CN113241147A - Fitness plan generation method and device and electronic equipment - Google Patents
Fitness plan generation method and device and electronic equipment Download PDFInfo
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- CN113241147A CN113241147A CN202110465590.5A CN202110465590A CN113241147A CN 113241147 A CN113241147 A CN 113241147A CN 202110465590 A CN202110465590 A CN 202110465590A CN 113241147 A CN113241147 A CN 113241147A
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Abstract
The application provides a fitness plan generation method, a fitness plan generation device and electronic equipment, and relates to the technical field of fitness, wherein the method comprises the following steps: the method comprises the steps of obtaining and determining an initial crowd type to which a user currently belongs according to body data of the user, then determining and displaying a step crowd type corresponding to the initial crowd type according to a preset crowd step knowledge graph, responding to selection operation of the user, determining the step crowd selected by the user as a target crowd, then determining a body measurement project of the user according to the initial crowd type and the target crowd type, then obtaining body measurement data corresponding to the body measurement project of the user and body building preference of the user, and finally generating a training plan according to the body measurement data and the body building preference. According to the technical scheme, the physical quality and the fitness preference of the user can be deeply known, the actual situation of the user can be more accurately determined, and then the fitness plan capable of meeting the personalized requirements of the user is generated.
Description
Technical Field
The application relates to a fitness technology, in particular to a fitness plan generating method and device and electronic equipment, and belongs to the technical field of fitness plans.
Background
As the standard of living increases, more and more people begin to build body. Scientific and reasonable body building can improve physical fitness and build up health, and blind excessive body building can damage the body.
In order to scientifically and reasonably exercise, a user usually selects an exercise room with private education service. The personal coach can provide a set of fitness plan for the user according to the current physical state and fitness purpose of the user, and provide relatively scientific and reasonable training instruction for the user in the fitness process of the user. However, in real life, the fitness levels of personal coaches are different, the fitness plan made by the personal coaches with poor levels is not scientific, and the user can not be well distinguished as a novice. To this end, some businesses have introduced a fitness plan generation service for users that can provide scientific fitness plans based on the user's physical data and fitness objectives.
However, the existing fitness plan generating service simply generates a fitness plan according to the physical data and the fitness purpose of the user, and therefore, the fitness plan cannot meet the personalized fitness requirement of the user.
Disclosure of Invention
In view of this, embodiments of the present application provide a method and an apparatus for generating a fitness plan, and an electronic device, which can provide a personalized fitness plan for a user.
In order to achieve the above object, in a first aspect, an embodiment of the present application provides a fitness plan generating method, including:
acquiring body data of a user;
determining an initial crowd type to which the user currently belongs according to the body data, wherein the crowd type is used for distinguishing the users with different physiques;
determining and displaying an advanced crowd type corresponding to the initial crowd type according to a preset crowd advanced knowledge graph, wherein the crowd advanced knowledge graph comprises a plurality of advanced routes, each advanced route is composed of a plurality of different crowd types, and the advanced crowd type of each crowd type is the crowd type pointed by the advanced direction of the crowd type;
responding to the selection operation of the user, and determining the advanced population selected by the user as a target population;
determining a physical measurement project of the user according to the initial crowd type and the target crowd type;
acquiring body measurement data corresponding to a body measurement project of a user and body building preferences of the user;
and generating a training plan according to the physical measurement data and the fitness preference.
Optionally, the crowd advanced knowledge graph further includes a training type corresponding to the advanced route, and the determining of the physical testing project of the user according to the initial crowd type and the target crowd type includes:
determining a target step route between the initial crowd type and the target crowd type in the crowd step knowledge graph according to the initial crowd type and the target crowd type;
and determining the physical testing items according to the training types corresponding to the target step route.
Optionally, the target advanced route is a shortest advanced route between the initial crowd type and the target crowd type in the crowd advanced knowledge graph.
Optionally, the fitness preferences include a fitness frequency, a single fitness duration and fitness equipment desired by the user, and the training plan is generated according to the body measurement data and the fitness preferences, and includes:
determining a training link corresponding to each training type according to each training type and a preset training type knowledge graph, wherein the training type knowledge graph comprises a plurality of training types and training links corresponding to each training type, and each training link comprises a plurality of training actions;
screening target training actions according to the determined training links, the physical measurement data and the fitness equipment expected by the user;
determining the training time of the target training action according to the expected body-building frequency and the single body-building time length of the user;
determining the amount of exercise of the target training action according to the body measurement data and the training time;
and generating a training plan according to the target training action, the training time and the motion amount.
Optionally, the training session comprises one or more of the following: fascia relaxation, dynamic warming, strength training, circulatory resistance training, core strength training, aerobic and stretching.
Optionally, the population type comprises one or more of the following: underweight, wasting, mild wasting, lean, healthy, athletes, stealth obesity, bodybuilders, strong, overweight people, strong, mild obesity, moderate obesity, severe obesity, and very severe obesity.
Optionally, the method further includes:
and adjusting the training plan of the next training according to the execution condition of the training plan completed by the user.
In a second aspect, an embodiment of the present application provides an exercise plan generating apparatus, including:
the system comprises a body measurement module, a crowd model generation module and a crowd model display module, wherein the body measurement module is used for acquiring body data of a user and determining an initial crowd type to which the user belongs currently, the crowd type is used for distinguishing the users with different physiques, an advanced crowd type corresponding to the initial crowd type is determined and displayed according to a preset crowd advanced knowledge map, the crowd advanced knowledge map comprises a plurality of advanced routes, each advanced route comprises a plurality of different crowd types, and the advanced crowd type of each crowd type is the crowd type pointed by the advanced direction of the crowd type; responding to the selection operation of the user, and determining the advanced population selected by the user as a target population; then determining a physical measurement project of the user according to the initial crowd type and the target crowd type;
and the plan module is used for acquiring the body measurement data corresponding to the body measurement project of the user and the body building preference of the user, and generating a training plan according to the body measurement data and the body building preference.
Optionally, the crowd advanced knowledge graph further includes a training type corresponding to the advanced route, and the body measurement module is specifically configured to:
determining a target step route between the initial crowd type and the target crowd type in the crowd step knowledge graph according to the initial crowd type and the target crowd type;
and determining the physical testing items according to the training types corresponding to the target step route.
Optionally, the target advanced route is a shortest advanced route between the initial crowd type and the target crowd type in the crowd advanced knowledge graph.
Optionally, the fitness preferences include a user-desired fitness frequency, a single fitness duration, and fitness equipment, and the planning module is specifically configured to:
determining a training link corresponding to each training type according to each training type and a preset training type knowledge graph, wherein the training type knowledge graph comprises a plurality of training types and training links corresponding to each training type, and each training link comprises a plurality of training actions;
screening target training actions according to the determined training links, the physical measurement data and the fitness equipment expected by the user;
determining the training time of the target training action according to the expected body-building frequency and the single body-building time length of the user;
determining the amount of exercise of the target training action according to the body measurement data and the training time;
and generating a training plan according to the target training action, the training time and the motion amount.
Optionally, the training session comprises one or more of the following: fascia relaxation, dynamic warming, strength training, circulatory resistance training, core strength training, aerobic and stretching.
Optionally, the population type comprises one or more of the following: underweight, wasting, mild wasting, lean, healthy, athletes, stealth obesity, bodybuilders, strong, overweight people, strong, mild obesity, moderate obesity, severe obesity, and very severe obesity.
Optionally, the apparatus further comprises:
and the adjusting module is used for adjusting the training plan of the next training according to the execution condition of the training plan completed by the user.
In a third aspect, an embodiment of the present application provides an electronic device, including: a memory for storing a computer program and a processor; the processor is configured to perform the method of the first aspect or any of the embodiments of the first aspect when the computer program is invoked.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method according to the first aspect or any embodiment of the first aspect.
The fitness plan generating method, the fitness plan generating device and the electronic equipment, which are provided by the embodiment of the application, can acquire the body data of a user, determine the current initial crowd type of the user according to the body data, determining and displaying an advanced crowd type corresponding to the initial crowd type according to a preset crowd advanced knowledge graph, wherein the crowd advanced knowledge graph comprises a plurality of advanced routes, each advanced route is composed of a plurality of different crowd types, the advanced crowd type of each crowd type is the crowd type indicated by the advanced direction of the crowd type, the advanced crowd selected by the user is determined as a target crowd in response to the selection operation of the user, then, determining a body measurement project of the user according to the initial crowd type and the target crowd type, acquiring body measurement data corresponding to the body measurement project of the user and body building preference of the user, and finally generating a training plan according to the body measurement data and the body building preference. According to the technical scheme, the initial crowd type and the target crowd type of the user can be determined firstly, the physical measurement project is determined in a targeted mode, the training plan is generated according to the physical measurement data and the fitness preference, the actual situation of the user can be determined more accurately by deeply knowing the physical quality and the fitness preference of the user, and then the fitness plan capable of meeting the personalized requirements of the user is generated.
Drawings
Fig. 1 is a schematic flow chart of a method for generating a fitness plan according to an embodiment of the present disclosure;
FIG. 2 is a partial schematic view of a population-step knowledge-graph as provided in an embodiment of the present application;
FIG. 3 is a schematic flow chart of generating a training program according to an embodiment of the present disclosure;
FIG. 4 is a partially schematic illustration of a training-type knowledge-graph as provided by an embodiment of the present application;
FIG. 5 is a schematic structural diagram of an exercise program generating apparatus according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The service flow of the existing fitness plan generating service is generally that a user is guided to input physical data and a fitness purpose, and then a fitness plan is generated according to the difference between the physical data input by the user and the fitness purpose. However, different users have different exercise abilities, different living habits and different fitness preferences, and the body data cannot comprehensively and accurately reflect the exercise abilities of the users, so that the fitness purpose cannot reflect the living habits and the fitness preferences of the users. Therefore, the fitness plan generated by the existing fitness plan generating service, although scientific, cannot match the actual situation of the user, and further cannot meet the personalized requirements of the user.
In order to facilitate understanding of technical solutions in the embodiments of the present application, some terms referred to in the embodiments of the present application are first explained below:
physical data: used for reflecting a certain physical index of the human body.
The types of people are as follows: for distinguishing users of different physiques.
Knowledge graph: the data structure is based on a graph and consists of nodes and edges, wherein each node represents an entity, and each edge is a relationship between the entities.
Training type: according to different training purposes, the body-building action is classified into a classification mark.
And (3) physical examination items: the accurate exercise capacity data test project is obtained by specially testing some parts of the human body.
The technical solution of the present application will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
The fitness plan generating method provided by the embodiment of the application can be applied to electronic equipment such as a computer, a notebook or a server, and the embodiment of the application does not limit the specific type of the electronic equipment.
Fig. 1 is a schematic flow chart of a method for generating a fitness plan according to an embodiment of the present application, as shown in fig. 1, the method includes the following steps:
and S110, acquiring body data of the user.
The technical scheme provided by the application can be realized in a form of adding the server to the client, for example, the client is mobile equipment of a user, the user can interact with the mobile equipment, the mobile equipment can send various data to the server through the internet, the server generates a fitness plan for the user, and finally the mobile equipment displays the fitness plan to the user; it may also be implemented in the form of a stand-alone electronic device, for example, a computer in a gym that independently performs interaction with the user and generates a fitness plan. The embodiment of the present application does not limit the specific implementation of the technical solution.
The body data may include a variety of data from the following: height, weight, waist circumference, chest circumference, hip circumference, age, sex, muscle content, body fat content, moisture content, heart rate, blood pressure basal metabolic value, and the like. The body data can be actively input into the electronic device by the user, or the user can firstly measure in the related body data measuring device and then the body data measuring device sends the measured body data to the electronic device. The embodiment of the application does not limit the specific process of acquiring the body data of the user by the electronic equipment.
And S120, determining the current initial crowd type of the user and the target crowd type expected to be reached by the user according to the body data.
The electronic device may store a preset crowd classification table. Developers can collect a large amount of body data to analyze and classify, and a crowd classification table is established according to the relationship between the body data and the actual physique of the corresponding human body. The crowd classification table may include a plurality of crowd types, body data corresponding to each crowd type, and specific numerical values of each body data. The crowd types may include multiple types from the following: underweight, emaciation, mild emaciation, lean, healthy, athletes, stealthy obesity, bodybuilding athletes, strong, overweight people, strong, mild obesity, moderate obesity, severe obesity, and very severe obesity, etc. Referring to table 1, table 1 is a population classification table, and only a part of population types and a part of body data are shown in table 1.
TABLE 1
The electronic device can compare the specific numerical value of the body data of the user with the specific numerical value of the body data in the crowd classification table, and then determine the crowd type with each numerical value in the crowd classification table being most similar to the specific numerical value of the body data of the user as the initial crowd type to which the user belongs currently.
The electronic equipment can also store a preset crowd advanced knowledge map. Developers can analyze the conversion relation among different crowd types according to the kinematics knowledge and the fitness knowledge to construct a crowd advanced knowledge map. The people group advanced knowledge-graph can comprise a plurality of advanced routes, each advanced route can be composed of a plurality of different people groups, and the advanced people group type of each people group is the people group type pointed by the advanced direction of the people group type. Referring to fig. 2, fig. 2 is a partial schematic view of a crowd-derived knowledge-graph according to an embodiment of the present disclosure. In fig. 2, the nodes are population types, and the edges are advanced routes, wherein the population types specifically include overweight, emaciation, health, athletes, invisible obesity, body building athletes, strong and overweight people, strong soldiers, mild obesity, moderate obesity, and severe obesity. For example, a progression route of moderate obesity is connected to mild obesity, the mild obesity indicated by the progression direction is a progression population of moderate obesity, and if the initial population type to which the user belongs is moderate obesity, the user can progress to the population type of mild obesity after passing exercise.
The electronic equipment can determine and display the advanced crowd type corresponding to the initial crowd type according to the preset crowd advanced knowledge graph. Specifically, the electronic device may preset 5 advanced people types corresponding to the initial person type to be displayed, for example, if the initial people type to which the user belongs is medium obesity, the electronic device may determine and display light obesity, overweight people, health, lean and athletes. Then, the user can select the expected people type according to the requirement of the user, and if the user also expects to achieve a better people type, the user can operate the electronic equipment to display all advanced people corresponding to the moderate obesity. After the user selects the displayed crowd type, the electronic device can respond to the selection operation of the user and determine the advanced crowd selected by the user as the target crowd type.
In one possible implementation, the user may also input desired values desired for each body data while inputting the body data. The electronic equipment can determine the corresponding crowd type according to the ideal value of each body data and the crowd classification table, and determine the crowd type as the target crowd type of the user.
And S130, determining the physical measurement project of the user according to the initial crowd type and the target crowd type.
Different users have different exercise abilities, and users with strong exercise abilities may feel relaxed during standard body-building actions, so that the exercise effect cannot be achieved; users with poor exercise performance may feel very tired and easily become fatigued during standard fitness activities. Thus, the user's fitness data may be acquired while the fitness plan is generated for the user.
In order to scientifically and reasonably meet the individual requirements of the user, the electronic equipment can determine the physical measurement items of the user according to the initial crowd type and the target crowd type under the condition that the initial crowd type and the target crowd type of the user are determined. Therefore, on one hand, the user can be prevented from carrying out invalid physical measurement projects, the service efficiency is improved, on the other hand, the physical measurement projects which meet the body building requirements of the user can be provided in a targeted mode, more accurate physical measurement data are obtained, and then the personalized body building plan which meet the real exercise capacity of the user better is generated according to the more accurate physical measurement data.
Specifically, the electronic device may determine a target advance route between the initial crowd type and the target crowd type in the crowd advance knowledge graph according to the initial crowd type and the target crowd type.
When there is only one step route between the initial crowd type and the target crowd type, the step route is the target step route. For example, referring to fig. 2, if the initial population type is mildly obese and the target population type is lean, there is a step between the initial population type and the target population type, and the target step is mildly obese-overweight-healthy-lean.
When there are multiple step routes between the initial crowd type and the target crowd type, the target step route may be the shortest step route between the initial crowd type and the target crowd type in the crowd step knowledge map. For example, referring to fig. 2, if the initial population type is healthy and the target population type is bodybuilder, there are two step routes between the initial population type and the target population type, one step route is healthy-athlete-bodybuilder and the other is healthy-athlete-strong-bodybuilder, and thus the target step route is healthy-athlete-bodybuilder.
The crowd advanced knowledge graph can also comprise training types corresponding to the advanced routes. In the process of constructing the crowd-ranking knowledge graph, developers can determine the most reasonable training types of different crowd types in ranking, and the training types can comprise various types in the following steps: increase muscle mass, reduce fat, lose weight and strength. The training purposes of each training type can be different, the muscle increasing training purpose is to increase the muscle dimension, and the principle is to stimulate the muscle growth through high-capacity training and increase the muscle by matching with the positive heat difference; the fat reduction training aims at reducing the fat proportion while maintaining the muscle content, and the principle is that the fat consumption proportion is higher through specific exercise intensity; the weight reduction training aims at reducing the weight of the whole body and developing a long-term training habit, and the principle is to reduce the weight by improving basic metabolism through movement and producing negative heat difference; the strength training aims to increase the strength level, and the principle is to increase the strength level through high strength training.
For example, referring to fig. 2, the most important part of the weight loss process is the reduction of fat content, and thus the type of training corresponding to the advanced route between moderate and mild obesity is fat reduction. The most important part of the process of weight gain is the increase in muscle content, and therefore the type of training corresponding to the advanced route between underweight and wasting is muscle gain.
The electronic device can determine the physical examination items according to the training types corresponding to the target step route. The training purposes of different training types are different, the testing purposes of different physical testing items are different, and the electronic equipment can determine the physical testing items matched with the training types according to the training purposes and the testing purposes so as to obtain more accurate physical testing data. Wherein the physical examination items may include a plurality of items of: upper limb pushing force, upper limb pulling force, lower limb squatting force, upper limb pushing endurance, upper limb pulling endurance, lower limb squatting endurance, core endurance, flexibility test, cardiopulmonary ability and the like.
And S140, acquiring body test data corresponding to the body test items of the user and body building preferences of the user.
The user can test according to the physical testing items given by the electronic equipment. If the intelligent physical testing equipment is used by the user, after the physical testing project is finished, the intelligent physical testing equipment can actively send physical testing data to the electronic equipment; if the user uses the traditional physical testing equipment, the user can actively input physical testing data into the electronic equipment after finishing the physical testing project.
In order to fully understand the fitness requirements of the user, the user can input the fitness preferences of the user into the electronic equipment. The fitness preferences may include a user-desired fitness frequency, a single fitness duration, and fitness equipment, wherein the fitness frequency may refer to the number of times a user may participate in fitness within a week; a single workout duration may refer to the total time a user may spend in a workout; an exercise machine may refer to an exercise machine that a user may use. For example, user a is an office clerk who likes to exercise at home, and has time to exercise every night, then user a's exercise frequency may be 4-6 times a week, and a single exercise duration may be 1-2 hours, and the exercise machine may include yoga mats, dumbbells, and tension bands. The user B is a full-time user who likes to build body in a gymnasium, only has time to build body at the end of a week, the body-building frequency of the user B can be 1-2 times a week, the single body-building duration can be 2-4 hours, and the body-building equipment can comprise a treadmill, a butterfly machine, a barbell, a multifunctional trainer and a spinning bike.
And S150, generating a training plan according to the physical measurement data and the fitness preference.
After the body measurement data and the fitness preferences of the user are obtained, the electronic equipment can generate a training plan according to the body measurement data and the fitness preferences.
Specifically, fig. 3 is a schematic flowchart of a process of generating a training program according to an embodiment of the present application. As shown in fig. 3, the method comprises the steps of:
and S151, determining a training link corresponding to each training type according to each training type and a preset training type knowledge graph.
The preset training type knowledge graph can be stored in the electronic equipment. The training type knowledge graph is also constructed by developers according to the kinematics knowledge and the fitness knowledge, the training type knowledge graph can comprise a plurality of training types and training links corresponding to the training types, and each training link can comprise a plurality of training actions. Wherein, the training link may comprise a plurality of links of the following: fascia relaxation, dynamic warming, strength training, circulatory resistance training, core strength training, aerobic and tensile, and the like. Referring to fig. 4, fig. 4 is a partial schematic view of a training-type knowledge graph provided in an embodiment of the present application. In fig. 4, the nodes are training types, training links and training actions, the edges are corresponding relations between the training types and the training links and between the training links and the training actions, the training types specifically include muscle building, fat reduction, weight reduction and strength, the training links specifically include strength training and aerobic training, and the training actions specifically include hard-drawing, deep-squatting, rocket pushing and bench-pushing.
In the training type knowledge graph, training links corresponding to different training types may be different, for example, when the training type is strength, the training links may include fascia relaxation, dynamic warming, strength training, core strength training, and stretching; when the type of training is fat reduction, the training sessions may include fascia relaxation, dynamic warming, strength training, resistance to circulation training, aerobic and stretching. The electronic equipment can determine training links corresponding to the training types according to the training types and a preset training type knowledge graph.
S152, screening target training actions according to the determined training links, the physical measurement data and the fitness equipment expected by the user.
In the training type knowledge graph, each training link is provided with a plurality of corresponding training actions, and based on the physical measurement data and the fitness equipment of the user, the electronic equipment can screen out target training actions meeting the fitness requirements of the user from the plurality of training actions.
S153, determining the training time of the target training action according to the expected body-building frequency and the single body-building time length of the user.
The electronic device may determine a training time for the target training action based on a user desired fitness frequency and a single fitness duration. Therefore, the schedule in the fitness plan can better accord with the actual living habits of the user, and the enthusiasm of the user for training is improved.
Further, in order to improve the scientificity of the fitness plan, the developer may set different training schedules for different training types, for example, when the training type is strength, the training schedule may include a training day, an active recovery day, and an intensity day; when the training type is weight loss, the training schedule can comprise aerobic days and aerobic intensity days; when the training type is muscle augmentation, the training schedule may include a training day and an active recovery day; where the type of training is fat reduction, the training schedule may include a training day, an active recovery day, and an aerobic day. The electronic device may determine training schedules when generating training times specifically, and then schedule specific training dates and training durations for each training schedule.
And S154, determining the motion amount of the target training motion according to the physical measurement data and the training time.
Specifically, the electronic device can reasonably determine the amount of exercise for each target training action according to the body measurement data and the training time. For example, if the training link to which the target training action belongs is strength training, the electronic device may determine the amount of exercise according to data related to strength testing in the physical testing data, and may further adjust the amount of exercise in each training according to the specific training time. If the training link to which the target training action belongs is fascia relaxation or stretching, the electronic device may determine the amount of exercise according to other training actions performed on the same day and the training sequence of the other training actions.
And S155, generating a training plan according to the target training action, the training time and the motion amount.
After the electronic device determines the target training motion, training time, and motion amount, a training plan may be generated according to a pre-stored training plan template. Wherein, the developer can respectively design the template format of the training plan according to the characteristics of different training types.
Furthermore, because the physical quality of the human body is a dynamic changing process, the physical quality or the athletic ability of the user can be changed to a certain extent after a plurality of times of body-building training. In order to make the fitness plan more consistent with the actual situation of the user, the electronic device may further adjust the training plan of the next training according to the execution situation of the training plan completed by the user.
Specifically, after the user completes the schedule in the training program every time, the actual situation of completion of the training action can be input into the electronic device. The electronic equipment evaluates the training quality of the current time, and can increase the training amount in the next schedule for finishing better training action on the training quality; for the training action with poor training quality, the training amount can be reduced in the next schedule, so that the training plan can better meet the actual condition of the user.
The fitness plan generating method provided by the embodiment of the application can acquire the body data of the user, determine the current initial crowd type of the user according to the body data, determining and displaying an advanced crowd type corresponding to the initial crowd type according to a preset crowd advanced knowledge graph, wherein the crowd advanced knowledge graph comprises a plurality of advanced routes, each advanced route is composed of a plurality of different crowd types, the advanced crowd type of each crowd type is the crowd type indicated by the advanced direction of the crowd type, the advanced crowd selected by the user is determined as a target crowd in response to the selection operation of the user, then, determining a body measurement project of the user according to the initial crowd type and the target crowd type, acquiring body measurement data corresponding to the body measurement project of the user and body building preference of the user, and finally generating a training plan according to the body measurement data and the body building preference. According to the technical scheme, the initial crowd type and the target crowd type of the user can be determined firstly, the physical measurement project is determined in a targeted mode, the training plan is generated according to the physical measurement data and the fitness preference, the actual situation of the user can be determined more accurately by deeply knowing the physical quality and the fitness preference of the user, and then the fitness plan capable of meeting the personalized requirements of the user is generated.
Based on the same inventive concept, as an implementation of the foregoing method, an embodiment of the present application provides a fitness plan generating device, where the embodiment of the device corresponds to the foregoing method embodiment, and for convenience of reading, details in the foregoing method embodiment are not repeated in this device embodiment one by one, but it should be clear that the device in this embodiment can correspondingly implement all the contents in the foregoing method embodiment.
Fig. 5 is a schematic structural diagram of a fitness plan generating device provided in the embodiment of the present application, and as shown in fig. 5, the device provided in the embodiment includes:
the physical measurement module 110 is configured to acquire body data of a user and determine an initial crowd type to which the user currently belongs, where the crowd type is a classification identifier for distinguishing users of different physical constitutions, and determine and display an advanced crowd type corresponding to the initial crowd type according to a preset crowd advanced knowledge graph, the crowd advanced knowledge graph includes multiple advanced routes, each advanced route is composed of multiple different crowd types, and the advanced crowd type of each crowd type is a crowd type pointed by an advanced direction of the crowd type; responding to the selection operation of the user, and determining the advanced population selected by the user as a target population; then determining a physical measurement project of the user according to the initial crowd type and the target crowd type;
the planning module 120 is configured to obtain the body measurement data corresponding to the body measurement project of the user and the fitness preference of the user, and generate a training plan according to the body measurement data and the fitness preference.
Optionally, the people advanced knowledge graph further includes a training type corresponding to the advanced route, and the body measurement module 110 is specifically configured to:
determining a target step route between the initial crowd type and the target crowd type in the crowd step knowledge graph according to the initial crowd type and the target crowd type;
and determining the physical testing items according to the training types corresponding to the target step route.
Optionally, the target advanced route is a shortest advanced route between the initial crowd type and the target crowd type in the crowd advanced knowledge graph.
Optionally, the fitness preferences include a user-desired fitness frequency, a single fitness duration, and fitness equipment, and the planning module is specifically configured to:
determining a training link corresponding to each training type according to each training type and a preset training type knowledge graph, wherein the training type knowledge graph comprises a plurality of training types and training links corresponding to each training type, and each training link comprises a plurality of training actions;
screening target training actions according to the determined training links, the physical measurement data and the fitness equipment expected by the user;
determining the training time of the target training action according to the expected body-building frequency and the single body-building time length of the user;
determining the amount of exercise of the target training action according to the body measurement data and the training time;
and generating a training plan according to the target training action, the training time and the motion amount.
Optionally, the training link includes a plurality of links selected from the following: fascia relaxation, dynamic warming, strength training, circulatory resistance training, core strength training, aerobic and stretching.
Optionally, the population types include multiple types from the following: underweight, wasting, mild wasting, lean, healthy, athletes, stealth obesity, bodybuilders, strong, overweight people, strong, mild obesity, moderate obesity, severe obesity, and very severe obesity.
Optionally, the apparatus further comprises:
an adjusting module 130, configured to adjust the training plan for the next training according to the execution condition of the training plan completed by the user.
The fitness plan generating device provided in this embodiment may implement the method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
Embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements the method described in the above method embodiments.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Based on the same inventive concept, the embodiment of the application also provides the electronic equipment. Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 6, the electronic device according to the embodiment includes: a memory 21 and a processor 20, the memory 21 being for storing a computer program; the processor 20 is arranged to perform the method according to the above-described method embodiment when the computer program 22 is invoked.
The electronic device provided by this embodiment may perform the above method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
The integrated unit may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable storage medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signal, telecommunication signal, and software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/device and method may be implemented in other ways. For example, the above-described apparatus/device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A method of generating a fitness plan, comprising:
acquiring body data of a user;
determining an initial crowd type to which the user currently belongs according to the body data, wherein the crowd type is used for distinguishing users with different physiques;
determining and displaying an advanced crowd type corresponding to the initial crowd type according to a preset crowd advanced knowledge graph, wherein the crowd advanced knowledge graph comprises a plurality of advanced routes, each advanced route is composed of a plurality of different crowd types, and the advanced crowd type of each crowd type is the crowd type pointed by the advanced direction of the crowd type;
in response to the selection operation of the user, determining the advanced crowd selected by the user as a target crowd;
determining a physical measurement project of the user according to the initial crowd type and the target crowd type;
acquiring body measurement data corresponding to the body measurement item of the user and body building preference of the user;
and generating a training plan according to the physical measurement data and the fitness preference.
2. The method of claim 1, wherein the population advanced knowledge-graph further comprises a training type corresponding to the advanced route, and wherein determining the fitness item of the user according to the initial population type and the target population type comprises:
determining a target advance route between the initial crowd type and the target crowd type in the crowd advance knowledge graph according to the initial crowd type and the target crowd type;
and determining the physical examination project according to each training type corresponding to the target advanced route.
3. The method of claim 2, wherein the target step route is the shortest step route between the initial population type and the target population type in the population step knowledge-graph.
4. The method of claim 2, wherein the workout preferences include a frequency of workouts, a length of time for a single workout, and exercise equipment desired by the user, and wherein generating a training plan based on the body measurement data and the workout preferences comprises:
determining a training link corresponding to each training type according to each training type and a preset training type knowledge graph, wherein the training type knowledge graph comprises a plurality of training types and training links corresponding to each training type, and each training link comprises a plurality of training actions;
screening target training actions according to the determined training links, the physical measurement data and the fitness equipment expected by the user;
determining the training time of the target training action according to the expected body-building frequency and the single body-building time length of the user;
determining the amount of exercise of the target training action according to the physical measurement data and the training time;
and generating the training plan according to the target training action, the training time and the motion amount.
5. The method of claim 4, wherein the training session comprises one or more of: fascia relaxation, dynamic warming, strength training, circulatory resistance training, core strength training, aerobic and stretching.
6. The method of claim 1, wherein the population types include one or more of the following: underweight, wasting, mild wasting, lean, healthy, athletes, stealth obesity, bodybuilders, strong, overweight people, strong, mild obesity, moderate obesity, severe obesity, and very severe obesity.
7. The method according to any one of claims 1-6, further comprising:
and adjusting the training plan of the next training according to the execution condition of the training plan completed by the user.
8. An exercise program generation apparatus, comprising:
the system comprises a body measurement module, a crowd model generation module and a crowd model display module, wherein the body measurement module is used for acquiring body data of a user and determining an initial crowd type to which the user belongs currently, the crowd type is used for distinguishing users with different physical constitutions, then an advanced crowd type corresponding to the initial crowd type is determined and displayed according to a preset crowd advanced knowledge graph, the crowd advanced knowledge graph comprises a plurality of advanced routes, each advanced route comprises a plurality of different crowd types, and the advanced crowd type of each crowd type is the crowd type pointed by the advanced direction of the crowd type; in response to the selection operation of the user, determining the advanced crowd selected by the user as a target crowd; determining a physical measurement project of the user according to the initial crowd type and the target crowd type;
and the plan module is used for acquiring the body measurement data corresponding to the body measurement project of the user and the body building preference of the user, and generating a training plan according to the body measurement data and the body building preference.
9. An electronic device, comprising: a memory for storing a computer program and a processor; the processor is adapted to perform the method of any of claims 1-7 when the computer program is invoked.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113674833A (en) * | 2021-08-23 | 2021-11-19 | 成都拟合未来科技有限公司 | Body-building video generation method, system, terminal and storage medium |
| CN115531832A (en) * | 2022-09-29 | 2022-12-30 | 厦门艾地运动科技有限公司 | Body-building guidance information generation method, terminal equipment and storage medium |
| CN115862807A (en) * | 2022-09-02 | 2023-03-28 | 深圳市智云医康医疗科技有限公司 | Body-building training method, system, medium and electronic equipment based on machine learning |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107335205A (en) * | 2017-06-22 | 2017-11-10 | 北京卡路里信息技术有限公司 | Body-building course recommends method, apparatus, storage medium and processor |
| CN109920510A (en) * | 2019-03-01 | 2019-06-21 | 常州市贝叶斯智能科技有限公司 | A kind of scientific fitness guidance system and method for knowledge based map |
| US20200242305A1 (en) * | 2017-09-18 | 2020-07-30 | Microsoft Technology Licensing, Llc | Fitness assistant chatbots |
| CN111737552A (en) * | 2020-06-04 | 2020-10-02 | 中国科学院自动化研究所 | Method, device and equipment for extracting training information model and acquiring knowledge graph |
| CN111782826A (en) * | 2020-08-27 | 2020-10-16 | 清华大学 | Knowledge graph information processing method, device, equipment and storage medium |
| CN111883228A (en) * | 2020-07-28 | 2020-11-03 | 平安科技（深圳）有限公司 | Health information recommendation method, device, equipment and medium based on knowledge graph |
| CN112084344A (en) * | 2020-09-11 | 2020-12-15 | 清华大学 | Knowledge graph reasoning method, device and storage medium |
| CN112182412A (en) * | 2020-11-26 | 2021-01-05 | 南京吉拉福网络科技有限公司 | Method, computing device, and computer storage medium for recommending physical examination items |
-
2021
- 2021-04-28 CN CN202110465590.5A patent/CN113241147B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107335205A (en) * | 2017-06-22 | 2017-11-10 | 北京卡路里信息技术有限公司 | Body-building course recommends method, apparatus, storage medium and processor |
| US20200242305A1 (en) * | 2017-09-18 | 2020-07-30 | Microsoft Technology Licensing, Llc | Fitness assistant chatbots |
| CN109920510A (en) * | 2019-03-01 | 2019-06-21 | 常州市贝叶斯智能科技有限公司 | A kind of scientific fitness guidance system and method for knowledge based map |
| CN111737552A (en) * | 2020-06-04 | 2020-10-02 | 中国科学院自动化研究所 | Method, device and equipment for extracting training information model and acquiring knowledge graph |
| CN111883228A (en) * | 2020-07-28 | 2020-11-03 | 平安科技（深圳）有限公司 | Health information recommendation method, device, equipment and medium based on knowledge graph |
| CN111782826A (en) * | 2020-08-27 | 2020-10-16 | 清华大学 | Knowledge graph information processing method, device, equipment and storage medium |
| CN112084344A (en) * | 2020-09-11 | 2020-12-15 | 清华大学 | Knowledge graph reasoning method, device and storage medium |
| CN112182412A (en) * | 2020-11-26 | 2021-01-05 | 南京吉拉福网络科技有限公司 | Method, computing device, and computer storage medium for recommending physical examination items |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113674833A (en) * | 2021-08-23 | 2021-11-19 | 成都拟合未来科技有限公司 | Body-building video generation method, system, terminal and storage medium |
| CN113674833B (en) * | 2021-08-23 | 2024-02-06 | 成都拟合未来科技有限公司 | Body-building video generation method, system, terminal and storage medium |
| CN115862807A (en) * | 2022-09-02 | 2023-03-28 | 深圳市智云医康医疗科技有限公司 | Body-building training method, system, medium and electronic equipment based on machine learning |
| CN115862807B (en) * | 2022-09-02 | 2024-02-02 | 深圳市智云医康医疗科技有限公司 | Body-building training method, system, medium and electronic equipment based on machine learning |
| CN115531832A (en) * | 2022-09-29 | 2022-12-30 | 厦门艾地运动科技有限公司 | Body-building guidance information generation method, terminal equipment and storage medium |
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