CN113657302A - State analysis system based on expression recognition - Google Patents
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Abstract
The invention belongs to the technical field of classroom monitoring, and particularly relates to a state analysis system based on expression recognition, which comprises a collection unit, an analysis unit and a receiving unit, wherein the collection unit is used for collecting the state of a user; the acquisition unit is used for acquiring images of students; the analysis unit is used for analyzing the individual state according to the student images and performing fuzzy marking on students with poor individual states; the analysis unit is also used for sending the vague marked student information to the receiving unit; the receiving unit is used for receiving and displaying the student information marked with the vague marks. The system can accurately monitor the learning and monitoring of students in the whole course, liberate the energy of teachers and improve the quality of classrooms.
Description
Technical Field
The invention belongs to the technical field of classroom monitoring, and particularly relates to a state analysis system based on expression recognition.
Background
The quality of class of students greatly affects the study. The traditional education mode is that the teacher observes student's action of listening to remind when finding student's the condition of listening to class goes wrong.
However, usually, a class has dozens of students, and a class has 40 minutes or 45 minutes, and the teacher monitors the students' listening behavior in the whole course, so that the energy requirement of the teacher is very high; in addition, the teacher mainly has the task of teaching knowledge, and puts too much energy into the observation of the student state, so that the teaching quality of the student is influenced in many times; besides, in the course of lecturing, the teacher needs to explain knowledge points, problem explanation, etc., and thus the attention of the teacher cannot always be paid to monitoring the learning state of the students.
In conclusion, the monitoring mode performed by the teaching teacher is not only difficult to ensure the monitoring effect, but also may affect the teaching quality of the teacher, and the situation of low classroom quality is likely to occur.
Disclosure of Invention
The invention aims to provide a state analysis system based on expression recognition, which can accurately monitor the learning monitoring of students in the whole course, liberate the energy of teachers and improve the quality of classes.
The basic scheme provided by the invention is as follows:
the state analysis system based on expression recognition comprises a collecting unit, an analyzing unit and a receiving unit;
the acquisition unit is used for acquiring images of students; the analysis unit is used for analyzing the individual state according to the student images and performing fuzzy marking on students with poor individual states; the analysis unit is also used for sending the vague marked student information to the receiving unit; the receiving unit is used for receiving and displaying the student information marked with the vague marks.
Basic scheme theory of operation and beneficial effect:
when the system is used, the acquisition unit acquires images of students in a classroom during class, the analysis unit analyzes the individual state, and the current individual state of the students can be known through the individual state analysis, namely whether the students are studying seriously at present. If the individual state of a certain student is poor, the student does not learn seriously, and therefore the student is marked with vague marks. Meanwhile, the analysis unit sends the student information to the receiving unit, the receiving unit can be integrated at a teacher end (such as a tablet computer loaded with corresponding APP), and the receiving unit displays the student information after receiving the vague marks. The teacher can know which students are not studying seriously at present through the receiving unit.
Because the system can automatically analyze and identify the learning states of the students, a teacher does not need to spend too much energy to know the current learning states of the students in class, and only needs to know the display content of the receiving unit, so that the teacher can concentrate on giving lessons and ensure the quality of the teaching. Except this, the system carries out state analysis through student's image, can guarantee the uniformity of analysis yardstick standard, can avoid appearing the condition that different teachers' supervision yardstick differs. Moreover, the system can carry out continuous monitoring analysis in the course of lessons, and can ensure that the learning state of students is monitored in the whole class time.
In conclusion, the system can accurately monitor the learning monitoring of students in the whole process, liberate the energy of teachers and improve the quality of classrooms.
Further, the device also comprises an input unit; the analysis unit analyzes the individual state by combining expression recognition and individual comparison; when the analysis unit analyzes that the state of a certain student is vague, the individual state of the student is regarded as a difference; the analysis unit is also used for identifying the student as suspected vague nerve and sending doubt information to the receiving unit when the analysis result shows that the students are thinking but no expression change occurs in the thinking process of the student, wherein the doubt information comprises the student information of the suspected vague nerve; the receiving unit is also used for receiving and displaying the in-doubt information; the input unit is used for inputting verification information of the doubt information; the analysis unit is also used for vaguely marking the corresponding individual state of the suspected nerve when the verification information is poor.
Has the advantages that: the expression recognition is combined with the individual comparison mode, so that the comprehensiveness and accuracy of the individual state analysis can be ensured. However, even when such a case is adopted, there may be a case where the analysis unit is not held accurately. When students need to think about problems brought forward by teachers or arranged accompanying exercises, as facial expressions in thinking of many people are different, some students can be glabellied and locked, and some students are calmer in expression in thinking, although the progress of most thinking can be obviously seen from the expression, the expression change is very small when thinking with a few students. At this time, if there is a student who is vaguely thinking about something else, it is difficult for the analysis unit to distinguish whether the student is thinking or vague.
When students think but no expression changes in the thinking process of a certain student, the analysis unit considers the student as a suspected vague nerve and sends doubt information to the receiving unit by using the system. The teaching teacher can know the above conditions through the suspected information, and can carry out spot check on the students in doubt when carrying out subsequent classroom interaction, and although the students do not need to stand immediately after thinking, the knowledge mastering degree is much better than that of the vague students. And then, the teaching teacher can input verification information of the suspicious information through the input unit. And if the verification information is poor, the analysis unit carries out vague marking on the corresponding individual state of the suspected vagus nerve. By the mode, even if the situation that whether the distraction exists is difficult to identify can be verified through subsequent processing, so that the classroom state of the student can be effectively monitored.
The system further comprises a storage unit, a data processing unit and a data processing unit, wherein the storage unit is used for storing suspected vague student information and generating a special archive corresponding to students; the analysis unit is also used for extracting the thinking habits of students with special files from the analysis result and storing the thinking habits in the corresponding special files when the students think; the analysis unit is also used for analyzing the thinking habits of the corresponding students when the number of the thinking habits stored in a special file reaches a preset value, so as to obtain the thinking expression rules of the students and store the thinking expression rules in the special file of the students; the analysis unit is also used for judging whether a special file of a certain student is stored in the storage unit when the certain student is determined to be suspected to be vague, judging whether the special file has a thinking expression rule if the special file exists, and determining that the individual state of the student corresponding to the suspected information is poor if the special file has the thinking expression rule and the expression changes.
Has the advantages that: if students suspected to be distracted appear each time, the verification needs to be carried out by a teaching teacher, two problems exist, firstly, the energy of the teaching teacher is consumed relatively; second, when there are many students suspected to be vague, the teacher giving lessons may not verify the result. In the scheme, the storage unit can store suspected distracted student information and generate a special file corresponding to students. Then, when the analysis result shows that the students are thinking, the analysis unit extracts the thinking habits of the students with the special files and stores the thinking habits in the corresponding special files. When the number of the thinking habits stored in a certain special file reaches a preset value, the students are analyzed for the thinking habits to obtain the thinking expression rules of the students and store the thinking expression rules in the special file of the students. Since thinking in class often occurs, the time actually required for the above process is not long. Then, when the analysis unit determines that a certain student is suspected to be distracted, whether a special file of the student is stored in the storage unit can be judged, if the special file exists and the expression law of thinking of the student is that the expression changes, the student is in a distraction state currently, therefore, the analysis unit directly determines that the individual state of the student corresponding to the suspected information is poor, and the student doubt information is not sent to the receiving end.
In this way, the recognition capability of the system can be continuously improved, and the workload of teachers can be continuously reduced. Moreover, because students without expression changes in thinking are originally few, as the service time of the system increases, the situations that the teaching teacher needs to perform auxiliary verification become less and less, and the time and energy that the teaching teacher needs to spend on verifying whether the system is distracted or not also become less and less.
Further, the analysis unit is also used for carrying out overall state analysis according to the result of the individual state analysis to obtain the overall state of the classroom and associating the overall state with the information of the teaching teacher; the analysis unit is also used for sending an improvement signal to the receiving unit when the overall state is poor; the receiving unit is also used for sending out an improvement prompt after receiving the improvement signal.
Has the advantages that: if the students in the classroom are in bad overall status, the teacher giving lessons may have some places to be improved in addition to the students' own problems. For example, managing too loose results in a student being careless in class, or the way in which a lecture is too boring results in a student being distracted. When this happens, the teacher giving lessons in the class needs to make some changes, otherwise, a large number of students may have poor learning effect in the class. Thus, the analysis unit sends an improvement signal to the receiving unit. And the receiving unit sends out improvement reminding to let the teaching teacher know the situation, even if making adjustment.
Further, the storage unit is also used for storing the analysis result of the overall state; the teaching device further comprises a statistical unit used for analyzing the teaching conditions of the teachers according to the analysis results of the overall states, and if the occurrence frequency of the poor overall states in the overall states related to the teachers is larger than a preset value, the statistical unit generates the lifting signals.
Has the advantages that: after the storage unit stores the analysis result of the overall state, the statistical unit can analyze the teaching condition of the teacher through the analysis result of the overall state because the overall state is associated with the information of the teaching teacher. If the occurrence frequency of the poor overall state is greater than the preset value in the overall state associated with a teacher, the situation that the state of the whole classroom is poor frequently occurs in the course of the teacher. If the development continues, the situation that most students of the teaching teacher do not master the classroom knowledge is likely to develop. Therefore, the statistical unit generates a lifting signal, school managers can conveniently know the situation, and the teacher is timely urged to lift the state quality.
Further, when the analysis unit performs the overall state analysis, the overall state is also associated with the classroom time.
Has the advantages that: when the classroom quality of a teacher needs to be improved, the problem that the classroom of the teacher is low in quality easily at which stage can be quickly known, and the teacher can conveniently improve pertinence according to the specific classroom stage.
Furthermore, the statistical unit divides the classroom into a plurality of time intervals based on the classroom time; the statistical unit is further used for analyzing the overall state of the classes of the teachers in each time period, ranking the classes, and screening out a preset number of teachers as recommended learning objects in each time period to generate a learning object suggestion table.
Has the advantages that: many teachers have different teaching styles and different control modes for each time period in a classroom, teachers with good classroom quality control in each time period can be screened out in such a mode, and when the teachers want to pertinently improve the quality of the teachers in the classroom in a certain time period, the teachers can quickly and accurately find learning objects.
Further, when the statistical unit analyzes that the occurrence frequency of the poor overall state in the overall states associated with a teacher is greater than a preset value, the statistical unit also analyzes the teaching conditions of the teacher at each time period and generates a promotion suggestion plan, and the content of the promotion suggestion plan comprises the time period needing promotion.
Has the advantages that: when the teaching skill of a teacher needs to be improved, the statistical unit can further analyze to know the performance of the teacher in each time interval and generate a promotion suggestion plan, and the teacher can promote the time interval in which the teaching quality needs to be improved according to the promotion suggestion plan. The teacher can supplement the short board time period of the teacher as soon as possible, and the teaching quality is guaranteed.
Further, the promotion suggestion plan further includes a sequence of the time periods that need to be promoted.
Has the advantages that: when a teaching teacher improves the capacity according to a promotion suggestion plan, the time interval which needs to be improved most is promoted preferentially, namely, the time interval which is easiest for students to be in a bad state in a classroom is reinforced, and for the teacher, the confidence that the promotion effect is enhanced can be seen quickly; for students, the teaching quality is improved in the time period when the easiest state is not good, and the learning quality can be stably improved.
Furthermore, the device also comprises a monitoring unit, and when the statistical unit generates the lifting signal, the lifting signal is sent to the monitoring unit.
Has the advantages that: the managers can know which teachers' classroom quality needs to be improved through the supervision unit, and are convenient to supervise and urge the teachers.
Drawings
Fig. 1 is a logic block diagram of a first embodiment of the invention.
Detailed Description
The following is further detailed by the specific embodiments:
example one
As shown in fig. 1, the device comprises a collecting unit, an analyzing unit, a storing unit, a receiving unit and an input unit; wherein, the collection unit is the camera of setting in the classroom, the quantity and the mounted position of camera, and the spatial structure that technical personnel in the field can be based on the classroom specifically sets up. The analysis unit and the storage unit are integrated at a back end, which is a server in this embodiment. The receiving unit and the input unit are integrated at a teaching end, and the teaching end is a tablet computer loaded with a corresponding APP in the embodiment.
The acquisition unit is used for acquiring student images. The analysis unit is used for analyzing the individual state according to the student images and performing fuzzy marking on students with poor individual states; the analysis unit is also used for sending the vague marked student information to the receiving unit; the receiving unit is used for receiving and displaying the student information marked with the vague marks. Specifically, the analysis unit performs individual state analysis by combining expression recognition and individual comparison.
Wherein, when the analysis unit analyzes that the state of a certain student is vague, the individual state of the student is regarded as poor. The analysis unit is also used for identifying the student as suspected vague nerve and sending doubt information to the receiving unit when the analysis result shows that the students are thinking but no expression change occurs in the thinking process of the student, wherein the doubt information comprises the student information of the suspected vague nerve; the receiving unit is also used for receiving and displaying the in-doubt information; the input unit is used for inputting verification information of the doubt information; the analysis unit is also used for vaguely marking the corresponding individual state of the suspected nerve when the verification information is poor.
The storage unit is used for storing suspected vague student information and generating a special file corresponding to the student; the analysis unit is also used for extracting the thinking habits of students with special files from the analysis result and storing the thinking habits in the corresponding special files when the students think; the analysis unit is also used for analyzing the thinking habits of the corresponding students when the number of the thinking habits stored in a special file reaches a preset value, so as to obtain the thinking expression rules of the students and store the thinking expression rules in the special file of the students; the analysis unit is also used for judging whether a special file of a certain student is stored in the storage unit when the certain student is determined to be suspected to be vague, judging whether the special file has a thinking expression rule if the special file exists, and determining that the individual state of the student corresponding to the suspected information is poor if the special file has the thinking expression rule and the expression changes.
The specific implementation process is as follows:
in class, the acquisition unit acquires images of students in a classroom, the analysis unit analyzes individual states, and the current individual states of the students can be known through the individual state analysis, namely whether the students are studying seriously at present. If the individual state of a certain student is poor, the student does not learn seriously, and therefore the student is marked with vague marks. Meanwhile, the analysis unit sends the student information to the receiving unit, and the receiving unit receives the vague marked student information and then displays the vague marked student information. The teacher can know which students are not studying seriously at present through the receiving unit.
Because the system can automatically analyze and identify the learning states of the students, a teacher does not need to spend too much energy to know the current learning states of the students in class, and only needs to know the display content of the receiving unit, so that the teacher can concentrate on giving lessons and ensure the quality of the teaching. Except this, the system carries out state analysis through student's image, can guarantee the uniformity of analysis yardstick standard, can avoid appearing the condition that different teachers' supervision yardstick differs. Moreover, the system can carry out continuous monitoring analysis in the course of lessons, and can ensure that the learning state of students is monitored in the whole class time.
When the system is used for analyzing the individual state, the comprehensiveness and the accuracy of the individual state analysis can be ensured due to the adoption of a mode of combining expression recognition and individual comparison. Even then, however, there may be cases where the analysis unit is not held correctly. For example, when students need to think about problems posed by teachers or accompanying exercises arranged in a hall, some students may look over the eyebrows tightly because facial expressions of many people when thinking about the students are different, and some students may have calmer expressions when thinking about the students, and although the progress of most thinking can be obviously seen from the expressions, the expression changes very little when thinking about the students. At this time, if there is a student who is vaguely thinking about something else, it is difficult for the analysis unit to distinguish whether the student is thinking or vague. In the system, when students think but no expression changes exist in the thinking process of a certain student, the analysis unit considers the student as a suspected vague nerve and sends doubt information to the receiving unit. The teaching teacher can know the above conditions through the suspected information, and can carry out spot check on the students in doubt when carrying out subsequent classroom interaction, and although the students do not need to stand immediately after thinking, the knowledge mastering degree is much better than that of the vague students. And then, the teaching teacher can input verification information of the suspicious information through the input unit. And if the verification information is poor, the analysis unit carries out vague marking on the corresponding individual state of the suspected vagus nerve. By the mode, even if the situation that whether the distraction exists is difficult to identify can be verified through subsequent processing, so that the classroom state of the student can be effectively monitored.
If students suspected to be distracted appear each time, the verification needs to be carried out by a teaching teacher, two problems exist, firstly, the energy of the teaching teacher is consumed relatively; second, when there are many students suspected to be vague, the teacher giving lessons may not verify the result. In the scheme, the storage unit can store suspected distracted student information and generate a special file corresponding to students. Then, when the analysis result shows that the students are thinking, the analysis unit extracts the thinking habits of the students with the special files and stores the thinking habits in the corresponding special files. When the number of the thinking habits stored in a certain special file reaches a preset value, the students are analyzed for the thinking habits to obtain the thinking expression rules of the students and store the thinking expression rules in the special file of the students. Since thinking in class often occurs, the time actually required for the above process is not long. Then, when the analysis unit determines that a certain student is suspected to be distracted, whether a special file of the student is stored in the storage unit can be judged, if the special file exists and the expression law of thinking of the student is that the expression changes, the student is in a distraction state currently, therefore, the analysis unit directly determines that the individual state of the student corresponding to the suspected information is poor, and the student doubt information is not sent to the receiving end. In this way, the recognition capability of the system can be continuously improved, and the workload of teachers can be continuously reduced. Moreover, because students without expression changes in thinking are originally few, as the service time of the system increases, the situations that the teaching teacher needs to perform auxiliary verification become less and less, and the time and energy that the teaching teacher needs to spend on verifying whether the system is distracted or not also become less and less.
Example two
Different from the first embodiment, the embodiment further includes a statistics unit and a supervision unit, wherein the statistics unit is integrated at the background end, and the supervision unit is integrated at the management end, where the management end is a smart phone loaded with a corresponding APP in this embodiment.
In this embodiment, the analysis unit is further configured to perform overall state analysis according to the result of the individual state analysis to obtain an overall state of the classroom, and associate the overall state with information of a teaching teacher; the analysis unit is also used for sending an improvement signal to the receiving unit when the overall state is poor; the receiving unit is also used for sending out an improvement prompt after receiving the improvement signal.
The storage unit is also used for storing the analysis result of the overall state; the system also comprises a statistical unit used for analyzing the teaching conditions of the teachers according to the analysis results of the overall states, and if the occurrence frequency of the poor overall state in the overall states associated with a certain teacher is greater than a preset value, the statistical unit generates a lifting signal and sends the lifting signal to the supervision unit.
When the analysis unit analyzes the whole state, the whole state is associated with the classroom time. The statistical unit also divides the classroom into a plurality of time intervals based on the classroom time; the statistical unit is further used for analyzing the overall state of the classes of the teachers in each time period, ranking the classes, and screening out a preset number of teachers as recommended learning objects in each time period to generate a learning object suggestion table.
When the statistical unit analyzes that the occurrence frequency of the poor overall state in the overall states associated with a teacher is greater than a preset value, the statistical unit also analyzes the teaching conditions of the teacher in each time period and generates a promotion suggestion plan, and the content of the promotion suggestion plan comprises the time period needing promotion. The content of the promotion suggestion plan comprises the time period needing promotion and the sequence of the time periods needing promotion.
The specific implementation process is as follows:
if the students in the classroom are in bad overall status, the teacher giving lessons may have some places to be improved in addition to the students' own problems. For example, managing too loose results in a student being careless in class, or the way in which a lecture is too boring results in a student being distracted. When this happens, the teacher giving lessons in the class needs to make some changes, otherwise, a large number of students may have poor learning effect in the class. Thus, the analysis unit sends an improvement signal to the receiving unit. And the receiving unit sends out improvement reminding to let the teaching teacher know the situation, even if making adjustment.
On the other hand, after the storage unit stores the analysis result of the overall state, the statistical unit can analyze the teaching condition of the teacher according to the analysis result of the overall state because the overall state is associated with the information of the teacher giving lessons. If the occurrence frequency of the poor overall state is greater than the preset value in the overall state associated with a teacher, the situation that the state of the whole classroom is poor frequently occurs in the course of the teacher. If the development continues, the situation that most students of the teaching teacher do not master the classroom knowledge is likely to develop. Therefore, the statistical unit generates a lifting signal and sends the lifting signal to the supervision unit, school managers can conveniently know the situation, and the teacher is timely supervised to promote the state quality. Because the analysis unit also associates the whole state with the classroom time when analyzing the whole state, when the classroom quality of a teacher needs to be improved, the problem that the classroom of the teacher is low in quality easily appears in which stage can be quickly known, and the teacher can conveniently improve pertinence according to the specific classroom stage.
When a teacher needing to improve the teaching quality is found, the statistical unit analyzes the teaching condition of each period of the teacher to generate a promotion suggestion plan, and the content of the promotion suggestion plan comprises the period needing to be promoted and the sequence of the period needing to be promoted. When the teaching skill of a teacher needs to be improved, the statistical unit can further analyze to know the performance of the teacher in each time interval and generate a promotion suggestion plan, and the teacher can promote the time interval in which the teaching quality needs to be improved according to the promotion suggestion plan. The teacher can supplement the short board time period of the teacher as soon as possible, and the teaching quality is guaranteed. Moreover, when the teaching teacher improves the capacity according to the improvement suggestion plan, the time interval which needs to be improved most is improved preferentially, namely, the time interval which is easiest for students to be in a bad state in a classroom is reinforced, and the teacher can see the improvement effect quickly to enhance the confidence of the improvement capacity; for students, the teaching quality is improved in the time period when the easiest state is not good, and the learning quality can be stably improved.
Besides, because many teachers have different teaching styles, the control modes of all time intervals in a classroom are different, and the teachers can learn conveniently. In the scheme, the statistical unit analyzes teaching conditions of teachers in each time period and ranks the teaching conditions, and screens out a preset number of teachers as recommended learning objects in each time period to generate a learning object suggestion table. By the mode, teachers with good classroom quality control in each time interval are screened out, and when the teachers want to pertinently improve the quality of the teachers in the classroom in a certain time interval, learning objects can be quickly and accurately found.
The foregoing is merely an example of the present invention, and common general knowledge in the field of known specific structures and characteristics is not described herein in any greater extent than that known in the art at the filing date or prior to the priority date of the application, so that those skilled in the art can now appreciate that all of the above-described techniques in this field and have the ability to apply routine experimentation before this date can be combined with one or more of the present teachings to complete and implement the present invention, and that certain typical known structures or known methods do not pose any impediments to the implementation of the present invention by those skilled in the art. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (10)
1. State analysis system based on expression discernment, its characterized in that: comprises an acquisition unit, an analysis unit and a receiving unit;
the acquisition unit is used for acquiring images of students; the analysis unit is used for analyzing the individual state according to the student images and performing fuzzy marking on students with poor individual states; the analysis unit is also used for sending the vague marked student information to the receiving unit; the receiving unit is used for receiving and displaying the student information marked with the vague marks.
2. The expression recognition-based state analysis system of claim 1, wherein: also includes an input unit; the analysis unit analyzes the individual state by combining expression recognition and individual comparison; when the analysis unit analyzes that the state of a certain student is vague, the individual state of the student is regarded as a difference; the analysis unit is also used for identifying the student as suspected vague nerve and sending doubt information to the receiving unit when the analysis result shows that the students are thinking but no expression change occurs in the thinking process of the student, wherein the doubt information comprises the student information of the suspected vague nerve; the receiving unit is also used for receiving and displaying the in-doubt information; the input unit is used for inputting verification information of the doubt information; the analysis unit is also used for vaguely marking the corresponding individual state of the suspected nerve when the verification information is poor.
3. The expression recognition-based state analysis system of claim 2, wherein: the system also comprises a storage unit, a data processing unit and a data processing unit, wherein the storage unit is used for storing suspected vague student information and generating a special file corresponding to a student; the analysis unit is also used for extracting the thinking habits of students with special files from the analysis result and storing the thinking habits in the corresponding special files when the students think; the analysis unit is also used for analyzing the thinking habits of the corresponding students when the number of the thinking habits stored in a special file reaches a preset value, so as to obtain the thinking expression rules of the students and store the thinking expression rules in the special file of the students; the analysis unit is also used for judging whether a special file of a certain student is stored in the storage unit when the certain student is determined to be suspected to be vague, judging whether the special file has a thinking expression rule if the special file exists, and determining that the individual state of the student corresponding to the suspected information is poor if the special file has the thinking expression rule and the expression changes.
4. The expression recognition-based state analysis system of claim 3, wherein: the analysis unit is also used for carrying out overall state analysis according to the result of the individual state analysis to obtain the overall state of the classroom and associating the overall state with the information of the teaching teacher; the analysis unit is also used for sending an improvement signal to the receiving unit when the overall state is poor; the receiving unit is also used for sending out an improvement prompt after receiving the improvement signal.
5. The expression recognition-based state analysis system of claim 4, wherein: the storage unit is also used for storing the analysis result of the overall state; the teaching device further comprises a statistical unit used for analyzing the teaching conditions of the teachers according to the analysis results of the overall states, and if the occurrence frequency of the poor overall states in the overall states related to the teachers is larger than a preset value, the statistical unit generates the lifting signals.
6. The expression recognition-based state analysis system of claim 5, wherein: when the analysis unit analyzes the overall state, the overall state is also associated with the classroom time.
7. The expression recognition-based state analysis system of claim 6, wherein: the statistical unit also divides the classroom into a plurality of time intervals based on the classroom time; the statistical unit is further used for analyzing the overall state of the classes of the teachers in each time period, ranking the classes, and screening out a preset number of teachers as recommended learning objects in each time period to generate a learning object suggestion table.
8. The expression recognition-based state analysis system of claim 7, wherein: when the statistical unit analyzes that the occurrence frequency of the poor overall state in the overall states associated with a teacher is greater than a preset value, the statistical unit also analyzes the teaching conditions of the teacher in each time period and generates a promotion suggestion plan, and the content of the promotion suggestion plan comprises the time period needing promotion.
9. The expression recognition-based state analysis system of claim 8, wherein: the promotion suggestion plan also includes a precedence order of the periods of time that need to be promoted.
10. The expression recognition-based state analysis system of claim 5, wherein: the device also comprises a monitoring unit, and when the statistical unit generates the lifting signal, the lifting signal is sent to the monitoring unit.
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