Atmospheric environment exercise health monitoring method and monitoring system

Abstract

The invention discloses an atmospheric environment exercise health monitoring method and system, and the method comprises the steps: S1, generating a pollution level distribution diagram based on air quality index data collected in real time, S2, determining an air quality risk value of a current region according to the pollution level distribution diagram, and S3, determining the air quality risk value of the current region according to the air quality risk value. S3, based on the air quality risk value, calculating a dynamic movement duration threshold value adaptive to human health; S4, regulating and controlling the actual movement duration of the user according to the dynamic movement duration threshold value so as to prevent pollutant inhalation from exceeding the standard; according to the atmospheric environment movement health monitoring method and monitoring system, the movement duration is regulated and controlled according to the real-time fluctuation of the air quality index, so that the problem that pollutants inhaled by the human body exceed the standard is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent health monitoring and environmental perception, and particularly to a method and system for monitoring the health of movement in the atmospheric environment. Background Art

[0002] In the current movement health management system, the physiological parameters of users (such as heart rate, step frequency, movement time, etc.) are usually used as the main basis for formulating and evaluating the amount of exercise. However, most of these methods ignore the impact of the atmospheric environmental quality on human health. Especially under the condition of high air pollutant concentration, continuous exercise may lead to the excessive inhalation of harmful substances by the human body, thus causing adverse effects on the respiratory system and cardiovascular system.

[0003] Some existing technologies attempt to combine environmental monitoring data to guide movement behavior. For example, it is recommended to reduce outdoor exercise when the air quality is poor. However, its control strategy is mostly static prompt, lacking a dynamic regulation mechanism linked to the real-time fluctuation of the Air Quality Index (AQI), and it is impossible to adjust the exercise plan or exercise intensity as needed. In addition, these systems often rely on coarse-grained regional data and fail to combine the environmental changes at the user's current location, resulting in lagged or overly general responses, reducing the accuracy and effectiveness of health intervention. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for monitoring the health of movement in the atmospheric environment, and to regulate the movement duration according to the real-time fluctuation of the air quality index to solve the problem of excessive inhalation of pollutants by the human body.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A method for monitoring the health of movement in the atmospheric environment, the method comprising:

[0006] S1. Generating a pollution level distribution map based on the real-time collected air quality index data;

[0007] S2. Determining the air quality risk value of the current area according to the pollution level distribution map, including calculating the short-term pollution index rising trend from the real-time data, obtaining the pollution index rising probability, using the air quality index of the current area as the real-time air pollution concentration value of the current user's area, and combining the residence time of the user's planned outdoor activity to calculate the actual air quality risk value;

[0008] S3. Calculating a dynamic movement duration threshold suitable for human health based on the air quality risk value, including setting the maximum recommended movement time in a healthy state, comparing the current air quality risk value with the ideal reference air quality risk value in a clean environment, linearly reducing the movement time, and calculating the upper limit of the movement time for the user to exercise;

[0009] S4. Regulate the actual exercise duration of the user according to the dynamic exercise duration threshold to prevent excessive inhalation of pollutants, including continuously recording the cumulative time that the user has been exercising outdoors in real time, comparing the upper limit of the exercise time of the user, calculating the remaining time for continued exercise, setting the remaining time threshold for continued exercise, and when the remaining time for continued exercise is lower than the remaining time threshold for continued exercise, starting a reminder mechanism to ensure that the exercise does not exceed the time limit.

[0010] Preferably, S1 includes normalizing the air quality index of each region, calculating the specific color value used to render the color of the region in the pollution level map according to the pollution degree, implementing a pollution level map with continuous color gradation changes, and outputting an image for visualizing the geographical distribution of pollution risks.

[0011] Preferably, the specific steps for calculating the specific color value used to render the color of a certain region in the pollution level map in S1 include: obtaining the real-time air quality index value of the region, normalizing it according to the preset minimum and maximum values, converting its numerical range to between 0 and 1, setting the color value representing the optimal air quality as the starting color, setting the color value representing the most serious pollution as the target color, calculating the difference between the two colors, multiplying the difference by the normalized air quality index value to obtain the corresponding color offset, and adding the offset to the starting color value to obtain the specific color value for rendering the color of the region in the pollution level map.

[0012] Preferably, the specific steps for calculating the air quality risk value in S2 include: first extracting historical data from the real-time air quality monitoring system, comparing the current value with the past data, estimating the possibility of pollution intensification as the probability of the pollution index rising; reading the real-time air quality index of the current region as the air pollution concentration value at the user's current location; calling the planned outdoor stay duration of the user; multiplying the probability of the pollution index rising by the air pollution concentration value, and then dividing by the planned outdoor stay duration of the user to obtain the actual air quality risk value.

[0013] Preferably, the specific steps for calculating the upper limit of the user's exercise time in S3 include: multiplying the maximum recommended exercise time in a healthy state by the ideal reference air quality risk value in a clean environment, and then dividing the result by the actual air quality risk value of the current region to obtain the upper limit of the exercise time for the user under the current pollution conditions.

[0014] Preferably, the specific steps for calculating the remaining time for the user to continue exercising in S4 include: subtracting the cumulative time that the user has been exercising outdoors from the upper limit of the exercise time of the user to obtain the remaining time for the user to continue exercising.

[0015] Preferably, S1 further includes obtaining the real-time air quality index value of the area where the user is currently located, setting a reference concentration range for dividing the pollution level, which ranges from the lowest pollution concentration to the highest pollution concentration, dividing this concentration range into multiple pollution level sections, determining the pollution level to which it belongs according to the relative position of the current air quality index in this concentration range, and if the result exceeds the preset highest pollution level range, limiting this value to the highest level.

[0016] Preferably, the determination of the pollution level in S1 adopts an integer-level segmentation method, and there is a linear or non-linear correspondence between the pollution level and the air quality index.

[0017] Preferably, S3 further includes obtaining the air quality risk level of the area where the current user is located, calling the initial exercise duration threshold that matches the user's health status, and exponentially adjusting the initial exercise duration threshold according to the relationship between the risk level of the user's environment and the preset benchmark risk level to generate a new dynamic exercise duration threshold. If the adjusted exercise duration threshold is lower than the minimum acceptable value set by the system, it is set to the minimum exercise duration threshold.

[0018] An atmospheric environment exercise health monitoring system for implementing the steps of the above-mentioned atmospheric environment exercise health monitoring method, the system includes:

[0019] An air quality collection module for obtaining air quality index data in real time and generating a pollution level distribution map;

[0020] A risk assessment module connected to the air quality collection module for determining the air quality risk value of the user's current location according to the pollution level distribution map;

[0021] A duration calculation module connected to the risk assessment module for calculating a dynamic exercise duration threshold adapted to the user's health status based on the air quality risk value;

[0022] An exercise control module connected to the duration calculation module for regulating the actual exercise duration of the user according to the dynamic exercise duration threshold to prevent the inhaled amount of pollutants from exceeding the health safety limit.

[0023] As can be seen from the above technical solutions, the present invention has the following beneficial effects:

[0024] The method and system for monitoring the health of atmospheric environment movement generate a pollution level distribution map based on the real-time collected air quality index data, determine the air quality risk value of the current area according to the pollution level distribution map, calculate the dynamic exercise duration threshold suitable for human health based on the air quality risk value, and regulate the actual exercise duration of the user according to the dynamic exercise duration threshold to prevent excessive inhalation of pollutants. It overcomes the deficiency in the prior art of only relying on physiological parameters to formulate exercise plans and ignoring environmental pollution factors. By introducing a pollution level distribution map, an air quality risk assessment model, and a dynamic exercise duration regulation mechanism, the linkage management of the user's exercise duration and the environmental pollution situation is realized, effectively avoiding the problem of excessive inhalation of pollutants caused by long-term exercise in a polluted environment. It also has the advantages of high positioning accuracy, fast response speed, and personalized regulation strategies. It can not only obtain refined air quality data based on the user's current location, but also calculate the exercise threshold in real time according to the risk value and actively intervene, significantly improving the intelligent level of exercise health management and the scientific nature of health protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flowchart of the method of the present invention;

[0026] Figure 2 It is a connection diagram of system modules of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all 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 shall fall within the protection scope of the present invention.

[0028] As Figure 1 shown, the present invention provides a technical solution: a method for monitoring the health of atmospheric environment movement, the method includes:

[0029] S1. Generate a pollution level distribution map based on the real-time collected air quality index data;

[0030] S2. Determine the air quality risk value of the current area according to the pollution level distribution map, including calculating the short-term pollution index rising trend from the real-time data, obtaining the pollution index rising probability, using the air quality index of the current area as the real-time air pollution concentration value of the current user's area, and combining the staying time of the user's planned outdoor activities to calculate the actual air quality risk value;

[0031] S3. Calculate the dynamic exercise duration threshold adapted to human health based on the air quality risk value, including setting the maximum recommended exercise time in a healthy state, comparing the current air quality risk value with the ideal reference air quality risk value in a clean environment, linearly reducing the exercise time, and calculating the upper limit of the exercise time for the user.

[0032] S4. Regulate the actual exercise duration of the user according to the dynamic exercise duration threshold to prevent excessive inhalation of pollutants, including real-time recording of the cumulative time that the user has been exercising outdoors, comparing with the upper limit of the exercise time for the user, calculating the remaining time for continued exercise, setting the remaining time threshold for continued exercise, and when the remaining time for continued exercise is lower than the remaining time threshold for continued exercise, starting the reminder mechanism to ensure that the exercise does not exceed the time limit.

[0033] This method first obtains the real-time data of the Air Quality Index (AQI) through an air quality monitoring device or by accessing a third-party open environmental data interface, and generates a corresponding pollution level distribution map through a visualization algorithm to mark the pollution status of different geographical regions. On this basis, by analyzing the historical pollution trend and the AQI change rate in the current region, the probability of the pollution level rising in the short term is evaluated, and the risk factor of the pollution index rising is calculated. This factor combines the actual air pollution concentration value where the user is located and the length of time planned to stay outdoors, and then comprehensively forms the air quality risk value. Then, by setting an upper limit threshold for the air pollution exposure time allowed for human health, referring to the optimal pollution risk value in a clean environment, the actual recommended outdoor exercise duration is determined by using a linear compression method. This exercise duration will be dynamically adjusted according to the results of the environmental risk assessment. Finally, the system continuously monitors the actual exercise time of the user, compares it with the calculated upper limit of the time, and when the remaining available exercise time is lower than the set threshold, immediately issues a prompt or warning through a smart device (such as a smart bracelet, mobile phone App) to ensure that the user does not inhale excessive pollutants due to exercising beyond the time limit. The method provided by the present invention effectively and intelligently integrates the real-time data of the environmental air quality with the individual exercise behavior, enabling the user to carry out healthy and safe outdoor exercise in a polluted environment. By constructing a pollution level map, quantifying the pollution rising risk, dynamically adjusting the exercise threshold and intervening in real time, the risk of inhaling harmful substances can be minimized without sacrificing the exercise plan, and the environmental adaptability of the exercise behavior can be improved. In addition, this method also has a significant protective effect on special populations (such as the elderly, people with sensitive respiratory systems), enhances the scientific nature and individualization level of exercise recommendations, and helps to improve the refinement and intelligent level of public health management.

[0034] S1 includes normalizing the air quality index of each region, calculating the specific color value used to render the color of the region in the pollution level map according to the pollution degree, realizing a pollution level map with continuous color scale changes, and outputting an image for visualizing the geographical distribution of pollution risks.

[0035] In this embodiment, for the distribution of the air quality index in different regions, first, the original AQI data is normalized so that its numerical range is unified to a preset standard interval (such as 0 to 1), which is convenient for subsequent image rendering processing. The normalized value is used to map to the corresponding color value interval, and the color value can adopt a gradient color band scheme, for example, continuously changing from green (excellent) to red (severe pollution), ensuring that the colors presented in the pollution level map are comparable and visually coherent. Subsequently, the image processing module colors the pixels of each region, assigns specific RGB or HSV color values according to the normalized index value of each region, and realizes a pollution level map with continuous color scale changes. This map is output in the form of an image and can be integrated into a map system or a mobile application terminal to clearly display the spatial distribution of pollution risks in each region, assisting users to intuitively understand the current environmental situation. This method improves the expressiveness and information visualization effect of the pollution level map through the normalization and color value mapping processing of air quality data, enabling users to more accurately and quickly identify the air quality differences between different regions. The pollution level map with continuous color scale changes avoids the information loss caused by the discontinuous representation method in traditional classification maps, enhancing the accuracy and aesthetics of data expression. In addition, the output image is convenient to be superimposed and displayed with other information layers (such as geographical location information, movement route), which helps users make more forward-looking movement path selection and risk avoidance decisions, improving the practicality and intelligent interaction experience of the system.

[0036] The specific steps for calculating the specific color value used to render the color of a certain region in the pollution level map in S1 include: obtaining the real-time air quality index value of this region, normalizing it according to the preset minimum and maximum values, converting its numerical range to between 0 and 1, setting the color value representing the best air quality as the starting color, setting the color value representing the most serious pollution as the target color, calculating the difference between the two colors, then multiplying this difference by the normalized air quality index value to obtain the corresponding color offset, and adding this offset to the starting color value to obtain the specific color value for rendering the color of this region in the pollution level map.

[0037] The specific formula for calculating the specific color value used to render the color of a certain region in the pollution level map is:

[0038] G = G1 + (G2 - G1) × m;

[0039] Where G represents the specific color value used to render the color of a certain region in the pollution level map, G1 represents the color value when the air quality is the best, G2 represents the color value when the pollution is the most serious, and m represents the normalized air quality index value.

[0040] In this embodiment, by constructing a color value mapping model, each real-time collected Air Quality Index (AQI) value can be accurately mapped to the color representation in the pollution level map. First, set the minimum value of AQI (for example, 0, corresponding to excellent air) and the maximum value (such as 500, corresponding to extremely heavy pollution) as the upper and lower limits for normalization processing. Apply a formula to linearly normalize any input AQI value and convert it into a decimal value between 0 and 1. Then, specify a color range, for example, the starting color is green (representing excellent air) and the target color is red (representing severe pollution), and calculate the differences in the three channels in the RGB color space for this range. Next, multiply the normalized value by the differences in the three channels respectively to obtain the color offset, and add this offset to the starting color value to generate the final RGB color value used to render the pollution level of this area, realizing the continuous mapping from air quality data to color expression. This color is then used to color the corresponding area in the pollution level map. This method establishes a continuous mapping relationship between the air quality index and the color value based on a linear interpolation model, ensuring that the pollution level map has good gradient effects and numerical correspondence visually. The normalization and difference interpolation mechanisms simplify the color generation logic, improving the operation efficiency of image rendering and the system response speed. At the same time, this mapping method supports personalized color settings, enhancing the readability and interactivity of the pollution map, facilitating users to quickly identify the changing trend of air quality, and improving the accuracy and timeliness of environmental risk identification. In addition, this method has strong versatility and is easy to integrate into different visualization platforms or devices, improving the scalability of the system.

[0041] The specific steps for calculating the air quality risk value in S2 include: First, extract historical data from the real-time air quality monitoring system, compare the current value with past data, and estimate the possibility of pollution intensification as the probability of the pollution index rising; read the current air quality index of the current area as the air pollution concentration value at the user's current location in real time; call the planned outdoor stay duration of the user; multiply the probability of the pollution index rising by the air pollution concentration value, and then divide by the planned outdoor stay duration of the user to obtain the actual air quality risk value.

[0042] The specific formula for calculating the air quality risk value is: A = B × D / T;

[0043] Where, A represents the actual air quality risk value, B represents the probability of the pollution index rising, D represents the air pollution concentration value, and T represents the planned outdoor stay duration of the user.

[0044] In this embodiment, by constructing a pollution risk assessment model, the air quality of the area where the user plans to exercise is quantitatively analyzed. First, the system calls the data interface of the accessed air quality monitoring platform to obtain the AQI values of the past several hours including the current moment, analyzes the pollution trend by means of difference comparison, moving average or regression model, etc., calculates the change rate of the current moment relative to the historical average value, so as to judge whether the pollution is in the rising stage, and generates a pollution index rising probability value between 0 and 1. Subsequently, the system synchronously reads the current air quality index of the user's location area, and this value is used as the reference data for the current air pollution concentration. At the same time, the system reads or the user inputs the length of time they plan to stay outdoors. Multiply the pollution rising probability by the current pollution concentration value to represent the weighted pollution risk level in the upcoming time period in this area, and then divide this product by the stay duration to reflect the pollution risk intensity exposed per unit time, and finally obtain the air quality risk value, which is used as the basic index for subsequent exercise recommendations and risk control. This method combines static air quality data with dynamic pollution development trends to achieve forward-looking prediction of future short-term environmental health risks. By introducing the concept of pollution rising probability, the response ability to sudden pollution events is enhanced, and the scientificity and timeliness of exercise behavior decision-making are improved. In addition, incorporating the stay time into the risk assessment formula makes the air quality risk assessment result time-sensitive, closer to the actual exposure situation, which is conducive to personalized recommendation of exercise intensity and duration, and effectively avoids the outdoor exposure risk of users during the period of increasing pollution. The entire assessment process has clear logic and simple calculation, which is convenient for rapid deployment and real-time update on terminal devices, and improves the practicality and interactive response ability of the system.

[0045] The specific steps for calculating the upper limit of the time for the user to perform exercise in S3 include: multiplying the maximum recommended exercise time in a healthy state by the ideal reference air quality risk value in a clean environment, and then dividing the result by the actual air quality risk value in the current area, so as to obtain the upper limit of the time for the user to perform exercise under the current pollution conditions.

[0046] The specific formula for calculating the upper limit of the time for the user to perform exercise is:

[0047] Where, T s represents the upper limit of the time for the user to perform exercise, T max represents the maximum recommended exercise time in a healthy state, A0 represents the ideal reference air quality risk value, and A represents the actual air quality risk value.

[0048] In this embodiment, a linear inverse model between air pollution and exercise time is constructed to scientifically adjust the upper limit of exercise time on the premise of ensuring the health and safety of users. First, the system sets the "maximum recommended exercise time in a healthy state" according to the user's health record or standard reference data, and this time value can correspond to the most ideal exercise duration in a clean air environment. Then, a standard reference air quality risk value is set as the ideal pollution exposure level that the human body can tolerate in a clean environment. Multiply the maximum exercise time by the ideal reference risk value to obtain a pollution-time exposure baseline value. Then, read the actual air quality risk value of the current area (already calculated in S2), and divide the above baseline value by this actual risk value to calculate the upper limit of the safe exercise time for the user under the current pollution conditions. Based on the principle that the heavier the pollution, the shorter the exercise time, this model ensures that the exercise recommendations are dynamically adaptable and scientific. This method uses the air pollution risk value as an adjustment factor to accurately control the exposure time of users in a polluted environment, thereby reducing the possibility of damage to the respiratory system by pollutants. By dynamically compressing the recommended exercise duration in a healthy state, the system can intelligently adjust exercise recommendations according to the external pollution situation, avoiding the misleading caused by a unified standard, and improving the effectiveness and safety of individualized health management. In addition, the calculation logic of this method is simple and highly operable, and it is easy to be integrated into wearable devices or health applications for real-time adjustment, enhancing the user experience and the system response speed, and significantly enhancing the environmental adaptability of outdoor healthy exercise.

[0049] The specific steps for calculating the remaining time for the user to continue exercising in S4 include: subtracting the cumulative time the user has currently exercised outdoors from the upper limit of the user's exercise time to obtain the remaining time for the user to continue exercising.

[0050] The specific formula for calculating the remaining time for the user to continue exercising is T1 = T s − T r ;

[0051] where T1 represents the remaining time for the user to continue exercising, T s represents the upper limit of the user's exercise time, and T r represents the cumulative time the user has currently exercised outdoors.

[0052] This implementation method constructs a real-time motion time monitoring mechanism to dynamically track the actual duration of the user's movement in the outdoor environment and perform a difference operation with the time limit pre-calculated by the system. Specifically, the system first obtains the upper limit of the movement time calculated based on the current air quality risk value, which has been obtained in step S3. Subsequently, the system uses terminal devices (such as smart bracelets, mobile APPs, GPS modules, etc.) to continuously record the cumulative time of the user since the start of outdoor exercise. By subtracting the used time from the time limit, the system can calculate in real time the remaining time for the user to safely exercise. This remaining time will be used as a basis for further determining whether to trigger an exercise end reminder or adjust the exercise plan, ensuring that the user is within a controlled range during the pollution exposure time and preventing health risks caused by excessive exercise. Through simple and effective difference calculations, this method can evaluate the remaining exercise time of the user in real time, which helps to achieve precise exercise time management. The system can dynamically limit the exercise time according to the environmental pollution intensity and avoid risk accumulation by real-time monitoring of the user's status, improving the controllability and safety of exercise behavior under air pollution conditions. Without the need for the user to manually record or calculate, the system automatically completes the time difference calculation and reminder judgment, greatly improving the convenience and automation level of exercise monitoring, and significantly enhancing the intelligent response ability and user stickiness of the health management system.

[0053] S1 also includes obtaining the real-time air quality index value of the area where the user is currently located, setting a reference concentration interval range for dividing the pollution level, which ranges from the lowest pollution concentration to the highest pollution concentration, dividing this concentration interval into multiple pollution level sections, and determining the pollution level to which it belongs according to the relative position of the current air quality index in this concentration interval. If the result exceeds the preset highest pollution level range, the value is limited to the highest level.

[0054] This implementation method further introduces pollution classification recognition logic on the basis of pollution level map rendering to enhance the structured interpretation ability of image data. Specifically, the system first obtains the AQI value of the area where the user is currently located in real time through the environmental monitoring data source. Subsequently, a standard reference air pollution concentration interval range is set, such as AQI from 0 to 500, and this concentration interval is divided into several level sections according to the pollution level at equal intervals or custom rules, such as excellent (0 - 50), good (51 - 100), mild pollution (101 - 150), etc. Then, according to the specific position of the current AQI value in this interval, it is judged which level section it is in, and an integer or text-type pollution level label is output accordingly. If the current AQI value exceeds the maximum threshold of this concentration interval, it is uniformly classified as the highest pollution level to prevent misjudgment of the pollution level caused by abnormal values.

[0055] This method provides a standardized pollution level identification mechanism, enabling air quality images to not only have a visual hierarchy but also be convenient for programmed analysis and hierarchical management. By classifying continuous AQI values into different pollution level segments, the system can quickly judge and classify responses to pollution events of different degrees, improving the accuracy of health tips and exercise recommendations. At the same time, setting a maximum level threshold and restricting the exceeded value helps improve the robustness of the system and the stability of user tips, avoiding abnormal image rendering or user misjudgment of the risk level caused by extreme value fluctuations. In addition, the pollution level, as structured tag information, can also be used for big data analysis and trend assessment, expanding the application scenarios of the system in health research and environmental supervision.

[0056] In S1, the determination of the pollution level adopts an integer-level segmentation method, and there is a linear or non-linear correspondence between the pollution level and the air quality index. This implementation method uses an integer-level discrete segmentation strategy for the division of the pollution level to enhance the clarity of hierarchical judgment and program executability. After setting the reference interval of air quality concentration, the system divides the overall AQI interval into several integer-level segments according to the preset segmentation rules, such as level 0 (excellent), level 1 (good), level 2 (mild pollution), etc., and the level numbers are integer values for unified reference in subsequent image rendering, risk assessment, and information prompting. In terms of the mapping relationship between the pollution level and the AQI, the system supports two modes: one is a linear correspondence relationship, that is, the AQI interval is evenly divided, and each level corresponds to an AQI range of equal width; the other is a non-linear correspondence relationship, that is, according to the non-linear characteristics of the impact of pollution on the human body, the AQI interval is segmented with different weights. For example, a more dense segmentation is adopted for the medium and high pollution segments to improve the response sensitivity. The system determines the corresponding level integer of the current AQI value through table lookup or calculation to complete the determination of the pollution level. By adopting the integer-level segmentation and linear / non-linear mapping mode, this method makes the division of the pollution level more flexible and scalable. The integer segmentation method helps the system make conditional judgments and classification displays, improving data processing efficiency; at the same time, supporting the non-linear segmentation mechanism enhances the perception and response ability to the high pollution interval, conforms to the distribution characteristics of human health sensitivity, and makes the risk prompt more accurate and effective. In addition, this method is convenient to uniformly output the pollution level in the form of numbers, colors, labels, etc. in the user interface, enhancing the user's understanding and interaction experience and improving the overall intelligent level of the system.

[0057] S3 also includes obtaining the air quality risk level of the current user's location, calling the initial exercise duration threshold that matches the user's health status, and exponentially adjusting the initial exercise duration threshold according to the relationship between the risk level of the user's environment and the preset reference risk level to generate a new dynamic exercise duration threshold. If the adjusted exercise duration threshold is lower than the minimum acceptable value set by the system, it is set as the minimum exercise duration threshold.

[0058] This embodiment introduces an exponential adjustment mechanism to perform more flexible and personalized dynamic control over the recommended exercise time for users. The system first obtains the air quality risk level at the user's current location, which value can be obtained through the aforementioned air quality risk value calculation module. Then, it calls the corresponding initial exercise duration threshold from the user's health record. For example, for users with relatively weak cardiopulmonary function, the initial recommended time is shorter. Next, an environmental baseline risk value is set as a pollution safety reference point. According to the ratio relationship between the current risk level and this baseline risk value, an exponential decay function is constructed to adjust the initial exercise time. Specifically, if the current risk value is higher than the baseline, the exercise time will be shortened exponentially; if it is lower than the baseline, the exercise duration will be maintained or slightly extended. If the finally obtained new dynamic exercise duration threshold is lower than the minimum exercise safety lower limit set by the system (such as 5 minutes), then it is limited to this minimum value to avoid excessive compression of the exercise recommendation. This method realizes refined control of the exercise time through an exponential function, and can better reflect the non-linear impact of pollution risk on human health compared to linear compression. The response to high-risk environments is faster and the compression is more significant, thus effectively reducing the pollution exposure risk; while in low-pollution environments, sufficient exercise recommendations are maintained to improve the user experience and health benefits. In addition, the setting of the minimum acceptable value ensures that the system recommendation has a lower limit protection mechanism, avoiding extreme situations such as "recommendation of 0 minutes" that are not executable by users, and improving the practicality and stability of the algorithm. At the same time, the exponential adjustment method combined with the user's health status enables the system to have stronger individual adaptation ability and intelligent judgment ability.

[0059] As shown in Figure 2, an atmospheric environment exercise health monitoring system is also provided for implementing the steps of the aforementioned atmospheric environment exercise health monitoring method. The system includes:

[0060] An air quality collection module for obtaining air quality index data in real time and generating a pollution level distribution map;

[0061] A risk assessment module connected to the air quality collection module for determining the air quality risk value at the user's current location according to the pollution level distribution map;

[0062] A duration calculation module connected to the risk assessment module for calculating a dynamic exercise duration threshold adapted to the user's health condition based on the air quality risk value;

[0063] An exercise control module connected to the duration calculation module for regulating the actual exercise duration of the user according to the dynamic exercise duration threshold to prevent the inhaled amount of pollutants from exceeding the health safety limit.

[0064] The system consists of multiple functional modules and operates collaboratively around the logical chain of "air quality - health exercise recommendations". The air quality collection module collects real-time data on the Air Quality Index (AQI) of a region through built-in or external sensors, API interfaces, etc., and generates a pollution level distribution map in combination with geographical information, providing a geographical and numerical basis for subsequent risk identification. The risk assessment module extracts the current AQI value and relevant historical data based on the pollution level map and the user's current location, and evaluates the pollution upward trend, risk factors, and outdoor exposure parameters through algorithms to generate the air quality risk value faced by the user currently. This risk value is transmitted to the duration calculation module, which dynamically adjusts the recommended exercise time according to the reference exercise data and preset models (linear or exponential) in the user's health record to form a duration threshold. The exercise control module is linked with wearable devices or mobile applications to monitor the user's exercise process in real time. By comparing the actual exercise duration with the duration threshold, it activates a reminder or intervention mechanism to prevent the user from being exposed for a long time in an environment with excessive pollution. This system realizes the full-link intelligent monitoring from data collection, environmental assessment to personalized exercise recommendations and behavior intervention, improving the scientificity and safety of healthy exercise under air pollution conditions. The modular design facilitates the expansion and integration of system functions and can adapt to various device platforms and application scenarios. Both risk assessment and exercise duration regulation support the input of individual health data, improving the pertinence and accuracy of the recommendations. The system has a high degree of automation, and users can obtain real-time and dynamic healthy exercise guidance without manual intervention, greatly enhancing the initiative and intelligent level of exercise health management.

[0065] 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 method for monitoring the health of atmospheric environmental movement, characterized in that, The method includes: S1. Generating a pollution level distribution map based on the real-time collected air quality index data; S2. Determining the air quality risk value of the current area according to the pollution level distribution map, including calculating the short-term pollution index rising trend from the real-time data to obtain the pollution index rising probability, using the air quality index of the current area as the real-time air pollution concentration value of the current user's location area, and combining the residence time that the user plans to stay outdoors to calculate the actual air quality risk value; S3. Calculating a dynamic exercise duration threshold adapted to human health based on the air quality risk value, including setting the maximum recommended exercise time in a healthy state, comparing the current air quality risk value with the ideal reference air quality risk value in a clean environment, linearly reducing the exercise time, and calculating the upper limit of the exercise time for the user; S4. Controlling the actual exercise duration of the user according to the dynamic exercise duration threshold to prevent excessive inhalation of pollutants, including real-time recording of the cumulative time that the user has been exercising outdoors currently, comparing with the upper limit of the exercise time for the user, calculating the remaining time for continued exercise, setting a remaining time threshold for continued exercise, and when the remaining time for continued exercise is lower than the remaining time threshold for continued exercise, starting a reminder mechanism to ensure non-overtime exercise.

2. The atmospheric environment movement health monitoring method according to claim 1, wherein: The S1 includes normalizing the air quality index of each area, calculating the specific color value used to render the color of the area in the pollution level map according to the pollution degree, realizing a pollution level map with continuous color scale changes, and outputting an image for visualizing the geographical distribution of pollution risks.

3. The atmospheric environment movement health monitoring method according to claim 2, characterized in that: The specific steps of calculating the specific color value used to render the color of a certain area in the pollution level map in the S1 include: obtaining the real-time air quality index value of the area, normalizing it according to the preset minimum value and maximum value, converting its value range to between 0 and 1, setting the color value representing the best air quality as the starting color, setting the color value representing the most serious pollution as the target color, calculating the difference between the two colors, multiplying the difference by the normalized air quality index value to obtain the corresponding color offset, and adding the offset to the starting color value to obtain the specific color value used to render the color of the area in the pollution level map.

4. The atmospheric environment movement health monitoring method according to claim 1, wherein: The specific steps of calculating the air quality risk value in the S2 include: first extracting historical data from the real-time air quality monitoring system, comparing the current value with the past data, estimating the possibility of pollution intensification as the pollution index rising probability; real-time reading of the air quality index of the current area as the air pollution concentration value of the current user's location; calling the outdoor residence duration planned by the user; multiplying the pollution index rising probability by the air pollution concentration value and then dividing by the residence time that the user plans to stay outdoors to obtain the actual air quality risk value.

5. The atmospheric environment movement health monitoring method according to claim 1, wherein: The specific steps of calculating the upper limit of the exercise time for the user's memory exercise in the S3 include: multiplying the maximum recommended exercise time in a healthy state by the ideal reference air quality risk value in a clean environment, and then dividing the result by the actual air quality risk value of the current area to obtain the upper limit of the exercise time for the user under the current pollution conditions.

6. The atmospheric environment movement health monitoring method according to claim 1, characterized in that: The specific steps for calculating the remaining time for the user to continue exercising in S4 are as follows: Subtract the cumulative time the user has currently exercised outdoors from the upper limit of the time for the user to exercise, and the result is the remaining time for the user to continue exercising.

7. The atmospheric environment movement health monitoring method according to claim 1, wherein: S1 further includes obtaining the real-time air quality index value of the area where the user is currently located, setting a reference concentration interval range for dividing the pollution level, which ranges from the lowest pollution concentration to the highest pollution concentration. The concentration interval is divided into multiple pollution level sections. According to the relative position of the current air quality index in this concentration interval, determine the pollution level to which it belongs. If the result exceeds the preset highest pollution level range, limit the value to the highest level.

8. The atmospheric environment movement health monitoring method according to claim 7, characterized in that: In S1, the determination of the pollution level adopts an integer-level segmentation method, and there is a linear or non-linear correspondence between the pollution level and the air quality index.

9. The atmospheric environment movement health monitoring method according to claim 1, characterized in that: S3 further includes obtaining the air quality risk level of the area where the current user is located, calling the initial exercise duration threshold matching the user's health status, and making an exponential adjustment to the initial exercise duration threshold according to the relationship between the risk level of the environment where the user is located and the preset benchmark risk level to generate a new dynamic exercise duration threshold. If the adjusted exercise duration threshold is lower than the minimum acceptable value set by the system, set it as the minimum exercise duration threshold.

10. An atmospheric environment exercise health monitoring system for implementing the steps of the atmospheric environment exercise health monitoring method according to any one of claims 1-9, characterized in that, The system includes: An air quality collection module for obtaining air quality index data in real time and generating a pollution level distribution map; A risk assessment module connected to the air quality collection module for determining the air quality risk value of the user's current location according to the pollution level distribution map; A duration calculation module connected to the risk assessment module for calculating a dynamic exercise duration threshold adapted to the user's health status based on the air quality risk value; An exercise control module connected to the duration calculation module for regulating the actual exercise duration of the user according to the dynamic exercise duration threshold to prevent the inhaled amount of pollutants from exceeding the health safety limit.

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