CN112370039A - 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 classroom quality detection method and device based on an intelligent classroom, which are applied to intelligent wearable equipment, wherein the method comprises the following steps: respectively obtaining pulse wave signals and skin electric signal information of a first student and a first teacher, and filtering the electric signals; obtaining first characteristic information and second characteristic information corresponding to the physiological signals of the first student and the first teacher by a training model; sequentially obtaining N groups of score information from the characteristic information of the electric signal, and sequencing the N groups of score information to obtain M pieces of student information within a first threshold value; and judging that the class quality of the students of which the duration time of the score information in the first threshold exceeds the second threshold is unqualified, obtaining first reminding information, and reminding the second students to attend classes through the intelligent wearable equipment. The technical purpose that the classroom quality can be monitored in real time, problems can be found in time and reminded, and therefore the classroom teaching quality is improved is achieved.
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 created by a thinking mode of internet plus and new-generation information technologies such as big data, cloud computing and the like based on a construction-oriented learning theory. The data are processed, mined and analyzed by adopting a modern analysis tool and an analysis method, so that a teaching decision is made, and learning conditions are accurately mastered and a teaching strategy is adjusted by depending on the data. The phenomenon that the attention of students is not concentrated in class is the most common phenomenon and the most direct phenomenon influencing the classroom quality, and no effective solution exists for the phenomenon at present.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
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
The embodiment of the application provides a classroom quality detection method and device based on smart classroom, solves the technical problems that classroom quality is difficult to monitor in real time, classroom management is weak, problems are difficult to solve in time when finding, and the like in the prior art, and achieves the technical purpose of monitoring classroom quality in real time, finding problems in time and reminding, and therefore classroom teaching quality is improved.
The embodiment of the application provides a classroom quality detection method based on an intelligent classroom, wherein the method comprises the steps of obtaining first electrophysiological signal information, wherein the first electrophysiological signal information is pulse wave signals 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; carrying out filtering pretreatment on the first electrophysiological signal to obtain third electrophysiological signal information, wherein the third electrophysiological signal information is the pretreated first electrophysiological signal information; obtaining fourth electrophysiological signal information, wherein the fourth electrophysiological signal information is the second electrophysiological signal information after being preprocessed; inputting the third electrophysiological signal information and the fourth electrophysiological signal information into a first training model, wherein the first training model is obtained by training multiple sets of training data, and each set of training data in the multiple sets 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; first output information and second output information of the first training model are obtained, 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 score information, wherein each group of score information comprises score information of an Nth student and a first teacher and score information between the Nth student and other N-1 students; sequencing the N groups of score information to obtain M pieces of student information within a first threshold value; judging whether the duration of the score information of the second student in the first threshold is within a second threshold; and if the duration time of the score information of the second student in the first threshold value is not in the second threshold value, obtaining first reminding information, wherein the first reminding information is used for reminding that the class quality of the second student is unqualified through the intelligent wearable equipment.
On the other hand, this 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 a pulse wave signal and skin electrical signal information of the first teacher; a third obtaining unit, 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, configured to obtain fourth electrophysiological signal information, where the fourth electrophysiological signal information is the second electrophysiological signal information after being preprocessed; a first input unit, 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 by training multiple sets of training data, and each set of training data in the multiple sets 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, 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 different. 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, configured to obtain first score information of the first student and the first teacher according to the first feature information and the second feature information, where the first score is any one of values between 0 and 100; a seventh obtaining unit, configured to obtain N sets of score information in sequence, 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 groups of score information, and obtain M pieces of student information within a first threshold; a first judgment unit for judging whether the duration of the score information of the second student within the first threshold is within a second threshold; a ninth obtaining unit, configured to obtain first reminding information if the duration of the 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, through the intelligent wearable device, that the class quality of the second student is not qualified.
On the other hand, the embodiment of the present application further provides a classroom quality detection apparatus based on an intelligent classroom, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the program to implement the steps of the method according to the first aspect.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
due to the fact that the technology based on the internet of things is adopted, electrophysiological signals of teachers and students are obtained through the intelligent wearable equipment, the signals are processed and calculated to obtain index information used for evaluating the classroom quality of the students, real-time monitoring is conducted on the classroom quality of the students, the situation that the classroom quality is unqualified can be timely reminded, the classroom quality can be monitored in real time, problems can be timely found out, reminding is conducted, and the technical purpose of improving classroom teaching quality is achieved.
The foregoing is a summary of the present disclosure, and embodiments of the present disclosure are described below to make the technical means of the present disclosure more clearly understood.
Drawings
Fig. 1 is a schematic flowchart illustrating a classroom quality detection method based on intelligent classroom according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of an intelligent classroom-based classroom quality detection apparatus according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: 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
The embodiment of the application provides a classroom quality detection method and device based on smart classroom, solves the technical problems that classroom quality is difficult to monitor in real time, classroom management is weak, problems are difficult to solve in time when finding, and the like in the prior art, and achieves the technical purpose of monitoring classroom quality in real time, finding problems in time and reminding, and therefore 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 merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Summary of the application
The intelligent classroom can process, mine and analyze data by adopting a modern analysis tool and an analysis method, so that a teaching decision is made, and learning conditions and a teaching strategy are accurately mastered and adjusted by means of the data. The phenomenon that the attention of students is not concentrated in class is the most common phenomenon and the most direct phenomenon to influence the quality of a classroom, and no effective solution exists at present. The technical problems that the classroom quality is difficult to monitor in real time, classroom management is weak, problems are difficult to solve in time and the like exist in the prior art.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides a classroom quality detection method based on an intelligent classroom, wherein the method comprises the steps of obtaining first electrophysiological signal information, wherein the first electrophysiological signal information is pulse wave signals 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; carrying out filtering pretreatment on the first electrophysiological signal to obtain third electrophysiological signal information, wherein the third electrophysiological signal information is the pretreated first electrophysiological signal information; obtaining fourth electrophysiological signal information, wherein the fourth electrophysiological signal information is the second electrophysiological signal information after being preprocessed; inputting the third electrophysiological signal information and the fourth electrophysiological signal information into a first training model, wherein the first training model is obtained by training multiple sets of training data, and each set of training data in the multiple sets 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; first output information and second output information of the first training model are obtained, 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 score information, wherein each group of score information comprises score information of an Nth student and a first teacher and score information between the Nth student and other N-1 students; sequencing the N groups of score information to obtain M pieces of student information within a first threshold value; judging whether the duration of the score information of the second student in the first threshold is within a second threshold; and if the duration time of the score information of the second student in the first threshold value is not in the second threshold value, obtaining first reminding information, wherein the first reminding information is used for reminding that the class quality of the second student is unqualified through the intelligent wearable equipment.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, an embodiment of the present application provides a classroom quality detection method based on a smart classroom, which is applied to an intelligent wearable device, wherein the method includes:
step S100: obtaining first electrophysiological signal information, wherein the first electrophysiological signal information is pulse wave signal and skin electrical 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;
particularly, intelligence wearing equipment embeds there are a skin electric sensor and a pulse wave sensor, the sensor carries out signal acquisition through the detection position that contacts the human body, will be surveyed the small change at position and convert the signal of telecommunication into, through the sensor obtains first student's pulse wave signal and skin signal of telecommunication information, has laid a foundation for accurate detection student's classroom quality.
Step S300: carrying out filtering pretreatment on the first electrophysiological signal to obtain third electrophysiological signal information, wherein the third electrophysiological signal information is the pretreated first electrophysiological signal information;
step S400: obtaining fourth electrophysiological signal information, wherein the fourth electrophysiological signal information is the second electrophysiological signal information after being preprocessed;
specifically, the intelligent wearable device inputs the electrophysiological signals acquired by the sensor into a built-in filter, further performs noise reduction on the original signals, and removes or retains signals of a certain threshold value, so as to avoid electromagnetic interference or other physiological signal interference caused by other factors, enhance useful information, and restore information degradation caused by interference information. The accuracy of the obtained electrophysiological signals 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 by training multiple sets of training data, and each set of training data in the multiple sets 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;
specifically, the machine model is obtained by training a plurality of sets of training data, and the process of training the neural network model by the training data is essentially a process of supervised learning. 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 of obtaining the third electrophysiological signal information and the fourth electrophysiological signal information, the machine learning model outputs the identified first characteristic information and 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 output second characteristic information are consistent with the identified first characteristic information and second characteristic information, the data supervised learning is finished, and then the next group of data supervised learning is carried out; and if the output first characteristic information and the output second characteristic information are not consistent with the identified first characteristic information and the identified second characteristic information, adjusting the machine learning model by the machine learning model, and performing supervised learning of the next group of data after the machine learning model reaches the expected accuracy. 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 follow-up 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 processing to obtain a third electrophysiological signal and a fourth electrophysiological signal, the training model firstly performs normalization processing on the obtained data; then extracting the statistical characteristics which can represent the signal variation range most from the frequency domain of the third electrophysiological signal and the fourth electrophysiological signal to form a frequency domain characteristic set; in order to facilitate uniform comparison and statistical distribution of data, normalization processing is performed on the data, and therefore feature extraction is completed by the training model.
Step S600: first output information and second output information of the first training model are obtained, 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 electrical 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 stimulation, and the rising and falling of the SCL are changed along with the difference of individual reaction, skin dryness or autonomic regulation capacity; the characteristic value SCR is phase reaction above a basic level, has higher change amplitude and higher speed, and 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 through calculation by the training model. The machine learning model is used for analyzing and processing data, so that more accurate data acquisition is realized, and the accuracy and the 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 characteristic 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. Obtaining a correlation coefficient of the first teacher and the first student through correlation analysis, and then obtaining score information between the first student and the first teacher through constructing a consistency equation between teachers and students, wherein the score is any value between 0 and 100. And a foundation is laid for judging whether the classroom quality of the first student is qualified.
Step S800: sequentially obtaining N groups of score information, wherein each group of score information comprises score information of an Nth student and a first teacher and score information between the Nth student and other N-1 students;
specifically, each of the N groups 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, to obtain N groups of score information in turn. And a foundation is laid for accurately judging the classroom quality of the first student.
Step S900: sequencing the N students according to the N groups of score information to obtain M pieces of student information within a first threshold value;
specifically, the N students are sequenced by combining score information between each student and score information between each student and each teacher, M pieces of student information with the ranking in the last 1% region are obtained, the first threshold value is the last 1% interval of the ranking region, and the basis is laid for further analyzing the class quality of the students by obtaining the M pieces of student information.
Step S1000: judging whether the duration of the score information of the second student in the first threshold is within a second threshold;
specifically, the second threshold is 15 seconds, and by obtaining the real-time score information of the second student, if the duration of the score of the second student within the first threshold exceeds 15 seconds through judgment, it is determined that the class attendance quality of the second student is not qualified.
Step S1100: and if the duration time of the score information of the second student in the first threshold value is not in the second threshold value, obtaining first reminding information, wherein the first reminding information is used for reminding that the class quality of the second student is unqualified through the intelligent wearable equipment.
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 attend classes through vibration and other modes. The technical purpose that the classroom quality can be monitored in real time, problems can be found in time and reminded, and therefore classroom teaching quality is improved is achieved.
Further, in order to implement the filtering process on the electrophysiological signal, an 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: obtaining output signal information of the first filter, wherein the output signal information includes the third electrophysiological signal and a fourth electrophysiological signal.
Specifically, the data processing center of the intelligent wearable device automatically obtains the wave frequency database information of the skin electrical signals and the pulse wave signals, and then the wave frequency database obtains the frequency threshold information of the electrophysiological signals, so that the frequency information required to be filtered by the electrophysiological signals is determined. The first filtering threshold value is 0.05Hz-20Hz, the first electrophysiological signal and the second electrophysiological signal are input to the first filter, the first filter is used for carrying out noise reduction and filtering processing on the first electrophysiological signal and the second electrophysiological signal, and the signal with the 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 this embodiment of the present application further includes:
step S601: inputting the first feature information and the second feature information into a second training model, wherein the second training model is obtained by training a plurality of sets of training data, and each set of training data in the plurality of sets includes: first characteristic information, second characteristic information and 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 the first teacher, and the first correlation coefficient P is a numerical value between-1 and 1.
Specifically, the machine model is obtained by training a plurality of sets of training data, and the process of training the neural network model by the training data is essentially a process of supervised learning. Each set of training data in the plurality of sets of training data comprises: first characteristic information, second characteristic information and identification information of the first correlation coefficient; under the condition of obtaining the first characteristic information and the second characteristic information, the machine learning model outputs 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 finished, and then the next group of data supervised learning is carried out; and if the output first correlation coefficient information is inconsistent with the identified first correlation coefficient information, adjusting the machine learning model by the machine learning model, and performing supervised learning of the next group of data until the machine learning model reaches the expected accuracy. The machine learning model is continuously corrected and optimized through training data, and the accuracy of the machine learning model for processing the data is improved through the process of supervised learning.
Further, the second training model performs correlation analysis on the first teacher and the first student, and the correlation analysis method includes 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, namely the first correlation coefficient P, by correlation analysis, wherein the first correlation coefficient P is a numerical value between-1 and 1. And a foundation is laid for detecting the classroom quality of the first student subsequently.
In order to obtain the consistency score information between the teacher and the student, step S602 in the embodiment of the present application further includes:
step S6021: obtaining first regression model information;
step S6022: obtaining first population 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 score information, wherein each group of score information comprises the score information of the Nth student and the first teacher and the score information between the Nth student and other N-1 students.
Specifically, the first regression model is a multivariate regression model, and a group identity equation between the first student and the first teacher is constructed according to the multivariate regression model. Obtaining score information for characterizing consistency between the first student and the first teacher from the equation, i.e., the first score information. And by analogy, N groups of score information are respectively obtained, wherein each group of score information comprises the score information of the Nth student and the first teacher and the score information between the Nth student and other N-1 students. By obtaining the score information, a foundation is laid for accurately analyzing whether the classroom quality of the students is qualified.
In order to further monitor the class quality information of the student, step S1100 in the embodiment of the present application further includes:
step S1101 a: obtaining first time information that the second student classroom quality is unqualified within a third time threshold;
step S1102 a: judging whether the first time information exceeds a fourth threshold value;
step S1103 a: if yes, obtaining second reminding information;
step S1104 a: 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 total class listening quality condition of the second student within a fourth time threshold is judged by obtaining the frequency information that the class quality of the second student is not qualified within the third time threshold, the fourth threshold is an evaluation index of the class listening quality within the third time threshold, if the evaluation index exceeds the evaluation index, the fact that the class listening quality of the second student within the third time threshold is overall poor and needs to be emphasized is represented, the intelligent wearable device automatically acquires second reminding information, and the second reminding information is sent to the first teacher. The technical purpose of further improving the classroom teaching quality is achieved by monitoring the classroom quality, finding out problems in time and reminding.
In order to improve the classroom quality by monitoring the teaching quality of the teacher, step S1100 in the embodiment of the present application further includes:
step S1101 b: acquiring time length information of a first classroom;
step S1102 b: obtaining first student quantity information detected as being unqualified in classroom quality in a first classroom;
step S1103 b: judging whether the first student quantity information exceeds a fifth threshold value;
step S1104 b: and if the first student quantity information exceeds a fifth threshold value, third reminding information is obtained and used for reminding the first teacher to pay attention to the classroom quality.
Specifically, the first student number information is the number of students detected as being unqualified in classroom quality in the first classroom. And analyzing the probability of the inattention of the students by obtaining the duration of the first classroom so as to obtain fifth threshold information, wherein the fifth threshold is the threshold information for evaluating the lecture quality of the first teacher by obtaining the number of the students with unqualified classroom quality in the first classroom. And if the first student quantity information exceeds a fifth threshold value through evaluation, third reminding information is obtained and sent to a first teacher for reminding the first teacher to pay attention to the classroom quality. The technical purpose of timely finding and reminding the problems in the teacher teaching and improving the classroom teaching quality is achieved.
In order to improve the classroom quality by analyzing the classroom quality, step S1100 in the embodiment of the present application further includes:
step S1101 c: obtaining first image information when a second student is detected as unqualified classroom quality;
step S1102 c: obtaining first behavior category information of which the classroom quality of the second student is unqualified according to the first image information;
step S1103 c: sequentially obtaining behavior category information of students with unqualified classroom quality in a first classroom;
step S1104 c: obtaining a behavior class analysis report of unqualified class quality of students in the first class;
step S1105 c: and sending the behavior category analysis report to the first teacher.
Specifically, first image information of the second student detected as unqualified classroom quality is obtained by an intelligent image capturing device of the intelligent wearable device, and the first image is analyzed by a data processing center to obtain behavior categories of the second student, such as fool, speech, sleep and the like. And sequentially obtaining the behavior category information of students with unqualified classroom quality in the first classroom, generating a behavior category analysis report, and sending the report to the first teacher. The technical purpose of finding problems and improving the classroom quality by taking corresponding measures by analyzing the reason that the classroom quality of students is unqualified is 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. due to the fact that the technology based on the internet of things is adopted, electrophysiological signals of teachers and students are obtained through the intelligent wearable equipment, the signals are processed and calculated to obtain index information used for evaluating the classroom quality of the students, real-time monitoring is conducted on the classroom quality of the students, the situation that the classroom quality is unqualified can be timely reminded, the classroom quality can be monitored in real time, problems can be timely found out, reminding is conducted, and the technical purpose of improving classroom teaching quality is achieved.
2. The method for acquiring data by using the machine learning model is adopted, and the training model can continuously optimize learning and obtain experience to process more accurate data based on the characteristic that the training model can continuously process data, so that more accurate characteristic information and correlation coefficient information of the electric signals are obtained, a score for evaluating the class quality of students is more accurately obtained, and the technical purpose of accurately evaluating the class quality of the students is realized.
Example two
Based on the same inventive concept as the intelligent classroom-based classroom quality detection method in the foregoing embodiment, the present invention further provides an intelligent classroom-based classroom quality detection apparatus, as shown in fig. 2, the apparatus 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, wherein 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 being preprocessed;
a first input unit 15, where 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 by training multiple sets of training data, and each set of training data in the multiple sets 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, wherein the sixth obtaining unit 16 is configured to obtain 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;
a sixth obtaining unit 17, where the sixth obtaining unit 17 is configured to obtain first score information of the first student and the first teacher according to the first feature information and the second feature information, where the first score is any one value between 0 and 100;
a seventh obtaining unit 18, where the seventh obtaining unit 18 is configured to obtain N sets of score information in sequence, 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 19, where the eighth obtaining unit 19 is configured to sort the N groups of score information to obtain M pieces of student information within a first threshold;
a first judging unit 20, the first judging unit 20 being configured to judge whether a duration of the score information of the second student within the first threshold is within a second threshold;
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, and the first reminding information is used to remind, through the intelligent wearable device, that the class quality of the second student is not qualified.
Further, the apparatus further comprises:
a tenth obtaining unit for obtaining wave frequency database information of the electrophysiological signal;
an eleventh obtaining unit, configured to obtain first filter information according to the wave frequency database;
a twelfth obtaining unit, configured to obtain a first filtering threshold;
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 comprises the third and fourth electrophysiological signals.
Further, the apparatus further comprises:
a third input unit, configured to input the first feature information and the second feature information into a second training model, where the second training model is obtained by training multiple sets of training data, and each set of training data in the multiple sets includes: first characteristic information, second characteristic information and 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 the first teacher, and the first correlation coefficient P is a numerical value between-1 and 1.
Further, the apparatus further comprises:
a fifteenth obtaining unit configured to obtain first regression model information;
a sixteenth obtaining unit, configured to obtain first population consistency equation information;
a seventeenth obtaining unit, configured to obtain first score information according to the first group identity equation information;
an eighteenth obtaining unit, configured to obtain N sets of score information in sequence, 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.
Further, the apparatus further comprises:
an eighteenth obtaining unit, configured to obtain first time information that the second student classroom quality is not qualified within a third time threshold;
a second judging unit, configured to judge whether the first count information exceeds a fourth threshold;
a nineteenth obtaining unit, configured to obtain second reminding information if the first count information exceeds a fourth threshold;
a first sending unit, configured to send the second reminding information to the first teacher.
Further, the apparatus further comprises:
a twentieth obtaining unit configured to obtain time length information of the first class;
a twenty-first obtaining unit configured to obtain information on the number of first students detected as being unqualified in class quality in the first class;
a third judging unit configured to judge whether the first student quantity information exceeds a fifth threshold;
a twenty-second obtaining unit, configured to obtain third reminding information for reminding the first teacher to pay attention to the classroom quality if the first student quantity information exceeds a fifth threshold.
Further, the apparatus further comprises:
a twenty-third obtaining unit configured to obtain first image information when the second student is detected as having unqualified classroom quality;
a twenty-fourth obtaining unit, configured to obtain, according to the first image information, first action category information that the second student classroom quality is not qualified;
a twenty-fifth obtaining unit, configured to sequentially obtain behavior category information of students with unqualified classroom quality in the first classroom;
a twenty-sixth obtaining unit, configured to obtain a behavior category analysis report that the classroom quality of the student in the first classroom is not qualified;
a second sending unit, configured to send the behavior category analysis report to the first teacher.
Various changes and specific examples of the intelligent classroom quality detection method in the first embodiment of fig. 1 are also applicable to the intelligent classroom quality detection apparatus in this embodiment, and through the foregoing detailed description of the intelligent classroom quality detection method, those skilled in the art can clearly know that the intelligent classroom quality detection apparatus in this embodiment is a classroom quality monitoring apparatus based on intelligent classroom, and therefore, for the sake of brevity of the description, detailed descriptions are omitted here.
Exemplary electronic device
The electronic device of the 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 the intelligent classroom-based classroom quality detection method in the foregoing embodiment, the present invention further provides an intelligent classroom-based classroom quality detection apparatus, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any one of the foregoing intelligent classroom-based classroom quality detection methods.
Where in fig. 3a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 306 provides an interface between the bus 300 and the 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, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
As will be appreciated by one skilled in the art, 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 has been 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing 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. Therefore, it is intended that the appended claims be interpreted as including 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 changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A classroom quality detection method based on an intelligent classroom is applied to intelligent wearable equipment, wherein the method comprises the following steps:
obtaining first electrophysiological signal information, wherein the first electrophysiological signal information is pulse wave signal and skin electrical 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;
carrying out filtering pretreatment on the first electrophysiological signal to obtain third electrophysiological signal information, wherein the third electrophysiological signal information is the pretreated first electrophysiological signal information;
obtaining fourth electrophysiological signal information, wherein the fourth electrophysiological signal information is the second electrophysiological signal information after being preprocessed;
inputting the third electrophysiological signal information and the fourth electrophysiological signal information into a first training model, wherein the first training model is obtained by training multiple sets of training data, and each set of training data in the multiple sets 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;
first output information and second output information of the first training model are obtained, 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 score information, wherein each group of score information comprises score information of an Nth student and a first teacher and score information between the Nth student and other N-1 students;
sequencing the N groups of score information to obtain M pieces of student information within a first threshold value;
judging whether the duration of the score information of the second student in the first threshold is within a second threshold;
and if the duration time of the score information of the second student in the first threshold value is not in the second threshold value, obtaining first reminding information, wherein the first reminding information is used for reminding that the class quality of the second student is unqualified through the intelligent wearable equipment.
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;
obtaining output signal information of the first filter, wherein the output signal information includes the third electrophysiological signal and a fourth electrophysiological signal.
3. The method of claim 1, wherein after obtaining the output information of the training model, the method further comprises:
inputting the first feature information and the second feature information into a second training model, wherein the second training model is obtained by training a plurality of sets of training data, and each set of training data in the plurality of sets includes: first characteristic information, second characteristic information and identification information of the first correlation coefficient;
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 the first teacher, and the first correlation coefficient P is a numerical value between-1 and 1.
4. The method of claim 3, wherein after obtaining third output information for the second training model, the method further comprises:
obtaining first regression model information;
obtaining first population consistency equation information;
obtaining first score information according to the first group consistency equation information;
and sequentially obtaining N groups of score information, wherein each group of score information comprises the score information of the Nth student and the first teacher and the score information between the Nth student and other N-1 students.
5. The method of claim 1, wherein the method further comprises:
obtaining first time information that the second student classroom quality is unqualified within a third time threshold;
judging whether the first time information exceeds a fourth threshold value;
if the first time information exceeds a fourth threshold value, second reminding information is obtained;
and sending the second reminding information to the first teacher.
6. The method of claim 1, wherein the method further comprises:
acquiring time length information of a first classroom;
obtaining first student quantity information detected as being unqualified in classroom quality in a first classroom;
judging whether the first student quantity information exceeds a fifth threshold value;
and if the first student quantity information exceeds a fifth threshold value, third reminding information is obtained and used for reminding the first teacher to pay attention to the classroom quality.
7. The method of claim 5, applied to an intelligent image capture device, wherein the method further comprises:
obtaining first image information when a second student is detected as unqualified classroom quality;
obtaining first behavior category information of which the classroom quality of the second student is unqualified according to the first image information;
sequentially obtaining behavior category information of students with unqualified classroom quality in a first classroom;
obtaining a behavior class analysis report of unqualified class quality of students in the first class;
and sending the behavior category analysis report to the first teacher.
8. The utility model 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 a pulse wave signal and skin electrical signal information of the first teacher;
a third obtaining unit, 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, configured to obtain fourth electrophysiological signal information, where the fourth electrophysiological signal information is the second electrophysiological signal information after being preprocessed;
a first input unit, 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 by training multiple sets of training data, and each set of training data in the multiple sets 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, 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 different. 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, configured to obtain first score information of the first student and the first teacher according to the first feature information and the second feature information, where the first score is any one of values between 0 and 100;
a seventh obtaining unit, configured to obtain N sets of score information in sequence, 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 groups of score information, and obtain M pieces of student information within a first threshold;
a first judgment unit for judging whether the duration of the score information of the second student within the first threshold is within a second threshold;
a ninth obtaining unit, configured to obtain first reminding information if the duration of the 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, through the intelligent wearable device, that the class quality of the second student is not qualified.
9. An intelligent classroom-based classroom quality detection device comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor executes the program to realize the steps of the method 1-7.
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