CN113889272A - Healthy life recommendation system based on intelligent body fat scale - Google Patents
Healthy life recommendation system based on intelligent body fat scale Download PDFInfo
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
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Abstract
The invention discloses a healthy life recommendation system based on an intelligent body fat scale, relates to the technical field of information recommendation, and solves the technical problem of high failure rate of a user health scheme; the processor is used for acquiring a health management directed graph according to the body data and generating health recommendation according to the health management directed graph; the health management directed graph comprises a plurality of nodes, a plurality of sub-paths are arranged among the nodes, and each sub-path comprises a plurality of sub-nodes; the node information of the node comprises body data and data acquisition time, the node information of the child node comprises consumed energy and intake energy, and the generation process of the health recommendation comprises the following steps: according to the recommended consumed energy and the recommended intake energy, acquiring corresponding exercise recommendations and diet lists in an exercise database and a diet database respectively, and then sending the exercise recommendations and the diet lists to a transmission module; and after the data acquisition time of the node is reached, acquiring the body data of the user and carrying out comparison analysis. The invention has reasonable design and is convenient for health life recommendation.
Description
Technical Field
The invention belongs to the technical field of information pushing, and particularly relates to a healthy life recommendation system based on an intelligent body fat scale.
Background
The intelligent body fat scale can measure a plurality of body data such as fat and water besides the body weight. The principle is that muscle contains more moisture such as blood and the like, and can conduct electricity, and fat is not conductive. Since the channel conductor of the in-vivo current is a muscle, the weight of the muscle can be known from the difficulty of passing the current, and the proportion of the body weight can be determined.
In the prior art, measurement data of the intelligent body fat scale are obtained, then simple statistical analysis is carried out on the measurement data, then some health schemes are given, supervision and guidance are not carried out in the execution process of the health schemes, changes of the measurement data of a user are not adjusted in time, the health schemes are difficult to execute, and the failure rate of the health schemes of the user is relatively high.
Disclosure of Invention
The invention provides a healthy life recommendation system based on an intelligent body fat scale, which is used for solving the technical problem of high failure rate of a user health scheme.
The purpose of the invention can be realized by the following technical scheme:
a healthy life recommendation system based on a smart body fat scale, comprising:
the processor is used for acquiring a health management directed graph according to the body data and generating health recommendation according to the health management directed graph; the health management directed graph comprises a plurality of nodes, a plurality of sub-paths are arranged among the nodes, and the sub-paths comprise a plurality of sub-nodes; node information is stored in the node, the node information comprises body data and data acquisition time, child node information is stored in the child node, the child node information comprises energy consumption and energy intake, and the generation process of the health recommendation comprises the following steps:
selecting a guide path from the plurality of sub-paths, acquiring node information of the sub-nodes, acquiring corresponding exercise suggestions and diet lists from an exercise database and a diet database respectively according to the suggested consumed energy and the suggested ingested energy, and then sending the exercise suggestions and the diet lists to a transmission module;
then, acquiring actual child node information of the user, and continuing to perform health recommendation according to the current child path when the actual child node information conforms to the current child node information; when the actual child node information does not accord with the current child node information, acquiring the child node, the previous consumed total energy and the intake total energy, acquiring a corresponding child node, and taking a child path where the child node is as a guide path for health recommendation;
and after the data acquisition time of the node is reached, acquiring the body data of the user and carrying out comparison analysis.
Further, the determining process of the actual child node information includes:
acquiring a difference A between actual energy intake and energy intake of a user at a child node, acquiring a difference B between actual energy consumption and recommended energy consumption corresponding to the child node, and judging that the current child node is met and acquiring the current child node when both A and B are smaller than corresponding set thresholds; and when one of the A and the B is larger than the corresponding set threshold, judging that the user does not conform to the current child node.
Further, the generation process of the health management directed graph comprises the following steps:
the method comprises the steps of obtaining initial body data of a user as a starting point, obtaining target body data of the user as a terminal point, then obtaining target achievement time set by the user, and setting a plurality of nodes to establish a health management directed graph according to expert experience.
Further, the body data includes muscle weight and fat weight.
Further, the alignment analysis process of the body data comprises:
and when both C and D are smaller than a set threshold value, health recommendation is continuously carried out according to the current health management directed graph, and when one of C and D is larger than or equal to the set threshold value, the health management directed graph is reestablished.
Further, the process of reestablishing the health management directed graph includes:
and taking the actual body data of the nodes as a starting point and the target body data as an end point, acquiring the residual time from the nodes to the end point, setting a plurality of nodes according to expert experience, and establishing a health management directed graph.
Further, the intelligent body fat scale is used for acquiring body data of a user and sending the body data to the transmission module; and the transmission module is used for transmitting the body data of the user to the processor.
Further, the process of acquiring body data by the transmission module comprises:
the transmission module is provided with the communication unit, and the storage has the communication protocol of intelligent body fat balance among the communication unit, utilizes the weight to awaken up intelligent body fat balance, then intelligent body fat balance broadcast feature code, and transmission module receives and discerns the feature code, utilizes corresponding communication unit and intelligent body fat balance communication connection, measures user's health data and sends to transmission module on the intelligent body fat balance.
Further, the feature code comprises a communication protocol and a mac address of the intelligent body fat scale.
Further, the transmission module comprises a smart phone, a smart watch and a tablet computer.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, a health management directed graph is established, wherein a plurality of nodes are arranged according to expert experience, the health management process of a user is divided into a plurality of stages, the user can conveniently subdivide a target, the user can conveniently achieve the goal, a plurality of sub-paths are arranged among the nodes, each sub-path comprises a plurality of sub-nodes, and the sub-nodes are used for controlling the intake energy and the consumption energy of the sub-nodes corresponding to time and generating corresponding diet lists and exercise suggestions, so that the user can conveniently control the amount of food and the amount of exercise; after the data acquisition time of the arrival node, measuring body data by using an intelligent body fat scale; and judging whether the body data of the user accords with the current node, and generating a new health management directed graph when the body data of the user does not accord with the current node, so that the possibility that the body data of the user reaches the target body data is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used herein is for the purpose of describing embodiments and is not intended to be limiting and/or limiting of the present disclosure; it should be noted that the singular forms "a," "an," and "the" include the plural forms as well, unless the context clearly indicates otherwise; also, although the terms first, second, etc. may be used herein to describe various elements, the elements are not limited by these terms, which are only used to distinguish one element from another.
As shown in fig. 1, a healthy life recommendation system based on an intelligent body fat scale includes:
an intelligent body fat scale for acquiring body data of a user; the body data comprise weight, fat weight and muscle weight, and after a user stands on the intelligent body fat scale, the intelligent body fat scale is awakened by the weight sensor to acquire the body data of the user such as the weight, the body fat and the like; the intelligent body fat scale is in communication connection with the storage and transmission module, and the intelligent body fat scale sends the body data to the transmission module; the transmission module comprises a smart phone, a tablet computer and wearable smart equipment. The intelligent body fat scale comprises a weight sensor, the intelligent body fat scale can be awakened by weight, and the characteristic code of the intelligent body fat scale is broadcasted after the intelligent body fat scale is awakened by weight.
The transmission module is used for transmitting the body data to the processor, and in real life, a user often goes to multiple places, such as home, gymnasium, company or on business. It is difficult to always obtain body data on the same intelligent body fat scale, and the intelligent body fat scale is inconvenient to carry about. In reality, many places are equipped with intelligent weighing scale, and the convenient measurement acquires the health data. The transmission module comprises a plurality of communication units, and a communication protocol of the intelligent body fat scale is stored in each communication unit; the intelligent body fat scale communication connection method comprises the steps that the intelligent body fat scales are used for being in communication connection with intelligent body fat scales of different brands, a transmission module obtains feature codes of the intelligent body fat scales before the communication connection is carried out, the feature codes comprise communication protocols and mac addresses of the intelligent body fat scales, and then corresponding communication units are selected according to the feature codes to carry out communication connection; transmission module is behind with intelligent body fat balance communication connection, and intelligent body fat balance measures the user, acquires the health data and sends it to transmission module, after the measurement, the user can select the characteristic code of saving intelligent body fat balance, will intelligent body fat balance as body fat balance commonly used, and it is more convenient when being convenient for communication connection, also can select not to save the characteristic code of body fat balance, will intelligent body fat balance is as interim body fat balance.
The health recommendation system comprises a processor, a database and a database server, wherein the processor is used for analyzing body data of a user to generate health recommendation, and a health management database is stored in the processor and is obtained from big data; the processor acquires initial body data of a user, including target body data established by the user for the body of the user;
then taking the initial body data of the user as a starting point, taking the target body data of the user as an end point, and then achieving time according to a target set by the user; constructing a health management directed graph; setting a plurality of nodes according to expert experience, wherein node information is arranged in the nodes, and the node information comprises preset body data and preset data acquisition time corresponding to the nodes; the data acquisition time intervals of the adjacent nodes can be the same or different. Calculating various difference values of body data between adjacent nodes, wherein the difference values comprise muscle weight difference, fat weight difference and weight mass difference, and the weight mass difference is fat weight difference plus muscle weight difference; in this example, the energy to be taken up and other nutrients are calculated for muscle increase and the energy to be consumed for fat decrease; acquiring a corresponding energy difference value and nutrients, distributing the energy difference value and the nutrients to child nodes, and then acquiring information of the child nodes by combining daily energy consumption and daily required nutrients, wherein the daily energy consumption is acquired by a BMR algorithm, and the influencing factors of the BMR include the age, height, muscle weight and weight of a user.
It should be noted that the expert experience is obtained through a neural network model and big data, and is used for obtaining body data of each time point in the user health management process, making a body data curve, and obtaining a time point at which the body data changes obviously as a node, that is, a turning point of a body data curve slope. The time corresponding to the node can be obtained through a large amount of data, and the initial body data and the target body data of the user are input, so that the node can be obtained.
It should be noted that there are several methods for distributing energy and nutrients, and each method corresponds to a sub-path from a starting point to a focal point via several nodes.
A plurality of sub paths are arranged between adjacent nodes, and each sub path comprises a plurality of sub nodes. The child node information includes a recommended intake energy and a recommended consumption energy. It should be noted that branch paths are arranged between the sub-nodes of different sub-paths, the data acquisition time of the sub-nodes is relatively fixed, and the data sampling time of the sub-nodes is set to six points per night in this embodiment.
The nodes, i.e. several key points from the starting point to the end point, make it meaningless to collect the body data for comparison in a short time, since the improvement of the body data of an individual is a relatively long process. The initial body data, the target body data and the set change time of the user are input into a health management model, the health management model obtains key points, namely nodes and corresponding body data of body data change according to big data, meanwhile, the probability of reaching each key time point from a starting point can be obtained, the intervals among the key points are different under the common condition, the body data do not change linearly in a longer time range, and therefore the sampling time of the body data of the key nodes is set according to expert experience. And then, further refining the paths among the key points, setting a plurality of sub-paths, wherein the sub-paths are a plurality of scientific and healthy diet and training processes set according to expert experience in the process of reaching the next node from the node, and the sub-paths are all directed graphs. The sub-paths comprise a plurality of sub-nodes, obvious boundaries are not set among the sub-paths, and the nodes arrive at each other, so that the health management scheme of the user can be adjusted in time. The acquisition time of the child node data is relatively short, in this embodiment, the acquisition time of the child node data is daily, and in some other embodiments, the acquisition time of the child node data is half a day.
After a health management directed graph of a user is generated, acquiring one of a plurality of sub-paths from a starting point to an adjacent node as a guide path, acquiring recommended intake energy and recommended consumption energy of the nearest sub-node, inputting the intake energy into a healthy diet database, acquiring a diet list corresponding to the sub-node, inputting the consumption energy into an action database, acquiring exercise suggestions corresponding to the sub-node, and then transmitting the diet list and the exercise suggestions to a transmission module, wherein for the user, part of the daily consumption energy is relatively fixed and is used for maintaining normal life and work, and the other part of the consumption energy needs to be consumed through physical exercise; health management is achieved by controlling the daily consumption and intake of energy by the user. Acquiring body data of a user at regular time every day through an intelligent body fat scale, determining a corresponding sub-node, then acquiring a sub-path corresponding to the sub-node, acquiring consumed energy and ingested energy of the next sub-node of the sub-path, respectively forming a movement suggestion and a diet list according to the consumed energy and the ingested energy, reaching data acquisition time set by the node after passing through a plurality of sub-nodes, then acquiring body data, comparing and analyzing the body data with the body data set by the node, acquiring a difference C between the actual muscle weight of the node and the preset muscle weight, acquiring a difference D between the actual fat weight and the preset muscle weight, continuing to perform health recommendation according to the current health management directed graph when both C and D are less than a set threshold value, and reestablishing a health management directed graph when one of C and D is greater than or equal to the set threshold value; and taking the actual body data of the nodes as a starting point and the target body data as an end point, acquiring the residual time from the nodes to the end point, setting a plurality of nodes according to expert experience, and establishing a health management directed graph.
When the difference value between each item of data in the body data and the set data is smaller than the set threshold value, the body data of the user is judged to accord with the current node, the current health management directed graph is kept, a plurality of sub-paths leading to the next node are obtained, then one of the sub-paths is selected as a guide path, the node information of the first sub-node is obtained, and a corresponding diet list and a corresponding diet suggestion are generated according to the suggestion consumed energy and the suggestion ingested energy in the node information.
A plurality of exercise suggestions are stored in the action database corresponding to the consumed energy, and the exercise suggestions comprise exercise time, actions and consumed energy; the healthy diet database stores a plurality of diet lists, and the diet lists comprise food names, weights and intake energy.
The transmission module through the user is intelligent wearing equipment promptly and obtains the consumption energy of user's current day, and specific process includes:
before a user eats food each time, the user takes a picture of the food to obtain a food photo and uploads the food photo to the processor; the processor identifies and analyzes the food and the weight thereof in the food photo, obtains and counts the energy intake of each time in the food photo, and then obtains the total energy intake of the user on the day.
A storage module for body data, measurement records and a health management directed graph of a user.
The data in the above formulas are all calculated by removing dimensions and taking numerical values thereof, the formulas are obtained by acquiring a large amount of data and performing software simulation to obtain the formulas closest to the real conditions, and the preset parameters and the preset threshold values in the formulas are set by the technicians in the field according to the actual conditions or obtained by simulating a large amount of data.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (10)
1. A healthy life recommendation system based on intelligent body fat scale, comprising:
the processor is used for acquiring a health management directed graph according to the body data and generating health recommendation according to the health management directed graph; the health management directed graph comprises a plurality of nodes, a plurality of sub-paths are arranged among the nodes, and the sub-paths comprise a plurality of sub-nodes; node information is stored in the node, the node information comprises body data and data acquisition time, child node information is stored in the child node, the child node information comprises energy consumption and energy intake, and the generation process of the health recommendation comprises the following steps:
selecting a guide path from the plurality of sub-paths, acquiring node information of the sub-nodes, acquiring corresponding exercise suggestions and diet lists from an exercise database and a diet database respectively according to the suggested consumed energy and the suggested ingested energy, and then sending the exercise suggestions and the diet lists to a transmission module;
then, acquiring actual child node information of the user, and continuing to perform health recommendation according to the current child path when the actual child node information conforms to the current child node information; when the actual child node information does not accord with the current child node information, acquiring the child node, the previous consumed total energy and the intake total energy, acquiring a corresponding child node, and taking a child path where the child node is as a guide path for health recommendation;
and after the data acquisition time of the node is reached, acquiring the body data of the user and carrying out comparison analysis.
2. The system as claimed in claim 1, wherein the determination process of the actual child node information comprises:
acquiring a difference A between actual energy intake and energy intake of a user at a child node, acquiring a difference B between actual energy consumption and recommended energy consumption corresponding to the child node, and judging that the current child node is met and acquiring the current child node when both A and B are smaller than corresponding set thresholds; and when one of the A and the B is larger than the corresponding set threshold, judging that the user does not conform to the current child node.
3. The system of claim 1, wherein the generation of the health management directed graph comprises:
the method comprises the steps of obtaining initial body data of a user as a starting point, obtaining target body data of the user as a terminal point, then obtaining target achievement time set by the user, and setting a plurality of nodes to establish a health management directed graph according to expert experience.
4. The system of claim 3, wherein the physical data comprises a muscle weight and a fat weight.
5. The system of claim 4, wherein the comparative analysis of the body data comprises:
and when both C and D are smaller than a set threshold value, health recommendation is continuously carried out according to the current health management directed graph, and when one of C and D is larger than or equal to the set threshold value, the health management directed graph is reestablished.
6. The system of claim 5, wherein the process of re-establishing the health management directed graph comprises:
and taking the actual body data of the nodes as a starting point and the target body data as an end point, acquiring the residual time from the nodes to the end point, setting a plurality of nodes according to expert experience, and establishing a health management directed graph.
7. The intelligent body fat scale-based healthy life recommendation system according to claim 1, further comprising an intelligent body fat scale for acquiring body data of a user and sending the body data to the transmission module; and the transmission module is used for transmitting the body data of the user to the processor.
8. The system of claim 7, wherein the process of acquiring the body data by the transmission module comprises:
the transmission module is provided with the communication unit, and the storage has the communication protocol of intelligent body fat balance among the communication unit, utilizes the weight to awaken up intelligent body fat balance, then intelligent body fat balance broadcast feature code, and transmission module receives and discerns the feature code, utilizes corresponding communication unit and intelligent body fat balance communication connection, measures user's health data and sends to transmission module on the intelligent body fat balance.
9. The system of claim 1, wherein the feature code comprises a communication protocol and a mac address of the intelligent body fat scale.
10. The system of claim 1, wherein the transmission module comprises a smart phone, a smart watch, and a tablet computer.
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