CN114898251A - Data processing method, device, equipment and storage medium - Google Patents
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Abstract
The invention discloses a data processing method, a data processing device, data processing equipment and a storage medium. The method comprises the following steps: the method comprises the steps of obtaining classroom data in the teaching process, analyzing the classroom data, prompting alarm information to preset managers when preset red line behaviors appear in participants in the teaching process according to analysis results, grading the participants according to the classroom data and preset grading rules, wherein the preset grading rules are associated with deep learning, sequencing objects with preset dimensionality according to the classroom data and the grading results and according to preset sequencing rules, and generating corresponding reports, wherein the objects with the preset dimensionality comprise at least one of schools, students and teachers. According to the technical scheme of the embodiment of the invention, interaction between students and teachers in a classroom is deeply analyzed, teaching data processing based on deep learning is normalized, and important reference information is provided for classroom management and classroom quality evaluation.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a data processing method, apparatus, device, and storage medium.
Background
With the continuous development of informatization technology, more and more teachers adopt a teacher-student interaction mode during teaching, so that students in classroom learning can actively learn. How to make students in class more actively learn knowledge is a key concern of each school.
Deep Learning (Deep Learning) is also translated into Deep Learning, which is the essential difference in Learning published by the united states scholars, Ference Marton and Roger Saljo, in 1976 after an experimental study on students reading academic articles: results and procedures are presented in the text. It is a concept opposite to the shallow learning of isolated memory and non-critical knowledge acceptance, emphasizes the active learning of learners and flexibly and skillfully applies knowledge to solve the actual problem. Deep learning emphasizes the association of knowledge with abilities to communicate, collaborate, and learn autonomously. The students can obtain listening ability, thinking ability, cooperative communication ability, learning ability and application transferring ability through deep learning, and meanwhile positive emotional experience is achieved in the process of capability improvement.
However, in practical teaching, the deep learning method does not enter a real classroom to perform deep fusion with teachers and students in schools, and only flows on the surface, so how to combine the deep learning method with a computer technology to help teaching activities is a technical problem to be solved urgently.
Disclosure of Invention
The invention provides a data processing method, a data processing device, data processing equipment and a data processing storage medium, which can realize the auxiliary education and teaching by combining a deep learning method and a computer technology.
In a first aspect, an embodiment of the present invention provides a data processing method, including:
acquiring classroom data in a teaching process, wherein the classroom data comprises video data in the teaching process;
analyzing the classroom data, and prompting alarm information to a preset manager when determining that a participant in the teaching process has a preset red line behavior according to an analysis result, wherein the alarm information is used for assisting the preset manager in managing the teaching process, the participant comprises a teacher and a student, the preset red line behavior comprises a preset red line teacher behavior and a preset red line student behavior, and the preset red line behavior is associated with deep learning;
scoring the participants according to the classroom data and a preset scoring rule, wherein the preset scoring rule is associated with deep learning;
and sequencing the objects with preset dimensions according to the classroom data and the grading result and according to a preset sequencing rule, and generating a corresponding report, wherein the objects with the preset dimensions comprise at least one of schools, students and teachers.
In a second aspect, an embodiment of the present invention provides a data processing apparatus, including:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring classroom data in a teaching process, and the classroom data comprises video data in the teaching process;
the alarm information prompting module is used for analyzing the classroom data and prompting alarm information to a preset manager when determining that a participant in the teaching process has a preset red line behavior according to an analysis result, wherein the alarm information is used for assisting the preset manager in managing the teaching process, the participant comprises a teacher and a student, the preset red line behavior comprises a preset red line teacher behavior and a preset red line student behavior, and the preset red line behavior is associated with deep learning;
the scoring module is used for scoring the participators according to the classroom data and a preset scoring rule, wherein the preset scoring rule is associated with deep learning;
and the report generation module is used for sequencing the objects with preset dimensions according to the classroom data and the grading result and a preset sequencing rule and generating a corresponding report, wherein the objects with the preset dimensions comprise at least one of schools, students and teachers.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the data processing method of the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium, where computer instructions are stored, and the computer instructions are used to enable a processor to implement the data processing method of the first aspect when executed.
The data processing scheme provided by the embodiment of the invention is used for acquiring classroom data in a teaching process, wherein the classroom data comprises video data in the teaching process, analyzing the classroom data, and prompting warning information to a preset manager when determining that a participant in the teaching process has a preset red line behavior according to an analysis result, wherein the warning information is used for assisting the preset manager to manage the teaching process, the participant comprises a teacher and a student, the preset red line behavior comprises a preset red line teacher behavior and a preset red line student behavior, the preset red line behavior is associated with deep learning, and the participant is scored according to the classroom data and a preset scoring rule, wherein the preset scoring rule is associated with the deep learning, and sequencing the objects with the preset dimensions according to a preset sequencing rule, and generating a corresponding report, wherein the objects with the preset dimensions comprise at least one of schools, students and teachers. By adopting the technical scheme, based on the deep learning method, classroom data is collected and analyzed, whether a teacher or a student has red line behavior or not is judged, classroom performances of the teacher and the student are scored, when the red line behavior appears, alarm information is prompted to a manager, after the classroom is finished, sequencing is carried out according to the classroom performance scores of the teacher and the student, and a corresponding report is generated, so that the problem that the application of the deep learning method to the teaching method still stays in theory is solved, interaction between the student and the teacher in the classroom is deeply analyzed, teaching data based on deep learning is processed and normalized, important reference information is provided for classroom management and classroom quality evaluation, and education and teaching assistance by using a mode of combining the deep learning method and a computer technology is realized.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a data processing method according to an embodiment of the present invention;
fig. 2 is a flowchart of a data processing method according to a second embodiment of the present invention;
fig. 3 is a schematic diagram of rights information provided according to the second embodiment of the present invention;
FIG. 4 is a pie chart illustrating the scoring of a student according to a second embodiment of the present invention;
FIG. 5 is a pie chart illustrating teacher scoring according to a second embodiment of the present invention;
FIG. 6 is a diagram illustrating a teacher scoring report according to a second embodiment of the present invention;
fig. 7 is a schematic structural diagram of a data processing apparatus according to a third embodiment of the present invention;
fig. 8 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. In the description of the present invention, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a data processing method according to an embodiment of the present invention, where the data processing method is applicable to a case where classroom data is processed to improve teaching interaction effects, and the method may be executed by a data processing apparatus, where the data processing apparatus may be implemented in a form of hardware and/or software, and the data processing apparatus may be configured in an electronic device, and the electronic device may be formed by two or more physical entities or may be formed by one physical entity. The electronic device may be configured as a server of a teaching management system (also referred to as a teaching management platform, which may be referred to as a platform hereinafter).
As shown in fig. 1, a data processing method provided in the first embodiment of the present invention specifically includes the following steps:
s101, classroom data in the teaching process are obtained.
Wherein the classroom data comprises video data in the teaching process.
Illustratively, the teaching process may be a teaching process or a network teaching process, for example, network teaching may be performed by means of live broadcasting and the like. Taking the network teaching process as an example, in this embodiment, classroom data can be directly collected by embedding codes in a web page written in the HTML5 language, and a background operation and maintenance management interface and a customized authorization token are set. The client access mode comprises the following steps: direct access to web pages, access to platform applications, and the like. The direct access to the web page may be: embedding a Javascript code or a chrome plug-in into a client browser to acquire classroom data; platform application access may be: after the client collects the classroom data, the classroom data collected by the client needs to be transcoded by using a software development kit so as to ensure the compatibility of the content and format of the classroom data stored in different systems. In addition, a docking interface of the live broadcast platform cloud service can be set, and classroom data of the live broadcast cloud service can be directly acquired. It is worth noting that, before obtaining the classroom data in the teaching process, the method needs to obtain the authorization consent of the teachers, students and other related personnel in the teaching process, and if the classroom data is not obtained, the classroom data in the teaching process cannot be collected. Wherein the classroom data can include: video data, time of class, place, school district name, teacher's job number, classroom number, etc. in the teaching process.
And S102, analyzing classroom data, and prompting alarm information to preset management personnel when determining that the participant in the teaching process has the preset red line behavior according to the analysis result.
The warning information is used for assisting preset managers in managing the teaching process, the participators comprise teachers and students, the preset red line behaviors comprise preset red line teacher behaviors and preset red line student behaviors, and the preset red line behaviors are associated with deep learning. For example, the behavior related to the inhibition of the deep learning progress or the adverse effect on the improvement of the deep learning ability may be set as the preset red line behavior.
In this embodiment, after classroom data is collected, whether a teacher, a student and the like in a teaching process have a preset red line behavior or not can be judged through analysis, and if the preset red line behavior occurs, warning information is prompted to preset management personnel. The preset red line behavior is set based on a deep learning method, and can include that a teacher speaks for too long time or a student does not speak for a long time, for example; the warning information can be prompted by sending characters or video segments and the like to a client of a preset manager; the preset manager can be a teacher or a teacher supervisor, and the like.
And S103, grading the participants according to the classroom data and a preset grading rule.
Wherein the preset scoring rule is associated with deep learning.
In this embodiment, according to the obtained classroom data and the scoring rule set based on the deep learning method, the scoring is performed on the participants in the teaching process such as teachers and students. Wherein, preset scoring rules include: rules for scoring pre-set red line behavior, rules for scoring teacher affinity, rules for scoring student attention, etc., the pre-set scoring rules being associated with deep learning. For example, an index that hinders the progress of deep learning or is not favorable for improving the deep learning ability may be set as a deduction item, and an index that promotes the progress of learning or is favorable for improving the deep learning ability may be set as an addend item.
And S104, sequencing the objects of the preset dimensionality according to the classroom data and the grading result and a preset sequencing rule, and generating a corresponding report.
The preset dimension object comprises at least one of a school, a student and a teacher.
In this embodiment, the preset ordering rule includes: sorting in descending order of total score or sorting in descending order of single score, etc.; in addition to generating reports, pie charts, graphs, or the like may be generated. Illustratively, according to the classroom data and the scoring result of the teacher, the teacher is ranked according to the descending ranking rule of the affinity scoring, and a report of the affinity scoring of the teacher is generated.
The data processing method provided by the embodiment of the invention is used for acquiring classroom data in a teaching process, wherein the classroom data comprises video data in the teaching process, analyzing the classroom data, and prompting alarm information to a preset manager when the participant in the teaching process is determined to have a preset red line behavior according to an analysis result, wherein the alarm information is used for assisting the preset manager in managing the teaching process, the participant comprises a teacher and a student, the preset red line behavior comprises a preset red line teacher behavior and a preset red line student behavior, the preset red line behavior is associated with deep learning, and the participant is scored according to the classroom data and a preset scoring rule, wherein the preset scoring rule is associated with the deep learning, objects with preset dimensions are ranked according to the preset ranking rule according to the classroom data and the scoring result, and a corresponding report is generated, the preset dimension object comprises at least one of a school, a student and a teacher. According to the technical scheme, classroom data are collected and analyzed based on a deep learning method, whether a teacher or a student has red line behaviors or not is judged, classroom performances of the teacher and the student are scored, when the red line behaviors appear, alarm information is prompted to a manager, after the classroom is finished, sequencing is carried out according to the classroom performance scores of the teacher and the student, and corresponding reports are generated.
Example two
Fig. 2 is a flowchart of a data processing method according to a second embodiment of the present invention, and the technical solution according to the second embodiment of the present invention is further optimized based on the above optional technical solutions, and a specific manner for processing classroom data is given.
Optionally, classroom data is analyzed, and when determining that the participant in the teaching process appears a preset red line behavior according to the analysis result, warning information is prompted to preset managers, including: analyzing and processing video data in the teaching process to obtain an analysis result; when the speaking time of the teacher exceeds a first preset time length according to the analysis result, prompting warning information to preset management personnel; when the speaking time of the student is determined to be lower than the second preset time length according to the analysis result, warning information is prompted to a preset manager; when the situation that video data are lost in the teaching process is determined according to the analysis result, warning information is prompted to a preset manager; when the fact that the duration time of the participator leaving the video data acquisition range exceeds a third preset duration is determined according to the analysis result, warning information is prompted to preset management personnel; and when the accumulated time length of the teaching contents of the teachers, which do not accord with the requirements of the preset teaching outline, in the teaching process exceeds a fourth preset time length according to the analysis result, prompting warning information to preset management personnel. The advantage of setting up like this is, standardizes teacher's and student's classroom action, helps promoting teacher's ability of imparting knowledge to students and promotes student's concentration on.
Optionally, according to the classroom data and the preset scoring rule, scoring the participators includes: according to the classroom data and a preset teacher scoring rule, scoring the affinity, teaching language, classroom organization, classroom interaction and red line indexes of the teacher in the teaching process; and according to the classroom data and a preset student scoring rule, scoring the attention of students, the times of keyword hit, the times of interaction, red line behaviors and the average sentence length of single sentences in the teaching process. The advantage that sets up like this lies in, can the systematic analysis go out the not enough of teacher and student's classroom action, promote the teaching quality for the teacher and provide data support.
Optionally, according to the classroom data and the scoring result, sorting the objects of the preset dimension according to a preset sorting rule, and generating a corresponding report, including: sequencing the objects with preset dimensionality according to the classroom data and the scores and a preset sequencing rule to obtain a sequencing result; acquiring access authority information corresponding to a target user; and generating a report and a trend chart matched with the access authority information of the current target user according to the sequencing result for each target user. The method has the advantages that the requirements of users with different roles for data are met, and the multi-dimensional report is provided.
As shown in fig. 2, a data processing method provided in the second embodiment of the present invention specifically includes the following steps:
s201, classroom data in the teaching process are obtained.
The classroom data comprises video data in the teaching process.
It should be clear that step S201 is already explained in the first embodiment of the present invention, and will not be described herein.
S202, analyzing and processing the video data in the teaching process to obtain an analysis result.
Specifically, the video data in the teaching process includes image data and audio data, and the analysis result may be a result of recognizing the movement or language of a teacher, a student, or other related personnel.
Optionally, analyzing and processing the video data in the teaching process to obtain an analysis result, including: carrying out structuring processing on the obtained video data to obtain structured image data and structured audio data; extracting head information in the structured image data by using a visual detection algorithm, and recording the occurrence time of the head information; extracting voice information in the structured audio data by using a voice detection algorithm, and recording a speaking time period in the voice information; and analyzing the human voice information by using a voice recognition algorithm.
Specifically, the process of analyzing and processing video data in the teaching process includes: firstly, the collected video data is structurally processed, and the video data is layered into structured image data and structured audio data; then, detecting information such as facial expressions, head orientations and head coordinates of the teacher and the student by using a visual detection algorithm, and recording the time of appearance and disappearance of the information; detecting the voice information of the teacher and the student by using a voice detection algorithm, filtering background noise, and recording the starting time and the ending time of the voice information; and then analyzing the voice information by utilizing a voice recognition algorithm.
Further, the analyzing the voice information by using the voice recognition algorithm comprises: recognizing a speech adscription person in the voice information to obtain a speech adscription person recognition result; based on the recognition result of the speech attribution person, extracting the voice information fragment of the attribution person from the voice information, and analyzing the semantics of the voice information fragment of the speech attribution person to obtain a semantic analysis result; and matching the semantics of the speech attribution persons with preset keywords respectively based on the semantic analysis result to obtain a matching result, wherein the preset keywords of the teacher comprise encouragement words, and the preset keywords of the trainees comprise question answering words.
Specifically, a speech recognition algorithm can be used for recognizing a speech adscriptor, namely a speaker, of each sentence of voice in the voice information, and the speech adscriptor is matched with the voice information, so that the corresponding relation between the speech adscriptor and the voice information can be obtained; and then extracting the voice information fragment of each ascription person from the voice information according to the corresponding relation, analyzing the semantics of the voice information fragment of each speech ascription person, namely the speech content, and respectively matching the semantics of each speech ascription person with preset keywords to obtain a matching result.
S203, when the speaking time of the teacher is determined to exceed the first preset time length according to the analysis result, warning information is prompted to preset management personnel; when the speaking time of the student is determined to be lower than the second preset time length according to the analysis result, warning information is prompted to preset management personnel; when the situation that video data are lost in the teaching process is determined according to the analysis result, warning information is prompted to a preset manager; when the fact that the duration time of the participant leaving the video data acquisition range exceeds a third preset duration is determined according to the analysis result, warning information is prompted to preset management personnel; and when determining that the accumulated time length of the teaching content of the teacher, which does not meet the requirements of the preset teaching outline, exceeds a fourth preset time length according to the analysis result, prompting warning information to preset management personnel.
Specifically, the above analysis processing makes it possible to obtain the speaking time of the teacher, the speaking time of the student, whether or not video data is missing, the duration of time during which the heads of the teacher and student are out of the video data acquisition range, the teaching content of the teacher, and the like. The teaching content of the teacher does not meet the requirement of the preset teaching outline, and comprises content except the content of the teaching outline or Chinese speaking in English class and the like, the first preset time, the second preset time, the third preset time and the fourth preset time can be set according to actual conditions, if the first preset time and the second preset time are set to be 3 minutes, the third preset time is set to be 2 minutes, and the fourth preset time is set to be 10 minutes.
S204, according to the classroom data and a preset teacher grading rule, grading the affinity, teaching language, classroom organization, classroom interaction and red line index of a teacher in the teaching process; and according to the classroom data and a preset student scoring rule, scoring the attention of students, the times of keyword hit, the times of interaction, red line behaviors and the average sentence length of single sentences in the teaching process.
It should be noted that the preset teacher scoring rule and the scoring items thereof and the preset student scoring rule and the scoring items thereof may be set according to actual situations, including but not limited to the above.
For example, the preset teacher scoring rule includes rules of scoring rules and scoring weight ratios of the rules, such as: the total score of teacher score is 100, the affinity score rule is that the ratio of the time length of smile appearing on the teacher's face to the time length of video data is multiplied by 100, if the ratio is 50%, the affinity score is 50, the total score is 100, and the weight of the affinity score to the total score is 15%; the teaching language scoring rule can be that the proportion value of the keywords spoken by the teacher in the keyword library is multiplied by 100, if the proportion value is 50%, the teaching language scoring is 50 points, the full scoring is 100 points, and the weight of the teaching language scoring in the total scoring is 20%; the classroom organization scoring rules can comprise encouraging language scoring, person-selecting scoring, teaching duration scoring and the like, the classroom organization scoring is an average value of accumulated scores of the separate items of the encouraging language scoring, the person-selecting scoring and the teaching duration scoring, the classroom organization scoring accounts for 30% of the total score, wherein the encouraging language scoring rules can be that 100 is multiplied by a proportion value of encouraging language spoken by a teacher in an encouraging language word bank, if the proportion value is 50%, the encouraging language scoring is 50 points, and the full score is 100, the person-selecting scoring rules can be that 100 is multiplied by a proportion value of the number of questions and answers finished by the teacher and students, if the proportion value is 50%, the person-selecting scoring is 50 points, the full score is 100, the teaching duration scoring rules can be that 100 is multiplied by a proportion value of knowledge points of classroom contents spoken by the teacher, and if the proportion value is 50%, the teaching duration scoring is 50%, the full score is 100 points; the classroom interaction scoring rules can comprise interaction frequency scoring and interaction effectiveness scoring, the classroom interaction scoring is an average value of the sum of scoring items of the interaction frequency scoring and the interaction effectiveness scoring, the classroom interaction scoring accounts for 35% of the total score, the interaction frequency scoring rules can be that the ratio of classroom interaction frequency of teachers and trainees to preset interaction frequency is multiplied by 100, if the ratio is 50%, the interaction frequency scoring is 50 points, the full score is 100, the interaction effectiveness scoring rules can be that the ratio of effective interaction frequency of teachers and trainees to the preset interaction frequency is multiplied by 100, if the ratio is 50%, the interaction effectiveness scoring is 50 points, and the full score is 100 points; the red line index scoring rule can be that the proportion value of the times of the teacher appearing the red line behaviors in the preset red line behavior times is multiplied by 100, if the proportion value is 50%, the red line index scoring is 50 points, the full score is 100 points, and the weight of the red line index scoring in the total score is 30%; and summing the products of the affinity, the teaching language, the classroom organization and the classroom interaction score of the teacher and the respective weight proportion, and deducting the score of the product of the red line index score and the weight thereof to obtain the total score of the teacher score.
For example, the preset student scoring rules include various scoring rules, such as: the total score of the student score is 100, the attention score rule can be that the frame rate of the student face appearing in the video data acquisition range accounts for the proportion value of the total frame rate of the video data multiplied by 100, if the proportion value is 50%, the attention score is 50, and the total score is 100; the keyword hit number scoring rule may be that the ratio of words or sentences spoken by the learner to words or sentences in the preset lesson outline is multiplied by 100, and if the ratio is 50%, the keyword hit number scoring is 50 points, and the full scoring is 100 points; the interaction frequency scoring rule can be that the ratio of the number of times of answering the questions by the student to the preset number of times is multiplied by 100, if the ratio is 50%, the interaction frequency score is 50, and the full score is 100; the red line behavior scoring rule can be that the proportion value of the times of the trainee appearing the red line behavior to the preset times is multiplied by 100, if the proportion value is 50%, the red line behavior scoring is 50 points, and the full score is 100 points; the single sentence average sentence length scoring rule can be that the ratio of the average number of words in each sentence spoken by the student to the preset numerical value is multiplied by 100, if the ratio is 50%, the single sentence average sentence length scoring is 50 points, and the full score is 100 points; and summing the attention of the student, the hit times of the keywords, the interaction times, the red line behaviors and the average sentence length scores of the single sentences, deducting the scores of the red line behaviors, and calculating the average value to obtain the score which is the total score of the student.
S205, according to the classroom data and the grading result, sequencing the objects with preset dimensionality according to a preset sequencing rule to obtain a sequencing result; acquiring access authority information corresponding to a target user; and generating a report and a trend chart matched with the access authority information of the current target user according to the sequencing result for each target user.
Specifically, according to classroom data and scoring results of teachers and students, the classroom data and the scoring results can be sorted according to preset sorting rules, so that sorting results are obtained, corresponding reports, trend graphs, scoring pie charts and the like can be generated according to the sorting results, and different permissions are given to different target users for meeting the requirements of different target users on the data and protecting the data privacy of the teachers and the students. Wherein, the target user includes at least one of the multiple roles of the class patrol officer and the teacher, fig. 3 is a schematic diagram of authority information, as shown in fig. 3, the authority of the class patrol officer includes that a classroom list, a teacher list, a student list, real-time monitoring and the like can be viewed, and the authority of the teacher includes that a classroom list, a student list and the like can be viewed, and the authority of the teacher does not have the authority of viewing the teacher list and real-time monitoring compared with the class patrol officer. In order to protect the privacy of teachers and students, a part of data in fig. 3 is masked.
Illustratively, fig. 4 is a pie chart of student scores, and fig. 5 is a pie chart of teacher scores, such as: the preset ordering rule is that teacher affinity scores are arranged in a descending order, according to classroom data and scoring results and according to the teacher affinity scores arranged in the descending order, class patrol personnel serve as target users and can look up interfaces shown in fig. 4 and 5, so that classroom performances of students and teachers can be visually seen, fig. 6 is a teacher scoring report diagram, class patrol personnel serve as target users and can also look up teacher scoring reports shown in fig. 6, wherein in order to protect privacy of teachers and students, partial data of fig. 4 and 6 are covered.
The data processing method provided by the embodiment of the invention is based on a deep learning method, video data of a classroom is collected and analyzed, whether a teacher or a student has red line behavior or not is judged, classroom performance of the teacher and the student is scored, when the red line behavior appears, alarm information is prompted to a manager, after the classroom is finished, sequencing is carried out according to classroom performance scores of the teacher and the student, a corresponding report form and a trend graph are generated, a deep learning concept enters into the integration of real classroom and depth of the teacher and the student, knowledge is mastered, abilities of communication, cooperation, autonomous learning and the like are mastered, students are promoted to understand the classroom of active learning, deep analysis is carried out on interaction between the students and the teacher in classroom, and teaching data processing based on deep learning is standardized.
On the basis of the above embodiment, after scoring the participants according to the classroom data and the preset scoring rule, the method may further include: and generating a video clip of target participants in the teaching process, wherein the target participants comprise teachers with first preset scoring indexes higher than a first preset threshold value and/or students with second preset scoring indexes higher than a second preset threshold value.
Specifically, after scoring participants in the teaching process such as teachers and students, videos of highlight periods of target participants in a classroom can be generated for the target users to watch. Wherein the target participant can be understood as a participant with a score higher than a preset threshold value among participants such as teachers and students. For example, a video clip may be generated in which the student has a higher keyword score than 80 points in the classroom.
On the basis of the above embodiment, after obtaining the classroom data in the teaching process, the method may further include: the classroom data in the teaching process are stored in a local server or a cloud server and the like, a storage period of validity is set, the classroom data can be downloaded or deleted by a target user within the authority range of the target user within the storage period of validity, and when the storage period of the classroom data exceeds the storage period of validity, the classroom data can be automatically deleted.
EXAMPLE III
Fig. 7 is a schematic structural diagram of a data processing apparatus according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes: a data acquisition module 301, an alarm information prompt module 302, a scoring module 303 and a report generation module 304, wherein:
the data acquisition module is used for acquiring classroom data in the teaching process, wherein the classroom data comprises video data in the teaching process;
the alarm information prompting module is used for analyzing classroom data and prompting alarm information to a preset manager when the fact that a participant in the teaching process has a preset red line behavior is determined according to an analysis result, wherein the alarm information is used for assisting the preset manager in managing the teaching process, the participant comprises a teacher and a student, the preset red line behavior comprises a preset red line teacher behavior and a preset red line student behavior, and the preset red line behavior is associated with deep learning;
the scoring module is used for scoring the participators according to the classroom data and a preset scoring rule, wherein the preset scoring rule is associated with deep learning;
and the report generation module is used for sequencing the objects with the preset dimensionality according to the classroom data and the grading result and according to a preset sequencing rule and generating a corresponding report, wherein the objects with the preset dimensionality comprise at least one of schools, students and teachers.
The data processing device provided by the embodiment of the invention is based on a deep learning method, collects and analyzes classroom data, judges whether a teacher or a student has red line behavior and scores the classroom performance of the teacher and the student, prompts alarm information to managers when the red line behavior appears, sequences according to the classroom performance scores of the teacher and the student and generates a corresponding report after the classroom is finished, solves the problem that the application of the deep learning method to the teaching method stays in theory, deeply analyzes the interaction between the student and the teacher in the classroom, standardizes the teaching data processing based on the deep learning, provides important reference information for classroom management and classroom quality evaluation, and realizes the auxiliary education and teaching by combining the deep learning method and computer technology.
Optionally, the warning information prompting module includes:
the data analysis unit is used for analyzing and processing the video data in the teaching process to obtain an analysis result;
the alarm information prompting unit is used for prompting alarm information to preset management personnel when the speaking time of the teacher is determined to exceed the first preset time length according to the analysis result; when the speaking time of the student is determined to be lower than the second preset time length according to the analysis result, warning information is prompted to preset management personnel; when the situation that video data are lost in the teaching process is determined according to the analysis result, warning information is prompted to a preset manager; when the fact that the duration time of the participator leaving the video data acquisition range exceeds a third preset duration is determined according to the analysis result, warning information is prompted to preset management personnel; and when the accumulated time length of the teaching contents of the teachers, which do not accord with the requirements of the preset teaching outline, in the teaching process exceeds a fourth preset time length according to the analysis result, prompting warning information to preset management personnel.
Optionally, the data analysis unit is specifically configured to: carrying out structuring processing on the obtained video data to obtain structured image data and structured audio data; extracting head information in the structured image data by using a visual detection algorithm, and recording the occurrence time of the head information; extracting voice information in the structured audio data by using a voice detection algorithm, and recording a speaking time period in the voice information; and analyzing the human voice information by using a voice recognition algorithm.
Further, the analyzing the voice information by using the voice recognition algorithm comprises: recognizing a speech adscription person in the voice information to obtain a speech adscription person recognition result; based on the recognition result of the speech attribution person, extracting the voice information fragment of the attribution person from the voice information, and analyzing the semantics of the voice information fragment of the speech attribution person to obtain a semantic analysis result; and matching the semantics of the speech attribution persons with preset keywords respectively based on the semantic analysis result to obtain a matching result, wherein the preset keywords of the teacher comprise encouragement words, and the preset keywords of the trainees comprise question answering words.
Optionally, the scoring module includes:
the teacher scoring unit is used for scoring the affinity, teaching language, classroom organization, classroom interaction and red line indexes of the teacher in the teaching process according to the classroom data and preset teacher scoring rules;
and the student scoring unit is used for scoring the attention degree, the keyword hit times, the interaction times, the red line behavior and the average sentence length of the single sentence of the student in the teaching process according to the classroom data and the preset student scoring rule.
Optionally, the report generating module includes:
the sequencing result generating unit is used for sequencing the objects with preset dimensionality according to the classroom data and the grading result and a preset sequencing rule to obtain a sequencing result;
the authority information acquisition unit is used for acquiring access authority information corresponding to a target user;
and the report generation unit is used for generating a report and a trend chart which are matched with the access authority information of the current target user according to the sequencing result for each target user.
Optionally, the apparatus further comprises:
the video clip generating unit is used for generating video clips of target participators in the teaching process, wherein the target participators comprise teachers with first preset scoring indexes higher than a first preset threshold value and/or students with second preset scoring indexes higher than a second preset threshold value.
The data processing device provided by the embodiment of the invention can execute the data processing method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Example four
FIG. 8 shows a schematic block diagram of an electronic device 40 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 8, the electronic device 40 includes at least one processor 41, and a memory communicatively connected to the at least one processor 41, such as a Read Only Memory (ROM)42, a Random Access Memory (RAM)43, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 41 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)42 or the computer program loaded from the storage unit 48 into the Random Access Memory (RAM) 43. In the RAM 43, various programs and data necessary for the operation of the electronic apparatus 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other via a bus 44. An input/output (I/O) interface 45 is also connected to bus 44.
A number of components in the electronic device 40 are connected to the I/O interface 45, including: an input unit 46 such as a keyboard, a mouse, etc.; an output unit 47 such as various types of displays, speakers, and the like; a storage unit 48 such as a magnetic disk, an optical disk, or the like; and a communication unit 49 such as a network card, modem, wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
In some embodiments, the data processing method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 40 via the ROM 42 and/or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the data processing method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the method by any other suitable means (e.g., by way of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
The computer device provided above can be used to execute the data processing method provided in any of the above embodiments, and has corresponding functions and advantages.
EXAMPLE five
In the context of the present invention, a computer-readable storage medium may be a tangible medium, which when executed by a computer processor, is for performing a method of data processing, the method comprising:
acquiring classroom data in a teaching process, wherein the classroom data comprises video data in the teaching process;
analyzing the classroom data, and prompting alarm information to a preset manager when determining that a participant in the teaching process has a preset red line behavior according to an analysis result, wherein the alarm information is used for assisting the preset manager in managing the teaching process, the participant comprises a teacher and a student, the preset red line behavior comprises a preset red line teacher behavior and a preset red line student behavior, and the preset red line behavior is associated with deep learning;
scoring the participants according to the classroom data and a preset scoring rule, wherein the preset scoring rule is associated with deep learning;
and sequencing the objects with preset dimensionality according to the classroom data and the grading result and generating a corresponding report according to a preset sequencing rule, wherein the objects with the preset dimensionality comprise at least one of schools, students and teachers.
In the context of the present invention, a computer readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer device provided above can be used to execute the data processing method provided in any of the above embodiments, and has corresponding functions and advantages.
It should be noted that, in the embodiment of the data processing apparatus, the included units and modules are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in some detail by the above embodiments, the invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the invention, and the scope of the invention is determined by the scope of the appended claims.
Claims (10)
1. A data processing method, comprising:
acquiring classroom data in a teaching process, wherein the classroom data comprises video data in the teaching process;
analyzing the classroom data, and prompting alarm information to a preset manager when determining that a participant in the teaching process has a preset red line behavior according to an analysis result, wherein the alarm information is used for assisting the preset manager in managing the teaching process, the participant comprises a teacher and a student, the preset red line behavior comprises a preset red line teacher behavior and a preset red line student behavior, and the preset red line behavior is associated with deep learning;
scoring the participants according to the classroom data and a preset scoring rule, wherein the preset scoring rule is associated with deep learning;
and sequencing the objects with preset dimensions according to the classroom data and the grading result and according to a preset sequencing rule, and generating a corresponding report, wherein the objects with the preset dimensions comprise at least one of schools, students and teachers.
2. The method of claim 1, wherein the analyzing the classroom data and prompting a warning message to a preset manager when the participant in the teaching process is determined to have a preset red line behavior according to the analysis result comprises:
analyzing and processing the video data in the teaching process to obtain an analysis result;
when the speaking time of the teacher is determined to exceed a first preset time length according to the analysis result, warning information is prompted to preset management personnel;
when the speaking time of the student is determined to be lower than a second preset time length according to the analysis result, warning information is prompted to preset management personnel;
when the situation that video data are lost in the teaching process is determined according to the analysis result, warning information is prompted to a preset manager;
when the fact that the duration time of the participant leaving the video data acquisition range exceeds a third preset duration is determined according to the analysis result, warning information is prompted to preset management personnel;
and when determining that the accumulated time length of the teaching content of the teacher, which does not meet the requirements of the preset teaching outline, exceeds a fourth preset time length according to the analysis result, prompting warning information to preset management personnel.
3. The method of claim 2, wherein analyzing the video data of the teaching process to obtain an analysis result comprises:
carrying out structuring processing on the obtained video data to obtain structured image data and structured audio data;
extracting head information in the structured image data by using a visual detection algorithm, and recording the time when the head information appears;
extracting voice information in the structured audio data by using a voice detection algorithm, and recording a speaking time period in the voice information;
and analyzing the voice information by utilizing a voice recognition algorithm.
4. The method of claim 3, wherein said parsing said vocal information using a speech recognition algorithm comprises:
recognizing a speech adscription person in the voice information to obtain a speech adscription person recognition result;
based on the recognition result of the speech attribution person, extracting the voice information fragment of the attribution person from the voice information, and analyzing the semantics of the voice information fragment of the speech attribution person to obtain a semantic analysis result;
and matching the semantics of each speaker with preset keywords respectively based on the semantic analysis result to obtain a matching result, wherein the preset keywords of the teacher comprise encouragement words, and the preset keywords of the trainees comprise topic answer words.
5. The method of claim 1, wherein scoring the participant according to the classroom data and pre-set scoring rules comprises:
according to the classroom data and a preset teacher grading rule, grading the affinity, teaching language, classroom organization, classroom interaction and red line index of a teacher in the teaching process;
and according to the classroom data and a preset student scoring rule, scoring the attention of the students, the keyword hit times, the interaction times, the red line behaviors and the average sentence length of the single sentence in the teaching process.
6. The method according to claim 1, wherein the sorting the objects of the preset dimension according to the preset sorting rule and the scoring result and generating the corresponding report according to the classroom data and the scoring result comprises:
according to the classroom data and the grading result, sequencing the objects with preset dimensionality according to a preset sequencing rule to obtain a sequencing result;
acquiring access authority information corresponding to a target user;
and generating a report and a trend chart matched with the access authority information of the current target user according to the sequencing result for each target user.
7. The method of claim 1, further comprising, after said scoring the participant according to the classroom data and pre-set scoring rules:
and generating a video clip of target participants in the teaching process, wherein the target participants comprise teachers with first preset scoring indexes higher than a first preset threshold value and/or students with second preset scoring indexes higher than a second preset threshold value.
8. A data processing apparatus, comprising:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring classroom data in a teaching process, and the classroom data comprises video data in the teaching process;
the alarm information prompting module is used for analyzing the classroom data and prompting alarm information to preset managers when the fact that preset red line behaviors occur to participants in the teaching process is determined according to an analysis result, wherein the alarm information is used for assisting the preset managers in managing the teaching process, the participants comprise teachers and students, the preset red line behaviors comprise preset red line teacher behaviors and preset red line student behaviors, and the preset red line behaviors are associated with deep learning;
the scoring module is used for scoring the participators according to the classroom data and a preset scoring rule, wherein the preset scoring rule is associated with deep learning;
and the report generation module is used for sequencing the objects with preset dimensions according to the classroom data and the grading result and a preset sequencing rule and generating a corresponding report, wherein the objects with the preset dimensions comprise at least one of schools, students and teachers.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the data processing method of any one of claims 1-7.
10. A computer-readable storage medium, characterized in that it stores computer instructions for causing a processor to implement the data processing method of any of claims 1-7 when executed.
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