CN110543607A - Page data generation method and device, computer equipment and storage medium - Google Patents
Page data generation method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a page data generation method, a page data generation device, computer equipment and a storage medium, wherein after a trigger request of a user is obtained, a facial image of the user is obtained according to the trigger request; then, the facial image is identified to obtain the state information of the user; if the state information indicates that a risk exists, sending a health data acquisition request to a client; obtaining authorization information returned by a client, and obtaining health data of the user according to the authorization information; calculating according to the health data to obtain a health score of the user; and finally, acquiring corresponding page data according to the health score, and sending the page data to a client. After the facial images of the users are identified and detected, the users with the risks of the state information are further detected for the health state, and corresponding page data are formed, so that more targeted page data presentation is formed, and more accurate page data presentation is realized.
Description
Technical Field
the present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for generating page data, a computer device, and a storage medium.
background
with the continuous development of big data technology, many websites or application programs can intelligently match pages for users at present, so as to be more convenient for the users to operate and use. However, as the target users become more popular, many middle aged and elderly people also become target users. However, various data in the internet is complex, such as video data, and many video data with violent, bloody or thriller factors are obviously not suitable for being presented to a part of users. If the page data with the content is directly presented to a part of unsuitable users, the use experience of the part of users is influenced, and the physical and mental health of the users is even seriously influenced. Based on this, providing appropriate page data for users becomes an urgent problem to be solved.
Disclosure of Invention
the embodiment of the invention provides a page data generation method and device, computer equipment and a storage medium, and aims to solve the problem that the generation of page data is not accurate enough.
a page data generation method comprises the following steps:
acquiring a trigger request of a user, and acquiring a facial image of the user according to the trigger request;
identifying the facial image to obtain the state information of the user;
If the state information indicates that a risk exists, sending a health data acquisition request to a client;
Obtaining authorization information returned by a client, and obtaining health data of the user according to the authorization information;
Calculating according to the health data to obtain a health score of the user;
And acquiring corresponding page data according to the health score, and sending the page data to a client.
a page data generating apparatus comprising:
the facial image acquisition module is used for acquiring a trigger request of a user and acquiring a facial image of the user according to the trigger request;
The facial image recognition module is used for recognizing the facial image to obtain the state information of the user;
The first acquisition request sending module is used for sending a health data acquisition request to the client when the state information is in risk;
the health data acquisition module is used for acquiring authorization information returned by a client and acquiring the health data of the user according to the authorization information;
the first health score calculation module is used for calculating according to the health data to obtain the health score of the user;
And the first page data sending module is used for acquiring corresponding page data according to the health score and sending the page data to the client.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the above-mentioned page data generating method when executing the computer program.
a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-described page data generating method.
In the page data generation method, the page data generation device, the computer equipment and the storage medium, after the trigger request of the user is obtained, the facial image of the user is obtained according to the trigger request; then, the facial image is identified to obtain the state information of the user; if the state information indicates that a risk exists, sending a health data acquisition request to a client; obtaining authorization information returned by a client, and obtaining health data of the user according to the authorization information; calculating according to the health data to obtain a health score of the user; and finally, acquiring corresponding page data according to the health score, and sending the page data to a client. After the facial images of the users are identified and detected, the users with the state information risks are further detected for the health state, and corresponding page data are formed, so that more targeted page data presentation is formed, the health of the users is also ensured, and more accurate page data presentation is realized.
Drawings
in order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a schematic diagram of an application environment of a page data generation method according to an embodiment of the present invention;
FIG. 2 is a flowchart of a page data generation method according to an embodiment of the present invention;
FIG. 3 is another flow chart of a method for generating page data in an embodiment of the invention;
FIG. 4 is another flow chart of a method for generating page data in an embodiment of the invention;
FIG. 5 is another flow chart of a method for generating page data in an embodiment of the invention;
FIG. 6 is another flow chart of a method for generating page data in an embodiment of the invention;
FIG. 7 is a schematic block diagram of a page data generating apparatus according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a computer device according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. 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 page data generation method provided by the embodiment of the invention can be applied to the application environment shown in fig. 1, wherein a client (computer device) communicates with a server through a network. The server side obtains a trigger request of a user from the client side, and obtains a facial image of the user according to the trigger request; identifying the facial image to obtain the state information of the user; if the state information indicates that a risk exists, sending a health data acquisition request to a client; obtaining authorization information returned by a client, and obtaining health data of the user according to the authorization information; calculating according to the health data to obtain a health score of the user; and acquiring corresponding page data according to the health score, and sending the page data to a client. Among them, the client (computer device) may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices. The server can be implemented by an independent server or a server cluster composed of a plurality of servers.
In an embodiment, as shown in fig. 2, a page data generating method is provided, which is described by taking the application of the method to the server in fig. 1 as an example, and includes the following steps:
s10: acquiring a trigger request of a user, and acquiring a facial image of the user according to the trigger request.
The user trigger request may be a request triggered by a client in response to a login, registration or opening behavior of a user on a certain platform, website or application program. And after generating the trigger request, the client sends the trigger request to the server. And after the server side acquires the trigger request, acquiring the facial image of the user according to the trigger request. Specifically, the server side can send an image acquisition instruction to the client side, the client side performs image acquisition on a face area of the user through an image acquisition tool to obtain a face image, and then the face image is sent to the server side.
S20: and identifying the facial image to obtain the state information of the user.
The state information is information reflecting the health state of the user, and the age and/or the health state of the user can be identified according to the facial image so as to obtain the state information of the user. For example, the age of the user may be identified from the face image, the age information of the user may be determined, and if the age information of the user is determined to exceed a preset age threshold, the state information of the user may be considered as being at risk. Specifically, an age recognition model may be trained in advance using a large amount of sample data, and then the facial image may be subjected to age recognition using the age recognition model, and then the status information of the user may be further obtained according to the age information obtained by the recognition. For example, an age threshold (60, 65, 70, 75, or the like) may be set in advance, and the relationship between the age information identified by the age identification model and the age threshold may be determined, and if the age is greater than the age threshold, the state information of the user may be considered to be at risk.
furthermore, the health state of the user can be directly identified by the facial image through a preset health identification model, and the state information of the user can be obtained according to the identification result. Specifically, the health recognition model can be trained by the following steps: acquiring a training sample set, wherein the training sample comprises a face sample image and labeling information of the face sample image, and the labeling information is used for indicating health condition information of each category in a preset health condition information category set and the probability corresponding to the health condition information of each category; and taking the face sample image of each training sample in the training sample set as input, taking the labeling information corresponding to the input face sample image as output, and training the neural network to obtain the health recognition model.
Preferably, the status information of the user can be determined by integrating the age information and the health status information. Specifically, the age information of the user may be determined first, and if the age information exceeds a preset threshold, the health status of the user is further determined, so as to obtain the status information of the user. Or the health state of the user can be determined firstly, and if the health state exceeds a preset threshold, the age information of the user is further determined, so that the state information of the user is obtained. Or identifying the age information and the health state of the user, and then integrating the identification results of the age information and the health state of the user to determine the state information of the user.
S30: and if the state information indicates that the risk exists, sending a health data acquisition request to the client.
Health data is various types of data reflecting the relevance of a user to health, such as: body temperature, blood pressure, pulse, electrocardiogram, oxygen concentration, vital capacity, blood glucose, or respiratory rate. And the server side sends a health data acquisition request to the client side under the condition that the state information of the user is acquired as risk, so as to acquire the health data of the user.
S40: and acquiring authorization information returned by the client, and acquiring the health data of the user according to the authorization information.
After sending the health data acquisition request to the client, the client returns the authorization information after the agreement instruction of the user. And the server acquires the health data of the user according to the authorization information. Specifically, the health data can be obtained through a smart terminal (for example, a smart bracelet, a smart phone, a smart blood pressure meter, or the like), or obtained through a third-party application program. Illustratively, the third party application may be an APP for peace and quiet doctors, spring rain doctors, or Huan sports health, etc.
s50: and calculating according to the health data to obtain the health score of the user.
And calculating the health score of the user according to the health data. Specifically, a reference health parameter table may be set in advance. And matching according to each parameter in the health data and the corresponding health parameter table, obtaining the score of each item according to the matching degree, and integrating the scores of each item to obtain the health score of the user. Specifically, the health parameter table includes parameter items and standard data corresponding to each parameter item. For example, if a parameter item is heart rate, the standard data corresponding to the parameter item (heart rate) is 60 to 100 times/min. In the step, the heart rate data of the user in the health data is compared with the standard data of the heart rate in the health parameter table, if the heart rate data of the user is within the standard data (60-100 times/min), the score of the health data is higher, and if the heart rate data of the user is not within the standard data (60-100 times/min), the score of the health data is lower. Further, different score levels can be set according to the deviation degree of the specific health data and the corresponding reference interval value, the higher the deviation degree is, the lower the corresponding score is, and the specific adaptation and adjustment can be performed according to different parameter items, which is not described herein again.
specifically, if the health data of the user includes blood pressure and pulse. The blood pressure and the pulse of the user can be evaluated according to the preset standard data of the blood pressure and the pulse, so that the score corresponding to the blood pressure and the pulse of the user at the moment is obtained. And finally, directly or indirectly adding the scores corresponding to the blood pressure and the pulse to obtain the health score. The intermediate ground addition can be embodied as setting different weights for different health data in advance, and superposing the value of each health data after multiplying the value by the corresponding weight.
Exemplarily, assume that the standard data of blood pressure and pulse are:
Blood pressure: systolic pressure of 100-;
Pulse: 60-100 times/min.
if the blood pressure value and the pulse value in the health data of the user are respectively as follows: systolic pressure is 150mmHg, diastolic pressure is 100mmHg, and pulse is 90. Then the blood pressure value of the user deviates from the normal value at this time, which is the case of high blood pressure, so that the score corresponding to the blood pressure value in the health data of the user at this time is lower, and the score corresponding to the pulse value is normal. Alternatively, a normal score and an abnormal score may be preset, for example: the normal score was 1 and the abnormal score was 0. Then the health score for that user is 1+0 to 1. Further, if the corresponding weights a and b are set for the blood pressure and the pulse, the health score of the user at this time is 1 × a +0 × b.
Further, the criterion data may be further refined and layered in view of more accurate health score calculation. For example, the blood pressure data can be further divided into: health standard data: systolic pressure of 100-; sub-health criteria data: systolic blood pressure 131-: systolic pressure >140mmHg, diastolic pressure >90 mmHg. Then, different scores (e.g., 1, 0.7, 0.5) are set for the three cases, respectively, to perform the calculation more accurately. It is to be understood that the above example is merely one piece of reference data and should not be taken as the only possible data for the present application.
It will be appreciated that the calculation of the user health score may be made based on the user's current health data. Alternatively, a comprehensive measure is made in conjunction with the user's recent health data over a period of time. Illustratively, the health data of the user in the last week is calculated and averaged to obtain the health score of the user, so as to better reflect the recent health state of the user.
s60: and acquiring corresponding page data according to the health score, and sending the page data to a client.
different health score intervals are preset, and corresponding page data are configured for each health score interval. The page data may be different products or services, such as books, music, videos, or financial products. Wherein, different page data are divided according to different health score intervals. For example, if the health score interval corresponds to a higher numerical value, the severity or risk level of the corresponding page data may be correspondingly increased. And the lower the numerical value corresponding to the health score interval is, the more drastic or risky degree of the corresponding page data can be correspondingly reduced. For example, if the health score interval corresponding to the health score is higher in value, the music matched to the user may include more vigorous or violent music, the financing product matched to the user may include a product with a higher risk coefficient, the video matched to the user may include a suspicion or terrorist movie, and the like. It is understood that the page data may include a plurality of types at the same time, that is, the page data may include books, music, or more. The specific setting can be determined according to actual needs.
After the page data are obtained, the page data are sent to the client, so that the client can display the page data on a visual interface of the client for a user to select.
in the embodiment, after the trigger request of the user is acquired, the facial image of the user is acquired according to the trigger request; then, the facial image is identified to obtain the state information of the user; if the state information indicates that a risk exists, sending a health data acquisition request to a client; obtaining authorization information returned by a client, and obtaining health data of the user according to the authorization information; calculating according to the health data to obtain a health score of the user; and finally, acquiring corresponding page data according to the health score, and sending the page data to a client. After the facial images of the users are identified and detected, the users with the state information risks are further detected for the health state, and corresponding page data are formed, so that more targeted page data presentation is formed, the health of the users is also ensured, and more accurate page data presentation is realized.
in one embodiment, as shown in fig. 3, the recognizing the facial image to obtain the status information of the user includes:
S21: and identifying the facial feature points of the facial image to obtain target key points, and obtaining an accurate facial image according to the target key points.
The face feature point detection algorithm is an algorithm for automatically positioning key feature points of a face, such as eyes, nose tips, mouth corner points, eyebrows, contour points of each part of the face and the like, according to an input image containing the face. Specifically, the target key points in this step may be acquired in the following manner:
(1) the OpenCV self-contained Viola-Jones algorithm based on Harr characteristics;
the OpenCV is a cross-platform computer vision library, can run on Linux, Windows, Android and Mac OS operating systems, is composed of a series of C functions and a small number of C + + classes, provides interfaces of languages such as Python, Ruby, MATLAB and the like, realizes a plurality of general algorithms in the aspects of image processing and computer vision, and is a human face feature point detection algorithm based on Harr features. The Haar feature is a feature reflecting the gray level change of an image, and is a feature reflecting the difference value of pixel blocks. Haar features fall into three categories: edge features, linear features, and center-diagonal features. The Viola-Jones algorithm is a method of performing face detection based on the haar feature value of a face.
(2) Dlib based on HOG + SVM features;
Wherein dlib is a modern C + + tool box, which contains machine learning algorithm and tools for creating complex software in C + + to solve practical problems, HOG is Histogram of Oriented Gradient (HOG), SVM (support Vector machine) is a support Vector machine, which is a common discriminant method and is generally used for pattern recognition, classification and regression analysis, and HOG features are widely used in image recognition in combination with SVM classifiers.
(3) Three face detection methods (DPM, HeadHunter, and HeadHunter _ baseline) by doppia library.
among them, dpm (deformable Part model) is a target detection algorithm, and has become an important Part of many classifiers, segmentation, human body posture and behavior classification at present. DPM can be regarded as the extension of HOG, and the method comprises the steps of firstly calculating a gradient direction histogram, then training by using an SVM to obtain a target gradient model, and then classifying, so that the model is matched with a target. The HeadHunter and HeadHunter _ baseline algorithms are the same as DPM in terms of method, except that the model used is different.
the following process of obtaining face feature points is described by taking the face feature point detection algorithm (1) as an example:
Firstly, obtaining a sample image of an input face image, preprocessing (normalizing) the sample image, and then training to obtain a face characteristic point model, namely a Viola-Jones algorithm of Harr characteristics; and finally, carrying out matching calculation according to a Viola-Jones algorithm of Harr characteristics and the classification of the human face characteristic regions to obtain the human face characteristic point information of the human face image.
in one embodiment, the target keypoints may be three for the left ear, the right ear, and the chin. And then, determining the symmetry point of the chin point by taking the connecting line of the left ear point and the right ear point as a symmetry axis. And respectively taking the left ear point, the right ear point, the chin point and the symmetrical point as the middle points of each edge in the rectangle to construct a rectangular frame. And extracting an image from the face image according to the rectangular frame to obtain an accurate face image.
S22: and identifying the accurate facial image by adopting a preset age identification model to obtain age information.
And identifying the accurate face image through an age identification model of a pre-training number to obtain the age information of the user. The age identification model is obtained by pre-training a neural network. Alternatively, the age information may be a specific age value, or an age interval, which is determined according to the actual application requirement.
S23: and if the age information is in a preset high-risk age interval, the state information is that a risk exists.
The high-risk age interval is a preset interval range, and optionally, the high-risk age interval may be: [65, 100], [70, 100], [75, 100], or [80, 100], and the like. And if the age information is within the high-risk age interval, the user is considered as a high-age user, and the corresponding state information is risk.
s24: and if the age information is in a preset middle risk age interval, carrying out health state identification on the accurate facial image to obtain a health state.
the middle risk age interval is a preset interval range, and optionally, the middle risk age interval may be: [65, 70], [55, 60], [60, 65], or [60, 70], and the like. And if the age information of the user is in the risk age interval, further identifying the health state of the user. Specifically, the health state of the user can be identified through a preset health identification model, and the health state of the user is obtained according to an identification result. The health recognition model can be obtained by training the following steps: the method comprises the steps of obtaining a training sample set, wherein the training sample comprises a face sample image and labeling information of the face sample image, and the labeling information is used for indicating health condition information of each category in a preset health condition information category set and probability corresponding to the health condition information of each category. The annotation information may include: good, general, poor, etc.; and taking the face sample image of each training sample in the training sample set as input, taking the labeling information corresponding to the input face sample image as output, and training the neural network to obtain the health recognition model. Alternatively, the health status may include good, general, bad, and the like.
S25: and if the health state of the user is not good, the state information is that the risk exists.
and if the health state of the user is identified to be not good, the state information corresponding to the user is the risk.
in the embodiment, the facial feature points of the facial image are identified to obtain the accurate facial image, so that the accurate identification is facilitated. And then, identifying the accurate facial image by adopting a preset age identification model to obtain age information. And if the age information is in a preset high-risk age interval, the state information is that a risk exists. And if the age information is in a preset middle risk age interval, carrying out health state identification on the accurate facial image. And if the health state of the user is not good, the state information is that the risk exists. By carrying out different judgments on different age information, the accuracy of the state information is better ensured.
In one embodiment, the trigger request further includes user information.
the user information is self-related information input by the user through the client during registration, such as name, age, identification number, and the like.
in this embodiment, as shown in fig. 4, after the state information indicates that there is a risk, the page data generating method further includes:
s31: and acquiring the real age data of the user from the user information, and if the age information is not matched with the real age data, sending an image reacquisition request to a client.
in this step, if the status information indicates that there is a risk, the real age data of the user is obtained from the user information, where the real age data is the age data input by the user. If the age information does not match the real age data, it indicates that there is a possibility of an error in identification or an error in user input, and in order to make a more accurate determination, an image reacquisition request is sent to the client to reacquire the facial image of the user.
S32: and acquiring a plurality of correction face images returned by the client according to the image reacquisition request, and identifying the plurality of correction face images to obtain the corrected age information of the user.
After receiving the image reacquisition request, the client acquires the face image of the user again, and acquires a plurality of corrected face images of the user to avoid errors. And then sending the plurality of corrected face images to a server. After acquiring the plurality of correction face images, the server identifies the plurality of correction face images to obtain the corrected age information of the user. The corrected age information may be a mean value of a plurality of age information obtained by identifying a plurality of corrected face images.
S33: and if the corrected age information is in a preset high-risk age interval, the state information is that a risk exists.
s34: and if the corrected age information is in a preset middle risk age interval, carrying out health state identification on the accurate facial image.
S35: and if the health state of the user is not good, triggering and executing the step of sending the health data acquisition request to the client.
after obtaining the corrected age information, the user is judged again to be in the status information, and it is understood that steps S33-S34 are the same as steps S23-S24, and will not be described again. And triggering and executing the step of sending the health data acquisition request to the client when the health state of the user is not good.
In this embodiment, if the status information indicates that there is a risk, the real age data of the user is obtained from the user information, and if the age information does not match the real age data, an image reacquisition request is sent to a client. And acquiring a plurality of correction face images returned by the client according to the image reacquisition request, and identifying the plurality of correction face images to obtain the corrected age information of the user. Through the secondary confirmation of the age information of the user, the accuracy of the overall identification is better ensured.
In an embodiment, as shown in fig. 5, the calculating according to the health data to obtain the health score of the user includes:
s51: and acquiring a preset reference health value from a database according to the health data.
S52: and matching each health parameter in the health data according to a preset reference health value to obtain the score of each health parameter.
s53: and superposing the score of each health parameter according to a preset weight to obtain the health score of the user.
specifically, a reference health value is preset for each health parameter in the health data, and it is understood that the reference health value may be a numerical range or a threshold. According to the matching result of each health parameter in the health data and the corresponding reference health value, the score of each health parameter can be obtained. The score may be set according to the degree of deviation of the specific health parameter from the baseline health value. And then, the score of each health parameter is superposed according to the preset weight of each health parameter to obtain the health score of the user.
in the embodiment, the health data is quantitatively measured through the preset reference health value, so that the health score of the user is obtained, and the health state of the user is measured more intuitively.
In an embodiment, as shown in fig. 6, after the obtaining the corresponding page data according to the health score and sending the page data to the client, the page data generating method further includes:
S70: and acquiring data selection information of the user.
And after the page data is sent to the client, monitoring the operation behavior of the user, and if the user selects one or more specific products, forming data selection information by the client and sending the data selection information to the server. In particular, the data selection information may be a data identification of the product selected by the user.
S80: and acquiring a risk value of corresponding data according to the data selection information, and acquiring the associated terminal information of the user if the risk value is not matched with the health score.
each product is preset with a risk value, and the risk value is set differently according to different data types. For example, for a financial product, the setting of the risk value can be performed according to the specific situation of the product. For video, the risk value can be set according to the video type or video grading situation. And after the risk value of the corresponding product is obtained, if the risk value is not matched with the health score, acquiring the associated terminal information of the user.
the information of the associated terminal can be input for the user during registration or obtained through a third party data platform. For example, the associated side information may be an emergency contact of the user.
S90: and sending reminding information to the corresponding correlation terminal according to the correlation terminal information.
In this embodiment, after the data selection information of the user is acquired, a risk value of a corresponding product is acquired according to the data selection information, and if the risk value is not matched with the health score, the association end information of the user is acquired. And sending reminding information to the corresponding correlation terminal according to the correlation terminal information. The behavior of the user can be better monitored, and the better health monitoring of the user is ensured.
The embodiment of the invention also relates to a page data generation method, which is described by taking the application of the method to the server side in fig. 1 as an example, and comprises the following steps:
Acquiring a trigger request of a user, wherein the trigger request comprises user information.
and extracting age information of the user from the user information.
and if the age information is in a preset high-risk age interval, the state information of the user is that the user has a risk.
And if the age information is in a preset middle risk age interval, carrying out health state identification on the accurate facial image.
and if the health state of the user is not good, the state information is that the risk exists.
And if the state information indicates that the risk exists, sending a health data acquisition request to the client.
And acquiring authorization information returned by the client, and acquiring the health data of the user according to the authorization information.
And calculating according to the health data to obtain the health score of the user.
And acquiring corresponding page data according to the health score, and sending the page data to a client.
In the embodiment, after the user is registered, the age information of the user is extracted from the user information, further judgment of the user state information is carried out according to the age information, if the state information is in risk, the health score is further calculated, corresponding page data is generated according to the health score, further detection of the health state of the user in risk in the state information is carried out, and the corresponding page data is formed, so that more targeted data recommendation is formed, the health of the user is also ensured, more accurate page data presentation is realized, and the page data forming efficiency is improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
In an embodiment, a page data generation device is provided, and the page data generation device corresponds to the page data generation method in the above embodiment one to one. As shown in fig. 7, the page data generating apparatus includes a facial image acquisition module 10, a facial image recognition module 20, a first acquisition request transmitting module 30, a health data acquisition module 40, a first health score calculation module 50, and a first page data transmitting module 60. The functional modules are explained in detail as follows:
A facial image obtaining module 10, configured to obtain a trigger request of a user, and obtain a facial image of the user according to the trigger request;
a facial image recognition module 20, configured to recognize the facial image to obtain status information of the user;
A first obtaining request sending module 30, configured to send a health data obtaining request to the client when the state information indicates that there is a risk;
The health data acquisition module 40 is used for acquiring authorization information returned by the client and acquiring the health data of the user according to the authorization information;
A first health score calculation module 50, configured to calculate according to the health data to obtain a health score of the user;
And a first page data sending module 60, configured to obtain corresponding page data according to the health score, and send the page data to the client.
Preferably, the facial image recognition module 20 includes a face feature point recognition unit, an age information recognition unit, a risk information recognition unit, a health status recognition unit, and a risk health status recognition unit.
And the face characteristic point identification unit is used for identifying the face characteristic points of the face image to obtain target key points and obtaining an accurate face image according to the target key points.
And the age information identification unit is used for identifying the accurate facial image by adopting a preset age identification model to obtain age information.
And the risk information identification unit is used for determining that the state information is in risk when the age information is in a preset high-risk age interval.
and the health state identification unit is used for identifying the health state of the accurate facial image to obtain the health state when the age information is in a preset middle risk age interval.
And the risk health state identification unit is used for determining that the state information is in risk when the health state of the user is not good.
Preferably, the target key points may be left ear points, right ear points, and chin points. The face characteristic point identification unit is further used for determining a symmetrical point of the chin point by taking a connecting line of the left ear point and the right ear point as a symmetrical axis; respectively taking the left ear point, the right ear point, the chin point and the symmetrical point as the middle points of each edge in the rectangular frame to construct the rectangular frame; and extracting an image from the face image according to the rectangular frame to obtain an accurate face image.
Preferably, the page data generating device is further configured to acquire real age data of the user from the user information, and send an image reacquisition request to the client if the age information does not match the real age data. And acquiring a plurality of correction face images returned by the client according to the image reacquisition request, and identifying the plurality of correction face images to obtain the corrected age information of the user. And if the corrected age information is in a preset high-risk age interval, the state information is that a risk exists. And if the corrected age information is in a preset middle risk age interval, carrying out health state identification on the accurate facial image. And if the health state of the user is not good, the state information is that the risk exists.
Preferably, the first health score calculating module 50 is further configured to obtain a preset reference health value from a database according to the health data. And matching each health parameter in the health data according to the reference health value to obtain the score of each health parameter. And superposing the score of each health parameter according to a preset weight to obtain the health score of the user.
Preferably, the page data generating device is further configured to obtain data selection information of the user. And acquiring a risk value of corresponding data according to the data selection information, and acquiring the associated terminal information of the user if the risk value is not matched with the health score. And sending reminding information to the corresponding correlation terminal according to the correlation terminal information.
In an embodiment, a page data generation device is provided, and the page data generation device corresponds to the page data generation method in the above embodiment one to one. The functional modules are explained in detail as follows:
The trigger request acquisition module is used for acquiring a trigger request of a user, and the trigger request comprises user information.
and the age information acquisition module is used for extracting the age information of the user from the user information.
And the first risk information acquisition module is used for determining that the state information of the user is in risk when the age information is in a preset high-risk age interval.
and the health state identification module is used for identifying the health state of the accurate facial image if the age information is in a preset middle risk age interval.
and the first risk information acquisition module is used for determining that the state information is in risk when the health state of the user is not good.
And the second acquisition request sending module is used for sending a health data acquisition request to the client when the state information is in risk.
and the authorization information acquisition module is used for acquiring authorization information returned by the client and acquiring the health data of the user according to the authorization information.
And the second health score calculating module is used for calculating according to the health data to obtain the health score of the user.
And the second page data sending module is used for acquiring corresponding page data according to the health score and sending the page data to the client.
For specific limitations of the page data generation apparatus, reference may be made to the above limitations of the page data generation method, which is not described herein again. The various modules in the page data generation device can be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for the data used in the page data generating method in the above embodiment. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a page data generating method.
In one embodiment, a computer device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the page data generating method in the above embodiments when executing the computer program.
in one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements the page data generating method in the above-described embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
it will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A page data generation method is characterized by comprising the following steps:
Acquiring a trigger request of a user, and acquiring a facial image of the user according to the trigger request;
Identifying the facial image to obtain the state information of the user;
If the state information indicates that a risk exists, sending a health data acquisition request to a client;
Obtaining authorization information returned by a client, and obtaining health data of the user according to the authorization information;
Calculating according to the health data to obtain a health score of the user;
and acquiring corresponding page data according to the health score, and sending the page data to a client.
2. The page data generating method according to claim 1, wherein said recognizing the face image to obtain the state information of the user comprises:
Identifying the facial feature points of the facial image to obtain target key points, and obtaining an accurate facial image according to the target key points;
Identifying the accurate facial image by adopting a preset age identification model to obtain age information;
If the age information is in a preset high-risk age interval, the state information is that a risk exists;
If the age information is in a preset middle risk age interval, performing health state identification on the accurate facial image to obtain a health state;
and if the health state of the user is not good, the state information is that the risk exists.
3. The page data generating method according to claim 2, wherein said trigger request further includes user information;
After the state information indicates that a risk exists, the page data generation method further includes:
Acquiring real age data of the user from the user information, and if the age information is not matched with the real age data, sending an image reacquisition request to a client;
acquiring a plurality of correction face images returned by the client according to the image reacquisition request, and identifying the plurality of correction face images to obtain correction age information of the user;
If the corrected age information is in a preset high-risk age interval, the state information is that a risk exists;
If the corrected age information is in a preset middle risk age interval, performing health state identification on the accurate facial image;
and if the health state of the user is not good, triggering and executing the step of sending the health data acquisition request to the client.
4. The method for generating page data according to claim 1, wherein the calculating according to the health data to obtain the health score of the user comprises:
acquiring a preset reference health value from a database according to the health data;
Matching each health parameter in the health data according to a preset reference health value to obtain a score of each health parameter;
And superposing the score of each health parameter according to a preset weight to obtain the health score of the user.
5. The page data generating method according to claim 1, wherein after said sending said page data to a client, said page data generating method further comprises:
acquiring data selection information of the user;
acquiring a risk value of corresponding data according to the data selection information, and acquiring the associated terminal information of the user if the risk value is not matched with the health score;
And sending reminding information to the corresponding correlation terminal according to the correlation terminal information.
6. The page data generation method according to claim 2, wherein the target key points may be a left ear point, a right ear point, and a chin point;
the obtaining of the accurate face image according to the target key point comprises:
determining a symmetrical point of the chin point by taking a connecting line of the left ear point and the right ear point as a symmetrical axis;
respectively taking the left ear point, the right ear point, the chin point and the symmetrical point as the middle points of each edge in the rectangular frame to construct the rectangular frame;
And extracting an image from the face image according to the rectangular frame to obtain an accurate face image.
7. The page data generation method according to claim 1, wherein the health data of the user is obtained from data in an intelligent terminal or from a third-party application.
8. a page data generating apparatus, comprising:
The facial image acquisition module is used for acquiring a trigger request of a user and acquiring a facial image of the user according to the trigger request;
The facial image recognition module is used for recognizing the facial image to obtain the state information of the user;
the first acquisition request sending module is used for sending a health data acquisition request to the client when the state information is in risk;
the health data acquisition module is used for acquiring authorization information returned by a client and acquiring the health data of the user according to the authorization information;
The first health score calculation module is used for calculating according to the health data to obtain the health score of the user;
and the first page data sending module is used for acquiring corresponding page data according to the health score and sending the page data to the client.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the page data generating method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the page data generating method according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111640502A (en) * | 2020-05-29 | 2020-09-08 | 口碑(上海)信息技术有限公司 | Distribution object health state detection method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111640502A (en) * | 2020-05-29 | 2020-09-08 | 口碑(上海)信息技术有限公司 | Distribution object health state detection method and device |
| CN111640502B (en) * | 2020-05-29 | 2023-08-22 | 口碑(上海)信息技术有限公司 | Method and device for detecting health state of delivery object |
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