CN112370039B - Classroom quality detection method and device based on intelligent classroom - Google Patents
Classroom quality detection method and device based on intelligent classroom Download PDFInfo
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Abstract
The invention discloses a class quality detection method and device based on an intelligent class, which are applied to intelligent wearable equipment, wherein the method comprises the following steps: pulse wave signal and skin electric signal information of a first student and a first teacher are respectively obtained, and filtering processing is carried out on the electric signals; obtaining first characteristic information and second characteristic information corresponding to physiological signals of the first student and the first teacher by a training model; sequentially obtaining N groups of component information according to the characteristic information of the electric signals, and sequencing the N groups of component information to obtain M pieces of student information within a first threshold value; and judging that the class quality of the students with the duration time of the score information in the first threshold exceeds the second threshold is unqualified, acquiring first reminding information, and reminding the second students to pay attention to class through the intelligent wearable equipment. The technical purpose of monitoring the classroom quality in real time, finding problems in time and reminding is achieved, and therefore the classroom teaching quality is improved.
Description
Technical Field
The invention relates to the technical field of classroom quality detection, in particular to a classroom quality detection method and device based on an intelligent classroom.
Background
The intelligent classroom is an intelligent and efficient classroom which is built by the new generation of information technologies such as 'Internet+' thinking mode, big data, cloud computing and the like based on the construction sense learning theory. And processing, mining and analyzing the data by adopting modern analysis tools and analysis methods, so as to carry out teaching decision, and accurately grasp the learning condition and adjust the teaching strategy by means of the data. The phenomenon that students take lessons without concentration is the most common phenomenon which directly affects the quality of the class, and no effective solution is available for the phenomenon at present.
However, in the process of implementing the technical scheme of the invention in the embodiment of the application, the inventor of the application finds that at least the following technical problems exist in the above technology:
the classroom quality is difficult to monitor in real time, the classroom management is weak, the problem is difficult to solve in time, and the like.
Disclosure of Invention
According to the classroom quality detection method and device based on the intelligent classroom, the technical problems that in the prior art, the classroom quality is difficult to monitor in real time, the classroom management is weak, the problem finding is difficult to solve in time and the like are solved, the technical purposes of monitoring the classroom quality in real time, finding the problem in time and reminding are achieved, and therefore the classroom teaching quality is improved.
The embodiment of the application provides a class quality detection method based on a smart class, wherein the method comprises the steps of obtaining first electrophysiological signal information, wherein the first electrophysiological signal information is pulse wave signal and skin electric signal information of a first student; obtaining second electrophysiological signal information, wherein the second electrophysiological signal information is pulse wave signal and skin electrical signal information of a first teacher; filtering and preprocessing the first electrophysiological signal to obtain third electrophysiological signal information, wherein the third electrophysiological signal information is the preprocessed first electrophysiological signal information; obtaining fourth electrophysiological signal information, wherein the fourth electrophysiological signal information is the second electrophysiological signal information after pretreatment; inputting the third electrophysiological signal information and the fourth electrophysiological signal information into a first training model, wherein the first training model is obtained through training of multiple groups of training data, and each group of training data in the multiple groups of training data comprises: the third electrophysiological signal information, the fourth electrophysiological signal information, the identification information of the first characteristic information, and the identification information of the second characteristic information; and obtaining first output information and second output information of the first training model, wherein the first output information and the second output information are obtained. The first output information is first characteristic information of the third electrophysiological signal, and the second input information is second characteristic information of the fourth electrophysiological signal; obtaining first score information of a first student and a first teacher according to the first characteristic information and the second characteristic information, wherein the first score is any value between 0 and 100; sequentially obtaining N groups of fraction information, wherein each group of fraction information comprises fraction information of an Nth student and a first teacher and fraction information between the Nth student and other N-1 students; sorting the N groups of fraction information to obtain M pieces of student information within a first threshold value; judging whether the duration time of the score information of the second student in the first threshold value is in the second threshold value or not; and if the duration of the score information of the second student in the first threshold is not in the second threshold, obtaining first reminding information, wherein the first reminding information is used for reminding the second student that the class quality of the second student is unqualified through the intelligent wearable device.
On the other hand, the application still provides a classroom quality detection device based on wisdom classroom, wherein, the device includes: the first acquisition unit is used for acquiring first electrophysiological signal information, wherein the first electrophysiological signal information is pulse wave signal and skin electric signal information of a first student; a second obtaining unit configured to obtain second electrophysiological signal information, where the second electrophysiological signal information is pulse wave signal and skin electrical signal information of the first teacher; the third obtaining unit is used for carrying out filtering pretreatment on the first electrophysiological signal to obtain third electrophysiological signal information, wherein the third electrophysiological signal information is the first electrophysiological signal information after pretreatment; a fourth obtaining unit, configured to obtain fourth electrophysiological signal information, where the fourth electrophysiological signal information is the second electrophysiological signal information after preprocessing; the first input unit is used for inputting the third electrophysiological signal information and the fourth electrophysiological signal information into a first training model, wherein the first training model is obtained through training of multiple groups of training data, and each group of training data in the multiple groups of training data comprises: the third electrophysiological signal information, the fourth electrophysiological signal information, the identification information of the first characteristic information, and the identification information of the second characteristic information; and a fifth obtaining unit for obtaining the first output information and the second output information of the first training model, wherein the sixth obtaining unit is used for obtaining the first output information and the second output information of the first training model. The first output information is first characteristic information of the third electrophysiological signal, and the second input information is second characteristic information of the fourth electrophysiological signal; the sixth obtaining unit is used for obtaining first score information of a first student and a first teacher according to the first characteristic information and the second characteristic information, wherein the first score is any value between 0 and 100; a seventh obtaining unit, configured to sequentially obtain N sets of score information, where each set of score information includes score information of an nth student and a first teacher and score information between the nth student and other N-1 students; an eighth obtaining unit, configured to sort the N sets of score information to obtain M pieces of student information that are within a first threshold; the first judging unit is used for judging whether the duration time of the score information of the second student in the first threshold value is in the second threshold value or not; and the ninth obtaining unit is used for obtaining first reminding information if the duration time of the score information of the second student in the first threshold is not in the second threshold, wherein the first reminding information is used for reminding the second student that the class quality of the second student is unqualified through the intelligent wearable device.
On the other hand, the embodiment of the application also provides a class quality detection device based on intelligent class, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of the method in the first aspect when executing the program.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
because the technology based on the internet of things is adopted, the electrophysiological signals of the teacher and the students are obtained through the intelligent wearable equipment, and the signals are processed and calculated to obtain index information for evaluating the classroom quality of the students, so that the classroom quality of the students is monitored in real time, and the condition of unqualified classroom quality can be timely reminded, thereby realizing the technical purposes of monitoring the classroom quality in real time, timely finding problems and reminding, and improving the classroom teaching quality.
The foregoing description is a summary of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application is given.
Drawings
Fig. 1 is a schematic flow chart of a class quality detection method based on a smart class according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a class quality detection device based on a smart class according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Reference numerals illustrate: the device comprises a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a first input unit 15, a fifth obtaining unit 16, a sixth obtaining unit 17, a seventh obtaining unit 18, an eighth obtaining unit 19, a first judging unit 20, a ninth obtaining unit 21, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304 and a bus interface 306.
Detailed Description
According to the classroom quality detection method and device based on the intelligent classroom, the technical problems that in the prior art, the classroom quality is difficult to monitor in real time, the classroom management is weak, the problem finding is difficult to solve in time and the like are solved, the technical purposes of monitoring the classroom quality in real time, finding the problem in time and reminding are achieved, and therefore the classroom teaching quality is improved. Hereinafter, example embodiments of the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application and not all of the embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.
Summary of the application
The intelligent classroom can process, mine and analyze the data by adopting modern analysis tools and analysis methods, so that teaching decisions can be made, and the learning condition and the teaching strategy can be accurately mastered by the data. The phenomenon that students take lessons without concentration is the most common phenomenon which most directly affects the quality of the class, and no effective solution is available at present. In the prior art, the problems of difficult real-time monitoring of classroom quality, weak classroom management, difficult timely solution of found problems and the like exist.
Aiming at the technical problems, the technical scheme provided by the application has the following overall thought:
the embodiment of the application provides a class quality detection method based on a smart class, wherein the method comprises the steps of obtaining first electrophysiological signal information, wherein the first electrophysiological signal information is pulse wave signal and skin electric signal information of a first student; obtaining second electrophysiological signal information, wherein the second electrophysiological signal information is pulse wave signal and skin electrical signal information of a first teacher; filtering and preprocessing the first electrophysiological signal to obtain third electrophysiological signal information, wherein the third electrophysiological signal information is the preprocessed first electrophysiological signal information; obtaining fourth electrophysiological signal information, wherein the fourth electrophysiological signal information is the second electrophysiological signal information after pretreatment; inputting the third electrophysiological signal information and the fourth electrophysiological signal information into a first training model, wherein the first training model is obtained through training of multiple groups of training data, and each group of training data in the multiple groups of training data comprises: the third electrophysiological signal information, the fourth electrophysiological signal information, the identification information of the first characteristic information, and the identification information of the second characteristic information; and obtaining first output information and second output information of the first training model, wherein the first output information and the second output information are obtained. The first output information is first characteristic information of the third electrophysiological signal, and the second input information is second characteristic information of the fourth electrophysiological signal; obtaining first score information of a first student and a first teacher according to the first characteristic information and the second characteristic information, wherein the first score is any value between 0 and 100; sequentially obtaining N groups of fraction information, wherein each group of fraction information comprises fraction information of an Nth student and a first teacher and fraction information between the Nth student and other N-1 students; sorting the N groups of fraction information to obtain M pieces of student information within a first threshold value; judging whether the duration time of the score information of the second student in the first threshold value is in the second threshold value or not; and if the duration of the score information of the second student in the first threshold is not in the second threshold, obtaining first reminding information, wherein the first reminding information is used for reminding the second student that the class quality of the second student is unqualified through the intelligent wearable device.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, an embodiment of the present application provides a class quality detection method based on a smart class, which is applied to a smart wearable device, where the method includes:
step S100: acquiring first electrophysiological signal information, wherein the first electrophysiological signal information is pulse wave signal and skin electric signal information of a first student;
step S200: obtaining second electrophysiological signal information, wherein the second electrophysiological signal information is pulse wave signal and skin electrical signal information of a first teacher;
specifically, a skin electric sensor and a pulse wave sensor are arranged in the intelligent wearable device, the sensor is used for collecting signals through a detection part contacting a human body, tiny changes of the detected part are converted into electric signals, pulse wave signals and skin electric signal information of a first student are obtained through the sensor, and a foundation is laid for accurately detecting classroom quality of the student.
Step S300: filtering and preprocessing the first electrophysiological signal to obtain third electrophysiological signal information, wherein the third electrophysiological signal information is the preprocessed first electrophysiological signal information;
Step S400: obtaining fourth electrophysiological signal information, wherein the fourth electrophysiological signal information is the second electrophysiological signal information after pretreatment;
specifically, the intelligent wearable device inputs the electrophysiological signals acquired by the sensor to the built-in filter, further performs noise reduction processing on the original signals, and removes or retains signals with a certain threshold value, so as to avoid electromagnetic interference or other physiological signal interference caused by other factors, strengthen useful information, and recover information degradation caused by interference information. The accuracy of the obtained electrophysiological signal is ensured.
Step S500: inputting the third electrophysiological signal information and the fourth electrophysiological signal information into a first training model, wherein the first training model is obtained through training of multiple groups of training data, and each group of training data in the multiple groups of training data comprises: the third electrophysiological signal information, the fourth electrophysiological signal information, the identification information of the first characteristic information, and the identification information of the second characteristic information;
in particular, the machine model is trained through multiple sets of training data, and the neural network model is essentially a supervised learning process through the training data. Each set of training data in the plurality of sets of training data comprises: the third electrophysiological signal information, the fourth electrophysiological signal information, the identification information of the first characteristic information, and the identification information of the second characteristic information; under the condition that the third electrophysiological signal information and the fourth electrophysiological signal information are obtained, the machine learning model outputs the identified first characteristic information and the second characteristic information to verify the first characteristic information and the second characteristic information output by the machine learning model, and if the output first characteristic information and the second characteristic information are consistent with the identified first characteristic information and the second characteristic information, the data supervised learning is completed, and the next group of data supervised learning is performed; if the output first characteristic information and the second characteristic information are inconsistent with the first characteristic information and the second characteristic information, the machine learning model adjusts itself until the machine learning model reaches the expected accuracy, and then the next data set is supervised and learned. The machine learning model is continuously corrected and optimized through training data, the accuracy of the machine learning model for processing the data is improved through the process of supervised learning, the obtained first characteristic information and the obtained second characteristic information are more accurate, and a foundation is laid for the subsequent detection of the classroom quality of the students through the accurate acquisition of the first characteristic information and the second characteristic information.
Further, after the original signal is subjected to noise reduction treatment to obtain a third electrophysiological signal and a fourth electrophysiological signal, the training model firstly performs standardization treatment on the obtained data; then, extracting statistical features which can most represent the signal change range from the frequency domains of the third and fourth electrophysiological signals to form a frequency domain feature set; in order to facilitate unified comparison and statistical distribution of data, the data is normalized, so that feature extraction is completed by the training model.
Step S600: and obtaining first output information and second output information of the first training model, wherein the first output information and the second output information are obtained. The first output information is first characteristic information of the third electrophysiological signal, and the second input information is second characteristic information of the fourth electrophysiological signal;
specifically, the first characteristic information and the second characteristic information each include a skin electric signal characteristic and a pulse wave signal characteristic. The extracted skin electrical signal features mainly include Skin Conductance Level (SCL), skin Conductance Response (SCR). The characteristic value SCL is a skin conductance baseline without any environmental stimulus, and the rising and falling of the SCL change along with the difference of the reaction, the skin dryness or the self-regulating capacity of an individual; the characteristic value SCR is that the phase response is above the basic level, the change amplitude is higher, the speed is faster, and the characteristic value SCR is displayed in the form of GSR burst or GSR peak. The extracted skin pulse wave signal features mainly comprise heart rate and heart rate variability, and the obtained frequency domain features are obtained by calculation of the training model. The machine learning model is used for analyzing and processing the data, so that more accurate data acquisition is realized, and the accuracy and the high efficiency of information processing are improved.
Step S700: obtaining first score information of a first student and a first teacher according to the first characteristic information and the second characteristic information, wherein the first score is any value between 0 and 100;
specifically, according to the feature indexes obtained by the training model, the correlation between the first teacher and the first student is calculated, and the correlation analysis method comprises three methods of pearson correlation analysis, wavelet transformation correlation and phase locking value analysis. And obtaining a correlation coefficient of the first teacher and the first student by correlation analysis, and then obtaining score information between the first student and the first teacher by constructing a consistency equation between the teacher and the student, wherein the score is any numerical value between 0 and 100. Lays a foundation for judging whether the class quality of the first student is qualified.
Step S800: sequentially obtaining N groups of fraction information, wherein each group of fraction information comprises fraction information of an Nth student and a first teacher and fraction information between the Nth student and other N-1 students;
specifically, each set of the N sets of score information includes score information between the first student and the first teacher, and score information between the first student and each of the remaining students, and so on, N sets of score information are sequentially obtained. Lays a foundation for accurately judging the class quality of the first student.
Step S900: sorting N students according to the N groups of fraction information to obtain M pieces of student information within a first threshold;
specifically, the N students are ranked by combining score information between each student and the students and between the students and teachers to obtain M pieces of student information ranked in the last 1% area, the first threshold is the last 1% area of the ranking area, and a foundation is laid for further analysis of the lesson quality of the students by obtaining the M pieces of student information.
Step S1000: judging whether the duration time of the score information of the second student in the first threshold value is in the second threshold value or not;
specifically, the second threshold is 15 seconds, real-time score information of the second student is obtained, and if the duration of the score of the second student in the first threshold exceeds 15 seconds through judgment, the class quality of the second student is judged to be unqualified.
Step S1100: and if the duration of the score information of the second student in the first threshold is not in the second threshold, obtaining first reminding information, wherein the first reminding information is used for reminding the second student that the class quality of the second student is unqualified through the intelligent wearable device.
Specifically, if the intelligent data processing center of the intelligent wearable device determines that the second student is unqualified in class quality, first reminding information is automatically obtained, and the first wearable device reminds the second student to notice the class through vibration and the like. The technical purpose of monitoring the classroom quality in real time, finding problems in time and reminding is achieved, and therefore the classroom teaching quality is improved.
Further, in order to implement the filtering processing on the electrophysiological signal, the embodiment S300 of the present application further includes:
step S301: obtaining wave frequency database information of the electrophysiological signals;
step S302: obtaining first filter information according to the wave frequency database;
step S303: obtaining a first filtering threshold;
step S304: inputting the first and second electrophysiological signals to the first filter;
step S305: output signal information of the first filter is obtained, wherein the output signal information comprises the third and fourth electrophysiological signals.
Specifically, the data processing center of the intelligent wearable device automatically obtains wave frequency database information of the skin electric signal and the pulse wave signal, and then the wave frequency database obtains frequency threshold information of the electrophysiological signal, so that frequency information required to be filtered by the electrophysiological signal is determined. The first filtering threshold value is 0.05Hz-20Hz, the first electrophysiological signal and the second electrophysiological signal are input into the first filter, the first filter is used for carrying out noise reduction and filtering treatment on the first electrophysiological signal and the second electrophysiological signal, and the signal with wave frequency within the first filtering threshold value is reserved, so that more accurate electrophysiological signal information is output.
In order to obtain the first correlation coefficient, step S600 in the embodiment of the present application further includes:
step S601: inputting the first characteristic information and the second characteristic information into a second training model, wherein the second training model is obtained through training of multiple groups of training data, and each group of training data in the multiple groups of training data comprises: the first characteristic information, the second characteristic information and the identification information of the first correlation coefficient;
step S602: and obtaining third output information of the second training model, wherein the third output information is a first correlation coefficient P between the first student and a first teacher, and the first correlation coefficient P is a numerical value between-1 and 1.
In particular, the machine model is trained through multiple sets of training data, and the neural network model is essentially a supervised learning process through the training data. Each set of training data in the plurality of sets of training data comprises: the first characteristic information, the second characteristic information and the identification information of the first correlation coefficient; under the condition that the first characteristic information and the second characteristic information are obtained, the machine learning model outputs the identified first correlation coefficient information to verify the first correlation coefficient information output by the machine learning model, and if the output first correlation coefficient information is consistent with the identified first correlation coefficient information, the data supervised learning is completed, and then the next group of data supervised learning is performed; if the output first correlation coefficient information is inconsistent with the identified first correlation coefficient information, the machine learning model adjusts itself until the machine learning model reaches the expected accuracy, and then supervised learning of the next group of data is performed. The machine learning model is continuously corrected and optimized by training the data, and the accuracy of the machine learning model for processing the data is improved by supervising the learning process.
Further, the second training model performs correlation analysis on the first teacher and the first student, and the correlation analysis method comprises three methods of pearson correlation analysis, wavelet transformation correlation and phase locking value analysis. And obtaining a correlation coefficient between the first teacher and the first student through correlation analysis, namely the first correlation coefficient P, wherein the first correlation coefficient P is a numerical value between-1 and 1. Lays a foundation for the subsequent detection of the classroom quality of the first student.
In order to obtain consistency score information between the teacher and the students, step S602 in the embodiment of the present application further includes:
step S6021: obtaining first regression model information;
step S6022: obtaining first group consistency equation information;
step S6023: obtaining first score information according to the first group consistency equation information;
step S6024: and sequentially obtaining N groups of fraction information, wherein each group of fraction information comprises fraction information of the Nth student and the first teacher and fraction information between the Nth student and other N-1 students.
Specifically, the first regression model is a multivariate regression model, and a group consistency equation between the first student and the first teacher is constructed according to the multivariate regression model. Score information, i.e., the first score information, characterizing consistency between the first student and a first teacher is obtained from the equation. And similarly, respectively obtaining N groups of fraction information, wherein each group of fraction information comprises fraction information of the Nth student and the first teacher and fraction information between the Nth student and other N-1 students. By obtaining the score information, a foundation is laid for accurately analyzing whether the class quality of the student is qualified.
In order to further monitor the lesson quality information of the students, step S1100 of the embodiment of the present application further includes:
step S1101a: acquiring first time number information of unqualified class quality of the second student in a third time threshold;
step S1102a: judging whether the first time number information exceeds a fourth threshold value;
step S1103a: if yes, obtaining second reminding information;
step S1104a: and sending the second reminding information to the first teacher.
Specifically, the third time threshold is a preset time range for checking the class quality of the second student, the number of times information that the class quality of the second student is unqualified in the third time threshold is obtained, so that the overall class listening quality condition of the second student in the fourth time threshold is judged, the fourth time threshold is an evaluation index of the class listening quality in the third time threshold, if the evaluation index is exceeded, the evaluation index represents that the class listening quality of the second student in the third time threshold is overall worse, and the second reminding information is automatically acquired by the intelligent wearable device and is sent to the first teacher if the evaluation index is required to be paid attention to. The technical purpose of timely finding and reminding problems through monitoring the classroom quality is achieved, and therefore the classroom teaching quality is further improved.
In order to improve the class quality by monitoring the lecture quality of the teacher, step S1100 of the embodiment of the present application further includes:
step S1101b: acquiring duration information of a first classroom;
step S1102b: obtaining first student quantity information detected as failed class quality in a first class;
step S1103b: judging whether the first student quantity information exceeds a fifth threshold value;
step S1104b: and if the first student quantity information exceeds a fifth threshold value, obtaining third reminding information for reminding the first teacher of paying attention to class quality.
Specifically, the first student number information is the number of students detected as failed class quality in the first class. And analyzing the probability of the student having class distraction by obtaining the duration of the first class so as to obtain fifth threshold information, wherein the fifth threshold is threshold information for evaluating the lecture quality of the first teacher by obtaining the number of students with unqualified class quality in the first class. If the first student quantity information exceeds the fifth threshold through evaluation, third reminding information is obtained, and the third reminding information is sent to a first teacher and used for reminding the first teacher of paying attention to class quality. The technical purposes of timely finding out and reminding the problems existing in teacher teaching and improving classroom teaching quality are achieved.
In order to improve the class quality by analyzing the class quality, step S1100 of the embodiment of the present application further includes:
step S1101c: obtaining first image information when a second student is detected as unqualified in class quality;
step S1102c: acquiring first behavior category information with unqualified class quality of the second student according to the first image information;
step S1103c: sequentially obtaining behavior category information of all students with unqualified class quality in a first class;
step S1104c: obtaining a behavior category analysis report of unqualified class quality of students in a first class;
step S1105c: and sending the behavior class analysis report to the first teacher.
Specifically, the intelligent image capturing device of the intelligent wearable device obtains first image information when the second student is detected as unqualified in class quality, and the first image is analyzed through the data processing center to obtain behavior categories of the second student, such as fool, talk, sleep and the like. And sequentially obtaining the behavior category information of all students with unqualified class quality in the first class, generating a behavior category analysis report, and sending the report to a first teacher. The technical purposes of finding problems and improving class quality by taking corresponding measures by analyzing the unqualified reasons of the class quality of students are achieved.
In summary, the classroom quality detection method based on the intelligent classroom provided by the embodiment of the application has the following technical effects:
1. because the technology based on the internet of things is adopted, the electrophysiological signals of the teacher and the students are obtained through the intelligent wearable equipment, and the signals are processed and calculated to obtain index information for evaluating the classroom quality of the students, so that the classroom quality of the students is monitored in real time, and the condition of unqualified classroom quality can be timely reminded, thereby realizing the technical purposes of monitoring the classroom quality in real time, timely finding problems and reminding, and improving the classroom teaching quality.
2. The method for acquiring the data by using the machine learning model is adopted, and the characteristic that the data is more accurate is processed based on the training model, so that the characteristic information and the correlation coefficient information of more accurate electric signals are obtained, the score for evaluating the class quality of students is more accurate, and the technical aim of accurately evaluating the class quality of the students is fulfilled.
Example two
Based on the same concept as the classroom quality detection method based on the intelligent classroom in the foregoing embodiment, the present invention further provides a classroom quality detection device based on the intelligent classroom, as shown in fig. 2, where the device includes:
A first obtaining unit 11, where the first obtaining unit 11 is configured to obtain first electrophysiological signal information, where the first electrophysiological signal information is pulse wave signal and skin electrical signal information of a first student;
a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain second electrophysiological signal information, where the second electrophysiological signal information is pulse wave signal and skin electrical signal information of the first teacher;
a third obtaining unit 13, where the third obtaining unit 13 is configured to perform filtering preprocessing on the first electrophysiological signal to obtain third electrophysiological signal information, where the third electrophysiological signal information is the preprocessed first electrophysiological signal information;
a fourth obtaining unit 14, where the fourth obtaining unit 14 is configured to obtain fourth electrophysiological signal information, where the fourth electrophysiological signal information is the second electrophysiological signal information after preprocessing;
the first input unit 15, the first input unit 15 is configured to input the third electrophysiological signal information and the fourth electrophysiological signal information into a first training model, where the first training model is obtained through training of multiple sets of training data, and each set of training data in the multiple sets of training data includes: the third electrophysiological signal information, the fourth electrophysiological signal information, the identification information of the first characteristic information, and the identification information of the second characteristic information;
A fifth obtaining unit 16, where the sixth obtaining unit 16 is configured to obtain first output information and second output information of the first training model, where the first output information and the second output information are used. The first output information is first characteristic information of the third electrophysiological signal, and the second input information is second characteristic information of the fourth electrophysiological signal;
a sixth obtaining unit 17, where the sixth obtaining unit 17 is configured to obtain first score information of a first student and a first teacher according to the first feature information and the second feature information, where the first score is any value between 0 and 100;
a seventh obtaining unit 18, wherein the seventh obtaining unit 18 is configured to sequentially obtain N sets of score information, and each set of score information includes score information of an nth student and a first teacher and score information between the nth student and other N-1 students;
an eighth obtaining unit 19, where the eighth obtaining unit 19 is configured to sort the N sets of score information to obtain M pieces of student information that are within a first threshold;
a first judging unit 20, wherein the first judging unit 20 is used for judging whether the duration time of the score information of the second student in the first threshold value is in the second threshold value;
And a ninth obtaining unit 21, where the ninth obtaining unit 21 is configured to obtain first reminding information if the duration of the score information of the second student in the first threshold is not in the second threshold, where the first reminding information is used to remind, by the intelligent wearable device, that the class quality of the second student is unqualified.
Further, the device further comprises:
a tenth obtaining unit configured to obtain wave frequency database information of the electrophysiological signal;
an eleventh obtaining unit configured to obtain first filter information from the wave frequency database;
a twelfth obtaining unit for obtaining a first filtering threshold value;
a second input unit for inputting the first and second electrophysiological signals to the first filter;
a thirteenth obtaining unit for obtaining output signal information of the first filter, wherein the output signal information includes the third electrophysiological signal and a fourth electrophysiological signal.
Further, the device further comprises:
The third input unit is used for inputting the first characteristic information and the second characteristic information into a second training model, wherein the second training model is obtained through training of multiple groups of training data, and each group of training data in the multiple groups of training data comprises: the first characteristic information, the second characteristic information and the identification information of the first correlation coefficient;
a fourteenth obtaining unit, configured to obtain third output information of the second training model, where the third output information is a first correlation coefficient P between the first student and a first teacher, and the first correlation coefficient P is a value between-1 and 1.
Further, the device further comprises:
a fifteenth obtaining unit configured to obtain first regression model information;
a sixteenth obtaining unit for obtaining first group consistency equation information;
a seventeenth obtaining unit configured to obtain first score information according to the first group consistency equation information;
the eighteenth obtaining unit is used for sequentially obtaining N groups of fraction information, wherein each group of fraction information comprises fraction information of the Nth student and the first teacher and fraction information between the Nth student and other N-1 students.
Further, the device further comprises:
an eighteenth obtaining unit, configured to obtain first time number information of failed class quality of the second student within a third time threshold;
the second judging unit is used for judging whether the first time number information exceeds a fourth threshold value or not;
a nineteenth obtaining unit, configured to obtain second reminding information if the first time number information exceeds a fourth threshold;
the first sending unit is used for sending the second reminding information to the first teacher.
Further, the device further comprises:
the twentieth acquisition unit is used for acquiring the duration information of the first class;
a twenty-first obtaining unit for obtaining first student number information detected as failed class quality in a first class;
a third judging unit for judging whether the first student number information exceeds a fifth threshold;
and the twenty-second obtaining unit is used for obtaining third reminding information for reminding the first teacher of paying attention to the class quality if the first student quantity information exceeds a fifth threshold value.
Further, the device further comprises:
a twenty-third obtaining unit for obtaining first image information when the second student is detected as failed in class quality;
a twenty-fourth obtaining unit, configured to obtain first behavior category information with unqualified class quality of the second student according to the first image information;
the twenty-fifth obtaining unit is used for sequentially obtaining behavior category information of all students with unqualified class quality in the first class;
a twenty-sixth obtaining unit, configured to obtain a behavior class analysis report of unqualified class quality of the student in the first class;
and the second sending unit is used for sending the behavior category analysis report to the first teacher.
Various modifications and embodiments of the foregoing intelligent classroom-based classroom quality detection method in the first embodiment of fig. 1 are equally applicable to the intelligent classroom-based classroom quality detection device in the present embodiment, and those skilled in the art will clearly know about the foregoing detailed description of the intelligent classroom-based classroom quality detection method in the present embodiment, so that the detailed description thereof will not be repeated herein for the sake of brevity of the description.
Exemplary electronic device
An electronic device of an embodiment of the present application is described below with reference to fig. 3.
Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of a class quality detection method based on a smart class as in the previous embodiments, the present invention further provides a class quality detection device based on a smart class, on which a computer program is stored, which when executed by a processor, implements the steps of any one of the above-described class quality detection methods based on a smart class.
Where in FIG. 3 a bus architecture (represented by bus 300), bus 300 may comprise any number of interconnected buses and bridges, with bus 300 linking together various circuits, including one or more processors, represented by processor 302, and memory, represented by memory 304. Bus 300 may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., as are well known in the art and, therefore, will not be described further herein. Bus interface 306 provides an interface between bus 300 and receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e. a transceiver, providing a means for communicating with various other apparatus over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, while the memory 304 may be used to store data used by the processor 302 in performing operations.
It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (7)
1. A class quality detection method based on intelligent class is applied to intelligent wearable equipment, wherein the method comprises the following steps:
acquiring first electrophysiological signal information, wherein the first electrophysiological signal information is pulse wave signal and skin electric signal information of a first student;
obtaining second electrophysiological signal information, wherein the second electrophysiological signal information is pulse wave signal and skin electrical signal information of a first teacher;
filtering and preprocessing the first electrophysiological signal to obtain third electrophysiological signal information, wherein the third electrophysiological signal information is the preprocessed first electrophysiological signal information;
obtaining fourth electrophysiological signal information, wherein the fourth electrophysiological signal information is the second electrophysiological signal information after pretreatment;
inputting the third electrophysiological signal information and the fourth electrophysiological signal information into a first training model, wherein the first training model is obtained through training of multiple groups of training data, and each group of training data in the multiple groups of training data comprises: the third electrophysiological signal information, the fourth electrophysiological signal information, the identification information of the first characteristic information and the identification information of the second characteristic information;
Obtaining first output information and second output information of the first training model, wherein the first output information is first characteristic information of the third electrophysiological signal, and the second output information is second characteristic information of the fourth electrophysiological signal;
obtaining first score information of a first student and a first teacher according to the first characteristic information and the second characteristic information, wherein the first score is any value between 0 and 100;
sequentially obtaining N groups of fraction information, wherein each group of fraction information comprises fraction information of an Nth student and a first teacher and fraction information between the Nth student and other N-1 students;
sorting the N groups of fraction information to obtain M pieces of student information within a first threshold value;
judging whether the duration time of the score information of the second student in the first threshold value is in the second threshold value or not;
if the duration of the score information of the second student in the first threshold is not in the second threshold, obtaining first reminding information, wherein the first reminding information is used for reminding the second student that the class quality of the second student is unqualified through the intelligent wearable device;
wherein after obtaining the output information of the training model, the method further comprises:
Inputting the first characteristic information and the second characteristic information into a second training model, wherein the second training model is obtained through training of multiple groups of training data, and each group of training data in the multiple groups of training data comprises: the first characteristic information, the second characteristic information and the identification information of the first correlation coefficient;
obtaining third output information of the second training model, wherein the third output information is a first correlation coefficient P between the first student and a first teacher, and the first correlation coefficient P is a numerical value between-1 and 1;
obtaining first regression model information;
obtaining first group consistency equation information;
obtaining first score information according to the first group consistency equation information;
and sequentially obtaining N groups of fraction information, wherein each group of fraction information comprises fraction information of the Nth student and the first teacher and fraction information between the Nth student and other N-1 students.
2. The method of claim 1, wherein the method further comprises:
obtaining wave frequency database information of the electrophysiological signals;
obtaining first filter information according to the wave frequency database;
obtaining a first filtering threshold;
Inputting the first and second electrophysiological signals to the first filter;
output signal information of the first filter is obtained, wherein the output signal information comprises the third and fourth electrophysiological signals.
3. The method of claim 1, wherein the method further comprises:
acquiring first time number information of unqualified class quality of the second student in a third time threshold;
judging whether the first time number information exceeds a fourth threshold value;
if the first time number information exceeds a fourth threshold value, second reminding information is obtained;
and sending the second reminding information to the first teacher.
4. The method of claim 1, wherein the method further comprises:
acquiring duration information of a first classroom;
obtaining first student quantity information detected as failed class quality in a first class;
judging whether the first student quantity information exceeds a fifth threshold value;
and if the first student quantity information exceeds a fifth threshold value, obtaining third reminding information for reminding the first teacher of paying attention to class quality.
5. The method of claim 3 applied to an intelligent image capturing device, wherein the method further comprises:
Obtaining first image information when a second student is detected as unqualified in class quality;
acquiring first behavior category information with unqualified class quality of the second student according to the first image information;
sequentially obtaining behavior category information of all students with unqualified class quality in a first class;
obtaining a behavior category analysis report of unqualified class quality of students in a first class;
and sending the behavior class analysis report to the first teacher.
6. A class quality detection device based on a smart class, wherein the device comprises:
the first acquisition unit is used for acquiring first electrophysiological signal information, wherein the first electrophysiological signal information is pulse wave signal and skin electric signal information of a first student;
a second obtaining unit configured to obtain second electrophysiological signal information, where the second electrophysiological signal information is pulse wave signal and skin electrical signal information of the first teacher;
the third obtaining unit is used for carrying out filtering pretreatment on the first electrophysiological signal to obtain third electrophysiological signal information, wherein the third electrophysiological signal information is the first electrophysiological signal information after pretreatment;
A fourth obtaining unit, configured to obtain fourth electrophysiological signal information, where the fourth electrophysiological signal information is the second electrophysiological signal information after preprocessing;
the first input unit is used for inputting the third electrophysiological signal information and the fourth electrophysiological signal information into a first training model, wherein the first training model is obtained through training of multiple groups of training data, and each group of training data in the multiple groups of training data comprises: the third electrophysiological signal information, the fourth electrophysiological signal information, the identification information of the first characteristic information and the identification information of the second characteristic information;
a fifth obtaining unit, configured to obtain first output information and second output information of the first training model, where the first output information is first feature information of the third electrophysiological signal, and the second output information is second feature information of the fourth electrophysiological signal;
the sixth obtaining unit is used for obtaining first score information of a first student and a first teacher according to the first characteristic information and the second characteristic information, wherein the first score is any value between 0 and 100;
A seventh obtaining unit, configured to sequentially obtain N sets of score information, where each set of score information includes score information of an nth student and a first teacher and score information between the nth student and other N-1 students;
an eighth obtaining unit, configured to sort the N sets of score information to obtain M pieces of student information that are within a first threshold;
the first judging unit is used for judging whether the duration time of the score information of the second student in the first threshold value is in the second threshold value or not;
a ninth obtaining unit, configured to obtain first reminding information if a duration of score information of the second student within the first threshold is not within the second threshold, where the first reminding information is used to remind, by using the intelligent wearable device, that a class quality of the second student is unqualified;
the third input unit is used for inputting the first characteristic information and the second characteristic information into a second training model, wherein the second training model is obtained through training of multiple groups of training data, and each group of training data in the multiple groups of training data comprises: the first characteristic information, the second characteristic information and the identification information of the first correlation coefficient;
A fourteenth obtaining unit configured to obtain third output information of the second training model, where the third output information is a first correlation coefficient P between the first student and a first teacher, and the first correlation coefficient P is a value between-1 and 1;
a fifteenth obtaining unit configured to obtain first regression model information;
a sixteenth obtaining unit for obtaining first group consistency equation information;
a seventeenth obtaining unit configured to obtain first score information according to the first group consistency equation information;
the eighteenth obtaining unit is used for sequentially obtaining N groups of fraction information, wherein each group of fraction information comprises fraction information of the Nth student and the first teacher and fraction information between the Nth student and other N-1 students.
7. A class quality detection device based on intelligent class comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the method described in 1-5.
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| CN109063954A (en) * | 2018-06-20 | 2018-12-21 | 新华网股份有限公司 | The assessment method and system of teachers ' teaching |
| CN110916631A (en) * | 2019-12-13 | 2020-03-27 | 东南大学 | Student classroom learning state evaluation system based on wearable physiological signal monitoring |
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| CN108376305A (en) * | 2017-12-30 | 2018-08-07 | 中国移动通信集团福建有限公司 | Training quality appraisal procedure, device, equipment and medium |
| CN109063954A (en) * | 2018-06-20 | 2018-12-21 | 新华网股份有限公司 | The assessment method and system of teachers ' teaching |
| CN110916631A (en) * | 2019-12-13 | 2020-03-27 | 东南大学 | Student classroom learning state evaluation system based on wearable physiological signal monitoring |
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