Intelligent exercise equipment personalized control system and method

Abstract

This invention discloses a personalized control system and method for intelligent exercise equipment, relating to the field of exercise equipment control technology. The method includes: assessing the degree of similarity in physical characteristics between other users and the user to obtain approximate users; acquiring historical records of changes in physical characteristics and historical exercise records of the approximate users, acquiring the user's initial historical exercise records and target physical characteristic data, and analyzing the reference value of the approximate users for the user's exercise to obtain reference users; acquiring historical equipment operation records generated during the use of the intelligent exercise equipment by the reference users, analyzing the impact of the intelligent exercise equipment on the user's exercise effect under different operating states to obtain target operation data; and controlling the operating state of the intelligent exercise equipment based on the target operation data when the user exercises with the intelligent exercise equipment, and providing message prompts to the user.

Description

Technical Field

[0001] This invention relates to the field of exercise equipment control technology, specifically an intelligent personalized control system and method for exercise equipment. Background Technology

[0002] With the continuous development of intelligent exercise equipment, more and more people are using intelligent exercise equipment for exercise. The benefits of using intelligent exercise equipment include: 1. Real-time monitoring: Intelligent exercise equipment can monitor heart rate and calories in real time, allowing users to understand their physical condition. 2. Diverse exercise options: Intelligent exercise equipment stores various exercise modes and types, which users can adjust according to their preferences and needs, maintaining the effectiveness and novelty of their workouts. 3. High safety: By monitoring and providing feedback on the user's physical condition, the risk of injury caused by improper technique or overexertion can be effectively reduced.

[0003] Currently, most intelligent exercise equipment on the market monitors users' body data, such as calories burned during exercise, and then recommends exercise modes based on the user's exercise plan. However, in reality, different users have different physical conditions, and the same exercise intensity will have different effects on different users. In addition, different users have different exercise goals. There are very few intelligent exercise equipment that can develop personalized exercise strategies for the equipment and the user, thereby controlling the exercise equipment. This not only reduces the user's exercise effect, but may even lead to injury during exercise. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent personalized control system and method for exercise equipment to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a personalized control method for intelligent exercise equipment, the method comprising:

[0006] Step S100: Obtain initial vital sign data of users using intelligent exercise equipment, obtain historical initial vital sign data of other users using intelligent exercise equipment, assess the similarity of vital signs between other users and users, and obtain similar users.

[0007] Step S200: Obtain historical vital sign change records and historical exercise records of similar users, obtain the user's initial historical exercise records and target vital sign data, analyze the exercise reference value of similar users to the user, and obtain exercise reference users;

[0008] Step S300: Obtain the intelligent exercise equipment, analyze the historical equipment operation records generated during the exercise reference user's use, analyze the degree of influence of the intelligent exercise equipment on the user's exercise effect under different operating states, and obtain the target operation data;

[0009] Step S400: When a user exercises using the intelligent exercise equipment, the operating status of the intelligent exercise equipment is controlled according to the target operating data, and a message prompt is given to the user.

[0010] Furthermore, step S100 includes:

[0011] Step S101: Obtain the user's initial vital signs data, which includes the values ​​of various vital signs indicators of the user when they start using the intelligent exercise equipment;

[0012] Step S102: Obtain the historical initial vital signs data of other users using the intelligent exercise equipment. Other users exercised using the intelligent exercise equipment during the historical period. From the historical initial vital signs data, obtain the values ​​of various vital signs indicators of other users when they started using the intelligent exercise equipment.

[0013] Step S103: Obtain the characteristics of each other user using the intelligent exercise equipment, and calculate the characteristic value a of the user's vital signs in the initial vital signs data a=(a △ -a´) / σ, where σ is the standard deviation of the vital signs of other users when they first start using the intelligent exercise equipment, and a △ denoted as the value of the user's vital signs in the initial vital signs data, and a' as the average value of the vital signs of each other user when they started using the intelligent exercise equipment;

[0014] Step S104: Obtain the feature values ​​of various vital signs indicators of the user from the initial vital sign data, and aggregate them to obtain the user's initial feature vector A={a1, a2, ..., a...} n}, where a1, a2, ..., a n These are the feature values ​​of the user's 1st, 2nd, ..., nth vital signs, respectively.

[0015] Step S105: Evaluate the similarity of vital signs between each other user and the user, wherein the evaluation of the similarity of vital signs between the c-th other user and the user is carried out as follows:

[0016] Calculate the approximate value r of the vital signs between the c-th other user and the user. c :

[0017] ,

[0018] Among them, B cThis is the initial feature vector of the c-th other user;

[0019] When the approximate value of vital signs is r c If the feature approximation threshold is greater than the preset threshold, the physical characteristics of the c-th other user are determined to be similar to those of the user, and the c-th other user is recorded as the approximate user of the user.

[0020] The reason for calculating the characteristic values ​​of the user's vital signs in the above steps is that the numerical range and units of different characteristic indicators vary in actual processes. By calculating the characteristic values ​​of the characteristic indicators, the calculated approximate values ​​of vital signs can be made more accurate, providing strong data support for the control of intelligent exercise equipment described below.

[0021] Furthermore, step S200 includes:

[0022] Step S201: Obtain each approximate user of the user, record the physical condition of the approximate user after exercising with intelligent exercise equipment, obtain the historical characteristic change record of the approximate user, and extract the values ​​of various physical indicators of the approximate user after exercise from the historical characteristic change record.

[0023] Step S202: Obtain the user's target vital sign data, which includes the target values ​​of various vital sign indicators of the user, obtain historical characteristic change records of approximate users, and calculate the target vital sign value of the approximate user relative to the user in each historical characteristic change record, wherein the target vital sign value F of the approximate user relative to the user in the e-th historical characteristic change record. e :

[0024] ,

[0025] Where n is the total number of the user's various vital signs indicators; a i,target d is the target value for the user's i-th vital sign indicator; e,i For the e-th historical feature change record, the values ​​of various vital signs indicators approximate the user's post-exercise state;

[0026] Step S203: Obtain the maximum value of the target vital signs between approximate users in each historical feature change record, and record it as the target vital signs value between approximate users and users, and normalize the target vital signs value.

[0027] Step S204: Obtain the historical exercise records of similar users, and from the historical exercise records, obtain the values ​​of various exercise indicators of similar users when exercising with intelligent exercise equipment;

[0028] Record the user's exercise process using intelligent exercise equipment within a historical period to obtain the user's initial historical exercise record, obtain the total number g of the user's initial historical exercise record, obtain the first g historical exercise records that approximate the user, and record the time point of the (g-1)th historical exercise record as the feature time point.

[0029] Step S205: Obtain the values ​​of various exercise indicators of the user when exercising with intelligent exercise equipment from the initial historical exercise records, and analyze the exercise reference value of similar users to the user. The specific analysis process is as follows:

[0030] Calculate the similarity H of performance metrics between approximate users:

[0031] ,

[0032] Among them, Q x Let Q' be the value of the running metric in the x-th historical exercise record of the approximate user; Q' is the average value of the running metric in the previous g historical exercise records of the approximate user; W x Let W' be the value of the running metric in the user's xth initial historical exercise record; W' is approximately the average value of the running metric in the user's various initial historical exercise records.

[0033] Calculate the approximate user's training reference score K for the user:

[0034] ,

[0035] Where m is the total number of various sports indicators for the user; η1 and η2 are the preset first and second scoring coefficients, respectively, η1+η2=1, η1>0, η2>0; F´ max H represents the normalized target value of the target vital signs between approximate users; z This approximates the similarity of the z-th operational metric between users;

[0036] Step S206: When the exercise reference score K is greater than the preset reference score threshold, it is determined that the exercise of the approximate user is of reference value to the user. The approximate user is recorded as the user's exercise reference user, and the user's various exercise reference users are obtained.

[0037] Furthermore, step S300 includes:

[0038] Step S301: Obtain the intelligent exercise equipment. During the exercise reference user's use, the historical equipment operation records are generated. Set the unit duration. From the historical equipment operation records, obtain the average value of each operation indicator of the intelligent exercise equipment within each unit duration and aggregate them to obtain the operation dataset of each operation indicator.

[0039] Step S302: Analyze the impact of intelligent exercise equipment on the user's exercise effect under different operating conditions. The specific analysis process is as follows:

[0040] Obtain the first historical device running record of the exercise reference user after the characteristic time point, and record it as the characteristic historical device running record;

[0041] The average value of each element in the running dataset of each running indicator of each user in the corresponding characteristic historical device running record is obtained and recorded as the target value of each running indicator in the intelligent exercise equipment in each unit of time after the user starts exercising.

[0042] Step S303: Calculate the characteristic change rate p of a certain performance indicator of the intelligent exercise equipment during the v-th unit of time after the user starts exercising. v =(L v -L v-1 ) / L v-1 L v-1 L represents the target value of a certain performance indicator within the (v-1)th unit of time after the user begins exercising. v The target value of a certain performance indicator within the v-th unit of time after the user starts exercising;

[0043] When the characteristic rate of change p v If the rate of change is less than the preset threshold, use L. v-1 Replace L with its value. v The system determines that the best training effect for the user is achieved when the various operating indicators in the intelligent exercise equipment are at their target values. The system replaces the target values ​​of the various operating indicators in the intelligent exercise equipment within each unit of time after the user starts exercising, and then collects the data to obtain the target operating data.

[0044] Furthermore, step S400 includes:

[0045] Step S401: Obtain the user's target running data. When the user starts exercising with the intelligent exercise equipment in the current cycle, adjust the various operating indicators of the intelligent exercise equipment based on the target running data, control the operating status of the intelligent exercise equipment, and issue a prompt to the user.

[0046] Step S402: After the user completes exercise using the intelligent exercise equipment in the current period, obtain the mean value of each element in the running data set of various running indicators in the second historical device running record after the characteristic time point for each exercise reference user, and generate the user's target running data for the next period in the current period. In this way, obtain the user's target running data for each period after the current period, and control the intelligent exercise equipment when the user exercises using the intelligent exercise equipment.

[0047] To better implement the above methods, an intelligent personalized control system for exercise equipment is also proposed. The system includes a vital sign approximation assessment module, an exercise reference analysis module, a target running data module, and a running control module.

[0048] The vital signs approximation assessment module is used to assess the degree of similarity in vital signs between other users to obtain approximate users;

[0049] The exercise reference analysis module is used to analyze the exercise reference value of similar users to users, and to obtain exercise reference users;

[0050] The target operation data module is used to analyze the impact of intelligent exercise equipment on the user's exercise effect under different operating conditions, and obtain target operation data;

[0051] The operation control module is used to control the operating status of the intelligent exercise equipment based on the target operation data and to provide message prompts to the user.

[0052] Furthermore, the vital sign approximation assessment module includes a vital sign approximation value unit and a vital sign approximation assessment unit;

[0053] The vital sign approximation value unit is used to calculate the approximate vital sign values ​​between each other user;

[0054] The vital sign approximation assessment unit is used to assess the degree of similarity of vital signs between each other user based on the approximation values ​​of vital signs, and to obtain approximate users.

[0055] Furthermore, the exercise reference analysis module includes an exercise reference scoring unit and an exercise reference analysis unit;

[0056] The exercise reference rating unit is used to calculate the exercise reference rating of similar users to users;

[0057] The exercise reference analysis unit is used to analyze the exercise reference value of similar users to users based on the exercise reference score, and to obtain exercise reference users.

[0058] Furthermore, the target runtime data module includes a runtime dataset unit and a target runtime data unit;

[0059] The running dataset unit is used to obtain the historical equipment running records of exercise reference users. From the historical equipment running records, the average values ​​of various running indicators of the intelligent exercise equipment in each unit of time are obtained and aggregated to obtain the running dataset of various running indicators.

[0060] The target running data unit is used to analyze the impact of intelligent exercise equipment on the user's exercise effect under different operating states based on the running dataset, and obtain the target running data.

[0061] Furthermore, the operation control module includes an operation control unit;

[0062] The operation control unit adjusts various operating indicators of the intelligent exercise equipment based on the target operating data, controls the operating status of the intelligent exercise equipment, and issues prompts to the user.

[0063] Compared with the prior art, the beneficial effects of the present invention are: the present invention realizes personalized control of intelligent exercise equipment. Considering that in actual process, different users have different physical conditions, the same exercise intensity will have different exercise effects on different users, and different users have different exercise goals, the present invention formulates the optimal operating data of intelligent exercise equipment from multiple perspectives such as the user's exercise goals, physical characteristics and exercise characteristics, and controls the intelligent exercise equipment. This will not only reduce the physical damage to the user during exercise, but also greatly improve the user's exercise effect. Attached Figure Description

[0064] Figure 1 This is a flowchart of a personalized control method for intelligent exercise equipment according to the present invention;

[0065] Figure 2 This is a schematic diagram of a module of an intelligent personalized control system for exercise equipment according to the present invention. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] Example: Figures 1-2 As shown, the present invention provides a technical solution, a method for personalized control of intelligent exercise equipment, the method comprising:

[0068] Step S100: Obtain initial vital sign data of users using intelligent exercise equipment, obtain historical initial vital sign data of other users using intelligent exercise equipment, assess the similarity of vital signs between other users and users, and obtain similar users.

[0069] Step S100 includes:

[0070] Step S101: Obtain the user's initial vital signs data, which includes the values ​​of various vital signs indicators of the user when they start using the intelligent exercise equipment;

[0071] For example, various characteristic indicators include weight, muscle mass, etc.

[0072] Step S102: Obtain the historical initial vital signs data of other users using the intelligent exercise equipment. Other users exercised using the intelligent exercise equipment during the historical period. From the historical initial vital signs data, obtain the values ​​of various vital signs indicators of other users when they started using the intelligent exercise equipment.

[0073] Step S103: Obtain the characteristics of each other user using the intelligent exercise equipment, and calculate the characteristic value a of the user's vital signs in the initial vital signs data a=(a △ -a´) / σ, where σ is the standard deviation of the vital signs of other users when they first start using the intelligent exercise equipment, and a △ denoted as the value of the user's vital signs in the initial vital signs data, and a' as the average value of the vital signs of each other user when they started using the intelligent exercise equipment;

[0074] Step S104: Obtain the feature values ​​of various vital signs indicators of the user from the initial vital sign data, and aggregate them to obtain the user's initial feature vector A={a1, a2, ..., a...} n}, where a1, a2, ..., a n These are the feature values ​​of the user's 1st, 2nd, ..., nth vital signs, respectively.

[0075] Step S105: Evaluate the similarity of vital signs between each other user and the user, wherein the evaluation of the similarity of vital signs between the c-th other user and the user is carried out as follows:

[0076] Calculate the approximate value r of the vital signs between the c-th other user and the user. c :

[0077] ,

[0078] Among them, B c This is the initial feature vector of the c-th other user;

[0079] When the approximate value of vital signs is rc If the feature approximation threshold is greater than the preset threshold, the physical characteristics of the c-th other user are determined to be similar to those of the user, and the c-th other user is recorded as the approximate user of the user.

[0080] Step S200: Obtain historical vital sign change records and historical exercise records of similar users, obtain the user's initial historical exercise records and target vital sign data, analyze the exercise reference value of similar users to the user, and obtain exercise reference users;

[0081] Step S200 includes:

[0082] Step S201: Obtain each approximate user of the user, record the physical condition of the approximate user after exercising with intelligent exercise equipment, obtain the historical characteristic change record of the approximate user, and extract the values ​​of various physical indicators of the approximate user after exercise from the historical characteristic change record.

[0083] Step S202: Obtain the user's target vital sign data, which includes the target values ​​of various vital sign indicators of the user, obtain historical characteristic change records of approximate users, and calculate the target vital sign value of the approximate user relative to the user in each historical characteristic change record, wherein the target vital sign value F of the approximate user relative to the user in the e-th historical characteristic change record. e :

[0084] ,

[0085] Where n is the total number of the user's various vital signs indicators; a i,target d is the target value for the user's i-th vital sign indicator; e,i For the e-th historical feature change record, the values ​​of various vital signs indicators approximate the user's post-exercise state;

[0086] Step S203: Obtain the maximum value of the target vital signs between approximate users in each historical feature change record, and record it as the target vital signs value between approximate users and users, and normalize the target vital signs value.

[0087] Step S204: Obtain the historical exercise records of similar users, and from the historical exercise records, obtain the values ​​of various exercise indicators of similar users when exercising with intelligent exercise equipment;

[0088] For example, various sports indicators include the body's heart rate and calorie consumption;

[0089] Record the user's exercise process using intelligent exercise equipment within a historical period to obtain the user's initial historical exercise record, obtain the total number g of the user's initial historical exercise record, obtain the first g historical exercise records that approximate the user, and record the time point of the (g-1)th historical exercise record as the feature time point.

[0090] Step S205: Obtain the values ​​of various exercise indicators of the user when exercising with intelligent exercise equipment from the initial historical exercise records, and analyze the exercise reference value of similar users to the user. The specific analysis process is as follows:

[0091] Calculate the similarity H of performance metrics between approximate users:

[0092] ,

[0093] Among them, Q x Let Q' be the value of the running metric in the x-th historical exercise record of the approximate user; Q' is the average value of the running metric in the previous g historical exercise records of the approximate user; W x Let W' be the value of the running metric in the user's xth initial historical exercise record; W' is approximately the average value of the running metric in the user's various initial historical exercise records.

[0094] Calculate the approximate user's training reference score K for the user:

[0095] ,

[0096] Where m is the total number of various sports indicators for the user; η1 and η2 are the preset first and second scoring coefficients, respectively, η1+η2=1, η1>0, η2>0; F´ max H represents the normalized target value of the target vital signs between approximate users; z This approximates the similarity of the z-th operational metric between users;

[0097] For example, m is 3; F' max =0.80; η1 is 0.4; η2 is 0.6; H1 is 0.70; H2 is 0.80; H3 is 0.90;

[0098] Calculate the approximate user's training reference score K for the user:

[0099] ,

[0100] Step S206: When the training reference score K is greater than the preset reference score threshold, it is determined that the training of the approximate user has reference value for the user. The approximate user is recorded as the user's training reference user, and the user's various training reference users are obtained.

[0101] Step S300: Obtain the intelligent exercise equipment, analyze the historical equipment operation records generated during the exercise reference user's use, analyze the degree of influence of the intelligent exercise equipment on the user's exercise effect under different operating states, and obtain the target operation data;

[0102] Step S300 includes:

[0103] Step S301: Obtain the intelligent exercise equipment. During the exercise reference user's use, the historical equipment operation records are generated. Set the unit duration. From the historical equipment operation records, obtain the average value of each operation indicator of the intelligent exercise equipment within each unit duration and aggregate them to obtain the operation dataset of each operation indicator.

[0104] For example, various operating indicators include power output and operating speed;

[0105] Step S302: Analyze the impact of intelligent exercise equipment on the user's exercise effect under different operating conditions. The specific analysis process is as follows:

[0106] Obtain the first historical device running record of the exercise reference user after the characteristic time point, and record it as the characteristic historical device running record;

[0107] The average value of each element in the running dataset of each running indicator of each user in the corresponding characteristic historical device running record is obtained and recorded as the target value of each running indicator in the intelligent exercise equipment in each unit of time after the user starts exercising.

[0108] Step S303: Calculate the characteristic change rate p of a certain performance indicator of the intelligent exercise equipment during the v-th unit of time after the user starts exercising. v =(L v -L v-1 ) / L v-1 L v-1 L represents the target value of a certain performance indicator within the (v-1)th unit of time after the user begins exercising. v The target value of a certain performance indicator within the v-th unit of time after the user starts exercising;

[0109] When the characteristic rate of change p v If the rate of change is less than the preset threshold, use L. v-1 Replace L with its value. v The value is used to determine the optimal training effect for the user when the various operating indicators in the intelligent exercise equipment are at the target value. The target values ​​of various operating indicators in the intelligent exercise equipment are replaced and collected for each unit of time after the user starts exercising to obtain target operating data.

[0110] Step S400: When a user exercises using the intelligent exercise equipment, the operating status of the intelligent exercise equipment is controlled according to the target running data, and a message prompt is given to the user.

[0111] Step S400 includes:

[0112] Step S401: Obtain the user's target running data. When the user starts exercising with the intelligent exercise equipment in the current cycle, adjust the various operating indicators of the intelligent exercise equipment based on the target running data, control the operating status of the intelligent exercise equipment, and issue a prompt to the user.

[0113] Step S402: After the user completes the exercise using the intelligent exercise equipment in the current period, obtain the mean value of each element in the running data set of various running indicators in the second historical device running record after the characteristic time point for each exercise reference user, and generate the user's target running data for the next period in the current period. In this way, obtain the user's target running data for each period after the current period, and control the intelligent exercise equipment when the user exercises using the intelligent exercise equipment.

[0114] To better implement the above methods, an intelligent personalized control system for exercise equipment is also proposed. The system includes a vital sign approximation assessment module, an exercise reference analysis module, a target running data module, and a running control module.

[0115] The vital signs approximation assessment module is used to assess the degree of similarity in vital signs between other users to obtain approximate users;

[0116] The exercise reference analysis module is used to analyze the exercise reference value of similar users to users, and to obtain exercise reference users;

[0117] The target operation data module is used to analyze the impact of intelligent exercise equipment on the user's exercise effect under different operating conditions, and obtain target operation data;

[0118] The operation control module is used to control the operating status of the intelligent exercise equipment based on the target operation data and to provide message prompts to the user.

[0119] The vital signs approximation assessment module includes a vital signs approximation value unit and a vital signs approximation assessment unit.

[0120] The vital sign approximation value unit is used to calculate the approximate vital sign values ​​between each other user;

[0121] The vital sign approximation assessment unit is used to assess the degree of similarity of vital signs between each other user based on the approximation values ​​of vital signs, and to obtain approximate users;

[0122] The exercise reference analysis module includes an exercise reference scoring unit and an exercise reference analysis unit.

[0123] The exercise reference rating unit is used to calculate the exercise reference rating of similar users to users;

[0124] The exercise reference analysis unit is used to analyze the exercise reference value of similar users to users based on the exercise reference score, and to obtain the exercise reference users.

[0125] The target running data module includes a running dataset unit and a target running data unit;

[0126] The running dataset unit is used to obtain the historical equipment running records of exercise reference users. From the historical equipment running records, the average values ​​of various running indicators of the intelligent exercise equipment in each unit of time are obtained and aggregated to obtain the running dataset of various running indicators.

[0127] The target running data unit is used to analyze the impact of intelligent exercise equipment on the user's exercise effect under different operating states based on the running dataset, and to obtain the target running data;

[0128] The operation control module includes an operation control unit;

[0129] The operation control unit adjusts various operating indicators of the intelligent exercise equipment based on the target operating data, controls the operating status of the intelligent exercise equipment, and issues prompts to the user.

[0130] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A personalized control method for intelligent exercise equipment, characterized in that, The method includes: Step S100: Obtain initial vital sign data of users using intelligent exercise equipment, obtain historical initial vital sign data of other users using the intelligent exercise equipment, assess the degree of similarity between the vital signs of the other users and the user, and obtain similar users; Step S200: Obtain the historical vital sign change records and historical exercise records of the approximate user, obtain the initial historical exercise records and target vital sign data of the user, analyze the exercise reference value of the approximate user to the user, and obtain the exercise reference user; Specifically, the training reference value of the approximate users to the user is analyzed to obtain the training reference users. The specific analysis process is as follows: Calculate the similarity H of the operating metrics between the approximate user and the user: ， Among them, Q x Let Q' be the value of the running metric in the x-th historical exercise record of the approximate user; Q' be the average value of the running metric in the previous g historical exercise records of the approximate user; W x W' is the value of the running indicator in the xth initial historical exercise record of the user; W' is the average value of the running indicator in each initial historical exercise record of the approximate user; Calculate the approximate user's training reference score K for the user: ， Where m is the total number of various sports indicators of the user; η1 and η2 are the preset first scoring coefficient and second scoring coefficient, respectively, η1+η2=1, η1>0, η2>0; F´ max H represents the normalized target vital sign value between the approximate user and the user; z The similarity of the z-th operational metric between the approximate user and the user; When the exercise reference score K is greater than the preset reference score threshold, it is determined that the approximate user has reference value for the user's exercise, and the approximate user is recorded as the user's exercise reference user, and each exercise reference user of the user is obtained. Step S300: Obtain the intelligent exercise equipment, analyze the historical equipment operation records generated during the exercise reference user's use, and analyze the degree of influence of the intelligent exercise equipment on the user's exercise effect under different operating states to obtain target operation data; The analysis of the impact of the intelligent exercise equipment on the user's exercise effect under different operating states is as follows: Obtain the first historical device operation record of the exercise reference user after the characteristic time point, and record it as the characteristic historical device operation record; The mean of each element in the running dataset of each running indicator of each user in the corresponding feature history device running record of each user is obtained and recorded as the target value of each running indicator in the intelligent exercise equipment for each unit of time after the user starts exercising. Calculate the characteristic change rate p of a certain performance indicator of the intelligent exercise equipment during the v-th unit of time after the user begins exercising. v =(L v -L v-1 ) / L v-1 L v-1 L represents the target value of a certain performance indicator within the (v-1)th unit of time after the user begins exercising. v The target value of a certain performance indicator is defined as follows: within the v-th unit of time after the user begins exercising. When the characteristic change rate p v If the rate of change is less than a preset threshold, use the L v-1 Replace the value of L with the value of L. v The value is used to determine that when the various operating indicators in the intelligent exercise equipment are at the target value, the exercise effect on the user is optimal. The target values ​​of the various operating indicators in the intelligent exercise equipment are replaced and collected for each unit of time after the user starts exercising to obtain target operating data. Step S400: When the user uses the intelligent exercise equipment for exercise, the operating status of the intelligent exercise equipment is controlled according to the target running data, and a message prompt is given to the user.

2. The intelligent exercise equipment personalized control method according to claim 1, characterized in that, Step S100 includes: Step S101: Obtain the user's initial vital signs data, which includes the values ​​of various vital signs indicators of the user when they start using the intelligent exercise equipment; Step S102: Obtain historical initial vital sign data of other users using the intelligent exercise equipment, wherein the other users exercised using the intelligent exercise equipment within a historical period, and obtain the values ​​of various vital sign indicators of the other users when they started using the intelligent exercise equipment from the historical initial vital sign data; Step S103: Obtain the characteristics of each other user using the intelligent exercise equipment, and calculate the characteristic value a=(a) of the user's vital signs in the initial vital signs data. △ -a´) / σ, where σ is the standard deviation of the vital signs of each of the other users when they begin using the intelligent exercise equipment, and a △ The value of the user's vital signs in the initial vital signs data, and a' is the average value of the vital signs of each of the other users when they start using the intelligent exercise equipment; Step S104: Obtain the feature values ​​of various vital signs indicators of the user in the initial vital sign data, and aggregate them to obtain the user's initial feature vector A={a1, a2, ..., a...} n }, where a1, a2, ..., a n These are the feature values ​​of the 1st, 2nd, ..., nth vital signs of the user, respectively. Step S105: Evaluate the degree of similarity of vital signs between each of the other users and the user, wherein the evaluation of the degree of similarity of vital signs between the c-th other user and the user is specifically carried out as follows: Calculate the approximate vital sign value r between the c-th other user and the user. c : ， Among them, B c This is the initial feature vector of the c-th other user; When the approximate value of the vital signs is r c If the physical characteristics of the c-th other user are greater than the preset feature approximation threshold, it is determined that the physical characteristics of the c-th other user are similar to those of the user, and the c-th other user is recorded as the approximate user of the user.

3. The intelligent exercise equipment personalized control method according to claim 2, characterized in that, Step S200 includes: Step S201: Obtain each approximate user of the user, record the physical condition of the approximate user after exercising with the intelligent exercise equipment, obtain the historical characteristic change record of the approximate user, and extract the values ​​of various physical indicators of the approximate user after exercise from the historical characteristic change record; Step S202: Obtain the target vital sign data of the user, the target vital sign data including the target values ​​of various vital sign indicators of the user, obtain the historical feature change records of the approximate user, and calculate the target vital sign target value of the approximate user relative to the user in each historical feature change record, wherein the target vital sign value F of the approximate user relative to the user in the e-th historical feature change record. e : ， Where n is the total number of the user's vital signs indicators; a i,target d is the target value of the i-th vital sign indicator of the user; e,i The values ​​of various vital signs of the approximate user after exercise in the e-th historical feature change record; Step S203: Obtain the maximum value of the target vital signs of the approximate user relative to the user in each of the historical feature change records, and record it as the target vital signs target value between the approximate user and the user, and normalize the target vital signs target value; Step S204: Obtain the historical exercise records of the approximate user, and obtain the values ​​of various exercise indicators of the approximate user when exercising with the intelligent exercise equipment from the historical exercise records; The user's exercise process using the intelligent exercise equipment during the historical period is recorded to obtain the user's initial historical exercise record. The total number of the user's initial historical exercise records g is obtained. The first g historical exercise records of the approximate user are obtained, and the time point of the (g-1)th historical exercise record is recorded as the feature time point. Step S205: Obtain the values ​​of various exercise indicators of the user when exercising with the intelligent exercise equipment from the initial historical exercise records, analyze the exercise reference value of the approximate users to the user, and obtain the various exercise reference users of the user.

4. The intelligent exercise equipment personalized control method according to claim 3, characterized in that, Step S300 includes: Step S301: Obtain the intelligent exercise equipment. Based on the historical equipment operation records generated during the use of the exercise reference user, set the unit duration, obtain the average value of each operation indicator of the intelligent exercise equipment within each unit duration from the historical equipment operation records, and aggregate them to obtain the operation dataset of each operation indicator. Step S302: Analyze the impact of the intelligent exercise equipment on the user's exercise effect under different operating conditions to obtain target operating data.

5. The intelligent exercise equipment personalized control method according to claim 4, characterized in that, Step S400 includes: Step S401: Obtain the user's target running data. When the user starts exercising with the intelligent exercise equipment in the current cycle, adjust the various operating indicators of the intelligent exercise equipment based on the target running data, control the operating status of the intelligent exercise equipment, and issue a prompt to the user. Step S402: After the user completes exercise using the intelligent exercise equipment in the current period, obtain the mean value of each element in the running dataset of each running indicator in the second historical device running record after the characteristic time point for each exercise reference user, and generate the target running data of the user in the next period in the current period. Similarly, obtain the target running data of the user in each period after the current period, and control the intelligent exercise equipment when the user exercises using the intelligent exercise equipment.

6. A personalized control system for intelligent exercise equipment, used to execute the personalized control method for intelligent exercise equipment as described in any one of claims 1-5, characterized in that, The system includes a vital sign approximation assessment module, an exercise reference analysis module, a target operation data module, and an operation control module. The vital sign approximation assessment module is used to assess the degree of similarity of vital signs between the other users and the user, and to obtain approximate users; The exercise reference analysis module is used to analyze the exercise reference value of the approximate users to the user, and to obtain the exercise reference users; The target operation data module is used to analyze the degree of influence of the intelligent exercise equipment on the user's exercise effect under different operating states, and obtain target operation data; The operation control module is used to control the operation status of the intelligent exercise equipment based on the target operation data, and to provide message prompts to the user.

7. The intelligent personalized control system for exercise equipment according to claim 6, characterized in that, The vital signs approximation assessment module includes a vital signs approximation value unit and a vital signs approximation assessment unit; The vital sign approximation unit is used to calculate the vital sign approximation values ​​between each other user and the user. The vital sign approximation assessment unit is used to assess the degree of similarity of vital signs between each other user and the user based on the vital sign approximation value, and to obtain approximate users.

8. The intelligent personalized control system for exercise equipment according to claim 6, characterized in that, The exercise reference analysis module includes an exercise reference scoring unit and an exercise reference analysis unit; The exercise reference scoring unit is used to calculate the exercise reference score of the approximate user for the user; The exercise reference analysis unit is used to analyze the exercise reference value of the approximate user to the user based on the exercise reference score, and obtain the exercise reference user.

9. The intelligent personalized control system for exercise equipment according to claim 6, characterized in that, The target running data module includes a running dataset unit and a target running data unit; The running dataset unit is used to obtain the historical device running records of exercise reference users, obtain the average value of various running indicators of the intelligent exercise equipment in each unit of time from the historical device running records, and collect them to obtain the running dataset of the various running indicators. The target running data unit is used to analyze the impact of the intelligent exercise equipment on the user's exercise effect under different operating states based on the running dataset, and obtain target running data.

10. The intelligent personalized control system for exercise equipment according to claim 6, characterized in that, The operation control module includes an operation control unit; The operation control unit adjusts various operating indicators of the intelligent exercise equipment based on the target operating data, controls the operating status of the intelligent exercise equipment, and issues prompts to the user.

Images