Exercise suggestion method based on park environment and health condition

Abstract

The invention discloses an exercise suggestion method based on a park environment and a health condition, and the method comprises the steps: S1, constructing a sensor network, and collecting environment data and health data in a region; s2, uploading the collected data to a cloud computing platform, and performing data cleaning and data integration; s3, performing data fusion on the cleaned data environment data and health data to construct a comprehensive data set; s4, constructing an evaluation model, obtaining a comprehensive score based on the comprehensive data set, and predicting the health demand and exercise amount of the user through a regression analysis or classification algorithm; and S5, according to the health demand and the exercise amount of the user, obtaining the current health state data and the personal target of the user, and generating a personalized exercise suggestion. Through sharing and integration of all data in the park, sports health management based on environment and health conditions is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of park environment and health status management, and in particular to an exercise recommendation method based on park environment and health status. Background Art

[0002] A park refers to an area designated by the government under centralized and unified planning, where a specific type of industry or type of enterprise or company is set up for unified management. Typical examples include industrial parks, free trade zones, industrial parks, and animation parks.

[0003] At present, the development trend of park management is towards comprehensive intelligent supervision. Through the comprehensive use of multi-source data, the personalized and intelligent park management can be achieved. The existing parks are still in a situation where the data for the health management of people in the park is single, independent and imperfect. They can only simply obtain the single health data of the personnel to play the role of data collection. They are faced with the inability to effectively use the health data of the people in the park to conduct a comprehensive analysis of the health status, and cannot give relevant suggestions with guiding directions. They can only realize the concrete display and feedback of the collected data. The data obtained by each unit in the park cannot be effectively shared and integrated, forming a data island, lacking unified standards and specifications, and the management execution strength is inconsistent, and it cannot be targeted at all-round personalized management of health. Summary of the invention

[0004] To solve the above problems, the present invention provides an exercise recommendation method based on park environment and health status, which comprehensively analyzes park environment data and health status data and combines them with exercise recommendations to achieve effective sharing and integration of data of various dimensions collected in the park and realize health management of people in the park.

[0005] The present invention provides an exercise recommendation method based on park environment and health conditions. The specific technical solution is as follows:

[0006] S1: Build a sensor network to collect environmental and health data in the area;

[0007] S2: Upload the collected data to the cloud computing platform for data cleaning and data integration;

[0008] S3: Perform data fusion on the cleaned data environment data and health data to construct a comprehensive data set;

[0009] S4: Build an evaluation model to obtain a comprehensive score based on the comprehensive data set, and predict the user's health needs and exercise volume through regression analysis or classification algorithm;

[0010] S5: Based on the user's health needs and exercise volume, obtain the user's current health status data and personal goals, and generate personalized exercise recommendations.

[0011] Furthermore, environmental data is collected through IoT sensors installed in the area, and personal health data is obtained through personal devices owned by people in the park;

[0012] The environmental data includes air quality data, temperature and humidity data, and wind speed and direction data;

[0013] The personal health data includes steps, heart rate and sleep data.

[0014] Furthermore, the data cleaning includes removing duplicate records, filling missing values ​​and filtering outliers.

[0015] Furthermore, the specific process of data integration is as follows:

[0016] Standardize or normalize the cleaned data;

[0017] The processed data is integrated into the same format according to different types and sources, including timestamp alignment and data format conversion.

[0018] Furthermore, the personalized exercise suggestions include exercise type, corresponding exercise intensity, and exercise duration;

[0019] After the user selects the type of exercise, the system will output a combination of various exercise intensities and corresponding exercise durations based on the user's health needs and amount of exercise.

[0020] Furthermore, the personalized exercise recommendations also include: outputting preset dietary recommendations and life habits that require attention based on the current user's health data status, exercise type, and corresponding exercise intensity and exercise duration.

[0021] Furthermore, after step S5, the following steps are further included:

[0022] Collect personal health data in real time and monitor the changes in health data indicators after users follow exercise recommendations;

[0023] Based on changes in health data indicators, feedback is provided to adjust exercise intensity and type, and generate updated personalized exercise recommendations.

[0024] Furthermore, in step S5, the generating of personalized exercise suggestions further includes generating exercise suggestion recommendation content, and the generation of the exercise suggestion recommendation content is specifically as follows:

[0025] Obtain browsing records, click behaviors, and scores of historical exercise data posted by park personnel whose health data is similar to that of the current user;

[0026] Based on the number of views, clicks and ratings, the corresponding historical sports suggestion content is sorted and recommended to the current user.

[0027] Furthermore, in step S5, the generating of personalized exercise suggestions further includes generating exercise suggestion recommendation content, and the generation of the exercise suggestion recommendation content is specifically as follows:

[0028] Retrieve the user's historical exercise data to obtain the user's exercise mode and type;

[0029] Calculate the similarity between the current user and other users' exercise modes and types, obtain personalized exercise suggestions from the user with the highest similarity, and recommend them to the current user.

[0030] The beneficial effects of the present invention are as follows:

[0031] The present invention monitors the park's environmental data in real time through Internet of Things sensors, and collects the health data of users in the park through personal devices, conducts a comprehensive evaluation based on the environmental data and health data, predicts the users' health needs and exercise volume, and outputs personalized exercise recommendations based on the users' health needs and exercise volume. At the same time, through real-time monitoring data feedback, the personalized exercise recommendations are dynamically adjusted to achieve exercise health management based on environmental and health data in the park. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION

[0033] The following description clearly and completely describes the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] In the description of the embodiments of the present invention, it should be noted that the indicated orientation or positional relationship is based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the invented product is usually placed when in use, or the orientation or positional relationship commonly understood by those skilled in the art, or the orientation or positional relationship in which the invented product is usually placed when in use, which is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply 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 a limitation on the present invention. In addition, the terms "first" and "second" are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0035] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection, or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0036] Example 1

[0037] Embodiment 1 of the present invention discloses a method for suggesting exercise based on the park environment and health conditions. Figure 1 As shown, the details are as follows:

[0038] S1: Build a sensor network to collect environmental and health data in the area;

[0039] Collect environmental data through IoT sensors installed in the area;

[0040] The IoT sensors include air quality monitoring stations, temperature and humidity sensors, and wind speed and direction meters, which are responsible for collecting environmental data in real time;

[0041] Obtain personal health data through personal devices owned by people in the park;

[0042] Personal devices include smart bracelets and smart watches, which are used to collect personal health data and connect to the park's health management backend remote data;

[0043] The environmental data includes air quality data, temperature and humidity data, wind speed and direction data, noise intensity, light intensity, etc.;

[0044] The personal health data includes steps, heart rate and sleep data.

[0045] Specifically, the collection of environmental data also includes weather data. The weather data is connected to a third-party weather forecast server and the weather forecast data is integrated using an API interface to achieve synchronous updating of environmental data and weather conditions.

[0046] S2: Upload the collected data to the cloud computing platform for data cleaning and data integration;

[0047] The data collected by the sensor network is uploaded to the cloud computing platform through wireless gateways and routers, and the data collected by personal devices is uploaded to the cloud computing platform through the network;

[0048] Specifically, security measures such as encryption, access control and anonymization are also implemented in the process of data collection and transmission to prevent data leakage and abuse.

[0049] Data collection and transmission can be adaptively adjusted by setting the sampling frequency and data transmission mode.

[0050] Select appropriate transmission methods according to application scenarios, such as cellular networks, Wi-Fi, LoRa, and NB-IoT, to ensure the reliability and efficiency of data transmission, encrypt the transmitted data, ensure the security of data during transmission, prevent data leakage, and configure backup paths for data transmission to cope with emergencies such as network failures and improve the reliability of data transmission.

[0051] The data cleaning includes removing duplicate records, filling missing values ​​and filtering outliers.

[0052] Specifically, the collected raw data still needs to undergo basic verification, such as checking data integrity and rationality, to eliminate outliers. For cases with large amounts of data, data compression algorithms are used to reduce the load of data transmission while ensuring that the data is not distorted after recovery. Preliminary data processing is performed on the device side or edge computing node, such as data aggregation and simple calculations, to reduce the amount of data uploaded to the cloud.

[0053] The cloud computing platform receives data from the device through the API interface, performs identity authentication and data integrity verification, and stores the received data in the cloud storage system. The storage structure can adopt relational databases, NoSQL databases, and time series databases to optimize query and management efficiency, add indexes and tags to the stored data, and facilitate subsequent data retrieval and analysis;

[0054] Real-time monitoring is also carried out during the data collection and transmission process, including device status, data transmission rate and error log. Once equipment failure and data transmission interruption are found, the alarm mechanism is immediately triggered to notify relevant personnel to handle it. All activities in the data collection and transmission process are recorded in detail to provide a basis for subsequent problem troubleshooting and performance optimization.

[0055] The specific process of data integration is as follows:

[0056] Standardize or normalize the cleaned data to ensure that data of different dimensions can be compared and analyzed on the same scale;

[0057] The processed data is integrated into the same format according to different types and sources, including timestamp alignment and data format conversion.

[0058] S3: Perform data fusion on the cleaned data environment data and health data to construct a comprehensive data set;

[0059] Specifically, data fusion is performed by direct splicing, feature crossing or using graph neural networks, taking into account temporal synchrony and spatial correlation, to ensure that the fused dataset can fully reflect the interaction between individual health status and environmental factors.

[0060] S4: Construct an evaluation model and obtain a comprehensive score based on the comprehensive data set by weighted average method or principal component analysis (PCA);

[0061] Specifically, based on the comprehensive data set, appropriate assessment models are selected according to the assessment objectives, such as health risk level, quality of life assessment and disease prediction. For health risk level assessment and quality of life assessment, a statistically based scoring system is selected. This scoring system collects multi-dimensional data such as individual physiological and biochemical indicators, lifestyle and family health history, and combines statistical methods to calculate the comprehensive risk score. For complex problems such as disease prediction, a deep learning model is used to automatically learn the feature representation in the data for evaluation.

[0062] Then, through regression analysis or classification algorithms, such as random forest or support vector machine, the user's health needs and exercise amount are predicted based on the evaluation results;

[0063] The evaluation results include: health risk score, disease occurrence probability and quality of life index, etc.

[0064] S5: Based on the user's health needs and exercise volume, obtain the user's current health status data and personal goals, and generate personalized exercise recommendations.

[0065] The personalized exercise recommendations include exercise type, corresponding exercise intensity, and exercise duration;

[0066] After the user selects the type of exercise, the system will output a combination of various exercise intensities and corresponding exercise durations based on the user's health needs and amount of exercise.

[0067] As a preferred embodiment, the personalized exercise recommendation also includes: outputting preset dietary advice content and life habits content that need attention based on the current user's health data status, exercise type, and corresponding exercise intensity and exercise duration.

[0068] In this embodiment, after step S5, the following steps are further included:

[0069] Collect personal health data in real time and monitor the changes in health data indicators after users follow exercise recommendations;

[0070] Based on changes in health data indicators, feedback is provided to adjust exercise intensity and type, and generate updated personalized exercise recommendations.

[0071] For example, if the personalized exercise recommendation recommends walking 8,000 steps a day or increasing the frequency of cardiopulmonary exercise, in this process, through the user's real-time feedback data (such as physical condition after exercise) and real-time data changes (such as weather changes), the personalized recommendation is updated according to the new evaluation results, and the exercise recommendation content is dynamically adjusted to ensure the timeliness and effectiveness of the plan.

[0072] As a preferred embodiment, in step S5, the generating of personalized exercise suggestions further includes generating exercise suggestion recommendation content, and the generation of the exercise suggestion recommendation content is specifically as follows:

[0073] Obtain browsing records, click behaviors, and scores of historical exercise data posted by park personnel whose health data is similar to that of the current user;

[0074] Based on the number of views, clicks and ratings, the corresponding historical sports suggestion content is sorted and recommended to the current user.

[0075] Example 2

[0076] Embodiment 2 of the present invention discloses a method for suggesting exercise based on the park environment and health conditions, such as Figure 1 As shown, the details are as follows:

[0077] S1: Build a sensor network to collect environmental and health data in the area;

[0078] Collect environmental data through IoT sensors installed in the area;

[0079] The IoT sensors include air quality monitoring stations, temperature and humidity sensors, and wind speed and direction meters, which are responsible for collecting environmental data in real time;

[0080] Obtain personal health data through personal devices owned by people in the park;

[0081] Personal devices include smart bracelets and smart watches, which are used to collect personal health data and connect to the park's health management backend remote data;

[0082] The environmental data includes air quality data, temperature and humidity data, wind speed and direction data, noise intensity, light intensity, etc.;

[0083] The personal health data includes steps, heart rate and sleep data.

[0084] Specifically, the collection of environmental data also includes weather data. The weather data is connected to a third-party weather forecast server and the weather forecast data is integrated using an API interface to achieve synchronous updating of environmental data and weather conditions.

[0085] S2: Upload the collected data to the cloud computing platform for data cleaning and data integration;

[0086] The data collected by the sensor network is uploaded to the cloud computing platform through wireless gateways and routers, and the data collected by personal devices is uploaded to the cloud computing platform through the network;

[0087] Specifically, security measures such as encryption, access control and anonymization are also implemented in the process of data collection and transmission to prevent data leakage and abuse.

[0088] Data collection and transmission can be adaptively adjusted by setting the sampling frequency and data transmission mode.

[0089] Select appropriate transmission methods according to application scenarios, such as cellular networks, Wi-Fi, LoRa, and NB-IoT, to ensure the reliability and efficiency of data transmission, encrypt the transmitted data, ensure the security of data during transmission, prevent data leakage, and configure backup paths for data transmission to cope with emergencies such as network failures and improve the reliability of data transmission.

[0090] The data cleaning includes removing duplicate records, filling missing values ​​and filtering outliers.

[0091] Specifically, the collected raw data still needs to undergo basic verification, such as checking data integrity and rationality, to eliminate outliers. For cases with large amounts of data, data compression algorithms are used to reduce the load of data transmission while ensuring that the data is not distorted after recovery. Preliminary data processing is performed on the device side or edge computing node, such as data aggregation and simple calculations, to reduce the amount of data uploaded to the cloud.

[0092] The cloud computing platform receives data from the device through the API interface, performs identity authentication and data integrity verification, and stores the received data in the cloud storage system. The storage structure can adopt relational databases, NoSQL databases, and time series databases to optimize query and management efficiency, add indexes and tags to the stored data, and facilitate subsequent data retrieval and analysis;

[0093] Real-time monitoring is also carried out during the data collection and transmission process, including device status, data transmission rate and error log. Once equipment failure and data transmission interruption are found, the alarm mechanism is immediately triggered to notify relevant personnel to handle it. All activities in the data collection and transmission process are recorded in detail to provide a basis for subsequent problem troubleshooting and performance optimization.

[0094] The specific process of data integration is as follows:

[0095] Standardize or normalize the cleaned data to ensure that data of different dimensions can be compared and analyzed on the same scale;

[0096] The processed data is integrated into the same format according to different types and sources, including timestamp alignment and data format conversion.

[0097] S3: Perform data fusion on the cleaned data environment data and health data to construct a comprehensive data set;

[0098] Specifically, data fusion is performed by direct splicing, feature crossing or using graph neural networks, taking into account temporal synchrony and spatial correlation, to ensure that the fused dataset can fully reflect the interaction between individual health status and environmental factors.

[0099] S4: Construct an evaluation model and obtain a comprehensive score based on the comprehensive data set by weighted average method or principal component analysis (PCA);

[0100] Specifically, based on the comprehensive data set, appropriate assessment models are selected according to the assessment objectives, such as health risk level, quality of life assessment and disease prediction. For health risk level assessment and quality of life assessment, a statistically based scoring system is selected. This scoring system collects multi-dimensional data such as individual physiological and biochemical indicators, lifestyle and family health history, and combines statistical methods to calculate the comprehensive risk score. For complex problems such as disease prediction, a deep learning model is used to automatically learn the feature representation in the data for evaluation.

[0101] Then, through regression analysis or classification algorithms, such as random forest or support vector machine, the user's health needs and exercise amount are predicted based on the evaluation results;

[0102] The evaluation results include: health risk score, disease occurrence probability and quality of life index, etc.

[0103] S5: Based on the user's health needs and exercise volume, obtain the user's current health status data and personal goals, and generate personalized exercise recommendations.

[0104] The personalized exercise recommendations include exercise type, corresponding exercise intensity, and exercise duration;

[0105] After the user selects the type of exercise, the system will output a combination of various exercise intensities and corresponding exercise durations based on the user's health needs and amount of exercise.

[0106] As a preferred embodiment, the personalized exercise recommendation also includes: outputting preset dietary advice content and life habits content that need attention based on the current user's health data status, exercise type, and corresponding exercise intensity and exercise duration.

[0107] In this embodiment, after step S5, the following steps are further included:

[0108] Collect personal health data in real time and monitor the changes in health data indicators after users follow exercise recommendations;

[0109] Based on changes in health data indicators, feedback is provided to adjust exercise intensity and type, and generate updated personalized exercise recommendations.

[0110] For example, if the personalized exercise recommendation recommends walking 8,000 steps a day or increasing the frequency of cardiopulmonary exercise, in this process, through the user's real-time feedback data (such as physical condition after exercise) and real-time data changes (such as weather changes), the personalized recommendation is updated according to the new evaluation results, and the exercise recommendation content is dynamically adjusted to ensure the timeliness and effectiveness of the plan.

[0111] As a preferred embodiment, in step S5, the generating of personalized exercise suggestions further includes generating exercise suggestion recommendation content, and the generation of the exercise suggestion recommendation content is specifically as follows:

[0112] Retrieve the user's historical exercise data to obtain the user's exercise mode and type;

[0113] Calculate the similarity between the current user and other users' exercise modes and types, obtain personalized exercise suggestions from the user with the highest similarity, and recommend them to the current user.

[0114] The present invention is not limited to the above-mentioned specific embodiments, but extends to any new features or any new combination disclosed in this specification, as well as any new method or process steps or any new combination disclosed.

Claims

1. A method for suggesting exercise based on park environment and health conditions, characterized in that: include: S1: Build a sensor network to collect environmental and health data in the area; S2: Upload the collected data to the cloud computing platform for data cleaning and data integration; S3: Perform data fusion on the cleaned data environment data and health data to construct a comprehensive data set; S4: Build an evaluation model to obtain a comprehensive score based on the comprehensive data set, and predict the user's health needs and exercise volume through regression analysis or classification algorithm; S5: Based on the user's health needs and exercise volume, obtain the user's current health status data and personal goals, and generate personalized exercise recommendations.

2. The method for suggesting exercise based on park environment and health conditions according to claim 1, characterized in that: Collect environmental data through IoT sensors installed in the area, and obtain personal health data through personal devices owned by people in the park; The environmental data includes air quality data, temperature and humidity data, and wind speed and direction data; The personal health data includes steps, heart rate and sleep data.

3. The exercise recommendation method based on park environment and health status according to claim 1, characterized in that: The data cleaning includes removing duplicate records, filling missing values ​​and filtering outliers.

4. The method for suggesting exercise based on park environment and health conditions according to claim 1, characterized in that: The specific process of data integration is as follows: Standardize or normalize the cleaned data; The processed data is integrated into the same format according to different types and sources, including timestamp alignment and data format conversion.

5. The exercise recommendation method based on park environment and health conditions according to claim 1, characterized in that: The personalized exercise recommendations include exercise type, corresponding exercise intensity, and exercise duration; After the user selects the type of exercise, the system will output a combination of various exercise intensities and corresponding exercise durations based on the user's health needs and amount of exercise.

6. The method for suggesting exercise based on park environment and health conditions according to claim 5, characterized in that: The personalized exercise suggestions also include: outputting preset dietary suggestions and life habits that require attention based on the current user's health data status, exercise type, and corresponding exercise intensity and exercise duration.

7. The method for suggesting exercise based on park environment and health conditions according to any one of claims 1 to 6, characterized in that: After step S5, the following steps are also included: Collect personal health data in real time and monitor the changes in health data indicators after users follow exercise recommendations; Based on changes in health data indicators, feedback is provided to adjust exercise intensity and type, and generate updated personalized exercise recommendations.

8. The method for suggesting exercise based on park environment and health conditions according to claim 7, characterized in that: In step S5, the generating of personalized exercise suggestions further includes generating exercise suggestion recommendation content, and the generation of the exercise suggestion recommendation content is specifically as follows: Obtain browsing records, click behaviors, and scores of historical exercise data posted by park personnel whose health data is similar to that of the current user; Based on the number of views, clicks and ratings, the corresponding historical sports suggestion content is sorted and recommended to the current user.

9. The method for suggesting exercise based on park environment and health conditions according to claim 7, characterized in that: In step S5, the generating of personalized exercise suggestions further includes generating exercise suggestion recommendation content, and the generation of the exercise suggestion recommendation content is specifically as follows: Retrieve the user's historical exercise data to obtain the user's exercise mode and type; Calculate the similarity between the current user and other users' exercise modes and types, obtain personalized exercise suggestions from the user with the highest similarity, and recommend them to the current user.

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