Outdoor exercise risk intelligent assessment method and system

Abstract

The invention discloses an outdoor exercise risk intelligent assessment method and system, and relates to the technical field of exercise risk assessment. An intelligent assessment system for outdoor exercise risks comprises a data acquisition and processing module, a physiological risk assessment module, a state risk assessment module, an environment risk assessment module and a comprehensive assessment and processing module. According to the invention, the preprocessed motion state data is segmented based on the preprocessed surrounding environment data, and the motion state of the user is evaluated according to the historical outdoor motion data and the segmented motion state data. According to the intelligent assessment method and system for the outdoor exercise risk, the mental or fatigue state of the user during outdoor exercise can be quantified under the condition that the influence of surrounding environmental factors is eliminated, so that the change of the outdoor exercise risk caused by the poor mental state and muscle fatigue of the user is accurately assessed, and the assessment comprehensiveness of the intelligent assessment method and system for the outdoor exercise risk is improved.

Description

A method and system for intelligent risk assessment in outdoor sports Technical Field

[0001] This invention relates to the field of sports risk assessment technology, specifically to an intelligent assessment method and system for outdoor sports risks. Background Technology

[0002] Outdoor sports refer to various physical activities conducted in natural environments, such as hiking, cycling, mountaineering, camping, rock climbing, and rafting. These activities allow people to connect with nature, exercise, improve physical fitness, cultivate teamwork, and challenge themselves. However, outdoor sports often come with certain risks, such as complex terrain, changeable weather, potential threats from wildlife, and the risk of getting lost or injured. Risk assessment for outdoor sports is crucial because it helps participants understand potential dangers in advance and take appropriate preventative measures, such as preparing adequate equipment, planning reasonable routes, and learning necessary first aid skills. Furthermore, risk assessment helps organizers develop detailed safety plans, improve their ability to respond to emergencies, maximize participant safety, and ensure that outdoor sports are conducted in a safe and orderly environment, allowing people to enjoy the fun of sports while reducing the probability of accidents.

[0003] Existing intelligent risk assessment methods and systems for outdoor sports typically monitor users' physiological data and environmental data around the exercise route during outdoor activities and assess the risks accordingly. However, conventional physiological data monitoring can only be used to compare the data with an individual's physiological thresholds and obtain a corresponding risk level score. It cannot monitor and assess the user's mental or fatigue state during outdoor activities in real time. Therefore, it does not take into account the increased risk of outdoor sports caused by poor mental state and muscle fatigue, resulting in poor accuracy and comprehensiveness of existing intelligent risk assessment methods and systems for outdoor sports.

[0004] Based on the above, this invention proposes a comprehensive intelligent assessment method and system for outdoor sports risks. Summary of the Invention

[0005] To overcome the shortcomings of existing intelligent risk assessment methods and systems for outdoor sports, which typically monitor users' physiological data and environmental data around the exercise route during outdoor activities and assess the risks accordingly, conventional physiological data monitoring can only be used to compare with an individual's physiological thresholds and obtain a corresponding risk level score. However, it cannot monitor and assess the user's mental or fatigue state during outdoor activities in real time. Therefore, it does not take into account the increased risk of outdoor sports caused by poor mental state and muscle fatigue, resulting in poor accuracy and comprehensiveness of existing intelligent risk assessment methods and systems for outdoor sports. This invention proposes a comprehensive intelligent risk assessment method and system for outdoor sports.

[0006] An intelligent risk assessment method for outdoor sports includes the following steps:

[0007] The system obtains users' historical outdoor exercise data from historical records, and uses sensors to monitor users' basic physiological data, exercise status data, and surrounding environment data in real time during outdoor exercise. The monitored data is then preprocessed to obtain preprocessed basic physiological data, exercise status data, and surrounding environment data.

[0008] Based on historical outdoor sports data and preprocessed basic physiological data, and using Kalman filtering, the user's dynamic physiological baseline is obtained. Based on the dynamic physiological baseline and preprocessed basic physiological data, the user's physiological status is assessed to obtain the user's physiological status risk score.

[0009] Based on the preprocessed surrounding environment data, the preprocessed motion status data is segmented, and the user's motion status is evaluated based on historical outdoor sports data and the segmented motion status data to obtain the user's motion status risk score.

[0010] Based on the preprocessed surrounding environmental data and using the fuzzy evaluation method, the degree of danger of the outdoor sports environment is assessed, and the environmental status risk score of the surrounding environment is obtained.

[0011] Based on physiological state risk scores, exercise state risk scores, and environmental state risk scores, a real-time comprehensive assessment of the user's outdoor exercise risk is conducted to obtain the user's outdoor exercise risk score. Based on the outdoor exercise risk score, a corresponding treatment plan is planned for the user.

[0012] As a preferred aspect of the invention, the specific steps of obtaining the user's dynamic physiological baseline based on historical outdoor exercise data and preprocessed basic physiological data using Kalman filtering are as follows:

[0013] An initial physiological baseline for users is established based on historical outdoor sports data. After the start of outdoor sports, the initial physiological baseline is attenuated and adjusted at preset time intervals based on the duration and intensity of outdoor sports to obtain a predicted physiological baseline.

[0014] The moving average of preprocessed basic physiological data over a preset time period is calculated and used as the observed physiological baseline. The deviation between the predicted physiological baseline and the observed physiological baseline is calculated and the Kalman gain is obtained. Based on the Kalman gain and the deviation between the predicted physiological baseline and the observed physiological baseline, the predicted physiological baseline is dynamically adjusted and updated to obtain the user's dynamic physiological baseline.

[0015] As a preferred aspect of the invention, the specific steps for assessing the user's physiological state based on a dynamic physiological baseline and preprocessed basic physiological data to obtain the user's physiological state risk score are as follows:

[0016] Obtain the user's current basic physiological data, and calculate the deviation between each physiological data point in the current basic physiological data and the corresponding physiological data points at the initial physiological baseline and dynamic physiological baseline. The deviation represents the ratio of the deviation between each physiological data point to the historical maximum deviation for that physiological data point. Based on the calculated deviation, calculate the physiological risk index corresponding to the initial physiological baseline and dynamic physiological baseline using the following formula:

[0017]

[0018] in Indicates physiological risk index, Represents the calculated first... Bias of physiological data Represents the calculated first... The weighting coefficients corresponding to the deviation of each physiological data point, and the sum of the weighting coefficients corresponding to the deviation of each physiological data point is 1. The total number of items representing physiological data;

[0019] The physiological risk indices corresponding to the initial physiological baseline and the dynamic physiological baseline are weighted and fused to obtain the user's physiological state risk score.

[0020] As a preferred aspect of the invention, the specific steps for segmenting the preprocessed motion state data based on the preprocessed surrounding environment data, and assessing the user's motion state based on historical outdoor sports data and the segmented motion state data to obtain the user's motion state risk score are as follows:

[0021] Based on the user's surrounding environment during outdoor sports, the preprocessed motion state data is segmented and classified. The user's initial motion state data under different surrounding environment data is obtained from historical outdoor sports data and constructed into initial motion state vectors under different surrounding environment data.

[0022] The system acquires the user's current motion state data and surrounding environment data, constructs the current motion state data into a current motion state vector, calculates the deviation between the current motion state vector and the initial motion state vector under the corresponding surrounding environment data, and obtains the user's motion state risk score. The deviation represents the ratio of the Euclidean distance between the current motion state vector and the initial motion state vector to the historical maximum Euclidean distance between the two types of vectors.

[0023] As a preferred aspect of the invention, the specific steps for assessing the degree of danger of the outdoor sports environment based on preprocessed surrounding environmental data and using a fuzzy evaluation method to obtain an environmental state risk score for the surrounding environment are as follows:

[0024] All data types in the preprocessed surrounding environment data are used as environmental risk assessment indicators. Data on various environmental risk assessment indicators corresponding to multiple historical outdoor sports safety accidents are obtained and used as sample data.

[0025] The standard deviation of each environmental risk assessment indicator is calculated based on the sample data, and the proportion of the standard deviation of each environmental risk assessment indicator to the sum of the standard deviations of all environmental risk assessment indicators is used as the assessment weight of this environmental risk assessment indicator.

[0026] Based on the impact characteristics of various environmental risk assessment indicators on outdoor sports risks and their own data variation range, corresponding fuzzy membership functions are set. The user's current surrounding environment data is substituted into each fuzzy membership function to obtain the membership value of each environmental risk assessment indicator. The membership values ​​of each environmental risk assessment indicator and the assessment weight are weighted and summed to obtain the environmental status risk score of the surrounding environment.

[0027] As a preferred aspect of the invention, the specific steps for performing a real-time comprehensive assessment of the user's outdoor activity risk based on physiological state risk score, exercise state risk score, and environmental state risk score to obtain the user's outdoor activity risk score, and for planning a corresponding treatment plan for the user based on the outdoor activity risk score, are as follows:

[0028] The user's outdoor activity risk score is calculated based on physiological state risk score, activity state risk score, and environmental state risk score, using a specific formula:

[0029]

[0030] in This indicates the user's outdoor sports risk score. Indicates physiological risk score, Indicates the risk score of the movement status. Indicates the environmental status risk score. , and Represents the weighting coefficient, and ;

[0031] Based on the outdoor sports risk score and the preset classification rules, users are classified into the corresponding outdoor sports risk levels. According to the outdoor sports risk level, the corresponding level of alarm is issued and the corresponding level of handling plan is planned for the user.

[0032] An intelligent risk assessment system for outdoor sports includes:

[0033] The data acquisition and processing module is used to acquire the user's historical outdoor sports data from historical records. It uses sensors to monitor the user's basic physiological data, sports status data and surrounding environment data in real time during outdoor sports, and preprocesses the monitored data to obtain preprocessed basic physiological data, sports status data and surrounding environment data.

[0034] The physiological risk assessment module is used to obtain the user's dynamic physiological baseline based on historical outdoor sports data and preprocessed basic physiological data and Kalman filtering. Based on the dynamic physiological baseline and preprocessed basic physiological data, the module assesses the user's physiological status and obtains the user's physiological status risk score.

[0035] The status risk assessment module is used to segment the preprocessed motion status data based on the preprocessed surrounding environment data, and to assess the user's motion status based on historical outdoor sports data and the segmented motion status data, thereby obtaining the user's motion status risk score.

[0036] The environmental risk assessment module is used to assess the degree of danger of the outdoor sports environment based on the preprocessed surrounding environmental data and the fuzzy assessment method, and obtain the environmental status risk score of the surrounding environment.

[0037] The comprehensive assessment and processing module includes a risk comprehensive assessment unit and a processing plan planning unit. The risk comprehensive assessment unit is used to conduct a real-time comprehensive assessment of the user's outdoor sports risk based on physiological state risk score, exercise state risk score and environmental state risk score to obtain the user's outdoor sports risk score. The processing plan planning unit is used to plan the corresponding processing plan for the user based on the outdoor sports risk score.

[0038] The present invention has the following advantages:

[0039] 1. This invention segments the preprocessed motion state data based on preprocessed surrounding environment data, and evaluates the user's motion state based on historical outdoor exercise data and segmented motion state data. It can quantify the user's mental or fatigue state during outdoor exercise without excluding the influence of surrounding environmental factors, thereby accurately assessing the changes in outdoor exercise risk caused by poor mental state and muscle fatigue, and improving the comprehensiveness of this intelligent outdoor exercise risk assessment method and system.

[0040] 2. This invention obtains the user's dynamic physiological baseline based on historical outdoor sports data and preprocessed basic physiological data, using Kalman filtering. It can track the continuous evolution of the user's physical decline, environmental adaptation, and fatigue accumulation in real time, and accurately isolate physiological compensatory changes caused by external disturbances such as altitude, temperature, and terrain. This avoids misreporting normal physiological data drift as risk. Furthermore, assessing the user's physiological state based on the dynamic physiological baseline can capture early signs of acute decompensation and quantify the degree of chronic functional exhaustion, thus improving the accuracy of this intelligent risk assessment method and system for outdoor sports.

[0041] 3. This invention provides a real-time comprehensive assessment of users' outdoor sports risks based on physiological state risk scores, exercise state risk scores, and environmental state risk scores. It can capture microscopic signs of internal decompensation in real time by utilizing the deviation of physiological data, accurately identify motor breakdown and skill degradation caused by fatigue accumulation by using the deviation of exercise state, and objectively assess the risks posed by weather and terrain to outdoor sports by using environmental state risk scores. These three advantages complement each other, significantly improving the comprehensiveness of this intelligent assessment method and system for outdoor sports risks. Attached Figure Description

[0042] Figure 1 is a flowchart illustrating an intelligent risk assessment method for outdoor sports used in an embodiment of the present invention.

[0043] Figure 2 is a schematic diagram of the structure of an intelligent risk assessment system for outdoor sports used in an embodiment of the present invention. Detailed Implementation

[0044] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this invention.

[0045] Example 1, an intelligent risk assessment method for outdoor sports, as shown in Figure 1, includes the following steps:

[0046] The system obtains users' historical outdoor exercise data from historical records, and uses sensors to monitor users' basic physiological data, exercise status data, and surrounding environment data in real time during outdoor exercise. The monitored data is then preprocessed to obtain preprocessed basic physiological data, exercise status data, and surrounding environment data.

[0047] Based on historical outdoor sports data and preprocessed basic physiological data, and using Kalman filtering, the user's dynamic physiological baseline is obtained. Based on the dynamic physiological baseline and preprocessed basic physiological data, the user's physiological status is assessed to obtain the user's physiological status risk score.

[0048] Based on the preprocessed surrounding environment data, the preprocessed motion status data is segmented, and the user's motion status is evaluated based on historical outdoor sports data and the segmented motion status data to obtain the user's motion status risk score.

[0049] Based on the preprocessed surrounding environmental data and using the fuzzy evaluation method, the degree of danger of the outdoor sports environment is assessed, and the environmental status risk score of the surrounding environment is obtained.

[0050] Based on physiological state risk scores, exercise state risk scores, and environmental state risk scores, a real-time comprehensive assessment of the user's outdoor exercise risk is conducted to obtain the user's outdoor exercise risk score. Based on the outdoor exercise risk score, a corresponding treatment plan is planned for the user.

[0051] It should be noted that the historical outdoor sports data includes the user's historical basic physiological data, historical sports status data, and historical surrounding environment data. The basic physiological data typically includes the user's heart rate, body temperature, and blood oxygen saturation. The sports status data needs to be determined according to the specific type of outdoor sports. For example, for hiking, this data typically includes the user's movement speed, cadence, stride length, and respiratory rate. The surrounding environment data typically includes temperature, humidity, light intensity, and terrain slope. The preprocessing steps mainly include time alignment and outlier handling of the various real-time monitored data.

[0052] The specific steps for obtaining the user's dynamic physiological baseline based on historical outdoor exercise data and preprocessed basic physiological data using Kalman filtering are as follows:

[0053] An initial physiological baseline for users is established based on historical outdoor sports data. After the start of outdoor sports, the initial physiological baseline is attenuated and adjusted at preset time intervals based on the duration and intensity of outdoor sports to obtain a predicted physiological baseline.

[0054] The moving average of preprocessed basic physiological data over a preset time period is calculated and used as the observed physiological baseline. The deviation between the predicted physiological baseline and the observed physiological baseline is calculated and the Kalman gain is obtained. Based on the Kalman gain and the deviation between the predicted physiological baseline and the observed physiological baseline, the predicted physiological baseline is dynamically adjusted and updated to obtain the user's dynamic physiological baseline.

[0055] The specific steps for assessing the user's physiological state based on a dynamic physiological baseline and preprocessed basic physiological data to obtain the user's physiological state risk score are as follows:

[0056] Obtain the user's current basic physiological data, and calculate the deviation between each physiological data point in the current basic physiological data and the corresponding physiological data points at the initial physiological baseline and dynamic physiological baseline. The deviation represents the ratio of the deviation between each physiological data point to the historical maximum deviation for that physiological data point. Based on the calculated deviation, calculate the physiological risk index corresponding to the initial physiological baseline and dynamic physiological baseline using the following formula:

[0057]

[0058] in Indicates physiological risk index, Represents the calculated first... Bias of physiological data Represents the calculated first... The weighting coefficients corresponding to the deviation of each physiological data point, and the sum of the weighting coefficients corresponding to the deviation of each physiological data point is 1. The total number of items representing physiological data;

[0059] The physiological risk indices corresponding to the initial physiological baseline and the dynamic physiological baseline are weighted and fused to obtain the user's physiological state risk score.

[0060] It should be noted that the above-mentioned physiological risk index calculation formula will trigger the overall risk when any physiological data exceeds the standard, which can significantly reflect the barrel effect. That is, when assessing the physiological state of the human body, even if only one physiological data exceeds the preset threshold, it will remind the user that the physiological state is poor and there is a risk.

[0061] The above steps obtain the user's dynamic physiological baseline based on historical outdoor sports data and preprocessed basic physiological data, using Kalman filtering. This allows for real-time tracking of the continuous evolution of the user's physical decline, environmental adaptation, and fatigue accumulation. It also accurately isolates physiological compensatory changes caused by external disturbances such as altitude, temperature, and terrain, avoiding misreporting normal physiological data drift as risk. Furthermore, assessing the user's physiological state based on the dynamic physiological baseline can capture early signs of acute decompensation and quantify the degree of chronic functional exhaustion, thus improving the accuracy of this intelligent risk assessment method and system for outdoor sports.

[0062] The specific steps for segmenting the preprocessed motion state data based on the preprocessed surrounding environment data, and assessing the user's motion state based on historical outdoor exercise data and the segmented motion state data to obtain the user's motion state risk score are as follows:

[0063] Based on the user's surrounding environment during outdoor sports, the preprocessed motion state data is segmented and classified. The user's initial motion state data under different surrounding environment data is obtained from historical outdoor sports data and constructed into initial motion state vectors under different surrounding environment data.

[0064] The system acquires the user's current motion state data and surrounding environment data, constructs the current motion state data into a current motion state vector, calculates the deviation between the current motion state vector and the initial motion state vector under the corresponding surrounding environment data, and obtains the user's motion state risk score. The deviation represents the ratio of the Euclidean distance between the current motion state vector and the initial motion state vector to the historical maximum Euclidean distance between the two types of vectors.

[0065] The above steps segment the preprocessed motion state data based on the preprocessed surrounding environment data, and evaluate the user's motion state based on historical outdoor sports data and the segmented motion state data. This can quantify the user's mental or fatigue state during outdoor sports without excluding the influence of surrounding environmental factors, thereby accurately assessing the changes in outdoor sports risks caused by poor mental state and muscle fatigue, and improving the comprehensiveness of this intelligent outdoor sports risk assessment method and system.

[0066] The specific steps for assessing the risk level of the outdoor sports environment based on preprocessed surrounding environmental data and using fuzzy evaluation method to obtain the environmental state risk score of the surrounding environment are as follows:

[0067] All data types in the preprocessed surrounding environment data are used as environmental risk assessment indicators. Data on various environmental risk assessment indicators corresponding to multiple historical outdoor sports safety accidents are obtained and used as sample data.

[0068] The standard deviation of each environmental risk assessment indicator is calculated based on the sample data, and the proportion of the standard deviation of each environmental risk assessment indicator to the sum of the standard deviations of all environmental risk assessment indicators is used as the assessment weight of this environmental risk assessment indicator.

[0069] Based on the impact characteristics of various environmental risk assessment indicators on outdoor sports risks and their own data variation range, corresponding fuzzy membership functions are set. The user's current surrounding environment data is substituted into each fuzzy membership function to obtain the membership value of each environmental risk assessment indicator. The membership values ​​of each environmental risk assessment indicator and the assessment weight are weighted and summed to obtain the environmental status risk score of the surrounding environment.

[0070] The specific steps for conducting a real-time comprehensive assessment of a user's outdoor activity risk based on physiological state risk scores, exercise state risk scores, and environmental state risk scores to obtain the user's outdoor activity risk score, and then planning a corresponding treatment plan for the user based on the outdoor activity risk score, are as follows:

[0071] The user's outdoor activity risk score is calculated based on physiological state risk score, activity state risk score, and environmental state risk score, using a specific formula:

[0072]

[0073] in This indicates the user's outdoor sports risk score. Indicates physiological risk score, Indicates the risk score of the movement status. Indicates the environmental status risk score. , and Represents the weighting coefficient, and ;

[0074] Based on the outdoor sports risk score and the preset classification rules, users are classified into the corresponding outdoor sports risk levels. According to the outdoor sports risk level, the corresponding level of alarm is issued and the corresponding level of handling plan is planned for the user.

[0075] It should be noted that the above assessment method is more suitable for outdoor sports risk assessment under non-extreme conditions. However, once a user is in extreme conditions and a certain risk score exceeds the preset threshold, the outdoor sports activity will be directly marked as high risk and it will be recommended to stop the outdoor sports activity.

[0076] The above steps comprehensively assess the user's outdoor sports risk in real time based on physiological state risk scores, exercise state risk scores, and environmental state risk scores. This approach can capture microscopic signs of internal decompensation in real time by utilizing the deviation of physiological data, accurately identify motor breakdown and skill degradation caused by fatigue accumulation by using the deviation of exercise state, and objectively assess the risks posed by weather and terrain to outdoor sports by using environmental state risk scores. These three advantages complement each other, significantly improving the comprehensiveness of this intelligent outdoor sports risk assessment method and system.

[0077] Example 2, an intelligent risk assessment system for outdoor sports, as shown in Figure 2, includes:

[0078] The data acquisition and processing module is used to acquire the user's historical outdoor sports data from historical records. It uses sensors to monitor the user's basic physiological data, sports status data and surrounding environment data in real time during outdoor sports, and preprocesses the monitored data to obtain preprocessed basic physiological data, sports status data and surrounding environment data.

[0079] The physiological risk assessment module is used to obtain the user's dynamic physiological baseline based on historical outdoor sports data and preprocessed basic physiological data and Kalman filtering. Based on the dynamic physiological baseline and preprocessed basic physiological data, the module assesses the user's physiological status and obtains the user's physiological status risk score.

[0080] The status risk assessment module is used to segment the preprocessed motion status data based on the preprocessed surrounding environment data, and to assess the user's motion status based on historical outdoor sports data and the segmented motion status data, thereby obtaining the user's motion status risk score.

[0081] The environmental risk assessment module is used to assess the degree of danger of the outdoor sports environment based on the preprocessed surrounding environmental data and the fuzzy assessment method, and obtain the environmental status risk score of the surrounding environment.

[0082] The comprehensive assessment and processing module includes a risk comprehensive assessment unit and a processing plan planning unit. The risk comprehensive assessment unit is used to conduct a real-time comprehensive assessment of the user's outdoor sports risk based on physiological state risk score, exercise state risk score and environmental state risk score to obtain the user's outdoor sports risk score. The processing plan planning unit is used to plan the corresponding processing plan for the user based on the outdoor sports risk score.

[0083] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims. Parts not described in detail in this specification are prior art known to those skilled in the art.

Claims

1. A method for intelligent risk assessment in outdoor sports, characterized in that, Includes the following steps: The system obtains users' historical outdoor exercise data from historical records, and uses sensors to monitor users' basic physiological data, exercise status data, and surrounding environment data in real time during outdoor exercise. The monitored data is then preprocessed to obtain preprocessed basic physiological data, exercise status data, and surrounding environment data. Based on historical outdoor sports data and preprocessed basic physiological data, and using Kalman filtering, the user's dynamic physiological baseline is obtained. Based on the dynamic physiological baseline and preprocessed basic physiological data, the user's physiological status is assessed to obtain the user's physiological status risk score. Based on the preprocessed surrounding environment data, the preprocessed motion state data is segmented. The user's motion state is evaluated based on historical outdoor sports data and the segmented motion state data to obtain the user's motion state risk score. Based on the preprocessed surrounding environment data and using the fuzzy evaluation method, the degree of danger of the outdoor sports environment is evaluated to obtain the environmental state risk score of the surrounding environment. Based on physiological state risk scores, exercise state risk scores, and environmental state risk scores, a real-time comprehensive assessment of the user's outdoor exercise risk is conducted to obtain the user's outdoor exercise risk score. Based on the outdoor exercise risk score, a corresponding treatment plan is planned for the user.

2. The intelligent risk assessment method for outdoor sports according to claim 1, characterized in that, The specific steps for obtaining the user's dynamic physiological baseline based on historical outdoor exercise data and preprocessed basic physiological data and Kalman filtering are as follows: establish the user's initial physiological baseline based on historical outdoor exercise data, and adjust the initial physiological baseline attenuation based on the duration and intensity of outdoor exercise every preset time period after the start of outdoor exercise to obtain the predicted physiological baseline. The moving average of preprocessed basic physiological data over a preset time period is calculated and used as the observed physiological baseline. The deviation between the predicted physiological baseline and the observed physiological baseline is calculated and the Kalman gain is obtained. Based on the Kalman gain and the deviation between the predicted physiological baseline and the observed physiological baseline, the predicted physiological baseline is dynamically adjusted and updated to obtain the user's dynamic physiological baseline.

3. The intelligent risk assessment method for outdoor sports according to claim 2, characterized in that, The specific steps for assessing the user's physiological state based on a dynamic physiological baseline and preprocessed basic physiological data to obtain a physiological state risk score are as follows: Obtain the user's current basic physiological data; calculate the deviation between each physiological data point in the current basic physiological data and the corresponding physiological data points of the initial physiological baseline and dynamic physiological baseline; where the deviation represents the ratio of the deviation between each physiological data point to the historical maximum deviation corresponding to that physiological data point; and calculate the physiological risk index corresponding to the initial physiological baseline and dynamic physiological baseline based on the calculated deviation using the following formula: in Indicates physiological risk index, Represents the calculated first... Bias of physiological data Represents the calculated first... The weighting coefficients corresponding to the deviation of each physiological data point, and the sum of the weighting coefficients corresponding to the deviation of each physiological data point is 1. This represents the total number of items in the physiological data; The physiological risk indices corresponding to the initial physiological baseline and the dynamic physiological baseline are weighted and fused to obtain the user's physiological state risk score.

4. The intelligent risk assessment method for outdoor sports according to claim 3, characterized in that, The specific steps for segmenting the preprocessed motion state data based on preprocessed surrounding environment data and evaluating the user's motion state based on historical outdoor sports data and segmented motion state data to obtain the user's motion state risk score are as follows: The preprocessed motion state data is segmented and classified according to the surrounding environment in which the user is outdoors; the initial motion state data of the user under different surrounding environment data is obtained from historical outdoor sports data and constructed into initial motion state vectors under different surrounding environment data; the user's current motion state data and surrounding environment data are obtained, and the current motion state data is constructed into a current motion state vector; the deviation between the current motion state vector and the initial motion state vector under the corresponding surrounding environment data is calculated to obtain the user's motion state risk score, where the deviation represents the ratio of the Euclidean distance between the current motion state vector and the initial motion state vector to the historical maximum Euclidean distance between the two types of vectors.

5. The intelligent risk assessment method for outdoor sports according to claim 4, characterized in that, The specific steps for assessing the risk level of the outdoor sports environment based on preprocessed surrounding environmental data and using fuzzy evaluation method to obtain the environmental status risk score of the surrounding environment are as follows: all data types in the preprocessed surrounding environmental data are used as environmental risk assessment indicators; data of various environmental risk assessment indicators corresponding to multiple historical outdoor sports safety accidents are obtained and used as sample data; the standard deviation of each environmental risk assessment indicator is calculated based on the sample data, and the proportion of the standard deviation of each environmental risk assessment indicator to the sum of the standard deviations of all environmental risk assessment indicators is used as the assessment weight of this environmental risk assessment indicator. Based on the impact characteristics of various environmental risk assessment indicators on outdoor sports risks and their own data variation range, corresponding fuzzy membership functions are set. The user's current surrounding environment data is substituted into each fuzzy membership function to obtain the membership value of each environmental risk assessment indicator. The membership values ​​of each environmental risk assessment indicator and the assessment weight are weighted and summed to obtain the environmental status risk score of the surrounding environment.

6. The intelligent risk assessment method for outdoor sports according to claim 5, characterized in that, The specific steps for conducting a real-time comprehensive assessment of the user's outdoor activity risk based on physiological state risk score, exercise state risk score, and environmental state risk score to obtain the user's outdoor activity risk score, and then planning a corresponding treatment plan for the user based on the outdoor activity risk score, are as follows: The user's outdoor activity risk score is calculated based on the physiological state risk score, exercise state risk score, and environmental state risk score using a formula. The specific calculation formula is as follows: in This indicates the user's outdoor sports risk score. Indicates physiological risk score, Indicates the risk score of the movement status. Indicates the environmental status risk score. 、 and Represents the weighting coefficient, and Based on the outdoor sports risk score and the preset classification rules, users are classified into the corresponding outdoor sports risk levels. Based on the outdoor sports risk level, an alarm of the corresponding level is issued and a corresponding handling plan is planned for the user.

7. An intelligent risk assessment system for outdoor sports, applied to the intelligent risk assessment method for outdoor sports as described in any one of claims 1-6, characterized in that, It includes: a data acquisition and processing module, which is used to acquire the user's historical outdoor sports data from historical records, monitor the user's basic physiological data, sports status data and surrounding environment data in real time through sensors, and preprocess the monitored data to obtain preprocessed basic physiological data, sports status data and surrounding environment data. The physiological risk assessment module is used to obtain the user's dynamic physiological baseline based on historical outdoor sports data and preprocessed basic physiological data and Kalman filtering. Based on the dynamic physiological baseline and preprocessed basic physiological data, the module assesses the user's physiological status and obtains the user's physiological status risk score. The status risk assessment module is used to segment the preprocessed motion status data based on the preprocessed surrounding environment data, and to assess the user's motion status based on historical outdoor sports data and the segmented motion status data, thereby obtaining the user's motion status risk score. The environmental risk assessment module is used to assess the degree of danger of the outdoor sports environment based on the preprocessed surrounding environmental data and the fuzzy assessment method, and obtain the environmental status risk score of the surrounding environment. The comprehensive assessment and processing module includes a risk comprehensive assessment unit and a processing plan planning unit. The risk comprehensive assessment unit is used to conduct a real-time comprehensive assessment of the user's outdoor sports risk based on physiological state risk score, exercise state risk score and environmental state risk score to obtain the user's outdoor sports risk score. The processing plan planning unit is used to plan the corresponding processing plan for the user based on the outdoor sports risk score.

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