CN105204626A - Method and device for controlling grading of users - Google Patents
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Abstract
An embodiment of the invention provides a method and a device for controlling grading of users. The method comprises steps as follows: acquiring physiological data of a target user in a certain medium data acquiring process; calculating the average value mu of the physiological data of the target user; looking up the position of the average value mu in a value sequence [mu1, mu2, mu3, ...,mun] generated in advance; determining the age range grade of the target user according to the position in the value sequence [mu1, mu2, mu3, ...,mun]. According to the method and the device, the users are automatically graded according to the physiological data, the age range grades which the users belong to can be accurately and efficiently determined, and the users are effectively protected; meanwhile, the human cost of a media supplier is reduced.
Description
Technical Field
The present application relates to the field of media data processing technologies, and in particular, to a method and an apparatus for hierarchical control of users.
Background
With the development of the media industry, users have a large amount of media data such as video, audio, software and the like to choose from.
In order to satisfy diverse needs of users and protect children and teenagers from inappropriate media data such as violence, pornography, allergy, etc., media data is classified and media data that users of different ages can select is limited. For example, hong Kong ranks movies and limits the levels of movies that can be viewed by different age groups, with specific ranking criteria as follows:
level I, suitable for people of any age to watch;
class IIA, children are not suitable for watching, parents are advised to guide, the content and the processing method are not suitable for children to watch, and the film may use slight bad words and a small amount of naked body, sexual violence and terror content;
class IIB, which is not desirable for teenagers and children, viewers should expect the film content to be of a stronger unsuitable composition than class IIA, giving guidance to their parents, which may have some vernacular and sexually related words, which may implicitly describe sexual behavior and the appearance of nudity in the scene of desire, and which may have moderate violence and terrorism.
Stage iii (limiting stage): only people aged 18 years (including 18 years) or older.
Obviously, the movie classification method limits the movies that children and minors can watch, so that the children and minors are far away from the movies that are not suitable for the children and minors to watch, and the children and minors are protected.
The method for controlling the movie grading is mainly based on limiting the movies which can be watched by the user according to the age, and staff is required to check the identification information provided by the user, so that the user is graded according to the age of the user, the workload of the staff is increased, and the personnel cost of a media provider is increased.
In addition, the staff member relies on the identification information provided by the user to grade the user, so that the situation that the user provides false identification information easily occurs, the user cannot be graded correctly, the management of media content watched by the user is not facilitated, and the user is difficult to be protected effectively.
Disclosure of Invention
In view of the above problems, embodiments of the present application are proposed to provide a method for hierarchical control of users and a corresponding apparatus for hierarchical control of users, which overcome or at least partially solve the above problems.
In order to solve the above problem, an embodiment of the present application discloses a method for performing hierarchical control on a user, where the method includes:
acquiring physiological data of a target user in the process of acquiring certain media data;
calculating an average value mu of the physiological data of the target user;
searching the position of the average value mu in a pre-generated value sequence [ mu 1, mu 2, mu 3, …, mu n ], wherein the value sequence [ mu 1, mu 2, mu 3, …, mu n ] is a sequence formed by respectively counting the values mu 1, mu 2, mu 3, …, mu n corresponding to the intersection points of the distribution curves of the physiological data of users which belong to different age ranges and are collected in advance when the same media data is obtained;
determining an age range class to which the target user belongs from positions in the sequence of values [ mu 1, mu 2, mu 3, …, mu n ].
Optionally, before the step of finding the position of the average value μ in the pre-generated value sequence [ μ 1, μ 2, μ 3, …, μ n ], the method further includes:
determining a physiological data distribution characteristic interval to which the physiological data of the target user belongs, wherein the physiological data distribution characteristic interval is a Gaussian distribution characteristic interval of all users who have the same age range attribute agentype, the same acquired media data attribute videoType and the same physiological data attribute dataType;
the step of determining the age range class to which the target user belongs from the positions in the value sequence [ mu 1, mu 2, mu 3, …, mu n ] is:
and determining the age range level of the target user according to the physiological data distribution characteristic interval and the position in the value sequence [ mu 1, mu 2, mu 3, …, mu n ].
Optionally, the step of obtaining the physiological data of the target user in the process of obtaining some media data is:
the method comprises the steps of collecting physiological data of a target user in the process of acquiring certain media data through wearable equipment acting on the body of the target user.
Optionally, the sequence of values [ μ 1, μ 2, μ 3, …, μ n ] is generated by:
the method comprises the steps that one-dimensional vectors of physiological data corresponding to a time axis in the process of acquiring certain media data of a plurality of users are acquired in advance through wearable equipment acting on the bodies of the users, wherein the physiological data comprise blood pressure parameters, pulse parameters and/or pupil size parameters;
calculating a Gaussian distribution characteristic parameter of a one-dimensional vector of the physiological data;
respectively extracting feature users with the same age range and the same one-dimensional vector of the physiological data;
respectively calculating the average value and the variance of the Gaussian distribution characteristic parameters of the characteristic users;
generating a corresponding distribution curve by adopting the average value and the variance of the characteristic users;
and respectively counting the values mu 1, mu 2, mu 3, … and mu n corresponding to the intersection points of the distribution curves to form a value sequence [ mu 1, mu 2, mu 3, … and mu n ].
Optionally, the positions in the value sequence [ μ 1, μ 2, μ 3, …, μ n ] have corresponding age range levels, respectively, and the step of determining the age range level to which the target user belongs according to the positions in the value sequence [ μ 1, μ 2, μ 3, …, μ n ] comprises:
extracting age range levels corresponding to positions in the value sequence [ mu 1, mu 2, mu 3, …, mu n ];
and determining the extracted age range level as the age range level to which the target user belongs.
In order to solve the above problem, an embodiment of the present application further discloses an apparatus for hierarchical control of users, where the apparatus includes:
the physiological data acquisition module is used for acquiring physiological data of a target user in the process of acquiring certain media data;
a calculation module for calculating an average value μ of the physiological data of the target user;
the searching module is used for searching the position of the average value mu in a pre-generated value sequence [ mu 1, mu 2, mu 3, …, mu n ], wherein the value sequence [ mu 1, mu 2, mu 3, …, mu n ] is a sequence formed by respectively counting the values mu 1, mu 2, mu 3, … and mu n corresponding to the intersection points of the distribution curves of the physiological data of users which belong to different age ranges and are collected in advance when the same media data is obtained;
an age range level determination module for determining an age range level to which the target user belongs according to the positions in the value sequence [ mu 1, mu 2, mu 3, …, mu n ].
Optionally, the method further comprises:
the physiological data distribution characteristic interval determining module is used for determining a physiological data distribution characteristic interval to which the physiological data of the target user belongs, wherein the physiological data distribution characteristic interval is a Gaussian distribution characteristic interval of all users with the same age range attribute, media data attribute, video attribute and physiological data attribute, dataType;
and the age range level determining module is used for determining the age range level of the target user according to the physiological data distribution characteristic interval and the position in the value sequence [ mu 1, mu 2, mu 3, …, mu n ].
Optionally, the physiological data acquisition module comprises:
and the physiological data acquisition sub-module is used for acquiring physiological data of the target user in the process of acquiring certain media data through wearable equipment acting on the body of the target user.
Optionally, the sequence of values [ μ 1, μ 2, μ 3, …, μ n ] is generated by:
the method comprises the steps that one-dimensional vectors of physiological data corresponding to a time axis in the process of acquiring certain media data of a plurality of users are acquired in advance through wearable equipment acting on the bodies of the users, wherein the physiological data comprise blood pressure parameters, pulse parameters and/or pupil size parameters;
calculating a Gaussian distribution characteristic parameter of a one-dimensional vector of the physiological data;
respectively extracting feature users with the same age range and the same one-dimensional vector of the physiological data;
respectively calculating the average value and the variance of the Gaussian distribution characteristic parameters of the characteristic users;
generating a corresponding distribution curve by adopting the average value and the variance of the characteristic users;
and respectively counting the values mu 1, mu 2, mu 3, … and mu n corresponding to the intersection points of the distribution curves to form a value sequence [ mu 1, mu 2, mu 3, … and mu n ].
Optionally, the positions in the value sequence [ μ 1, μ 2, μ 3, …, μ n ] each have a corresponding age range class, the age range class determination module comprising:
an age range level extraction submodule for extracting an age range level corresponding to a position in the value sequence [ mu 1, mu 2, mu 3, …, mu n ];
and the age range level determining submodule is used for determining the extracted age range level as the age range level to which the target user belongs.
Compared with the prior art, the method has the following advantages:
firstly, the method and the device can automatically grade the user through the physiological data without grading the user by the staff, avoid the problem of wrong user grade caused by manual grading, reduce the workload of the staff, improve the efficiency of grading the user, and simultaneously reduce the personnel cost of a media provider.
Furthermore, the physiological data can be used for quantifiable, objective and real-time feedback of the user on the media data, which is generated naturally, and reflects the bearing capacity of the user on the media data to a certain extent. The age range grade of the user is determined through the physiological data, and compared with the user grade determined according to the legal age, the age range grade of the user is objective and accurate, so that the user can be effectively protected.
In addition, the physiological data of the user can be collected through the wearable device acting on the body of the user, the physiological data of the user is guaranteed to be real and effective, and the accuracy of grading the user is further guaranteed.
Drawings
FIG. 1 is a flow chart of the steps of a method embodiment 1 of the present application for hierarchical control of users;
FIG. 2 is a schematic illustration of a physiological data profile of an embodiment of the present application;
FIG. 3 is a schematic illustration of a physiological data profile for each age range according to an embodiment of the present application;
FIG. 4 is a flow chart of steps of method embodiment 2 of the present application for hierarchical control of users;
fig. 5 is a block diagram of an embodiment of an apparatus for hierarchical control of a user according to the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description.
The user rating control is generally to rate users according to legal age, so that the categories of media data which can be watched by children and minors are limited, inappropriate media data such as violence, pornography and metamorphosis are kept away, and the children and minors are protected.
However, this method of controlling users hierarchically based on legal age does not limit the categories of media data that some authorized audience groups can obtain, for example, a young heart disease patient is not suitable for watching horror media data, but according to the method of controlling users hierarchically based on age, a young heart disease patient can watch horror media data.
Obviously, this method of hierarchical control of users based on age cannot protect a specific audience group, i.e., cannot achieve effective protection of users.
In order to solve the problems, one of the core ideas of the embodiment of the application is that the physiological data can reflect the bearing capacity of the user on the media data to a certain extent, and the application automatically grades the user through the physiological data, so that the age range grade of the user can be accurately and efficiently determined, and the user is effectively protected; while reducing the human cost to the media provider.
Referring to fig. 1, a flowchart illustrating steps of embodiment 1 of a method for hierarchical user control according to the present application is shown, where the method may specifically include the following steps:
step 101, acquiring physiological data of a target user in the process of acquiring certain media data;
in this embodiment, the media data may include video, audio, FLASH, game software, and the like, and the media content carried by the media data may include movies, televisions, music, images, advertisements, interactive games, web page content, and the like, which are not limited in this embodiment of the present application.
In practical applications, a user may generally obtain media data through a browser or an application program (e.g., a media player), for example, watching a television show through the media player.
The physiological data can be used for quantifiable, objective and real-time feedback of the user on the media data which is generated naturally, and the bearing capacity of the user on the media data is reflected to a certain extent.
Therefore, in practical applications, when a user acquires media data, the physiological data of the user (i.e. a target user) may be acquired when the user is subjected to visual and auditory stimuli of the media data or emotional reactions to media content carried by the media data, for example, blood pressure may rise when hearing a violent audio clip, pupils may enlarge when seeing a horror movie clip, heartbeat may be accelerated due to a stressful game operation in an interactive game scene, and so on.
As an example of specific implementation of the embodiment of the present application, the physiological data may include one or more of a plurality of physiological data such as a blood pressure parameter, a pulse parameter, a pupil size parameter, and the like.
102, calculating an average value mu of the physiological data of the target user;
specifically, the same kind of physiological data of the target user may fluctuate within a fixed range, and a plurality of values of the same kind of physiological data of the target user during the media data acquisition process can be collected. The collected physiological data may be the same kind of physiological data (e.g., pulse data), and then the average value μ of such physiological data of the target user is determined by calculating all values of such collected physiological data. For example, the average of 5 pulse data collected (65, 63, 64, 66 and 67 pulses/min) is calculated as follows:
(65+63+64+66+67) ÷ 5 ═ 65 (times/min)
Of course, a plurality of kinds of physiological data of the target user in the process of acquiring the media data can be collected, and each kind of physiological data has a plurality of numerical values; then, an average value is calculated for each kind of physiological data, which is not limited in the embodiment of the present application.
Step 103, find the said flatMean value μ in a pre-generated sequence of values [ μ1，μ2，μ3，…，μn]The position of (1);
in the examples of the present application, the sequence of values [ mu ]1，μ2，μ3，…，μn]The method can be used for respectively counting the values mu corresponding to the intersection points of the distribution curves of the physiological data of the users which belong to different age ranges and are collected in advance when the same media data is obtained1，μ2，μ3，…，μnThe sequence formed.
In the embodiment of the present application, the physiological data corresponding to each age range may be set in advance, and the value sequence [ μ ] of the physiological data may be generated1，μ2，μ3，…，μn]. Physiological data is many-to-one related to age range. Sequence of values [ mu ]1，μ2，μ3，…，μn]Different positions in (a) correspond to different age range classes. The age range class is determined according to the user's affordability for media data.
Specifically, it is possible to collect physiological data of users belonging to different age ranges in advance, acquire physiological data of a certain media data, classify the collected physiological data according to the age range to which the user belongs and the type of the physiological data, perform normalization processing, and draw a distribution curve of the physiological data corresponding to each age range. The distribution curve of physiological data of all users belonging to a certain age range, as shown in FIG. 2, the horizontal axis can represent the size of the physiological data value (e.g., ux-1，u，uxRepresenting the size of the physiological data), the vertical axis may represent the number of times the physiological data of the user is acquired (e.g., n represents the number of times the physiological data is acquired to a size u). Respectively counting the values mu corresponding to the intersection points of the physiological data curves corresponding to the age ranges1，μ2，μ3，…，μnAnd generates a sequence of values [ mu ] of the physiological data1，μ2，μ3，…，μn]. E.g. mu2Is a minor userThe physiological data value corresponding to the intersection point of the physiological data distribution curve of the child user and the physiological data distribution curve of the child user; mu.s3The physiological data value of the minor user is corresponding to the intersection point of the physiological data distribution curve of the minor user and the physiological data distribution curve of the middle-aged and young users, and the physiological data of the minor user is in mu2And mu3In a position of between, i.e. mu2And mu3The age range class corresponding to the position in between is minor.
In a preferred embodiment of the present application, the sequence of values may be generated by:
the substep S11 is that one-dimensional vectors of physiological data corresponding to a time axis in the process of acquiring certain media data by a plurality of users are collected in advance through wearable equipment acting on the bodies of the users;
wherein the physiological data may include a blood pressure parameter, a pulse parameter, and/or a pupil size parameter. In a specific implementation, physiological data of a plurality of users changing with time in the process of acquiring the media data can be collected in advance through the wearable device, (the type of the physiological data can be labeled by datatype), and a one-dimensional vector of the physiological data (can be labeled as datatype) is generated according to the time sequence) For example, a one-dimensional vector is generated with the time axis as the X axis and the physiological data as the Y axis.
At present, wearable equipment is rapidly developed and widely applied to various fields such as health, entertainment, communication, photography and the like. The wearable device can acquire physiological data of a user in real time, for example, the wearable device acquires physiological data of blood sugar, blood pressure, heart rate, body temperature, respiratory rate and the like of the user of the wearable device in real time, and the acquired physiological data can be uploaded into a database to establish a physiological database covering a large number of user groups.
Wearable equipment is a smart terminal which embeds technologies such as sensors, wireless communication, multimedia and the like into clothes or accessories worn on a user directly or integrated on the user. Wearable equipment exists in the form of portable accessories with a computing function and capable of being connected with various terminals such as mobile phones and computers, and the following products are more mainstream:
1. watch, wristband and ring products such as smartwatches, smartrings, supported by the hands;
2. glasses products with the head as a support, such as intelligent earphones and intelligent glasses;
3. footwear products supported by the foot, such as smart sports shoes;
4. for direct ingestion of tablet-like products, such as smart tablets, inside the human body.
Substep S12, calculating a Gaussian distribution characteristic parameter of the one-dimensional vector of the physiological data;
the Gaussian distribution characteristic parameter may include an average of a one-dimensional vector of physiological data (which may be labeled as) And variance of one-dimensional vector of physiological data (which can be labeled as)。
After a plurality of one-dimensional vectors are generated, normalization processing is carried out on all the one-dimensional vectors, and abnormal one-dimensional vectors are eliminated; through calculation, the gaussian distribution parameters of the one-dimensional vectors (excluding abnormal one-dimensional vectors) of all the users are obtained and can be labeled asWherein,is an average value reflecting the user's ability to bear media data;is variance, reflecting the user's uniformity in the ability to withstand the media data.
A substep S13 of respectively extracting feature users with the same age range and the same one-dimensional vector of the physiological data;
a one-dimensional vector with the same kind of physiological data and all users belonging to the same age range may be taken as feature users. For example, all users who watch an educational film and have a one-dimensional vector of pulse data and all users who belong to underage are taken as feature users. Different characteristic users correspond to different age ranges.
Substep S14, respectively calculating the average value and the variance of the Gaussian distribution characteristic parameters of the characteristic users;
specifically, each feature user includes a plurality of users, each having a mean and variance of a one-dimensional vector of corresponding physiological data. The average of the one-dimensional vectors of physiological data of the characteristic users (which may be labeled as) (ii) a And the variance of the one-dimensional vector of physiological data of the feature users (which may be labeled as). Capable of calculating characteristic users corresponding to each age rangeAnd
a substep S15, generating a corresponding distribution curve by using the average value and the variance of the characteristic users;
in an embodiment of the application, the physiological data of the characteristic user may be subject to mathematical expectations asAnd standard deviationA gaussian distribution of (a). And drawing a Gaussian distribution curve corresponding to the physiological data of the characteristic user by using the average value and the variance of the characteristic user.
Substep S16 of calculating the values μ corresponding to the intersections of the distribution curves1，μ2，μ3，…，μnForm a sequence of values [ mu ]1，μ2，μ3，…，μn]。
The gaussian distribution curves of the physiological data corresponding to the respective age ranges have intersection points. The intersections of the Gaussian distribution curves corresponding to the age ranges are counted, and the values (e.g., μ) of the physiological data corresponding to all the intersections are extracted1，μ2，μ3，…，μn) Using the extracted value (e.g. mu)1，μ2，μ3，…，μn) Generating a sequence of values (e.g. [ mu ])1，μ2，μ3，…，μn])
In order that those skilled in the art will better understand the embodiments of the present application, the following description will be given with reference to specific examples.
The age range of the user may be divided into children, immature adults, young and middle-aged adults. Accordingly, the set user age range class may include children, minor, middle-aged and elderly.
Taking the pre-generated pulse data value sequence as an example, the specific generation steps are as follows:
1. through the intelligent bracelet, pulse data which change along with time in the process of acquiring certain media data by a plurality of users are collected in advance, and a one-dimensional vector of the pulse data is generated according to the time sequence;
2. normalizing the one-dimensional vectors of the pulse data of all users, and then calculating the average value and the variance of the normalized one-dimensional vectors;
3. specifically, all users belonging to the same age range are extracted as feature users of a corresponding level of the age range, for example, all users of an age range of children are extracted as feature users of an age range of children.
4. The average value and variance of the physiological data of the characteristic users of each age range level are respectively calculated by using the average value and variance of the one-dimensional vectors of the characteristic users of each age range level, for example, the average value of the physiological data of the characteristic users of children of the age range level is 61 times/minute, and the variance is 1.
5. And generating a corresponding distribution curve by adopting the average value and the variance of the characteristic users of all age range levels. Specifically, a pulse data distribution curve of a child user is generated using the average value and the variance of a child feature user, a pulse data distribution curve of a minor user is generated using the average value and the variance of a minor feature user, a pulse data distribution curve of a middle-aged user is generated using the average value and the variance of a middle-aged user, and a pulse data distribution curve of an elderly user is generated using the average value and the variance of an elderly feature user. The pulse data distribution curves corresponding to the age ranges are shown in fig. 3 (pulse data distribution curves corresponding to children, immature adults, middle-aged adults and elderly people, respectively, from left to right), and the intersection point of the pulse data distribution curve corresponding to the children and the pulse data distribution curve corresponding to the immature adults is μ2The value corresponding to the intersection of the pulse data distribution curve corresponding to the minor adult and the pulse data distribution curve corresponding to the young and middle-aged3Pulse data distribution curve corresponding to young and middle-aged people and corresponding pulse data of old peopleThe value corresponding to the intersection point of the distribution curve is μ4；
6. Determining minimum pulse data mu of a characteristic user of a child using the mean and variance of the characteristic user of the child1(e.g.. mu.)1The difference between the mean and the variance of the characteristic users who are children), the mean and the variance of the characteristic users who are elderly are used to determine the minimum pulse data mu of the characteristic users who are elderly5(e.g.. mu.)5The sum of the mean and variance of the characteristic users for the elderly), using μ1，μ2，μ3，μ4，μ5Generating a sequence of values [ mu ] for the user to acquire pulse data for the movie1，μ2，μ3，μ4，μ5]。
Step 104, according to the value sequence [ mu ]1，μ2，μ3，…，μn]Determines the age range class to which the target user belongs.
In the value sequence [ mu ]1，μ2，μ3，…，μn]The average value of the physiological data of the target user is found. Once found, the mean can be determined in the sequence of values [ mu ]1，μ2，μ3，…，μn]In a sequence of values [ mu ] of the mean value1，μ2，μ3，…，μn]The age range level corresponding to the position in (1) is determined as the age range level of the user.
In a preferred embodiment of the present application, the sequence of values [ mu ]1，μ2，μ3，…，μn]Respectively, have corresponding age range levels, and the above step 104 may comprise the following sub-steps:
substep 104-1 of extracting said sequence of values [ mu ] s1，μ2，μ3，…，μn]Age range class corresponding to the position in (1)
Sub-step 104-2, determining the extracted age range class as the age range class to which the target user belongs.
In the present embodiment, positions in the value sequence [ μ 1, μ 2, μ 3, …, μ n ] have corresponding age range classes, respectively. Finding the position of the average value of the physiological data of the target user in the value sequence extracts the age range level corresponding to the position from the database, and determines the extracted age range level as the age range level of the target user. For example, the age range class corresponding to the position between the value sequences μ 1 and μ 2 is a child, and finding the position of the average value u of the physiological data of the target user in the value sequence [ μ 1, μ 2, μ 3, …, μ n ] and then finding the average value u at the position between μ 1 and μ 2 extracts the age range class (child) corresponding to the position between μ 1 and μ 2. And determining the extracted children in the age range level as the level of the target user, namely completing the grading of the user, wherein the age range level of the user is children.
As a preferred example of an embodiment of the present application, the age range class may include children, minor, middle-aged, and elderly. The pre-generated value sequence is [ mu ] according to the physiological data of the users belonging to different age ranges1，μ2，μ3，μ4，μ5]. The physiological data distribution curves corresponding to the age ranges are shown in fig. 3 (from left to right, the physiological data distribution curves corresponding to children, immature adults, young and middle-aged adults, and the physiological data distribution curves corresponding to the elderly are shown). The value sequence is [ mu ]1，μ2，μ3，μ4，μ5]The different positions of (a) correspond to different age range classes, as follows:
1、μ1and mu2Position in between, corresponding age range class for children;
2、μ2and mu3Position in between, corresponding to an age range rating of minor;
3、μ3and mu4The corresponding age range is middle and young;
4、μ4and mu5The corresponding age range class is elderly.
Looking up the average value u at a pre-generated sequence of values is [ mu ]1，μ2，μ3，μ4，μ5]In (d), e.g. average u is in μ2And mu3In between (it being understood that u is greater than μ2，And is less than mu3) Then, the age range of the target user is rated as minor.
The age range level may be classified not according to the legal age but according to the user's bearing ability. For example, users who are 18 days old and 18 years old are indistinguishable by physiological data, but the bearing capacity of the users is distinguishable by physiological data.
In the embodiment of the application, the age range grade of the user can be updated regularly by continuously collecting the physiological data of the user in the process of acquiring the media data and grading the user, so that the user can be effectively protected. In addition, the physiological data and the age range grade of the user can be simultaneously stored, and the sampling samples are continuously expanded, so that better accuracy can be provided for grading the user according to the physiological data.
In the embodiment of the application, the user can be automatically classified through the physiological data without classifying the user by the staff, so that the problem of wrong user grade caused by manual classification is avoided, the workload of the staff is reduced, the efficiency of classifying the user is improved, and the personnel cost of a media provider is reduced. The physiological data can be used for quantifiable, objective and real-time feedback of the user to the media data, the bearing capacity of the user to the media data is reflected to a certain degree, the age range grade of the user is determined through the physiological data, and compared with the user grade determined according to the legal age, the age range grade of the user is objective and accurate, and the effective protection of the user is realized.
In addition, the embodiment of the present application may determine the age range level to which the target user belongs by averaging several kinds of physiological data, and then rank the users based on the determined age range level. Specifically, if all kinds of physiological data determine that the age range class is the same age range class, the age range class to which the user belongs may be determined, for example, if it is determined that the age range class to which the user belongs is a child through pulse data, blood pressure data, and pupil size data, respectively, the age range class of the user is a child; if the age range classes determined by all kinds of physiological data are not the same, the age range classes determined by most kinds of physiological data are searched, and the age range classes determined by most kinds of physiological data are determined as the age range classes of the user. According to the method and the device, the user can be graded through a plurality of kinds of physiological data, so that the error of grading the user is reduced, and the protection effect on the user is enhanced.
Referring to fig. 4, a flowchart of the steps of embodiment 2 of the method for hierarchical user control according to the present application is shown, where the method specifically includes the following steps:
step 201, acquiring physiological data of a target user in a process of acquiring certain media data through wearable equipment acting on the body of the target user;
in the embodiment of the application, when a user acquires media data through a mobile phone, a computer and other terminals, physiological data of a target user in the media data acquisition process can be acquired through associated wearable equipment, and then a plurality of numerical values of the physiological data of the target user in the media data acquisition process can be collected. For example, the pulse data of the target user in the process of watching the movie is collected by the smart band, and the pulse data are 65 times/minute, 63 times/minute, 64 times/minute, 66 times/minute and 67 times/minute respectively.
Step 202, calculating an average value mu of the physiological data of the target user;
step 203, determining a physiological data distribution characteristic interval to which the physiological data of the target user belongs;
the physiological data distribution characteristic interval is a Gaussian distribution characteristic interval of all users who acquire a media data attribute videoType and a physiological data attribute dataType aiming at an attribute agenType with the same age range;
the physiological data distribution characteristic interval can be understood as: all users belonging to the same age range have the same type of physiological data forming the gaussian distribution characteristic interval when the same media data attribute videoType is acquired (such as watching horror movies). For example, the pulse data distribution characteristic interval of a user with an age range of children when watching a certain movie may be [58, 64 ]; the pulse data distribution characteristic interval of a user with an age range of minor may be [64, 70] when watching a certain movie; the pulse data distribution characteristic interval of a user with the age range of middle and young years when watching a certain movie can be [70, 76 ]; the pulse data distribution characteristic interval of a user aged in the age range while watching a certain movie may be [76, 81 ].
After the average value of the physiological data of the target user is calculated, the physiological data distribution characteristic interval to which a plurality of numerical values of the physiological data belong is searched, and if most of the numerical values of the physiological data belong to the same physiological data distribution characteristic interval, the physiological data distribution characteristic interval can be determined as the physiological data distribution characteristic interval to which the physiological data belong. For example, the pulse data of the target user are 65 times/min, 63 times/min, 64 times/min, 66 times/min and 67 times/min, respectively, 4 values (64 times/min, 65 times/min, 66 times/min and 67 times/min) belong to the interval [64, 70], and only 2 values (63 times/min and 64 times/min) belong to the interval [58, 64], that is, the pulse data distribution characteristic interval to which the pulse data of the target user belongs is [64, 70 ].
Step 204, searching the average value mu in a pre-generated value sequence [ mu ]1，μ2，μ3，…，μn]The position of (1);
wherein the sequence of values [ mu ]1，μ2，μ3，…，μn]For the users which are collected in advance and belong to different age ranges, when the same media data is obtained, the values mu corresponding to the intersection points of the distribution curves of the physiological data are respectively counted1，μ2，μ3，…，μnThe sequence formed.
Step 205, distributing characteristic intervals and the value sequence [ mu ] according to the physiological data1，μ2，μ3，…，μn]Determining the age range class to which the target user belongs.
In this embodiment, the age range corresponding to the physiological data distribution characteristic interval to which the physiological data of the target user belongs is the same as the age range level corresponding to the position of the average value of the physiological data of the target user in the value sequence, so that the age range level to which the bookmarked user belongs can be determined.
For example, the sequence of values that is pre-generated for the user to view the beat data for a certain movie is [58, 64, 70, 76, 81 ]. The position between 58 and 64 corresponds to a child in the age range, between 64 and 70 corresponds to a minor age, between 70 and 76 corresponds to a medium young age, and between 76 and 81 corresponds to an elderly age. The average value of the pulse data of the target user 65 times/min is at a position between 64 and 70 of the value sequence [58, 64, 70, 76, 81 ]. The age range corresponding to the pulse data distribution characteristic interval [64, 70] to which the pulse data of the target user belongs is underage, and the age range level corresponding to the position between 64 and 70 in the value sequence [58, 64, 70, 76, 81] is underage, the age range level of the target user can be determined as underage.
Of course, the age range corresponding to the physiological data distribution characteristic interval to which the physiological data of the target user belongs is different from the age range level corresponding to the position of the average value of the physiological data of the target user in the value sequence, and the physiological data of the target user can be collected again and classified.
The physiological data of the user can be collected through the wearable device acting on the body of the user, the physiological data of the user is guaranteed to be real and effective, and the accuracy of grading the user is guaranteed
It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the embodiments are not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the embodiments. Further, those skilled in the art will also appreciate that the embodiments described in the specification are presently preferred and that no particular act is required of the embodiments of the application.
Referring to fig. 5, a block diagram of an embodiment of a user hierarchical control device according to the present application is shown, and specifically, the user hierarchical control device may include the following modules:
a physiological data obtaining module 501, configured to obtain physiological data of a target user in a process of obtaining certain media data;
a calculation module 502 for calculating an average value μ of the physiological data of the target user;
a searching module 503 for searching the average value μ in a pre-generated value sequence [ μ [ mu ] ]1，μ2，μ3，…，μn]Position in [ mu ] of the sequence of values [ mu ]1，μ2，μ3，…，μn]Aiming at the users which are collected in advance and belong to different age rangesWhen the same media data is obtained, respectively counting the values mu corresponding to the intersection points of the distribution curves of the physiological data1，μ2，μ3，…，μnThe sequence formed;
an age range level determination module 504 for determining a level of the age range according to the sequence of values [ mu ]1，μ2，μ3，…，μn]Determines the age range class to which the target user belongs.
In a preferred embodiment of the present application, the apparatus further comprises:
a physiological data distribution characteristic interval determining module 505, configured to determine a physiological data distribution characteristic interval to which the physiological data of the target user belongs, where the physiological data distribution characteristic interval is a gaussian distribution characteristic interval of all users who have the same age range attribute, the same acquired media data attribute, videoType, and the same physiological data attribute, dataType;
correspondingly, an age range level determination module 504 is configured to determine the value sequence [ mu ] according to the physiological data distribution characteristic interval1，μ2，μ3，…，μn]Determining the age range class to which the target user belongs.
In a preferred embodiment of the present application, the physiological data acquisition module 501 includes:
the physiological data acquisition sub-module 501-1 is configured to acquire physiological data of a target user in a process of acquiring certain media data by using wearable equipment acting on the body of the target user.
As a preferred embodiment of the present application, the sequence of values [ mu ] is1，μ2，μ3，…，μn]Is generated by the following method:
the method comprises the steps that one-dimensional vectors of physiological data corresponding to a time axis in the process of acquiring certain media data of a plurality of users are acquired in advance through wearable equipment acting on the bodies of the users, wherein the physiological data comprise blood pressure parameters, pulse parameters and/or pupil size parameters;
calculating a Gaussian distribution characteristic parameter of a one-dimensional vector of the physiological data;
respectively extracting feature users with the same age range and the same one-dimensional vector of the physiological data;
respectively calculating the average value and the variance of the Gaussian distribution characteristic parameters of the characteristic users;
generating a corresponding distribution curve by adopting the average value and the variance of the characteristic users;
respectively counting the values mu corresponding to the intersection points of the distribution curves1，μ2，μ3，…，μnForm a sequence of values [ mu ]1，μ2，μ3，…，μn]。
In a preferred embodiment of the present application, the sequence of values [ mu ]1，μ2，μ3，…，μn]Each having a corresponding age range level, the age range level determination module 504 comprising:
an age range level extraction submodule 504-1 for extracting the value sequence [ mu ]1，μ2，μ3，…，μn]The age range class corresponding to the position in (1);
an age range level determination sub-module 504-2 for determining the extracted age range level as the age range level to which the target user belongs.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one of skill in the art, embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the true scope of the embodiments of the application.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The method for hierarchically controlling the user and the device for hierarchically controlling the user provided by the present application are introduced in detail above, and a specific example is applied in the present application to explain the principle and the implementation manner of the present application, and the description of the above embodiment is only used to help understanding the method and the core idea of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A method for hierarchical control of a user, the method comprising:
acquiring physiological data of a target user in the process of acquiring certain media data;
calculating an average value mu of the physiological data of the target user;
looking up the mean value mu in a pre-generated sequence of values [ mu ]1，μ2，μ3，…，μn]Position in [ mu ] of the sequence of values [ mu ]1，μ2，μ3，…，μn]For the users which are collected in advance and belong to different age ranges, when the same media data is obtained, the values mu corresponding to the intersection points of the distribution curves of the physiological data are respectively counted1，μ2，μ3，…，μnThe sequence formed;
according to said value sequence [ mu ]1，μ2，μ3，…，μn]Determines the age range class to which the target user belongs.
2. The method of claim 1, wherein said searching for said average μ is in a pre-generated sequence of values [ μ [ [ μ ])1，μ2，μ3，…，μn]Before the step of the position in (b), further comprising:
determining a physiological data distribution characteristic interval to which the physiological data of the target user belongs, wherein the physiological data distribution characteristic interval is a Gaussian distribution characteristic interval of all users who have the same age range attribute agentype, the same acquired media data attribute videoType and the same physiological data attribute dataType;
said sequence of values [ mu ] according to1，μ2，μ3，…，μn]The step of determining the age range class to which the target user belongs by the location in (1) is:
distributing characteristic intervals and the value sequence [ mu ] according to the physiological data1，μ2，μ3，…，μn]Determining the age range class to which the target user belongs.
3. The method according to claim 1 or 2, wherein the step of obtaining the physiological data of the target user in the process of acquiring certain media data is:
the method comprises the steps of collecting physiological data of a target user in the process of acquiring certain media data through wearable equipment acting on the body of the target user.
4. Method according to claim 1, characterized in that said sequence of values [ μ [ ]1，μ2，μ3，…，μn]Is generated by the following method:
the method comprises the steps that one-dimensional vectors of physiological data corresponding to a time axis in the process of acquiring certain media data of a plurality of users are acquired in advance through wearable equipment acting on the bodies of the users, wherein the physiological data comprise blood pressure parameters, pulse parameters and/or pupil size parameters;
calculating a Gaussian distribution characteristic parameter of a one-dimensional vector of the physiological data;
respectively extracting feature users with the same age range and the same one-dimensional vector of the physiological data;
respectively calculating the average value and the variance of the Gaussian distribution characteristic parameters of the characteristic users;
generating a corresponding distribution curve by adopting the average value and the variance of the characteristic users;
respectively counting the values mu corresponding to the intersection points of the distribution curves1，μ2，μ3，…，μnForm a sequence of values [ mu ]1，μ2，μ3，…，μn]。
5. Method according to claim 1 or 4, characterized in that said sequence of values [ μ [ ]1，μ2，μ3，…，μn]Respectively, have a corresponding age range class, said value sequence [ mu ] according to1，μ2，μ3，…，μn]The step of determining the age range class to which the target user belongs includes:
extracting the value sequence [ mu ]1，μ2，μ3，…，μn]The age range class corresponding to the position in (1);
and determining the extracted age range level as the age range level to which the target user belongs.
6. An apparatus for hierarchical control of a user, the apparatus comprising:
the physiological data acquisition module is used for acquiring physiological data of a target user in the process of acquiring certain media data;
a calculation module for calculating an average value μ of the physiological data of the target user;
a search module for searching the average value mu in a pre-generated value sequence [ mu ]1，μ2，μ3，…，μn]Position in [ mu ] of the sequence of values [ mu ]1，μ2，μ3，…，μn]For the users which are collected in advance and belong to different age ranges, when the same media data is obtained, the values mu corresponding to the intersection points of the distribution curves of the physiological data are respectively counted1，μ2，μ3，…，μnThe sequence formed;
an age range level determination module for determining a level of the age range according to the sequence of values [ mu ]1，μ2，μ3，…，μn]Determines the age range class to which the target user belongs.
7. The apparatus of claim 6, further comprising:
the physiological data distribution characteristic interval determining module is used for determining a physiological data distribution characteristic interval to which the physiological data of the target user belongs, wherein the physiological data distribution characteristic interval is a Gaussian distribution characteristic interval of all users with the same age range attribute, media data attribute, video attribute and physiological data attribute, dataType;
the age range grade determining module is used for determining the value sequence [ mu ] according to the physiological data distribution characteristic interval1，μ2，μ3，…，μn]Determining the age range class to which the target user belongs.
8. The apparatus of claim 6 or 7, wherein the physiological data acquisition module comprises:
and the physiological data acquisition sub-module is used for acquiring physiological data of the target user in the process of acquiring certain media data through wearable equipment acting on the body of the target user.
9. The apparatus of claim 6, wherein the sequence of values [ μ [ mu ] ]1，μ2，μ3，…，μn]Is generated by the following method:
the method comprises the steps that one-dimensional vectors of physiological data corresponding to a time axis in the process of acquiring certain media data of a plurality of users are acquired in advance through wearable equipment acting on the bodies of the users, wherein the physiological data comprise blood pressure parameters, pulse parameters and/or pupil size parameters;
calculating a Gaussian distribution characteristic parameter of a one-dimensional vector of the physiological data;
respectively extracting feature users with the same age range and the same one-dimensional vector of the physiological data;
respectively calculating the average value and the variance of the Gaussian distribution characteristic parameters of the characteristic users;
generating a corresponding distribution curve by adopting the average value and the variance of the characteristic users;
respectively counting the values mu corresponding to the intersection points of the distribution curves1，μ2，μ3，…，μnForm a sequence of values [ mu ]1，μ2，μ3，…，μn]。
10. Method according to claim 6 or 9, characterized in that said sequence of values [ μ [ ]1，μ2，μ3，…，μn]Each having a corresponding age range class, the age range class determination module comprising:
an age range level extraction submodule for extracting the sequence of values [ mu ]1，μ2，μ3，…，μn]The age range class corresponding to the position in (1);
and the age range level determining submodule is used for determining the extracted age range level as the age range level to which the target user belongs.
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