CN115186014B - Data processing method for educational training - Google Patents

Data processing method for educational training Download PDF

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CN115186014B
CN115186014B CN202211107383.3A CN202211107383A CN115186014B CN 115186014 B CN115186014 B CN 115186014B CN 202211107383 A CN202211107383 A CN 202211107383A CN 115186014 B CN115186014 B CN 115186014B
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余正浪
余严江
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Jiangsu Juxin Zhonghui Digital Technology Co ltd
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Abstract

The invention relates to the technical field of data processing, in particular to a data processing method for educational training, wherein related electronic equipment is adopted to identify each child in a target classroom, the behavior membership degree corresponding to each child is determined based on the prestige frequency of each child in the classroom, the typical hesitation time of interaction and the hesitation stability degree, the child with the largest behavior membership degree is taken as a representative child in a target time period and is taken as an object for recommending simulation when a young teacher reviews the target time, the quality of a copy can be improved compared with a mode of randomly selecting a simulation object, the young teacher is helped to find the defects when interacting with the child, the training of professional ability is further deepened, the workload of the copy of the young teacher can be reduced compared with a mode of simulating all children one by one, and the copy efficiency is improved.

Description

Data processing method for educational training
Technical Field
The invention relates to the technical field of data processing, in particular to a data processing method for education training.
Background
Preschool education is foundation education of life development, people attach more and more importance to preschool education, and practical infant teacher training market prospect is wide.
At present, professional training of preschool education is difficult to realize by scene simulation. As most preschool education forms are indoor interactive activities, for a large number of education contents depending on children sitting on the ground, children are easy to be distracted, and the interactive ability of teachers is tested. Present VR (virtual reality) technique can provide an immersive experience, can guarantee that the teacher looks back, understand the administrative effect of education in-process, discover the problem that its classroom in-process exists, and then improve follow-up classroom quality, but there is effectual data drive mode temporarily, data processing mode promptly, lead to the teacher not to know when looking back that to put into which children's role and experience, be unfavorable for the teacher to discover the problem that its classroom in-process exists, thereby the effect that the teacher utilized the VR technique to look back in class has been reduced.
Disclosure of Invention
In order to solve the problem that the existing data processing mode can not enable a teacher to not know which child role to be placed for experience when the teacher looks back in a classroom by utilizing a VR technology, and therefore the effect of looking back in the classroom is reduced, the invention aims to provide a data processing method for educational training.
The invention discloses a data processing method for educational training, which comprises the following steps:
acquiring the frequent degree of the prestige of each child in a classroom, the typical hesitation time for interaction and the stability degree of the presthesion in the target classroom;
calculating the attraction degree of each child in the classroom to each child according to the typical delay time and the delay stability degree of each child to interaction in the target classroom;
calculating the behavior difference distance between any two children according to the attraction degree of the classroom in the target classroom to each child, the frequent degree of prestige and the lagged time sequence; classifying the children in the target classroom according to the behavior difference distance to obtain a plurality of types; the delay time sequence is a sequence formed by the interaction delay times of the corresponding children in the classroom;
for either type: calculating the spatial distance between each child in the type and other children of the type according to the prestige frequency of each child in the type and the prestige state sequence corresponding to the target time period, calculating the behavior membership degree corresponding to each child in the type according to the spatial distance, taking the child with the maximum behavior membership degree as a representative child of the type corresponding to the target time period, and taking the representative child of the type corresponding to the target time period as a recommended simulation object when a young teacher looks back at the target time; the periscopic state sequence corresponding to the target time period is a sequence formed by the periscopic states corresponding to the collecting moments of the corresponding children in the target time period.
Further, the method for obtaining the stability degree of the late doubt comprises the following steps:
the degree of hesitation stability is calculated using the following formula:
Figure 100002_DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 980145DEST_PATH_IMAGE002
the degree of hesitation stability for a child,
Figure 834968DEST_PATH_IMAGE003
the typical late time to doubt for that child,
Figure 303121DEST_PATH_IMAGE004
in order to find the maximum value,
Figure 432751DEST_PATH_IMAGE005
is the hesitation time series for that child.
Further, the late suspected time is typically the average of the first 30% of the maximum late suspected times in the late suspected time sequence.
Further, the attraction degree of the classroom to each child is calculated by using the following formula:
Figure 168495DEST_PATH_IMAGE006
wherein, the first and the second end of the pipe are connected with each other,
Figure 100002_DEST_PATH_IMAGE007
in order to attract a child in a classroom,
Figure 14791DEST_PATH_IMAGE003
represents a typical suspicion time of the child, K represents a suspicion stability degree of the child, tanh () is a hyperbolic tangent function, alpha is a neighborhood correction parameter,
Figure 468775DEST_PATH_IMAGE008
，
Figure 402096DEST_PATH_IMAGE009
is the maximum of the typical hesitation times for children in the eight neighbourhood of the child.
Further, the behavior difference distance between any two children is calculated by using the following formula:
Figure 493811DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure 346230DEST_PATH_IMAGE011
being the behavioural difference distance between child a and child B,
Figure 38242DEST_PATH_IMAGE012
to the extent of attraction of the class to the child a,
Figure 195161DEST_PATH_IMAGE013
to the extent of attraction of the class to the child B,
Figure 452967DEST_PATH_IMAGE014
the frequent degree of the look for the child a,
Figure 476286DEST_PATH_IMAGE015
the frequent degree of the look of the child B,
Figure 406327DEST_PATH_IMAGE016
is the late-suspected time series of child a,
Figure 353555DEST_PATH_IMAGE017
is the late-suspected time series of child B,
Figure 715135DEST_PATH_IMAGE018
the cosine similarity is solved.
Further, the calculating a spatial distance between each child in the category and another child in the category according to the prestige frequency of each child in the category and the prestige status sequence corresponding to the target time period includes:
Figure 987984DEST_PATH_IMAGE019
wherein the content of the first and second substances,
Figure 80355DEST_PATH_IMAGE020
is the spatial distance between the child p and the child q,
Figure 80541DEST_PATH_IMAGE021
for the sequence of states of the child p in the target time period,
Figure 312939DEST_PATH_IMAGE022
for the sequence of the children's q's state of view within the target time period,
Figure 773002DEST_PATH_IMAGE023
the frequent degree of the look of the child p,
Figure 395744DEST_PATH_IMAGE024
the frequent degree of prestige of the children q.
Further, calculating behavior membership corresponding to each child in the type according to the spatial distance, including:
the behavioral membership is calculated using the following formula:
Figure 465200DEST_PATH_IMAGE025
wherein the content of the first and second substances,
Figure 909694DEST_PATH_IMAGE026
the behavior membership of the child sample p,
Figure 789926DEST_PATH_IMAGE027
is the spatial distance between the child sample p and the child sample q,
Figure 211549DEST_PATH_IMAGE028
kth reachable distance for child sample p
Figure 304270DEST_PATH_IMAGE029
The set of child samples covered in, K being the set value.
Furthermore, the DBSCAN algorithm is used for classifying the children in the target classroom.
Has the beneficial effects that: the method obtains parameters capable of reflecting behaviors, attention and performance of children in an activity classroom, wherein the parameters comprise frequent degree of prestige of each child in the classroom, typical delay time for interaction and delay stability degree; classifying children in the classroom according to the parameters to obtain different types of children; next, analyzing the children included in each type, and selecting representative children in each type as recommended simulation objects in the later period of driving VR, compared with a mode of randomly selecting simulation objects, the method can improve the quality of the reply, help the young students find the defects in the interaction with the children, further deepen the training of professional ability, and improve the VR review effect; compared with a mode of simulating all children one by one, the tray-duplicating work load of the young teachers can be reduced, and the tray-duplicating efficiency is improved.
Drawings
Fig. 1 is a flow chart of a data processing method for educational training of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention.
As shown in fig. 1, the data processing method for educational training of the present embodiment includes:
step 1: and acquiring the frequent degree of the hope of each child in the classroom, the typical delay time for interaction and the delay stability degree in the target classroom.
This embodiment represents a study classroom as a target classroom with multiple children in the target area. In the embodiment, two cameras are arranged in a target classroom, wherein the first camera is a common camera for shooting a child and is used for acquiring and calculating the head direction of the child; the second is a VR camera, which is set up behind the sitting area of the children seat for the children to simulate the situation of the children watching the attention reduction.
In order to determine the listening and speaking habits of each child in the target classroom from the active classroom, the implementationFor example, the listening and speaking modes of all children are counted, and the frequent degree of looking and looking of all children is calculated
Figure 137359DEST_PATH_IMAGE030
Typical hesitation time of a child to an interaction
Figure 172180DEST_PATH_IMAGE031
And degree of tardy stability
Figure 35093DEST_PATH_IMAGE002
. The specific acquisition process of the parameters is as follows:
the head posture of each child can be estimated by collecting the facial key points and the three-dimensional Mesh through the first camera, and a more common way is to determine the ear key points by using OpenPose and roughly calculate the head posture by combining the recognition result of the facial key points. The deflection angle A of the head relative to the position of the young teacher can be obtained through conversion of the pose of the camera, and the specific conversion process is not repeated because the existing estimation technology of the head deflection angle is widely applied. For the head deflection angle of the child, the tolerance angle interval can be set based on the polar coordinate space of the head center of the child
Figure 571703DEST_PATH_IMAGE032
To distinguish the state of the child watching the teachers at the time t from the state of looking around
Figure 570883DEST_PATH_IMAGE033
Recording the state of each child's view of the whole class
Figure 776605DEST_PATH_IMAGE034
Sampling is performed every 5 seconds until time T is reached and the activity classroom is over. When the child is in the state of watching the young teacher,
Figure 549651DEST_PATH_IMAGE033
=1; when the child does not belong to the state of watching the young teacher, i.e., the state of showing a look-up,
Figure 46492DEST_PATH_IMAGE033
=0, the child belongs to the state of watching the young teacher, namely the child has the head deflection angle in the tolerance angle interval
Figure 415025DEST_PATH_IMAGE032
The child is not in the condition of watching the young teacher, i.e. the head deflection angle of the child is not in the tolerance angle interval
Figure 276802DEST_PATH_IMAGE032
. Tolerance angle interval
Figure 534215DEST_PATH_IMAGE032
Can be set by the user when in application.
Counting the frequent degree of observation
Figure 287276DEST_PATH_IMAGE030
: when the state of looking at the children does not belong to the state for 2 times continuously from a certain moment, the state of looking at the children is considered to be finished once, and the children continue to pay attention to the content of the activity classroom of the teachers. Calculating the time length of the child in the state of looking at, and then looking at the frequency degree
Figure 261048DEST_PATH_IMAGE030
The time that the deflection angle of the head posture exceeds the tolerance angle (namely the time in the state of looking at) accounts for the whole class time, and the greater the frequent degree of looking at, the less interested the children in the activity class information of the current teacher.
Calculating the typical hesitation time of a child to an interaction
Figure 106775DEST_PATH_IMAGE003
: the interaction time interval is specified by the teachers in each class, so that the late-doubt time of the children in the activity class is calculated. Every class all generally comprises a plurality of times of interdynamic, and when the child was in the state of looking after before the interaction began, the beginning of each time of interdynamic can attract children's attention to turn to the young teacher, when there is a plurality of interactive hesitancies in this in-processIn between, these late times may constitute a sequence of late times
Figure 165998DEST_PATH_IMAGE035
Wherein, in the process,
Figure 394854DEST_PATH_IMAGE036
the hesitation time for a child at the first interaction,
Figure 711216DEST_PATH_IMAGE037
the hesitation time of a certain child at the nth interaction, N is the total number of interactions involved in the class.
Typical hesitation times can be calculated for multiple attention shifts of children in an activity classroom
Figure 242691DEST_PATH_IMAGE038
And late stability degree K. For children, multiple times of attention inattention are common phenomena, how to attract children is a part of training of preschool education specialties, and the method is also an important link for guaranteeing education quality. For the phenomenon of inattention, existence of multiple interactive hesitation, and obvious hesitation for multiple times enough to influence the quality of the current activity class of the child, the typical hesitation time is calculated by the embodiment
Figure 445003DEST_PATH_IMAGE039
，
Figure 838069DEST_PATH_IMAGE040
Time series for question delay
Figure 786433DEST_PATH_IMAGE041
The average of the first 30% of the maximum late times included in the late times. In this embodiment, the typical interaction time is calculated according to the last 30% of the maximum late doubt time, and as another embodiment, the typical interaction time may be calculated by selecting the last 25% or 40% of the maximum late doubt time.
The lagged-suspected stability degree K can reflect children in the current activity classroomThe degree of stability of the late suspicion time of the child, in this example
Figure 347865DEST_PATH_IMAGE042
If the ratio of the typical latency to the maximum interactive latency is smaller, then this is said
Figure 926220DEST_PATH_IMAGE003
And
Figure 122977DEST_PATH_IMAGE043
the difference is large; if the ratio of the typical hesitation time to the maximum interactive hesitation time is large, it is stated
Figure 660269DEST_PATH_IMAGE003
And
Figure 658181DEST_PATH_IMAGE043
the difference is small, and the difference is small,
Figure 655656DEST_PATH_IMAGE003
and can represent the attraction degree of the classroom to the children. Implementer settings
Figure 639793DEST_PATH_IMAGE003
The threshold value of (a) is set,
Figure 812017DEST_PATH_IMAGE003
below the threshold, the hearing and speaking quality of the child is considered to be not greatly affected, and K =1; for children without hesitation time, K =1 was set.
Therefore, the frequent degree of the hope of each child in the target classroom, the typical delay time for interaction and the delay stability degree can be obtained.
Step 2: and calculating the attraction degree of the classroom to each child according to the typical delay time and the delay stability degree of each child to the interaction in the target classroom.
The shorter the time to doubt, i.e. the less distracted the child is, the more attracted the interaction and listening to the lessee in the activity class, the greater the child's attraction to the quality of the activity class. In this embodiment, the following formula is specifically used to calculate the attraction degree of the classroom to each child:
Figure 793880DEST_PATH_IMAGE006
wherein the content of the first and second substances,
Figure 959544DEST_PATH_IMAGE007
to the attraction degree of a classroom to a certain child,
Figure 996639DEST_PATH_IMAGE007
the larger the attraction, the larger the attraction degree;
Figure 774102DEST_PATH_IMAGE003
indicating the typical suspicion time of the child and K indicating the suspicion stability of the child.
Figure 346773DEST_PATH_IMAGE002
The smaller, the
Figure 560585DEST_PATH_IMAGE043
Compared with
Figure 152103DEST_PATH_IMAGE003
Larger, if K is not considered, but only
Figure 269226DEST_PATH_IMAGE003
Since the calculated attraction degree is large as the size of the attraction degree of the child in the class, the attraction degree of the child listening and speaking is corrected by using K as the correction index in this embodiment. tanh () is a hyperbolic tangent function, used here for normalization of parameters.
The method comprises the following steps of calculating the maximum typical hesitation length of eight neighborhoods, wherein alpha is a neighborhood correction parameter, the eight neighborhoods of the positions where children are located need to be referenced in a setting mode, and the eight neighborhoods can be mutually influenced, so that the maximum typical hesitation length of the eight neighborhoods is counted and used as a reference value. When a child is in a certain position, the typical hesitation time of the child around the child is greater than that of the child, and the child is considered to be relative to the weekIn the case where the surrounding child is attracted by other things, the activity classroom contents of the young teacher can be more concerned. In this example
Figure 779842DEST_PATH_IMAGE008
Wherein
Figure 497262DEST_PATH_IMAGE038
Indicating a typical late-to-doubt time for a child,
Figure 380554DEST_PATH_IMAGE044
is the maximum of the typical hesitation times for children in the eight neighbourhood of the child. When the child is at an edge location, no non-existent neighborhoods are included in the calculation. The degree of attraction of the children to the contents of the teacher's activity classroom in the activity classroom is obtained through analysis so far
Figure 601451DEST_PATH_IMAGE007
。
And step 3: calculating the behavior difference distance between any two children according to the attraction degree, the prestige frequency degree and the delay time sequence of each child in the classroom of the target classroom; classifying the children in the target classroom according to the behavior difference distance to obtain a plurality of types; the delay time sequence is a sequence formed by the interaction delay times of the corresponding children in the classroom.
The embodiment analyzes the in-class data of the children in the target classroom and determines the difference of the interaction behaviors of the children of different types in the activity classroom. In the embodiment, based on a dbscan algorithm, r and minpts are set for clustering to obtain more clustering clusters, wherein each clustering cluster is a type; wherein the child behavior difference distance function is constructed as follows:
Figure 282968DEST_PATH_IMAGE045
wherein, the first and the second end of the pipe are connected with each other,
Figure 35155DEST_PATH_IMAGE011
between child A and child BThe distance of the difference in the behavior is,
Figure 906159DEST_PATH_IMAGE012
in order to the attraction degree of the class to the child a,
Figure 168513DEST_PATH_IMAGE013
to the extent of attraction of the class to the child B,
Figure 847363DEST_PATH_IMAGE014
the frequent degree of the look for the child a,
Figure 336113DEST_PATH_IMAGE015
the frequent degree of the look of the child B,
Figure 994496DEST_PATH_IMAGE016
is the late-suspected time series of child a,
Figure 878401DEST_PATH_IMAGE017
is the late-suspected time series of child B,
Figure 777087DEST_PATH_IMAGE018
the cosine similarity is solved. In the embodiment, the cosine similarity distance is used for secondary comparison, and the attraction degree of the children during listening and speaking is determined according to the similarity
Figure 674504DEST_PATH_IMAGE007
Frequent degree of observation
Figure 590375DEST_PATH_IMAGE030
And constructing a behavior vector. The present embodiment utilizes
Figure 905950DEST_PATH_IMAGE046
The calculation is carried out for the purpose of avoiding coincidence of similar attraction degrees caused by different behavior modes when the attraction degrees are determined, so that the final calculation results of the values of different parameters are the same.
Thus, the difference in the degree of attraction to the activity class between any two children can be obtained. According to the mode, the distance comparison is carried out on any two sampled children at present, and the difference distance between any two children is obtained. Thus, a child group assumed space based on psychological behaviors is obtained, and based on the assumed space, children in different listening and speaking modes are divided into different child groups based on the improved DBSCAN, and each child group corresponds to one type. For example, some children can well draw their attention when the interaction between the teachers starts, so the late time is short and the attraction degree value is large, but the attention is often drawn when the children are not in the interaction ring, and the children in the listening and speaking mode are classified into one type. In addition, there is also a large attraction degree value, and children who continuously pay attention to the young teacher are classified into one type. Of course, there may be other listening and speaking modes of children classified as other types. The r and the minpts can be set by themselves during application, and the difference of the r and the minpts during application can cause the difference of the type quantity and the classification result of the later-stage division, but the division of the child types can be realized.
And 4, step 4: for either type: calculating the spatial distance between each child in the type and other children of the type according to the prestige frequency of each child in the type and the prestige state sequence corresponding to the target time period, calculating the behavior membership degree corresponding to each child in the type according to the spatial distance, taking the child with the maximum behavior membership degree as a representative child of the type corresponding to the target time period, and taking the representative child of the type corresponding to the target time period as a recommended simulation object when a young teacher looks back at the target time; the periscopic state sequence corresponding to the target time period is a sequence formed by enough periscopic states corresponding to the acquisition moments of the corresponding children in the target time period.
For each mode of listening and speaking, the teacher can simulate the head direction of the child in a classroom in a VR mode to simulate and experience the behavior of the child for teaching. This is inefficient, however, and does not allow for a good analysis of the key reference views and a determination of when the view may represent a location where the teacher needs to have increased expertise, resulting in the teacher having to traverse through many times to find the cause. Considering that for a plurality of children belonging to a listening and speaking mode, the young teacher only needs to select one of the most representative children for analysis, so that the present embodiment next selects the child representative in each listening and speaking mode, so that the young teacher only needs to simulate the child representative in each listening and speaking mode to quickly find the reason that the attention of the corresponding type of children is affected when reviewing at the later stage.
The present embodiment takes 8 minutes as a time period, which is a sliding sampling window, and calculates the following contents at each data update time as the time of the activity class changes. The present embodiment performs sampling once every 5 seconds, so the present embodiment updates the following calculation process every 5 seconds.
For any period of time in an activity classroom, a state space of attention of a child in a certain listening and speaking mode in the period of time is constructed. The present embodiment calculates the spatial distance between two children belonging to the same type at a certain time using the following formula:
Figure 224805DEST_PATH_IMAGE019
wherein the content of the first and second substances,
Figure 625830DEST_PATH_IMAGE020
is the spatial distance between the child p and the child q,
Figure 65164DEST_PATH_IMAGE021
the child p is in the periscopic state sequence in the period of time, the periscopic state sequence is the set of the periscopic states corresponding to the sampling moments in the period of time,
Figure 484513DEST_PATH_IMAGE022
for the sequence of the states of attentiveness of the child q during this time, since the included attentiveness states of a group of children are not completely similar but may be approximately of the same type, when the attentiveness state of a child belongs to the situation represented by most children,
Figure 459422DEST_PATH_IMAGE047
the state features that can represent attention are similar, so distances are scaled to close distances in the hypothetical space, i.e., the term goes to 1, whereas it is kept farther away to longer distances.
Figure 767650DEST_PATH_IMAGE030
Can reflect the condition that the attention of the children is lost in a tidy classroom,
Figure 633844DEST_PATH_IMAGE023
the frequent degree of the look of the child p,
Figure 658432DEST_PATH_IMAGE024
the frequent degree of the children q, the times of the children p and q may be different, so that the state space of attention can further reflect the concentration and the ignorant behavior difference of one type of children. In this embodiment, the period of time is recorded as a target time, and when a time period corresponding to the period of time changes, the target time period also changes correspondingly.
For any child sample in the state space, calculating the Kth reachable distance of each child
Figure 961499DEST_PATH_IMAGE029
Kth reachable distance
Figure 320805DEST_PATH_IMAGE029
I.e. the distance at which a child sample radiates outward in the associated state space until the K-th adjacent sample is covered. In this embodiment, K is 5, and for K which cannot reach 5, calculation is not required, and K is regarded as an isolated sample; for K up to 5, the analysis was continued. When the system is applied specifically, an implementer can set the K value according to the number of children in a class.
At Kth reachable distance of child sample p
Figure 475843DEST_PATH_IMAGE029
Can cover K childrenSample q, constructing a set of all child samples q covered in the covered space
Figure 46283DEST_PATH_IMAGE028
. Therefore, the behavior membership degree of the child sample p can be obtained:
Figure 815524DEST_PATH_IMAGE025
wherein, the first and the second end of the pipe are connected with each other,
Figure 475176DEST_PATH_IMAGE026
the behavior membership degree of the child sample p,
Figure 122320DEST_PATH_IMAGE027
is the spatial distance between the child sample p and the child sample q. When the behavior of the child is unified with other children, the higher the concentration degree of the characteristics of the concentration condition of the child p and the surrounding of the state space of the child p is, the smaller the reachable distance is, and the higher the membership degree is, which means that the behavior of the child is representative in the time period and has a higher reference value. Conversely, low concentration means that the degree of membership is lower, meaning that the behavior of the child is unique in the time period and has a smaller reference value.
The embodiment takes the child most representative in the period of time as the child representative to be observed in the period of time in the mode. And taking the set of most representative children corresponding to the time period in each mode as the child roles recommended to be simulated when the teacher looks back at the time period in the later period. The child representatives corresponding to different time periods are calculated, the roles of the recommended and simulated children are different when the young teacher looks back at the different time periods, the membership degree calculation is performed every 5 seconds, and the recommendation data are more real-time. As other embodiments, the membership level calculation may be performed at longer intervals to avoid large calculations or large fluctuations in the recommended data.
The embodiment obtains parameters capable of reflecting behaviors, attention and performance of children in an activity classroom, wherein the parameters comprise frequent degree of prestige of each child in the classroom, typical lagged time for interaction and lagged stability degree; classifying children in the classroom according to the parameters to obtain different types of children; next, analyzing the children included in each type, and selecting representative children in each type as recommended simulation objects for driving VR at a later stage, wherein compared with a mode of randomly selecting simulation objects, the method can improve the quality of a reply, help a young teacher find the defects when interacting with the children, and further deepen the training of professional ability; compared with a mode of simulating all children one by one, the tray-duplicating work load of the young teachers can be reduced, and the tray-duplicating efficiency is improved.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.

Claims (5)

1. A data processing method for educational training, comprising:
acquiring the frequent degree of prestige, typical delay time for interaction and delay stability degree of each child in a classroom of a target classroom;
the method for acquiring the frequent degree of the prospect specifically comprises the following steps: the proportion of the time that the deflection angle based on the head posture exceeds the tolerance angle to the whole class period; the typical late key time is the mean value of the first 30% of the maximum late key times in the late key times included in the late key time sequence; the late-suspected stability degree is calculated using the following formula:
Figure DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 674400DEST_PATH_IMAGE002
for a certain child's degree of hesitation to stabilize,
Figure 250875DEST_PATH_IMAGE003
the typical late time to doubt for that child,
Figure 708401DEST_PATH_IMAGE004
in order to find the maximum value,
Figure 585090DEST_PATH_IMAGE005
a late-suspected time series for the child;
calculating the attraction degree of each child in the classroom according to the typical delay time and the delay stability degree of each child to the interaction in the target classroom, comprising the following steps:
Figure 469869DEST_PATH_IMAGE006
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE007
in order to attract a child in a classroom,
Figure 658274DEST_PATH_IMAGE003
represents the typical hesitation time of the child, K represents the hesitation stability degree of the child, tanh () is a hyperbolic tangent function, alpha is a neighborhood correction parameter,
Figure 919491DEST_PATH_IMAGE008
，
Figure 851020DEST_PATH_IMAGE009
maximum of typical hesitation times for children within eight neighbourhoods of the child;
calculating the behavior difference distance between any two children according to the attraction degree, the prestige frequency degree and the delay time sequence of each child in the classroom of the target classroom; classifying the children in the target classroom according to the behavior difference distance to obtain a plurality of types; the delay time sequence is a sequence formed by the interaction delay times of the corresponding children in the class;
for either type: calculating the spatial distance between each child in the type and other children of the type according to the prestige frequency of each child in the type and the prestige state sequence corresponding to the target time period, calculating the behavior membership degree corresponding to each child in the type according to the spatial distance, taking the child with the maximum behavior membership degree as a representative child of the type corresponding to the target time period, and taking the representative child of the type corresponding to the target time period as a recommended simulation object when a young teacher looks back at the target time; the periscopic state sequence corresponding to the target time period is a sequence formed by the periscopic states corresponding to the acquisition moments of the corresponding children in the target time period.
2. The data processing method for educational training according to claim 1, wherein the behavior difference distance between any two children is calculated using the following formula:
Figure 906700DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure 988926DEST_PATH_IMAGE011
being the behavioural difference distance between child a and child B,
Figure DEST_PATH_IMAGE012
to the extent of attraction of the class to the child a,
Figure 584992DEST_PATH_IMAGE013
to the extent of attraction of the class to the child B,
Figure 170694DEST_PATH_IMAGE014
to the extent that child a is looking at frequently,
Figure 662856DEST_PATH_IMAGE015
the frequent degree of the prestige of the child B,
Figure 966798DEST_PATH_IMAGE016
is the late-suspected time series for child a,
Figure 307168DEST_PATH_IMAGE017
is the late-suspected time series of child B,
Figure 12956DEST_PATH_IMAGE018
the cosine similarity is solved.
3. The data processing method for educational training according to claim 1, wherein the calculating the spatial distance between each child in the category and other children in the category according to the frequent degree of views of each child in the category and the sequence of views corresponding to the target time period comprises:
Figure 410439DEST_PATH_IMAGE019
wherein the content of the first and second substances,
Figure 201678DEST_PATH_IMAGE020
is the spatial distance between the child p and the child q,
Figure 342809DEST_PATH_IMAGE021
for the sequence of states of the child p in the target time period,
Figure 903103DEST_PATH_IMAGE022
for children q in a target time periodThe sequence of states of the internal views,
Figure 737067DEST_PATH_IMAGE023
the frequent degree of the look of the child p,
Figure 750022DEST_PATH_IMAGE024
is the frequent degree of the child q.
4. The data processing method for educational practical training according to claim 3, wherein calculating the behavioral membership corresponding to each child in the category according to the spatial distance comprises:
the behavioral membership is calculated using the following formula:
Figure 691915DEST_PATH_IMAGE025
wherein the content of the first and second substances,
Figure 106716DEST_PATH_IMAGE026
the behavior membership of the child sample p,
Figure 111581DEST_PATH_IMAGE027
is the spatial distance between the child sample p and the child sample q,
Figure DEST_PATH_IMAGE028
kth reachable distance for child sample p
Figure 470887DEST_PATH_IMAGE029
The set of child samples covered in, K being the set value.
5. The data processing method for educational training according to claim 1, wherein each child in the target classroom is classified using DBSCAN algorithm.
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