CN112786202A

CN112786202A - Health awareness parameter detection method and device, electronic equipment and storage medium

Info

Publication number: CN112786202A
Application number: CN202110113786.8A
Authority: CN
Inventors: 李佳; 朱国康
Original assignee: Anhui Huami Health Technology Co Ltd
Current assignee: Anhui Huami Health Technology Co Ltd
Priority date: 2021-01-27
Filing date: 2021-01-27
Publication date: 2021-05-11

Abstract

The application provides a method and a device for detecting health consciousness parameters, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring a knowledge graph; wherein each node in the knowledge graph has an associated test question; extracting at least one target topic from the test topics associated with each node of the knowledge graph; acquiring the correct probability of the first user for answering each target question; and determining the health consciousness parameters of the first user according to the correct probability of each target subject and the guessability set by each target subject. Therefore, the health consciousness parameters of the user can be detected without offline, the detection cost can be reduced, and the detection coverage is improved. And the health consciousness parameters of the user are determined according to the correct probability of each target topic and the guessability set by each target topic, and compared with the mode that the health consciousness parameters of the user are represented by directly using questionnaire scores in the prior art, the reasonability and the reliability of the detection result can be improved.

Description

Health awareness parameter detection method and device, electronic equipment and storage medium

Technical Field

The present application relates to the field of deep learning technologies, and in particular, to a method and an apparatus for detecting health awareness parameters, an electronic device, and a storage medium.

Background

With the rapid development of economy and society, the living standard of people is gradually improved, and the cognition of health is continuously deepened for both individuals and society. Health awareness changes from being healthy as long as the body is not suffering from a disease to focusing on the prevention of the disease, and good health awareness plays an irreplaceable role in the disease prevention process, and accordingly, the need for health awareness assessment is becoming stronger. The health consciousness assessment is used for objectively evaluating the attention degree of a tested person to the self health condition, and can reflect the comprehensive health cognition of an individual level. Although health consciousness is not equivalent to health conditions, people with high health consciousness are easy to keep good living habits because the health consciousness is often strongly correlated with health behaviors, and thus the people with high health consciousness have good health conditions.

In the related art, the health consciousness assessment system mainly takes off-line assessment as the main, and usually takes a questionnaire or a manner of inquiry by a professional. The online Health consciousness assessment system has few cases, typically a Health intelligence quotient test of Health IQ company, measures and assesses Health consciousness for users by using a mode of filling out questionnaires online, and meanwhile, in order to prevent cheating, the online questionnaires generally have personalized characteristics, and questionnaires of different users are not identical. By means of the convenience of the Internet and the high efficiency of a computer, the on-line filling of the questionnaire can be conveniently applied to large-scale people, and the reading results of the questionnaire can be obtained in a short time.

However, the offline health consciousness assessment system needs to recruit the tested persons, is high in cost and can only be applied to a very small-scale population, and the filled questionnaire needs to be reviewed by professionals, so that the questionnaire only contains a small amount of health knowledge problems, the coverage is insufficient, and the health consciousness condition of the user cannot be accurately assessed. The questionnaire of the online health consciousness assessment system has the characteristic of individuation, different users face different questionnaires with different test questions, and it is obviously unreasonable to compare the health consciousness of different users according to the questionnaire scores.

Disclosure of Invention

The present application is directed to solving, at least to some extent, one of the technical problems in the related art.

The application provides a method and a device for detecting health consciousness parameters, electronic equipment and a storage medium, so that the health consciousness parameters of a user can be detected without offline, detected personnel do not need to be recruited, the detection cost can be reduced, and the coverage of detection is improved. And the health consciousness parameters of the user are determined according to the correct probability of each target topic and the guessability set by each target topic, and compared with the mode that the health consciousness parameters of the user are represented by directly using questionnaire scores in the prior art, the reasonability and the reliability of the detection result can be improved.

An embodiment of a first aspect of the present application provides a method for detecting health consciousness parameters, including:

acquiring a knowledge graph; wherein each node in the knowledge graph has an associated test topic;

extracting at least one target topic from the test topics associated with each node of the knowledge graph;

acquiring the correct probability of the first user for answering each target topic;

determining health consciousness parameters of the first user according to the correct probability of each target title and the guessability set by each target title; wherein the guessability is determined according to the number of the options of the target topic and the type of the topic and is used for indicating the probability that the randomly selected option is the standard answer of the target topic.

According to the detection method of the health consciousness parameters, the knowledge graph is obtained; wherein each node in the knowledge graph has an associated test question; extracting at least one target topic from the test topics associated with each node of the knowledge graph; acquiring the correct probability of the first user for answering each target question; and determining the health consciousness parameters of the first user according to the correct probability of each target subject and the guessability set by each target subject. Therefore, the health consciousness parameters of the user do not need to be detected offline, so that the detected personnel do not need to be recruited, the detection cost can be reduced, and the detection coverage is improved. And the health consciousness parameters of the user are determined according to the correct probability of each target topic and the guessability set by each target topic, and compared with the mode that the health consciousness parameters of the user are represented by directly using questionnaire scores in the prior art, the reasonability and the reliability of the detection result can be improved.

In order to achieve the above object, a second aspect of the present application provides a device for detecting a health awareness parameter, including:

the map acquisition module is used for acquiring a knowledge map; wherein each node in the knowledge graph has an associated test topic;

the extraction module is used for extracting at least one target topic from the test topics associated with each node of the knowledge graph;

the probability obtaining module is used for obtaining the correct probability of the first user answering each target topic;

the determining module is used for determining health consciousness parameters of the first user according to the correct probability of each target title and the guessability set by each target title; wherein the guessability is determined according to the number of the options of the target topic and the type of the topic and is used for indicating the probability that the randomly selected option is the standard answer of the target topic.

The detection device for the health consciousness parameters of the embodiment of the application acquires the knowledge graph; wherein each node in the knowledge graph has an associated test question; extracting at least one target topic from the test topics associated with each node of the knowledge graph; acquiring the correct probability of the first user for answering each target question; and determining the health consciousness parameters of the first user according to the correct probability of each target subject and the guessability set by each target subject. Therefore, the health consciousness parameters of the user do not need to be detected offline, so that the detected personnel do not need to be recruited, the detection cost can be reduced, and the detection coverage is improved. And the health consciousness parameters of the user are determined according to the correct probability of each target topic and the guessability set by each target topic, and compared with the mode that the health consciousness parameters of the user are represented by directly using questionnaire scores in the prior art, the reasonability and the reliability of the detection result can be improved.

To achieve the above object, a third aspect of the present application provides an electronic device, including: the present invention relates to a health awareness system, and more particularly, to a health awareness system for detecting health awareness parameters, a method for detecting health awareness parameters, and a computer program stored in a memory and executable on a processor.

In order to achieve the above object, a non-transitory computer-readable storage medium is provided in an embodiment of a fourth aspect of the present application, on which a computer program is stored, and the computer program, when executed by a processor, implements the method for detecting a health awareness parameter as provided in an embodiment of the first aspect of the present application.

In order to achieve the above object, a fifth aspect of the present application provides a computer program product, wherein when being executed by an instruction processor of the computer program product, the method for detecting a health awareness parameter as provided in the first aspect of the present application is performed.

Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.

Drawings

The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

fig. 1 is a schematic flowchart illustrating a method for detecting health awareness parameters according to an embodiment of the present disclosure;

FIG. 2 is a schematic diagram of a knowledge graph corresponding to an exercise knowledge system in an embodiment of the present application;

fig. 3 is a schematic flowchart of a method for detecting health awareness parameters according to a second embodiment of the present application;

fig. 4 is a schematic flowchart of a method for detecting health awareness parameters according to a third embodiment of the present application;

FIG. 5 is a diagram illustrating access data of a second user to network resources associated with a "basketball" node in the present application;

fig. 6 is a schematic flowchart of a method for detecting health awareness parameters according to a fourth embodiment of the present application;

FIG. 7 is a schematic flow chart illustrating evaluation of health awareness parameters based on access behavior in an embodiment of the present application;

fig. 8 is a schematic structural diagram of a health awareness parameter detecting device according to a fifth embodiment of the present application;

FIG. 9 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present application.

Detailed Description

Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.

A method, an apparatus, an electronic device, and a storage medium for detecting a health awareness parameter according to embodiments of the present application are described below with reference to the accompanying drawings.

Fig. 1 is a flowchart illustrating a method for detecting health awareness parameters according to an embodiment of the present disclosure.

In the embodiment of the application, the detection method of the health consciousness parameters can be applied to an online health consciousness evaluation system.

As shown in fig. 1, the method for detecting health awareness parameters may include the following steps:

step 101, acquiring a knowledge graph; wherein each node in the knowledge graph has an associated test topic.

The concept of the Knowledge Graph (Knowledge Graph) originally originated from the internet search engine field, and the Knowledge Graph is composed of nodes and edges which are connected with each other, wherein the nodes represent the concepts, and the edges represent the relationship between the concepts.

It should be understood that different domains may correspond to different knowledge systems, and different knowledge systems correspond to different knowledge maps, so that different knowledge maps may be created for different domains, and the present application mainly relates to the health domain, and therefore, in the present application, a knowledge map of the health domain may be created in advance based on the knowledge system of the health domain.

For example, when creating the knowledge graph of the health field, the basic concepts in the health field can be organized and summarized by professionals in the fields of chronic diseases, epidemiology, psychology, motion education and nutrition, seven categories of basic concepts including chronic diseases, acute diseases, psychological diseases, household medicines, scientific sleep, motion and dietary nutrition are covered, and corresponding network resources are associated with each leaf level basic concept (the most basic concept) and comprise videos, articles and test questions, wherein the video contents can comprise network public lessons, courses, expert live-broadcast lessons and the like, the article contents can comprise science popularization, news and self-media articles, and the test questions can be created by professionals in the health field. Therefore, each basic concept can be regarded as a knowledge point, the relation among seven categories of knowledge points is combed, and the knowledge graph in the health field can be obtained. For example, if knowledge point a belongs to knowledge point B, for example, "bicycle sport" belongs to "sport type", two knowledge points are connected and their affiliations are marked, and after the relationship between the seven categories of knowledge points is sorted in this way, the knowledge map of the health field can be obtained.

As an example, the exercise knowledge system in the health field is used as an example, and the knowledge map obtained by combing can be shown in fig. 2.

In the embodiment of the application, when health awareness parameters of a user are evaluated, for example, when the user fills out a questionnaire online using a health awareness evaluation system, the health awareness evaluation system may acquire an established knowledge graph.

And 102, extracting at least one target topic from the test topics associated with each node of the knowledge graph.

In the embodiment of the application, each node in the knowledge graph can comprise a plurality of testing questions, and the plurality of testing questions related to each node can be created by professionals in the health field. The types of the test questions may be single choice questions or multiple choice questions, which is not limited in the present application.

As an example, the test subjects may be:

what is the following exercise pattern belongs to anaerobic exercise?

(a) Jogging;

(b) slow swimming;

(c) sprinting;

(d) slowly riding;

is the same weight (100g) of the food below?

(a) Apple, apple;

(b) bananas;

(c) cucumber;

(d) shelled melon seeds;

it should be noted that, the above is only exemplified by using the test questions as single choice questions, and the options of each test question are four, in practical application, the options of the single choice questions may also be 2, 3, 5, and so on, and/or the test questions may also be multiple choice questions, which is not limited in this application. The number of the options of each test question may be the same or different, and the above example is only given by the same number of the options of each test question, which is four.

In the embodiment of the application, when health consciousness parameters of a user are evaluated, at least one target topic can be extracted from test topics associated with each node of a knowledge graph to form a questionnaire. The target questions refer to test questions extracted by the online health consciousness assessment system.

It should be understood that if the test questions in the questionnaire are the same when each user performs the health awareness parameter evaluation, cheating may occur, for example, the user who fills in the questionnaire may reveal the test questions, and then the user who fills in the questionnaire may search the internet for answers to the same test questions. Moreover, the same user may perform the health awareness parameter evaluation several times, and if the questionnaire filled in each time is the same, the user may lose the interest of answering.

Therefore, as a possible implementation manner of the embodiment of the present application, in order to prevent cheating by the user and to motivate the user to fill in the questionnaire, at least one target topic may be randomly extracted from the test topics associated with each node of the knowledge graph.

For example, a root node of the knowledge graph may have seven sub-nodes, which are respectively chronic disease, acute disease, psychological disease, household medicine, scientific sleep, exercise, and dietary nutrition, and 5 nodes may be randomly selected from the sub-nodes corresponding to the chronic disease, acute disease, psychological disease, household medicine, scientific sleep, exercise, and dietary nutrition, and a test question associated with each selected node is randomly extracted as a target question, so as to generate a personalized survey questionnaire including 7 × 5 — 35 target questions.

And 103, acquiring the correct probability of the first user for answering each target topic.

In the embodiment of the present application, the first user is a user who fills out a questionnaire online using the health consciousness assessment system, that is, the first user is a user who participates in an answer to the questionnaire.

In the embodiment of the application, the correct probability of each target topic is determined according to the response condition of the first user for the target topic.

In the embodiment of the application, after the first user finishes answering the questionnaire, for each target topic in the questionnaire, an index number of the target topic can be extracted, and the answering situation of the first user for the target topic is recorded as an actual answer made by the first user in the application, a standard answer of the target topic is retrieved from a knowledge graph according to the index number of the target topic, the standard answer of the target topic is compared with the actual answer made by the first user, and the correct probability of answering the target topic by the first user is determined according to the comparison result. For example, if it is determined that the actual answer is identical to the standard answer according to the comparison result, it is determined that the first user answers correctly, and if it is determined that the actual answer is not identical to the standard answer according to the comparison result, it is determined that the first user answers incorrectly. For each target test question, when the first user answers correctly, the probability of correctness for the target question may be 1, and when the first user answers incorrectly, the probability of correctness for the target question may be 0.

And step 104, determining health consciousness parameters of the first user according to the correct probability of each target topic and the guessability set by each target topic.

Wherein, the guessability is determined according to the number of options of the target topic and the type of the topic and is used for indicating the probability that the randomly selected option is the standard answer of the target topic.

In the embodiment of the application, the guessability of each test question is related to the number of options of the test question and the question type of the test question, wherein the question type can be, for example, a single choice question (single choice for short) or a multiple choice question (multiple choice for short). It is understood that after a professional in the health field creates each test question, the standard answer of the test question is known, and thus, the guessability of the test question can be calculated. For example, when the type of the test question is a single choice question and the number of options of the test question is N, the guessability of the test question is 1/N, for example, when N is 4, the guessability of the test question is 0.25. As another example, when the type of the test question is a multiple choice question, such as N-choice (N-2), the guessability of the test question is

For example, when N is 4, the guessability of the test question is

When N is selected as N (N-1), the guessability of the test question is

For example, when N is 4, the guessability of the test question is

Therefore, in the present application, for each test question, the guessability of the test question can be preset. The guessability of the test subjects may be the same or different.

It should be noted that the target subjects in different questionnaires may be different, and the guessability corresponding to different target subjects may be the same or may also be different, and in order to improve the accuracy of the health awareness parameter determination result, the health awareness parameter of the first user may be determined according to the correct probability of each target subject and the guessability set by each target subject. Compared with the prior art that the health consciousness parameters of the detected personnel are represented by directly using the questionnaire scores, the health consciousness parameters of the first user are determined according to the correct probability of each target subject and the guessability set by each target subject, and the reasonability and reliability of the health consciousness parameter evaluation result can be improved.

In a possible implementation manner of the embodiment of the application, for each target topic, the correct probability of the target topic and the guessability of the target topic can be brought into a prediction formula to obtain a prediction equation containing health consciousness parameters, and then the prediction equation of each target topic can be solved by adopting a maximum likelihood estimation algorithm to obtain the health consciousness parameters. The above process is described in detail with reference to example two.

Fig. 3 is a flowchart illustrating a method for detecting health awareness parameters according to a second embodiment of the present application.

As shown in fig. 3, based on the embodiment shown in fig. 1, step 104 may specifically include the following steps:

step 201, for each target topic, substituting the correct probability of the target topic and the guessability of the target topic into a prediction formula to obtain a prediction equation containing unknown parameters; wherein the unknown parameters include health awareness parameters.

In the embodiment of the present application, the guessability of the purpose of each topic title is set. The correct probability of each target topic is determined according to the response condition of the first user aiming at the target topic.

In the embodiment of the application, the health consciousness parameter of the first user can be calculated and recorded according to the prediction formula. Specifically, for each target topic, the correct probability of the target topic and the guessability of the target topic can be respectively substituted into the prediction formula to obtain a prediction equation containing unknown parameters, that is, each target topic has a corresponding prediction equation. The unknown parameters may include, among other things, health awareness parameters.

In one possible implementation manner of the embodiment of the present application, the health awareness parameter of the first user may be evaluated using Item Response Theory (IRT for short) in the field of cognitive diagnosis. The term in the term reaction theory model refers to a target topic in the questionnaire, and the term reaction refers to the response of the person to be tested, i.e. the first user, to the target topic in the questionnaire.

The IRT model may assume that a person to be tested, that is, a response result of a first user to a target question is affected by four aspects: the ability level of the first user, the difficulty of the target topic, the discrimination of the target topic, the guessability of the target topic, wherein the ability level of the first user is characterized by the health awareness parameter. The health consciousness parameter of the first user, the difficulty of the target topic, the discrimination of the target topic and the guessability of the target topic are respectively represented by theta, a, b and c, and then the correct probability P (theta) of the target topic can be determined according to the formula (1) for each target topic:

that is, the prediction formula may be formula (1), and the unknown parameters may include health awareness parameters, difficulty of each target topic, and degree of distinction.

For example, if the type of the test question is a single-item selection question and the number of options of the test question is 4, c is 0.25, and for the first-item target question, it is assumed that the user answers correctly, and at this time, the prediction equation of the first-item question can be obtained as

For the second target topic, assuming the user answers incorrectly, at this time, the prediction equation of the first topic can be obtained as

By analogy, a prediction equation corresponding to each target topic can be obtained.

And step 202, solving the prediction equation of each target topic by adopting a maximum likelihood estimation algorithm to obtain health consciousness parameters.

For example, the unknown parameters θ, a, and b in the IRT model may be determined by solving with a Maximum Likelihood Estimation (MLE) algorithm, so as to obtain the health awareness parameter θ of the first user.

Specifically, for a first user, the health awareness parameter θ of the first user is a fixed value for different target topics, and a and b are associated with each target topic, and when the target topics are different, the values of a and b may be different. Still taking the example in step 201, for the first track of target topic, the difficulty of the target topic is a1, and the degree of distinction of the target topic is b1, and for the second track of target topic, the difficulty of the target topic is a2, and the degree of distinction of the target topic is b 2.

When the maximum likelihood estimation algorithm is used to solve each parameter, random initialization may be performed on θ, a1, a2, b1, and b2, for example, θ, a1, a2, b1, and b2 are all initialized to 0.5, then P (θ) of the first and second target topics is 0.625, P (θ) of the first target topic is 1, and P (θ) of the second target topic is 0, so that the optimization target may be to minimize an error between P (θ) and 1 of the first target topic and minimize an error between P (θ) and 0 of the second target topic. A penalty function can be defined, the difference between 0.625 and 1 and the difference between 0.625 and 0 can be calculated, the optimization goal is to minimize the difference between the P (theta) of the first topic title and 1, and minimize the difference between the P (theta) of the second topic title and 0, at this time, the values of theta, a1, a2, b1 and b2 can be adjusted according to the difference between the calculated P (theta) and the P (theta) actually corresponding to the target topic based on an optimization algorithm, such as a Stochastic Gradient Descent (SGD) algorithm, and then substituted into the formula (1), a new P (theta) can be calculated, the difference between the new P (theta) and the P (theta) actually corresponding to the target topic can be calculated, and then the values of theta, a1, a2, b1 and b2 can be adjusted repeatedly until the difference is minimized, so as to obtain the optimal theta, The values of a1, a2, b1 and b 2.

It should be noted that, in the prior art, the online health consciousness assessment system completely depends on the questionnaire result, and can only assess the health consciousness parameters of the users who answer the questionnaire, however, since many users do not actively fill in the questionnaire, the users cannot participate in the assessment, and the applicability is not high.

In the method, aiming at the users who do not participate in the questionnaire, the health consciousness parameters of the users can be predicted according to the access behaviors and the basic information of the users, and the applicability of the method can be improved. The above process is described in detail with reference to example three.

Fig. 4 is a flowchart illustrating a method for detecting health awareness parameters according to a third embodiment of the present application.

As shown in fig. 4, the method for detecting health awareness parameters may include the following steps:

step 301, acquiring a knowledge graph; each node in the knowledge graph has an associated test topic and an associated network resource.

In the embodiment of the present application, each node in the knowledge graph may have associated network resources, such as videos, articles, and the like.

Step 302, monitoring the access behavior of the second user to the network resource.

In the embodiment of the present application, the second user is a user who does not use the health awareness assessment system to fill in the questionnaire on line, that is, a user who does not participate in the questionnaire answer, but browses the network resources associated with the nodes in the knowledge graph.

In the embodiment of the application, after a user logs in a health consciousness assessment system through the internet, the user can browse network resources associated with nodes in a knowledge graph, for example, browse content of interest of the user in the network resources, such as browsing popular science articles, news, self-media articles, network public lessons, sports courses, live courses and the like, the health consciousness assessment system can record information of multimedia resource types (such as popular science articles, news, network public lessons and the like) to which the network resources browsed by the user belong, health knowledge points included in the network resources, start browsing time and end browsing time of the network resources and the like, and for example, access records can be stored in an Elasticsearch database.

In the embodiment of the application, for a second user who does not participate in the answer of the questionnaire, the access behavior of the second user to the network resource can be monitored. The access behavior may include the number of times of accessing the network resource associated with each node by the second user, the access duration (i.e., the time difference between the end browsing time and the start browsing time), and the like.

Step 303, performing feature extraction on the access behavior of the second user to obtain the access feature of the second user.

In the embodiment of the application, the access characteristics can include total access times and total access duration of each knowledge type; each knowledge type corresponds to a target node in the knowledge graph, and each target node has a direct parent-child relationship with a root node in the knowledge graph.

For example, the root node of the knowledge graph may have seven target nodes, which are chronic disease, acute disease, psychological disease, family medication, scientific sleep, exercise and dietary nutrition respectively, and the access characteristics may include total number of accesses and total duration of accesses respectively corresponding to the seven nodes of chronic disease, acute disease, psychological disease, family medication, scientific sleep, exercise and dietary nutrition respectively.

The total number of access times of each knowledge type is obtained by counting the number of access times of the associated network resources of each child node having direct or indirect parent-child relationship with the target node; the total access time of each knowledge type is obtained by counting the access time of the associated network resources of each child node having direct or indirect parent-child relationship with the target node.

Specifically, for the total number of accesses of each knowledge type, a target node corresponding to the knowledge type and each child node under the target node may be determined, the number of accesses of network resources associated with each child node under the target node may be counted to obtain the total number of accesses of the target node, the total number of accesses of the target node is used as the total number of accesses of the corresponding knowledge type, the total access duration of network resources associated with each child node under the target node may be counted to obtain the total access duration of the target node, and the total access duration of the target node is used as the total access duration of the corresponding knowledge type.

For example, if the target node includes 5 child nodes, the access times and the access durations corresponding to the 5 child nodes may be respectively counted, the access times corresponding to the 5 child nodes are summed to obtain the total access times of the target node, and the access time durations corresponding to the 5 child nodes are used to obtain the total access duration of the target node.

As a possible implementation manner of the embodiment of the present application, each network resource may belong to a multimedia resource type (e.g., a network public class, a course, an expert live broadcast class, a science popularization, news, a self-media article); the total number of access times can be obtained by weighting and summing the access times of the network resources associated with each child node according to the weight corresponding to each multimedia resource type; the total access duration can be obtained by weighting and summing the access durations of the network resources associated with the sub-nodes according to the weights corresponding to the multimedia resource types.

Specifically, each subnode may be associated with network resources of multiple multimedia resource types, the number of the network resources of each multimedia resource type may be multiple, the access times and the access durations of the network resources of the same multimedia resource type associated with the subnode may be respectively summed to obtain the access times and the access durations of the network resources of each multimedia resource type associated with the subnode, and then, the access times and the access durations of the network resources of each multimedia resource type associated with the subnode may be respectively weighted and summed according to weights corresponding to the multimedia resource types to obtain the access times and the access durations of the subnode. Therefore, the access times and the access duration of each child node can be determined, the access times of all the child nodes under the target node are summed to obtain the total access times, and similarly, the access durations of all the child nodes under the target node are summed to obtain the total access duration.

That is, first, the access times and the access durations of the leaf nodes without child nodes in the knowledge-graph may be determined, and then, the access times and the access durations of the non-leaf nodes in the knowledge-graph are calculated, that is, the access times and the access durations of the child nodes included in the non-leaf nodes are summed respectively to obtain the access times and the access durations of the non-leaf nodes. For each target node, the access times and the access duration corresponding to each child node having a direct parent-child relationship with the target node can be determined, and the access times and the access durations corresponding to each child node having a direct parent-child relationship with the target node are respectively summed to obtain the total access times and the total access duration of the target node, so that the total access times and the total access duration of the target node can be used as the total access times and the total access duration of the knowledge type corresponding to the target node.

For example, taking the target node as the moving node in fig. 2 for example, the corresponding access times and access durations of the leaf nodes in fig. 2 may be first calculated, for example, see fig. 5, and fig. 5 is a schematic diagram of access data of a second user to a network resource associated with a "basketball" node in this application. Marking the number of access times of the network resource of each multimedia resource type as N, and the access time (minute) of the network resource of each multimedia resource type as t, then the access time of the basketball node is the weighted sum of the access times of the network resources of various multimedia resource types, and the access time of the basketball node is the weighted sum of the access times of the network resources of various multimedia resource types.

Assuming that the multimedia resource types include web public classes, courses, expert live classes, science popularization texts, news, and self-media articles, and the weights are 0.25, 0.20, 0.18, 0.15, 0.12, and 0.10, respectively, the access times of the "basketball" node are: 0.25 × 1+0.20 × 0+0.18 × 0+0.15 × 5+0.12 × 15+0.10 × 2 ═ 3.0, the access duration of the basketball "node is: 0.25 × 30+0.20 × 0+0.18 × 0+0.15 × 15+0.12 × 34.5+0.10 × 0.8 ═ 13.97.

Therefore, the access times and the access duration corresponding to all the leaf nodes can be calculated, for each non-leaf node, the access times and the access duration corresponding to each sub-node included in the non-leaf node can be summed up respectively to obtain the access times and the access duration corresponding to the non-leaf node, and finally, the total access times and the total access duration corresponding to seven target nodes (chronic disease, acute disease, psychological disease, household medication, scientific sleep, exercise and dietary nutrition) can be calculated.

In a possible implementation manner of the embodiment of the present application, the access characteristic may further include an average access duration of each knowledge type, where the average access duration of each knowledge type is a ratio of a total access duration of the knowledge type to a total number of accesses of the knowledge type.

And step 304, inputting the access characteristics of the second user and the basic information of the second user into the trained prediction model to obtain the health consciousness parameters of the second user.

And the prediction model learns the mapping relation between the access characteristics and the basic information and the health consciousness parameters.

In an embodiment of the present application, the basic information may include at least one of age, sex, height, weight, or height-weight Index (BMI).

In the embodiment of the application, because the prediction model learns the mapping relationship between the access characteristics and the basic information and the health consciousness parameters, after the access characteristics of the second user are extracted, the access characteristics of the second user and the basic information of the second user can be input into the prediction model, and the health consciousness parameters of the second user are obtained through output of the prediction model.

According to the method for detecting the health consciousness parameters, aiming at the second user who does not participate in the questionnaire answer, the health consciousness parameters of the second user can be predicted according to the access behavior of the second user to the network resources associated with the nodes in the knowledge graph, and the applicability of the method can be improved.

In a possible implementation manner of the embodiment of the application, in order to improve accuracy of a prediction result of the prediction model, the prediction model may be trained according to an access behavior of a first user who has participated in an answer to a questionnaire to a network resource and basic information of the first user. The above process is described in detail with reference to example four.

Fig. 6 is a flowchart illustrating a method for detecting health awareness parameters according to a fourth embodiment of the present application.

As shown in fig. 6, on the basis of the embodiment shown in fig. 4, before step 304, the method for detecting the health awareness parameter may further include the following steps:

step 401, monitoring the access behavior of the first user to the network resource.

Step 402, performing feature extraction on the access behavior of the first user to obtain the access feature of the first user.

In this embodiment, the access behavior of the first user may include the number of times the first user accesses the network resource associated with each node and the access duration.

In the embodiment of the application, the access characteristics may include total access times of each knowledge type, total access time of each knowledge type, and average access time of each knowledge type.

And 403, generating a training sample according to the access characteristics of the first user and the basic information of the first user, and labeling the training sample by using the health consciousness parameters of the first user.

In this embodiment of the application, the first users all have the health consciousness parameters determined in step 104, and may generate training samples according to the access characteristics of the first users and the basic information of the first users, and label the training samples with the health consciousness parameters of the first users to obtain the labeled training samples.

Step 404, training the prediction model by using the labeled training sample.

For example, referring to fig. 7, fig. 7 is a schematic flowchart illustrating a process of evaluating health awareness parameters based on access behavior in an embodiment of the present application. For example, a predictive model may be built using machine learning techniques based on the basic information of the first user, the access data of seven target nodes, and health awareness parameters, such as the feature fields may include: age, gender, height, weight, BMI, total number of chronic disease visits, average length of chronic disease visits, total number of acute disease visits, total length of acute disease visits, average length of acute disease visits, total number of psychological disease visits, total length of psychological disease visits, average length of psychological disease visits, total number of family medication visits, total length of family medication visits, average length of family medication visits, total number of scientific sleep visits, total length of scientific sleep visits, average length of scientific sleep visits, total length of exercise visits, average length of exercise visits, total number of dietary nutrition visits, total length of dietary nutrition visits, average length of dietary nutrition visits. Where the predictive model may use a gradient boosting decision tree.

And training to obtain a prediction model M for predicting the health consciousness parameters based on the access records, and calling the prediction model M to predict the health consciousness parameters of a second user who does not participate in answering the questionnaire only according to the basic information and the access records of the second user. Therefore, even if the user does not participate in answering of the questionnaire, the health consciousness parameters of the user can be predicted according to the access behaviors of the user, and the applicability of the method can be improved.

In order to implement the above embodiments, the present application further provides a device for detecting health consciousness parameters.

Fig. 8 is a schematic structural diagram of a health awareness parameter detection apparatus according to a fifth embodiment of the present application.

As shown in fig. 8, the health awareness parameter detecting device 100 may include: an atlas acquisition module 110, an extraction module 120, a probability acquisition module 130, and a determination module 140.

The map acquisition module 110 is configured to acquire a knowledge map; wherein each node in the knowledge graph has an associated test topic.

The extraction module 120 is configured to extract at least one target topic from the test topics associated with each node of the knowledge graph.

The probability obtaining module 130 is configured to obtain a correct probability that the first user answers each target topic.

The determining module 140 is configured to determine a health awareness parameter of the first user according to the correct probability of each target topic and the guessability set by each target topic; wherein, the guessability is determined according to the number of options of the target topic and the type of the topic and is used for indicating the probability that the randomly selected option is the standard answer of the target topic.

Further, in a possible implementation manner of the embodiment of the present application, each node in the knowledge-graph further has an associated network resource, and on the basis of the embodiment shown in fig. 8, the apparatus 100 for detecting health awareness parameters may further include:

and the detection module is used for monitoring the access behavior of the second user to the network resource.

And the extraction module is used for carrying out feature extraction on the access behavior of the second user to obtain the access feature of the second user.

The prediction module is used for inputting the access characteristics of the second user and the basic information of the second user into the trained prediction model to obtain health consciousness parameters of the second user; and the prediction model learns the mapping relation between the access characteristics and the basic information and the health consciousness parameters.

Further, in a possible implementation manner of the embodiment of the present application, the detecting apparatus 100 for health awareness parameters may further include:

the training module is used for monitoring the access behavior of the first user to the network resources, performing feature extraction on the access behavior of the first user to obtain the access feature of the first user, generating a training sample according to the access feature of the first user and the basic information of the first user, labeling the training sample by adopting the health consciousness parameters of the first user, and training the prediction model by adopting the labeled training sample.

Further, in a possible implementation manner of the embodiment of the present application, the access behavior includes access times and access duration of network resources associated with each node; the access characteristics comprise the total access times and the total access duration of each knowledge type; each knowledge type corresponds to a target node in the knowledge graph, and each target node has a direct parent-child relationship with a root node in the knowledge graph; the total number of access times of each knowledge type is obtained by counting the access times of the associated network resources of each child node having direct or indirect parent-child relationship with the target node; the total access time of each knowledge type is obtained by counting the access time of the associated network resources of each child node having direct or indirect parent-child relationship with the target node.

Further, in a possible implementation manner of the embodiment of the present application, each network resource belongs to a multimedia resource type; the total number of access times is obtained by weighting and summing the access times of the network resources associated with each sub-node according to the weight corresponding to each multimedia resource type; and the total access time length is obtained by weighting and summing the access time lengths of the network resources associated with the sub-nodes according to the weight corresponding to each multimedia resource type.

Further, in a possible implementation manner of the embodiment of the present application, the access characteristics further include an access average duration of each knowledge type; wherein, the average access duration of each knowledge type is the ratio of the total access duration of the knowledge type to the total access times of the knowledge type.

Further, in a possible implementation manner of the embodiment of the present application, the basic information includes at least one of age, gender, height, weight, or height-weight index.

Further, in a possible implementation manner of the embodiment of the present application, the determining module 140 is specifically configured to: for each target topic, substituting the correct probability of the target topic and the guessability of the target topic into a prediction formula to obtain a prediction equation containing unknown parameters; wherein the unknown parameters include health awareness parameters; and solving the prediction equation of each target topic by adopting a maximum likelihood estimation algorithm to obtain health consciousness parameters.

Further, in a possible implementation manner of the embodiment of the present application, the unknown parameters further include difficulty and discrimination of each target topic.

It should be noted that the foregoing explanation of the embodiment of the method for detecting health awareness parameters is also applicable to the device for detecting health awareness parameters of the embodiment, and is not repeated herein.

In order to implement the above embodiments, the present application also provides an electronic device, including: the health awareness system comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the method for detecting the health awareness parameter according to the embodiment of the present application.

In order to achieve the above embodiments, the present application also proposes a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of detecting a health awareness parameter as proposed in the previous embodiments of the present application.

In order to implement the foregoing embodiments, the present application further provides a computer program product, which when executed by an instruction processor in the computer program product, performs the method for detecting health awareness parameters as set forth in the foregoing embodiments of the present application.

FIG. 9 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present application. The electronic device 12 shown in fig. 9 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.

As shown in fig. 9, electronic device 12 is embodied in the form of a general purpose computing device. The components of electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.

Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. These architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus, to name a few.

Electronic device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by electronic device 12 and includes both volatile and nonvolatile media, removable and non-removable media.

Memory 28 may include computer system readable media in the form of volatile Memory, such as Random Access Memory (RAM) 30 and/or cache Memory 32. The electronic device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 9, and commonly referred to as a "hard drive"). Although not shown in FIG. 9, a disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a Compact disk Read Only Memory (CD-ROM), a Digital versatile disk Read Only Memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application.

A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally perform the functions and/or methodologies of the embodiments described herein.

Electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with electronic device 12, and/or with any devices (e.g., network card, modem, etc.) that enable electronic device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, the electronic device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network such as the Internet) via the Network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via the bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

The processing unit 16 executes various functional applications and data processing, for example, implementing the methods mentioned in the foregoing embodiments, by executing programs stored in the system memory 28.

In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means 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 application. In this specification, the schematic representations of the terms used above are not necessarily intended to 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. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.

The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.

It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.

In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.

The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.

Claims

1. A method for detecting health consciousness parameters is characterized by comprising the following steps:

2. The method of claim 1, wherein each node in the knowledge-graph further has associated network resources; after the obtaining of the knowledge graph, the method further comprises the following steps:

monitoring the access behavior of a second user to the network resource;

performing feature extraction on the access behavior of the second user to obtain the access feature of the second user;

inputting the access characteristics of the second user and the basic information of the second user into a trained prediction model to obtain health awareness parameters of the second user;

3. The detection method according to claim 2, wherein before inputting the access characteristics of the second user and the basic information of the second user into the trained predictive model to obtain the health awareness parameters of the second user, the method further comprises:

monitoring the access behavior of the first user to the network resource;

performing feature extraction on the access behavior of the first user to obtain the access feature of the first user;

generating a training sample according to the access characteristics of the first user and the basic information of the first user, and labeling the training sample by adopting the health consciousness parameters of the first user;

and training the prediction model by adopting the marked training samples.

4. The detection method according to claim 2 or 3, wherein the access behavior comprises access times and access duration of network resources associated with each node;

the access characteristics comprise the total access times and the total access duration of each knowledge type; each knowledge type corresponds to a target node in the knowledge graph, and each target node has a direct parent-child relationship with a root node in the knowledge graph;

the total number of access times of each knowledge type is obtained by counting the number of access times of associated network resources for each child node with direct or indirect parent-child relationship to the target node;

the total access time of each knowledge type is obtained by counting the access time of the associated network resources of each child node having direct or indirect parent-child relationship with the target node.

5. The method of claim 4, wherein each of the network resources belongs to a multimedia resource type;

the total number of access times is obtained by weighting and summing the number of access times of the network resources associated with each child node according to the weight corresponding to each multimedia resource type;

and the total access time length is obtained by weighting and summing the access time lengths of the network resources associated with the sub-nodes according to the weight corresponding to each multimedia resource type.

6. The detection method according to claim 4, wherein the access characteristics further include an average time duration of access for each knowledge type;

wherein the average access duration of each knowledge type is the ratio of the total access duration of the knowledge type to the total access times of the knowledge type.

7. The method as claimed in any one of claims 1 to 3, wherein the determining the health awareness parameter of the first user according to the correct probability of each target topic and the guessability of each target topic setting comprises:

for each target topic, substituting the correct probability of the target topic and the guessability of the target topic into a prediction formula to obtain a prediction equation containing unknown parameters; wherein the unknown parameter comprises the health awareness parameter;

and solving the prediction equation of each target title object by adopting a maximum likelihood estimation algorithm to obtain the health consciousness parameters.

8. A health awareness parameter sensing device, comprising:

9. The detection apparatus according to claim 8, wherein each node in the knowledge-graph further has an associated network resource; the device further comprises:

the detection module is used for monitoring the access behavior of a second user to the network resource;

the extraction module is used for carrying out feature extraction on the access behavior of the second user to obtain the access feature of the second user;

the prediction module is used for inputting the access characteristics of the second user and the basic information of the second user into a trained prediction model to obtain health awareness parameters of the second user;

10. The detection apparatus of claim 9, further comprising:

the training module is used for monitoring the access behavior of the first user to the network resources, performing feature extraction on the access behavior of the first user to obtain the access feature of the first user, generating a training sample according to the access feature of the first user and the basic information of the first user, marking the training sample by adopting the health consciousness parameters of the first user, and training the prediction model by adopting the marked training sample.

11. The detection apparatus according to any one of claims 8 to 10, wherein the determination module is specifically configured to:

12. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of detecting a health awareness parameter as claimed in any one of claims 1 to 7 when executing the program.

13. A non-transitory computer-readable storage medium having stored thereon a computer program, wherein the program, when executed by a processor, implements the method of detecting a health awareness parameter of any one of claims 1-7.