For example, whether there are free tennis courts, basketball courts, etc., to avoid wasting time for users when they arrive at the venue that has already been reserved.

[0120] Coach matching unit, used to recommend suitable personal trainers or group classes according to the user's exercise goals and needs.

[0121] Data display module, connected to the intelligent analysis module, used to display the user's exercise data and health status in the form of graphs, charts and reports;

[0122] The data display module includes:

[0123] Real-time data monitoring unit, used to display the data during the user's exercise in real time, such as steps, heart rate, exercise trajectory, etc.;

[0124] Specifically, the main function of the real-time data monitoring unit is to monitor and display the data generated by the user during exercise through sensors and related devices. The accurate display and timely feedback of this data help users better understand their exercise situation and health status, so that they can make immediate adjustments and optimizations.

[0125] Steps monitoring: Real-time monitor the user's steps through the built-in acceleration sensor or exercise tracking device (such as smart bracelet, mobile phone, etc.), and display the steps of the current walking, running or other exercise types.

[0126] Heart rate monitoring: Real-time record and display the user's heart rate data through a heart rate monitoring device (such as a heart rate belt, smart watch, etc.), to help users understand the current exercise intensity and physical load, and adjust the exercise method in time to avoid over-exercise or under-exercise.

[0127] Exercise trajectory display: Based on GPS or other positioning technologies (such as sports watches, mobile phones, etc.), record the user's exercise trajectory, movement route and path. This helps users view key information such as the area distribution, route selection, and distance of the exercise.

[0128] Exercise duration and calorie consumption: Display data such as exercise duration, calorie consumption, exercise intensity, etc., to help users track and evaluate the exercise effect.

[0129] Exercise analysis report unit, used to generate reports on the user's exercise progress and health status, and present them in the form of charts, such as exercise volume trends, changes in health status, etc.;

[0130] Specifically, the motion analysis report unit is used to systematically analyze the user's motion data, generate detailed motion reports, and help the user understand their motion progress and changes in health status. This unit can generate motion summaries and trend analyses regularly, enabling the user to adjust their exercise plan and goals based on the reports.

[0131] Motion effect evaluation: Comprehensively evaluate the user's motion effects, generate data reports such as motion intensity and motion efficiency, and help the user understand the effects of different motion intensities and forms.

[0132] Motion type analysis: Analyze the effect comparison of different types of exercises (such as running, cycling, swimming, etc.) to help the user choose the most suitable exercise method for themselves.

[0133] Periodic analysis report: Generate regular analysis reports according to the time periods set by the user (such as weekly, monthly), and help the user track long-term motion effects and health changes.

[0134] The health goal tracking unit is used to display the achievement status of the user's exercise goals and provide suggestions for regular updates and adjustments.

[0135] The user feedback module is connected to the service recommendation module and is used to collect the user's feedback on the system services, supporting the continuous optimization of the system functions and service content.

[0136] The user feedback module includes:

[0137] The service evaluation unit is used to collect the user's satisfaction evaluations of the services provided by the system, such as exercise programs, courses, venues, etc.;

[0138] The improvement suggestion collection unit is used to collect the user's improvement suggestions for the system functions, personalized recommendations, and service content;

[0139] The data analysis unit is used to analyze the user feedback information and optimize the system's service recommendation algorithm and user experience based on the feedback results.

[0140] The intelligent analysis module uses deep learning algorithms to model the user's motion data and uses convolutional neural networks for image recognition of motion trajectories to more accurately analyze the user's motion patterns and health conditions. The system realizes multi-terminal synchronization of data through the cloud platform, supporting the user to access personal motion data, health reports, and system recommendations on different devices such as smartphones, tablets, and smartwatches, enabling the user to manage their exercise and health anytime, anywhere.

[0141] In summary, by introducing big data analysis, machine learning algorithms, and deep learning technologies, the present invention can accurately model the user's motion data, provide personalized exercise recommendations, health trend predictions, and exercise effect evaluations. The intelligent analysis module of the present invention combines the user's motion data and health status to automatically generate personalized recommendations such as exercise items, exercise intensity, and exercise frequency suitable for the user, thereby helping the user scientifically arrange the exercise plan, avoid over-exercising or under-exercising, and effectively improve the fitness effect. The introduction of deep learning algorithms can also perform high-precision analysis on the user's motion trajectory, accurately identify changes in the user's motion patterns and health status, and provide a more scientific and personalized exercise management solution than traditional methods.

[0142] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart sports service management system based on big data, characterized in that, It includes: A data collection module, which is used to collect the user's motion data through motion devices, sensors and wearable devices, and transmit the motion data to the data processing module; A data processing module, connected to the data collection module, which is used to clean, standardize and store the collected motion data; A user management module, connected to the data processing module, which is used to manage user information, analyze motion behavior and set personalized preferences; An intelligent analysis module, connected to the data processing module, which is used to model the user's motion data based on big data analysis and machine learning algorithms, and provide health trend prediction, motion effect evaluation and personalized motion suggestions; A service recommendation module, connected to the intelligent analysis module, which is used to intelligently recommend suitable sports items, venues and coaches according to the user's motion data, preferences and analysis results; A data display module, connected to the intelligent analysis module, which is used to display the user's motion data and health status in the form of graphs, charts and reports; A user feedback module, connected to the service recommendation module, which is used to collect the user's feedback on the system service and support the continuous optimization of the system function and service content.

2. The intelligent sports service management system based on big data according to claim 1, characterized in that, The data collection module includes: A motion sensor unit, which is used to monitor and collect the user's motion data in real time, including information such as steps, motion intensity, heart rate, etc.; A wearable device interface unit, which is used to collect the user's real-time motion data through wearable devices such as smart watches and smart bands, and transmit it to the data processing module; A positioning system unit, which is used to collect the user's motion trajectory data and analyze the motion path and motion environment in combination with GPS positioning information.

3. The intelligent sports service management system based on big data according to claim 1, characterized in that The data processing module includes: A data cleaning unit, which is used to filter noise, fill in missing values and standardize the collected original motion data; A data storage unit, which is used to store the processed data in a database and ensure the security and integrity of the data; A data preprocessing unit, which is used to perform feature engineering processing on the data and extract key information such as the user's motion frequency, motion duration, motion intensity, etc.

4. A smart sports service management system based on big data according to claim 1, characterized in that, The user management module includes: A user information management unit, which is used to store and manage the user's basic information, fitness history and motion preferences; A user behavior analysis unit, which is used to analyze the user's motion behavior patterns and identify the user's motion habits, strengths and weaknesses; A personalized preference setting unit, which is used to adjust personalized motion plans, recommendations and reminder settings according to the user's needs and feedback.

5. A smart sports service management system based on big data according to claim 1, characterized in that, The intelligent analysis module includes: A motion effect evaluation unit, which is used to evaluate the user's motion effect, including motion intensity, calorie consumption, recovery status, etc.; A health trend prediction unit, which is used to predict the user's future health status based on historical data and trend analysis, and issue early warning prompts; A personalized recommendation generation unit, which is used to generate personalized motion recommendations based on the user's motion data and health status, and recommend appropriate sports items and exercise intensities.

6. The intelligent sports service management system based on big data according to claim 1, characterized in that, The service recommendation module includes: A sports item recommendation unit, which is used to recommend suitable sports items according to the user's motion preferences and health goals, such as running, yoga, swimming, etc.; A venue recommendation unit, which is used to recommend suitable sports venues and times based on the user's location, sports needs, and venue availability; A coach matching unit, which is used to recommend suitable personal coaches or group courses according to the user's sports goals and needs.

7. A smart sports service management system based on big data according to claim 1, characterized in that, The data display module includes: A real-time data monitoring unit, which is used to display the data during the user's exercise in real time, such as steps, heart rate, exercise trajectory, etc.; An exercise analysis report unit, which is used to generate reports on the user's exercise progress and health status and present them in the form of charts, such as exercise volume trends, changes in health status, etc.; A health goal tracking unit, which is used to display the achievement of the user's exercise goals and provide suggestions for regular updates and adjustments.

8. An intelligent sports service management system based on big data according to claim 1, characterized in that, The user feedback module includes: A service evaluation unit, which is used to collect the user's satisfaction evaluations of the services provided by the system, such as sports events, courses, venues, etc.; An improvement suggestion collection unit, which is used to collect the user's improvement suggestions for the system functions, personalized recommendations, and service content; A data analysis unit, which is used to analyze the user feedback information and optimize the service recommendation algorithm and user experience of the system according to the feedback results.

9. A smart sports service management system based on big data according to claim 1, characterized in that, The intelligent analysis module uses deep learning algorithms to model the user's exercise data and uses convolutional neural networks for image recognition of the exercise trajectory to more accurately analyze the user's exercise patterns and health status.

10. A smart sports service management system based on big data according to claim 1, characterized in that, The system realizes multi-terminal synchronization of data through the cloud platform, supports users to access personal exercise data, health reports, and system recommendations on different devices such as smartphones, tablets, and smart watches, and enables users to manage exercise health anytime, anywhere.

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