A motion monitoring method and device based on smart watch

Abstract

The present application discloses a method and device for motion monitoring based on a smart watch, which relates to the technical field of motion monitoring. The method includes obtaining the user's historical motion data; establishing a user motion model; obtaining the user's location information and obtaining the environmental data around the user; based on the environmental data, analyzing the environmental data to obtain the user's first motion feature, obtaining the user's current sweating data, current heartbeat data and current position change data, and obtaining the user's second motion feature; obtaining the user's current motion state; based on the current motion state, obtaining the user's current health state, generating a health reminder and sending it to the user; obtaining the user's mobile phone usage habits, and performing power management and control on the software in the smart watch. The present application has the effect of improving the accuracy of the smart watch's positioning when monitoring the user's motion, the intelligence of the user's motion monitoring and tracking, and the battery life of the watch itself.

Description

Technical Field

[0001] The present application relates to the technical field of motion monitoring, and in particular to a motion monitoring method and device based on a smart watch. Background Art

[0002] With the development of technology, smart watches have gradually become an extension of smartphones in people's daily lives. Many functions that can be realized on mobile phones can now be realized on watches, such as calls, chatting, sports monitoring, etc.

[0003] As the functions of smart watches gradually improve, more and more people will choose to buy smart watches instead of traditional watches. The sports monitoring function in smart watches can also help users monitor their physical health and exercise status, obtain relevant data, and help users see their physical condition more intuitively.

[0004] In the prior art, some common exercise states are pre-set in general smart watches. When the user needs to exercise, the smart watch will monitor the user's body data after clicking the corresponding exercise state. However, these smart watches only simply record or calculate the user's heartbeat and other data to obtain data such as calorie consumption. However, these smart watches only collect the user's body data in the exercise state to calculate the exercise results. Such results are poorly adaptable to individual users, and cannot intelligently identify the user's exercise state, which is more troublesome to operate. Summary of the invention

[0005] The purpose of the present invention is to provide a motion monitoring method and device based on a smart watch to solve the problems raised in the above background technology.

[0006] In a first aspect, the present application provides a method for motion monitoring based on a smart watch, the method comprising the following steps:

[0007] Acquire the user's historical motion data, and process the historical motion data to obtain the user's historical sweat data, historical heartbeat data, historical location change data, historical environment data, and historical motion status;

[0008] Establishing a user motion model based on the historical sweating data, the historical heartbeat data, the historical position change data, the historical environment data, and the historical motion state;

[0009] Acquire the user's location information, identify the user's surroundings based on the location information, and obtain environmental data of the user's surroundings;

[0010] Based on the environmental data, the environmental data is analyzed to obtain a first motion feature of the user, current sweating data, current heartbeat data, and current position change data of the user are acquired, and a second motion feature of the user is obtained by analyzing the current sweating data, the current heartbeat data, and the current position change data;

[0011] Substituting the first motion feature and the second motion feature into the user motion model to obtain the user's current motion state;

[0012] Based on the current exercise state, obtaining the user's current health state, and generating a health reminder based on the current health state and sending it to the user;

[0013] The user's mobile phone usage habits are obtained, and the power of the software in the smart watch is controlled based on the mobile phone usage habits and the current exercise status.

[0014] Preferably, the step of establishing a user motion model based on the historical sweat data, the historical heartbeat data, the historical position change data, the historical environment data and the historical motion state is specifically:

[0015] Based on the historical sweat data, extracting data from the historical sweat data to obtain target historical sweat data at the user's wrist, and generating historical humidity data at the wrist based on the target historical sweat data;

[0016] Based on the historical heartbeat data, obtain the user's historical heartbeat frequency, and obtain the user's historical exercise intensity according to the historical heartbeat frequency;

[0017] Based on the historical position change data, the historical position change data is traversed to obtain the position change record and position change rate of the user during the movement;

[0018] Based on the historical environmental data, extracting the historical environmental data to obtain historical environmental temperature data and historical environmental sound data;

[0019] An initial user motion model is generated based on the historical motion state, and the historical humidity data, the historical motion intensity, the position change record, the position change rate, the historical ambient temperature data and the historical ambient sound data are substituted into the initial user motion model to generate a user motion model.

[0020] Preferably, the steps of obtaining the user's location information, identifying the user's surroundings based on the location information, and obtaining the environmental data of the user's surroundings are specifically:

[0021] Acquire the user's location information, identify the user's surroundings based on the location information, and obtain building data and facility data around the user;

[0022] The user's location information is updated according to the building data and the facility data to obtain specific location data;

[0023] Based on the specific location data, data recognition is performed on the surrounding environment to obtain environmental data around the user.

[0024] Preferably, based on the environmental data, the step of analyzing the environmental data to obtain the first motion feature of the user is specifically:

[0025] Based on the environmental data, the environmental data is screened to obtain environmental temperature data, environmental humidity data and environmental sound data;

[0026] generating a selection influence for selecting a user's exercise type based on the ambient temperature data and the ambient humidity data;

[0027] Based on the ambient sound data, extracting sound lines from the ambient sound data to obtain a plurality of ambient sound lines in the ambient sound data;

[0028] Based on the plurality of environmental sound lines, extracting a plurality of sound features in the plurality of environmental sound lines, and extracting a correlation value between each of the sound features and the motion;

[0029] Determining whether the correlation value exceeds a preset correlation threshold, and if it is determined that the correlation value exceeds the correlation threshold, extracting the sound feature corresponding to the correlation value and generating a sound feature set;

[0030] A first motion feature of the user is obtained according to the sound feature set and the selection influence.

[0031] Preferably, the step of obtaining the user's current sweating data, current heartbeat data, and current position change data, and analyzing the current sweating data, the current heartbeat data, and the current position change data to obtain the user's second motion feature is specifically:

[0032] Get the user's current sweat data, current heart rate data, and current location change data;

[0033] Based on the current sweat data, obtain the user's physical activity level;

[0034] Generate a user's heart rate curve based on the current heart rate data, and obtain the user's exercise interval according to the heart rate curve;

[0035] Based on the current position change data, a movement point map of the user in space is obtained, and based on the movement point map, a spatial movement map of the user is generated;

[0036] A second motion feature of the user is generated by combining the physical activity level, the motion interval, and the spatial movement map.

[0037] Preferably, the steps of obtaining the user's current health status based on the current exercise status, and generating a health reminder based on the current health status and sending it to the user are specifically:

[0038] Based on the current motion state, collecting current blood oxygen data of the user in the current motion state;

[0039] Based on the current blood oxygen data, the current sweating data and the current heartbeat data, obtaining the current health status of the user;

[0040] Based on the current health status, generating a user's sports health status table, and comparing the sports health status table with a preset health table to obtain health difference data;

[0041] It is determined whether the health difference data is abnormal. If it is determined that the health difference data is abnormal, a health reminder is generated according to the health difference data and sent to the user.

[0042] Preferably, the steps of obtaining the user's mobile phone usage habits and performing power control on the software in the smart watch in combination with the mobile phone usage habits and the current exercise state are specifically as follows:

[0043] Acquire the user's mobile phone usage habits, obtain the user's commonly used software based on the mobile phone usage habits, and filter the commonly used software to obtain target software that can be run on the smart watch;

[0044] Based on the target software, extracting the software power consumption of the target software when it works on the smart watch;

[0045] Prioritize each target software according to the mobile phone usage habits to obtain a priority score for each target software;

[0046] According to the priority score and the current motion state, the necessary usage value of each target software is obtained, and the target software is managed and controlled in combination with the necessary usage value and the software power consumption.

[0047] Preferably, the steps of obtaining the necessary usage value of each target software according to the priority score and the current motion state, and controlling the target software in combination with the necessary usage value and the power consumption of the software are specifically as follows:

[0048] Obtaining a necessary usage value for each target software according to the priority score and the current motion state;

[0049] Based on the current motion state, the user's immersion in the current motion is obtained, and a user's attention change curve for the smartwatch is generated by combining the immersion and the motion interval, and the necessary usage value is added to the attention change curve to obtain a necessary change curve;

[0050] Adding a preset necessary threshold line to the necessary change curve to obtain a first curve segment higher than the necessary threshold and a second curve segment lower than the necessary threshold;

[0051] Based on the first curve segment and the second curve segment, respectively obtain a first time period corresponding to the first curve segment and a second time period corresponding to the second curve segment;

[0052] In the first time period, the power usage permissions of the target software are all enabled to support the operation of the target software. In the second time period, only the three target software with the highest priority scores among the target software are supported to run.

[0053] In a second aspect, the present application provides a motion monitoring device based on a smart watch, the device comprising:

[0054] Historical data acquisition module: used to acquire the user's historical motion data, process the historical motion data, and obtain the user's historical sweat data, historical heartbeat data, historical position change data, historical environment data, and historical motion status;

[0055] Model building module: used to build a user motion model based on the historical sweating data, the historical heartbeat data, the historical position change data, the historical environment data and the historical motion state;

[0056] Location identification module: used to obtain the location information of the user, identify the user's surroundings based on the location information, and obtain the environmental data of the user's surroundings;

[0057] Feature analysis module: used to analyze the environmental data based on the environmental data to obtain the first motion feature of the user, obtain the current sweating data, current heartbeat data and current position change data of the user, and analyze according to the current sweating data, the current heartbeat data and the current position change data to obtain the second motion feature of the user;

[0058] A motion tracking module: used for substituting the first motion feature and the second motion feature into the user motion model to obtain the user's current motion state;

[0059] Health analysis module: used to obtain the user's current health status based on the current exercise status, and generate a health reminder based on the current health status and send it to the user;

[0060] Battery life management module: used to obtain the user's mobile phone usage habits, and combine the mobile phone usage habits and the current exercise status to control the power of the software in the smart watch.

[0061] In summary, the present application includes at least one of the following beneficial technical effects:

[0062] By acquiring multiple historical body data and historical motion states of the user, a user motion model is established, and then the user's location information is acquired to obtain the environmental data around the user. Then, the user's first motion feature and second motion feature are respectively obtained based on the current sweating data, current heartbeat data and current position change data of the user. Based on the first motion feature and the second motion feature, the user's current motion state is obtained. Then, the user's current physical health state is judged based on the current motion state. If an abnormality is found, a health reminder is generated and sent to the user. Then, the user's mobile phone usage habits are acquired, and the power consumption of the software in the smart watch is managed after combining the mobile phone usage habits with the current motion state. This improves the accuracy of the smart watch's positioning when monitoring the user's exercise, the intelligence of the user's exercise monitoring and tracking, and the battery life of the watch itself. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 is a flowchart of a method for monitoring exercise based on a smart watch provided in an embodiment of the present application;

[0064] Figure 2 It is a module block diagram of a motion monitoring method based on a smart watch provided in an embodiment of the present application.

[0065] Explanation of the accompanying drawings: 1. Historical data acquisition module; 2. Model building module; 3. Location recognition module; 4. Feature analysis module; 5. Motion tracking module; 6. Health analysis module; 7. Battery life management module. DETAILED DESCRIPTION

[0066] The following combination Figure 1-Figure 2 The present application is further described in detail, but the embodiments of the present invention are not limited thereto.

[0067] The embodiments of the present application disclose a motion monitoring method and device based on a smart watch.

[0068] In this embodiment, a method and device for motion monitoring based on a smart watch are provided, and the method comprises the following steps:

[0069] S100: Acquire historical motion data of the user, process the historical motion data, and obtain historical sweating data, historical heartbeat data, historical position change data, historical environment data, and historical motion status of the user;

[0070] S200: Establishing a user motion model based on historical sweating data, historical heartbeat data, historical location change data, historical environment data, and historical motion status;

[0071] S300: Acquire the user's location information, identify the user's surroundings based on the location information, and obtain environmental data of the user's surroundings;

[0072] S400: Analyze the environmental data based on the environmental data to obtain a first motion feature of the user, obtain current sweat data, current heartbeat data, and current position change data of the user, and analyze the current sweat data, current heartbeat data, and current position change data to obtain a second motion feature of the user;

[0073] S500: Substituting the first motion feature and the second motion feature into the user motion model to obtain the user's current motion state;

[0074] S600: Based on the current exercise state, obtain the current health state of the user, and generate a health reminder based on the current health state and send it to the user;

[0075] S700: Obtain the user's mobile phone usage habits, and manage the power of the software in the smart watch based on the mobile phone usage habits and the current exercise status.

[0076] It should be pointed out that the above modules are only the basic steps of this embodiment. In the specific implementation process, some steps can be appropriately added, reduced or modified without affecting the overall implementation effect.

[0077] The steps of establishing a user motion model based on historical sweat data, historical heartbeat data, historical location change data, historical environment data, and historical motion status are specifically as follows:

[0078] Based on the historical sweat data, data extraction is performed on the historical sweat data to obtain target historical sweat data at the user's wrist, and historical humidity data at the wrist is generated based on the target historical sweat data;

[0079] Based on the historical heartbeat data, the user's historical heartbeat frequency is obtained, and the user's historical exercise intensity is obtained according to the historical heartbeat frequency;

[0080] Based on the historical location change data, the historical location change data is traversed to obtain the user's location change record and location change rate during the movement;

[0081] Based on the historical environmental data, extract the historical environmental data to obtain the historical environmental temperature data and the historical environmental sound data;

[0082] An initial user motion model is generated based on the historical motion state, and historical humidity data, historical motion intensity, position change records, position change rate, historical ambient temperature data, and historical ambient sound data are substituted into the initial user motion model to generate a user motion model.

[0083] During application, taking a certain user as an example, the user's historical sweat data A, historical heartbeat data B, historical position change data C, historical environmental data D and the user's historical motion status are obtained, and the data A, B, C, and D are processed respectively to obtain the historical humidity data A1 at the user's wrist, the user's historical exercise intensity B1, the user's position change record C1 and position change rate C2, historical environmental temperature data D1 and historical environmental sound data D2, and an initial user motion model is generated according to the user's historical motion status. The model is mainly reflected in the fact that the type of exercise selected by the user is strength training. According to the above data A1, B1, C1, C2, D1 and D2, the changes in various body data of the user during the strength training process are obtained, and these changes are brought into the initial user motion model to obtain a more accurate user motion model.

[0084] The steps of obtaining the user's location information, identifying the user's surroundings based on the location information, and obtaining the environmental data of the user's surroundings are specifically as follows:

[0085] Obtain the user's location information, identify the user's surroundings based on the location information, and obtain the building data and facility data around the user;

[0086] The user's location information is updated according to the building data and facility data to obtain specific location data;

[0087] Based on the specific location data, the surrounding environment is identified to obtain the environmental data around the user.

[0088] During application, taking a certain user as an example, the location information of the user is obtained, and the surroundings of the user are identified based on the location information to obtain the building data around the user as high-rise buildings, gyms, etc., and the facility data as a variety of fitness equipment, etc. According to the building data, it can be obtained that the user is in the gym, and then the various fitness equipment in the facility data are used as references to obtain the specific location data of the user in the gym, and then based on the specific location data, the environmental data around the user is obtained, such as temperature data, humidity data, sound data, etc. in the gym.

[0089] Based on the environmental data, the step of analyzing the environmental data to obtain the first motion feature of the user is specifically as follows:

[0090] Based on the environmental data, the environmental data is screened to obtain environmental temperature data, environmental humidity data and environmental sound data;

[0091] Generate selection influence on user's exercise type based on ambient temperature data and ambient humidity data;

[0092] Based on the ambient sound data, extracting sound lines from the ambient sound data to obtain multiple ambient sound lines in the ambient sound data;

[0093] Based on the multiple ambient sound lines, extract multiple sound features in the multiple ambient sound lines, and extract a correlation value between each sound feature and the motion;

[0094] Determine whether the correlation value exceeds a preset correlation threshold, and if it is determined that the correlation value exceeds the correlation threshold, extract the sound feature corresponding to the correlation value and generate a sound feature set;

[0095] According to the sound feature set and the selection influence, a first motion feature of the user is obtained.

[0096] In application, taking a user as an example, the user is currently in a gym. After screening the environmental data in the gym, the environmental temperature data X, the environmental humidity data Y and the environmental sound data Z are obtained respectively. The selection influence of the user on the type of exercise is generated according to the environmental temperature data X and the environmental temperature data Y. For example, when the temperature and humidity are too high or too low, the type of exercise selected by the user will be more inclined to the type with less physical energy consumption. According to the environmental sound data Z, the environmental sound line in the environmental sound data is extracted, and multiple environmental sound lines are processed to obtain multiple sound features, and the correlation value between each sound feature and the exercise is extracted. It is found that there are multiple sound features that are not related to exercise in these sound features, such as the sound of chatting, the sound of eating and the sound of drinking water. After deleting these sound features with low correlation values, sound features with high correlation values ​​with exercise are obtained, such as shouting, heavy breathing and the sound of equipment falling. These sound features are collected to generate a sound feature set, and the sound feature set and the selection influence are integrated to obtain the first exercise feature of the user.

[0097] The steps of obtaining the current sweating data, the current heartbeat data, and the current position change data of the user, and analyzing the current sweating data, the current heartbeat data, and the current position change data to obtain the second motion feature of the user are specifically as follows:

[0098] Get the user's current sweat data, current heart rate data, and current location change data;

[0099] Based on the current sweat data, the user's physical activity level is obtained;

[0100] Generate a user's heart rate curve based on current heart rate data, and obtain the user's exercise interval according to the heart rate curve;

[0101] Based on the current position change data, a user's movement point map in space is obtained, and a user's spatial movement map is generated based on the movement point map;

[0102] The second motion feature of the user is generated by combining the physical activity level, the motion interval, and the spatial movement map.

[0103] In use, take a user as an example. The user is currently in a gym. The smart watch collects the user's current sweating data E through the humidity sensor, heart rate sensor, acceleration sensor and other sensors carried by the smart watch. The user's physical activity level is obtained based on the sweating data E. The user's heart rate frequency curve is generated based on the user's current heart rate data F collected by the heart rate sensor. The heart rate frequency curve reflects the frequency change of the user's heart rate over a period of time. The user's exercise interval is obtained based on the heart rate frequency curve. When the heart rate is high, it means that the user is exercising, and when the heart rate is low, it means that the user is resting. According to the user's current position change data G detected by the acceleration sensor, the user's movement point map in the gym is obtained based on the current position change data G, and the user's spatial movement map is generated based on the movement point map. The user's second movement feature is generated based on the user's physical activity level, exercise interval and spatial movement map in the gym.

[0104] The steps of obtaining the user's current health status based on the current exercise status, and generating a health reminder based on the current health status and sending it to the user are specifically as follows:

[0105] Based on the current motion state, the user's current blood oxygen data is collected in the current motion state;

[0106] Based on the current blood oxygen data, current sweat data and current heart rate data, the user's current health status is obtained;

[0107] Based on the current health status, generate a user's sports health status table, and compare the sports health status table with the preset health table to obtain health difference data;

[0108] Determine whether the health difference data is abnormal. If the health difference data is abnormal, generate a health reminder based on the health difference data and send it to the user.

[0109] In application, taking a certain user as an example, the user performs strength training in a gym and obtains an exercise state during the strength training. In this exercise state, the user's current blood oxygen content H is collected, and the user's current health status is obtained based on the user's current blood oxygen content H and current heart rate data F. The current blood oxygen content H and the current heart rate data F are compared with the preset health table, and it is found that the user's blood oxygen content H is lower than the normal value, and the current heart rate data F is super high compared to the normal value. In this case, health difference data HF is obtained, and it is further determined that the health difference data HF is in an obviously abnormal state, then a health reminder is generated, and the health reminder is sent to the user to remind the user to pause training.

[0110] The steps to obtain the user's mobile phone usage habits and control the power of the software in the smart watch based on the mobile phone usage habits and the current exercise status are as follows:

[0111] Obtain the user's mobile phone usage habits, and based on the mobile phone usage habits, obtain the user's commonly used software, filter the commonly used software, and obtain the target software that can be run on the smart watch;

[0112] Based on the target software, extract the software power consumption when the target software works on the smart watch;

[0113] Priority score each target software according to mobile phone usage habits to obtain the priority score of each target software;

[0114] According to the priority score and the current motion status, the necessary usage value of each target software is obtained, and the target software is managed and controlled in combination with the necessary usage value and the software power consumption.

[0115] In application, taking a certain user as an example, the user's mobile phone usage habits are obtained. The user usually turns off uncommon or unnecessary software notifications, leaving only message notifications of some commonly used chat software or some relatively important software, and allowing these software to run in the background. Then, according to these mobile phone usage habits, the software is screened to obtain the target software that can be run on the smart watch, which is chat software, information acquisition software, and payment software, etc. Then, the software power consumption generated when these software work on the smart watch is obtained, and it is found that the chat software and information acquisition software have higher power consumption, and the payment software has lower power consumption. Then, according to the user's mobile phone usage habits, it is found that the chat software and information acquisition software are more commonly used, and the payment software is only used in specific situations. Then, these target software are prioritized, and the commonly used software has a higher priority score, and the software used in specific situations has a lower score. Then, combined with the user's current exercise state of strength training in the gym, the necessary use value of each software is obtained, such as the necessary use value of the chat software is low, while the necessary use values ​​of the information acquisition software and the payment software are close to zero. Then, according to the necessary use value and software power consumption, the target software is managed and controlled, such as shutting down the background running of the software.

[0116] According to the priority score and the current motion status, the necessary usage value of each target software is obtained, and the steps of controlling the target software are combined with the necessary usage value and the software power consumption. Specifically, the following are performed:

[0117] According to the priority score and the current motion status, the necessary usage value of each target software is obtained;

[0118] Based on the current motion state, the user's immersion in the current motion is obtained, and a user's attention change curve for the smartwatch is generated by combining the immersion and the motion interval, and the necessary value is added to the attention change curve to obtain a necessary change curve;

[0119] Adding a preset necessary threshold line to the necessary change curve to obtain a first curve segment higher than the necessary threshold and a second curve segment lower than the necessary threshold;

[0120] Based on the first curve segment and the second curve segment, respectively obtain a first time period corresponding to the first curve segment and a second time period corresponding to the second curve segment;

[0121] In the first time period, the power usage permissions of all target software are enabled to support the operation of the target software. In the second time period, only the three target software with the highest priority scores are supported to run.

[0122] In use, if a user is doing strength training in a gym, the necessary value of each software is obtained according to the priority score of the target software in the smart watch and the current exercise state. The user's immersion in strength training is found to be very high according to the current exercise state. Then, the user's attention to the smart watch is obtained by combining the immersion and exercise interval. For example, the user's attention to the smart watch is very low during training, and the attention to the smart watch is very high during rest. Then, the user's attention change curve to the smart watch is obtained according to the above situation, and then the necessary change curve is generated according to the attention change curve. Then, the necessary change curve is divided by the preset necessary threshold line, and the first time period above the necessary threshold line and the second time period below the necessary threshold line after the division are recorded. In the first time period, the user is in the resting time period, and the user will use the smart watch to view information and other operations, so all software are allowed to run, such as chat software, information acquisition software, etc., to help the user view messages; in the second time period, the user is in the process of exercising, so only the three software with the highest priority score in the target software, such as QQ, WeChat, and Weibo, are allowed to be in operation to collect external information.

[0123] An embodiment of the present invention provides a motion monitoring device based on a smart watch. The device is based on any one of the above-mentioned motion monitoring methods based on a smart watch, and the device includes:

[0124] Historical data acquisition module 1: used to acquire the user's historical motion data, process the historical motion data, and obtain the user's historical sweat data, historical heartbeat data, historical location change data, historical environment data, and historical motion status;

[0125] Model building module 2: used to build a user motion model based on historical sweating data, historical heartbeat data, historical location change data, historical environment data and historical motion status;

[0126] Location identification module 3: used to obtain the user's location information, identify the user's surroundings based on the location information, and obtain the user's surrounding environmental data;

[0127] Feature analysis module 4: used to analyze the environmental data based on the environmental data to obtain the first motion feature of the user, obtain the current sweating data, current heartbeat data and current position change data of the user, and analyze the current sweating data, current heartbeat data and current position change data to obtain the second motion feature of the user;

[0128] Motion tracking module 5: used to substitute the first motion feature and the second motion feature into the user motion model to obtain the user's current motion state;

[0129] Health analysis module 6: used to obtain the user's current health status based on the current exercise status, and generate health reminders based on the current health status and send them to the user;

[0130] Battery life management module 7: used to obtain the user's mobile phone usage habits, and to control the power of the software in the smart watch based on the mobile phone usage habits and the current exercise status.

[0131] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A motion monitoring method based on a smart watch, characterized in that: The following steps are involved: Acquire the user's historical motion data, and process the historical motion data to obtain the user's historical sweat data, historical heartbeat data, historical location change data, historical environment data, and historical motion status; Establishing a user motion model based on the historical sweating data, the historical heartbeat data, the historical position change data, the historical environment data, and the historical motion state; Acquire the user's location information, identify the user's surroundings based on the location information, and obtain environmental data of the user's surroundings; Based on the environmental data, the environmental data is analyzed to obtain a first motion feature of the user, current sweating data, current heartbeat data, and current position change data of the user are acquired, and a second motion feature of the user is obtained by analyzing the current sweating data, the current heartbeat data, and the current position change data; Based on the environmental data, the step of analyzing the environmental data to obtain the first motion feature of the user is specifically: Based on the environmental data, the environmental data is screened to obtain environmental temperature data, environmental humidity data and environmental sound data; generating a selection influence for selecting a user's exercise type based on the ambient temperature data and the ambient humidity data; Based on the ambient sound data, extracting sound lines from the ambient sound data to obtain a plurality of ambient sound lines in the ambient sound data; Based on the plurality of environmental sound lines, extracting a plurality of sound features in the plurality of environmental sound lines, and extracting a correlation value between each of the sound features and the motion; Determining whether the correlation value exceeds a preset correlation threshold, and if it is determined that the correlation value exceeds the correlation threshold, extracting the sound feature corresponding to the correlation value and generating a sound feature set; Obtaining a first motion feature of the user according to the sound feature set and the selection influence; Substituting the first motion feature and the second motion feature into the user motion model to obtain the user's current motion state; Based on the current exercise state, obtaining the user's current health state, and generating a health reminder based on the current health state and sending it to the user; The user's mobile phone usage habits are obtained, and the power of the software in the smart watch is controlled based on the mobile phone usage habits and the current exercise status.

2. The method for monitoring exercise based on a smart watch according to claim 1, characterized in that: The step of establishing a user motion model based on the historical sweat data, the historical heartbeat data, the historical position change data, the historical environment data, and the historical motion state is specifically: Based on the historical sweat data, extracting data from the historical sweat data to obtain target historical sweat data at the user's wrist, and generating historical humidity data at the wrist based on the target historical sweat data; Based on the historical heartbeat data, obtain the user's historical heartbeat frequency, and obtain the user's historical exercise intensity according to the historical heartbeat frequency; Based on the historical position change data, the historical position change data is traversed to obtain the position change record and position change rate of the user during the movement; Based on the historical environmental data, extracting the historical environmental data to obtain historical environmental temperature data and historical environmental sound data; An initial user motion model is generated based on the historical motion state, and the historical humidity data, the historical motion intensity, the position change record, the position change rate, the historical ambient temperature data and the historical ambient sound data are substituted into the initial user motion model to generate a user motion model.

3. The method for monitoring exercise based on a smart watch according to claim 1, characterized in that: The steps of obtaining the user's location information, identifying the user's surroundings based on the location information, and obtaining the environmental data of the user's surroundings are specifically as follows: Acquire the user's location information, identify the user's surroundings based on the location information, and obtain building data and facility data around the user; The user's location information is updated according to the building data and the facility data to obtain specific location data; Based on the specific location data, data recognition is performed on the surrounding environment to obtain environmental data around the user.

4. The method for monitoring exercise based on a smart watch according to claim 1, characterized in that: The step of obtaining the current sweating data, the current heartbeat data, and the current position change data of the user, and analyzing the current sweating data, the current heartbeat data, and the current position change data to obtain the second motion feature of the user is specifically as follows: Get the user's current sweat data, current heart rate data, and current location change data; Based on the current sweat data, obtain the user's physical activity level; Generate a user's heart rate curve based on the current heart rate data, and obtain the user's exercise interval according to the heart rate curve; Based on the current position change data, a movement point map of the user in space is obtained, and based on the movement point map, a spatial movement map of the user is generated; A second motion feature of the user is generated by combining the physical activity level, the motion interval, and the spatial movement map.

5. The method for monitoring exercise based on a smart watch according to claim 4, characterized in that: The steps of obtaining the current health status of the user based on the current exercise status, and generating a health reminder based on the current health status and sending the health reminder to the user are specifically: Based on the current motion state, collecting current blood oxygen data of the user in the current motion state; Based on the current blood oxygen data, the current sweating data and the current heartbeat data, obtaining the current health status of the user; Based on the current health status, generating a user's sports health status table, and comparing the sports health status table with a preset health table to obtain health difference data; It is determined whether the health difference data is abnormal. If it is determined that the health difference data is abnormal, a health reminder is generated according to the health difference data and sent to the user.

6. The method for monitoring exercise based on a smart watch according to claim 5, characterized in that: The steps of obtaining the user's mobile phone usage habits and controlling the power of the software in the smart watch based on the mobile phone usage habits and the current exercise status are specifically as follows: Acquire the user's mobile phone usage habits, obtain the user's commonly used software based on the mobile phone usage habits, and filter the commonly used software to obtain target software that can be run on the smart watch; Based on the target software, extracting the software power consumption of the target software when it works on the smart watch; Prioritize each target software according to the mobile phone usage habits to obtain a priority score for each target software; According to the priority score and the current motion state, the necessary usage value of each target software is obtained, and the target software is managed and controlled in combination with the necessary usage value and the software power consumption.

7. The method for monitoring exercise based on a smart watch according to claim 6, characterized in that: The steps of obtaining the necessary usage value of each target software according to the priority score and the current motion state, and controlling the target software in combination with the necessary usage value and the power consumption of the software are specifically as follows: Obtaining a necessary usage value for each target software according to the priority score and the current motion state; Based on the current motion state, the user's immersion in the current motion is obtained, and a user's attention change curve for the smartwatch is generated by combining the immersion and the motion interval, and the necessary usage value is added to the attention change curve to obtain a necessary change curve; Adding a preset necessary threshold line to the necessary change curve to obtain a first curve segment higher than the necessary threshold and a second curve segment lower than the necessary threshold; Based on the first curve segment and the second curve segment, respectively obtain a first time period corresponding to the first curve segment and a second time period corresponding to the second curve segment; In the first time period, the power usage permissions of the target software are all enabled to support the operation of the target software. In the second time period, only the three target software with the highest priority scores among the target software are supported to run.

8. A motion monitoring device based on a smart watch, the device using a motion monitoring method based on a smart watch as claimed in any one of claims 1 to 7, characterized in that: The device comprises: Historical data acquisition module: used to acquire the user's historical motion data, process the historical motion data, and obtain the user's historical sweat data, historical heartbeat data, historical position change data, historical environment data, and historical motion status; Model building module: used to build a user motion model based on the historical sweating data, the historical heartbeat data, the historical position change data, the historical environment data and the historical motion state; Location identification module: used to obtain the location information of the user, identify the user's surroundings based on the location information, and obtain the environmental data of the user's surroundings; Feature analysis module: used to analyze the environmental data based on the environmental data to obtain the first motion feature of the user, obtain the current sweating data, current heartbeat data and current position change data of the user, and analyze according to the current sweating data, the current heartbeat data and the current position change data to obtain the second motion feature of the user; A motion tracking module: used for substituting the first motion feature and the second motion feature into the user motion model to obtain the user's current motion state; Health analysis module: used to obtain the user's current health status based on the current exercise status, and generate a health reminder based on the current health status and send it to the user; Battery life management module: used to obtain the user's mobile phone usage habits, and combine the mobile phone usage habits and the current exercise status to control the power of the software in the smart watch.

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