CN116193678A - Internet-based integrated management system and method for electrified education equipment - Google Patents
Internet-based integrated management system and method for electrified education equipment Download PDFInfo
- Publication number
- CN116193678A CN116193678A CN202310130332.0A CN202310130332A CN116193678A CN 116193678 A CN116193678 A CN 116193678A CN 202310130332 A CN202310130332 A CN 202310130332A CN 116193678 A CN116193678 A CN 116193678A
- Authority
- CN
- China
- Prior art keywords
- light intensity
- regulation
- data
- gesture
- integrated management
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
- H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
- H05B47/10—Controlling the light source
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B20/00—Energy efficient lighting technologies, e.g. halogen lamps or gas discharge lamps
- Y02B20/40—Control techniques providing energy savings, e.g. smart controller or presence detection
Landscapes
- Electrically Operated Instructional Devices (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an integrated management system and method for electrified education equipment based on the Internet, and belongs to the technical field of the Internet. Because the lighting positions of each classroom are different, the control modes are further divided into two modes of light control and light dark control through cluster analysis of light intensity historical data, and the probability values are judged by combining the regulation and control instructions in the two modes, so that a large amount of data are effectively integrated, the gesture data are ensured to be further analyzed only under the condition that the probability values are judged by the proper regulation and control instructions, and the gesture of a teacher is completely different in the two modes, so that a standard gesture characteristic data set is output through model training; and then combine big data analysis through internet technique, at the in-process of audio-visual education programme, can guarantee the accurate laminating mr's of control state of having class of light, avoid the waste of time of having class, make the student in time see clearly blackboard or projected content, liberated mr's both hands, make the operation of equipment more intelligent and automatic.
Description
Technical Field
The invention relates to the technical field of Internet, in particular to an integrated management system and method for electrified education equipment based on Internet.
Background
With the continuous development of society, information communication and communication become more frequent and more important; conference system devices such as various audio-visual devices, projection devices, monitoring devices, and light control devices are beginning to enter various industries. The existing conference room, audio-visual teaching room and the like are not a previous platform, chair and microphone, and along with the continuous progress of the internet technology, various advanced multimedia conference rooms and teaching equipment are replaced, and a simultaneous interpretation system, an electronic voting system and various light control systems are also configured for some large conference rooms. The use of multiple devices necessarily brings complicated device operations, such as frequent switching of light, simultaneous switching of multiple devices, continuous switching of projection pictures, and the like; at present, in the prior art, touch operation can be performed through an integrated terminal, but the method still needs human operation, and is not intelligent and automatic enough.
Disclosure of Invention
The invention aims to provide an integrated management system and method for electrified education equipment based on the Internet, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
an integrated management system of electrified education equipment based on internet, the system comprises: the system comprises an electrified education equipment integrated management model module, a regulation and control instruction judgment probability value calculation module, a standard attitude characteristic parameter training module and an intelligent regulation and control module;
the integrated management model module of the electrified education equipment is used for collecting light intensity data in the electrified education equipment, constructing an integrated management model of the electrified education equipment according to the light intensity data, wherein the integrated management model of the electrified education equipment comprises a light regulation model and a light dark regulation model; wherein, the light intensity data is collected by a light intensity sensor;
the regulation and control instruction judgment probability value calculation module is used for calculating a regulation and control instruction judgment probability value based on the electrified education equipment integrated management model;
the standard gesture feature parameter training module is used for acquiring historical gesture information data of a teacher in a classroom, extracting gesture feature parameters of the historical gesture information data, generating a gesture feature parameter set, training the gesture feature parameters under different gestures based on a regulation and control instruction judging probability value, and outputting the regulation and control instruction judging probability value and a standard gesture feature parameter set corresponding to the regulation and control instruction judging probability value;
the intelligent regulation and control module is used for collecting light intensity data in the current teaching room in real time and judging whether to start the integrated management model of the electrified teaching equipment based on the light intensity data in the current teaching room; if the integrated management model of the electrified education equipment is started, further starting gesture monitoring of a current teacher, acquiring gesture information data of the current teacher, calculating similarity of gesture feature parameters of the current teacher based on a standard gesture feature parameter set, and outputting a standard gesture feature parameter set corresponding to the maximum similarity; and performing intelligent regulation and control on the electrified education equipment according to the standard attitude characteristic parameter set corresponding to the maximum output similarity.
Further, the integrated management model module of the electrified education equipment further comprises a light intensity historical data clustering model unit and a light intensity data classifying unit;
the light intensity historical data clustering model unit is used for acquiring light intensity historical data within a preset time range to generate a light intensity historical data set which is marked as X= { X 1 ，X 2 ，...，X n -n is an integer, calculating an expected value and a standard deviation of the light intensity data in the teaching chamber from the light intensity history data, and recording the expected value as μ and the standard deviation as σ; according to the expected value and standard deviation of the light intensity historical data, a clustering model of the light intensity historical data is established
Wherein X is i ∈X，f(X i ) X represents i Cluster values of (2);
the light intensity data classifying unit is used for presetting a light intensity data threshold value, if f (X) i ) If the light intensity data threshold is greater than or equal to the light intensity data threshold, f (X i ) Corresponding light intensity data X i Classifying the data into a brightness control model data set, and recording the brightness control model data set as GL; if f (X) i ) Less than the intensity data threshold, f (X i ) Corresponding light intensity data X i Classified as a light-dark modulation model data set, and the light-dark modulation model data set was noted as GA.
Further, the regulation and control instruction evaluation probability value calculation module further comprises a regulation and control instruction evaluation value calculation unit and a regulation and control instruction evaluation probability value calculation unit;
the regulation and control instruction evaluation value calculation unit is used for acquiring historical light intensity data in the corresponding teaching room when the electrified education equipment integrated management model needs to be started at any time and generating a starting model data set GS= { Y 1 ，Y 2 ，...，Y m M is an integer, and a regulation instruction judgment probability value is calculated according to the following specific calculation formula:
wherein F is 1 (GS) represents a first evaluation value of the regulation instruction, F 2 (GS) represents a second evaluation value of the control instruction, Y j ∈GS；
The regulation instruction judgment probability value calculation unit is used for calculating a regulation instruction judgment probability value F (GS) =F according to a first evaluation value of a corresponding regulation instruction and a second evaluation value of the regulation instruction when the electrified education equipment integrated management model is started as required 1 (GS)/F 2 (GS); calculating a regulating instruction judging probability value corresponding to each time the electrified education equipment integrated management model is required to be started, and generating a regulating instruction judging probability value set { F (GS) 1 ，F(GS) 2 ，...，F(GS) L And L is an integer.
Further, the standard gesture feature parameter training module further comprises a gesture feature parameter training model unit and a standard gesture feature parameter set generating unit;
the gesture characteristic parameter training model unit is used for acquiring historical gesture information data of a teacher in a classroom, extracting gesture characteristic parameters of the historical gesture information data when the teacher writes on a blackboard through a neural network algorithm, and generating a judging probability value F (GS) of any regulating instruction V The lower corresponding set of attitude feature parameters, noted as f (GS) V ＝{U 1 ，U 2 ，...，U w W is an integer, where U 1 ，U 2 ，...，U w Any element represents a gesture feature parameter, f (GS) V ∈{f(GS) 1 ，f(GS) 2 ，...，f(GS) L -wherein f (GS) 1 ，f(GS) 2 ，...，f(GS) L F (GS) 1 ，F(GS) 2 ，...，F(GS) L Correspondingly generated gesture feature parameter sets; constructing a posture characteristic parameter training model, training the posture characteristic parameters under different postures, and calculating any regulation and control instruction judgment probability value F (GS) V Conditional probability P [ f (GS) V |G]＝P[G|f(GS) V ]*P[f(GS) V ]P (G), and
wherein G represents the presence of a set of gesture feature parameters f (GS) V A set of attitude characteristic parameters of any element, and +.>
...，f(GS) L }-{f(GS) V }，P[G|f(GS) V ]A conditional probability representing G;
the standard attitude feature parameter set generating unit is used for respectively training and generating an attitude feature parameter set f (GS) V Corresponding optimal attitude characteristic parameters, presetting an attitude characteristic parameter training threshold, and when P [ f (GS) V |G]When the training threshold value of the gesture characteristic parameter is larger than or equal to the training threshold value, the training is completed; integrate training results, and F (GS) V A set of data corresponding to the training is denoted as a standard posture feature parameter set, denoted as F (GS) V ＝{R 1 ，R 2 ，...，R w (wherein R is) 1 ，R 2 ，...，R w Respectively representing a standard attitude characteristic parameter.
Further, the intelligent regulation and control module further comprises a similarity calculation unit and an intelligent regulation and control unit;
the similarity calculation unit is used for collecting the light intensity data in the current teaching room in real time, and recording the light intensity data as Q, if Q is E GL U
The GA starts the electrified education equipment integrated management model, or does not start the electrified education equipment integrated management model; if the integrated management model of the electrified education equipment is started, further starting gesture monitoring of the current teacher, acquiring gesture information data of the current teacher, and extracting gesture characteristic parameters of the current teacher; the similarity of the gesture characteristic parameters of the current teacher is calculated, and a specific calculation formula is as follows:
wherein H is a Representing the gesture feature parameters of any current teacher, wherein C is the total number of the gesture feature parameters of the current teacher, and a is {1, 2.. The main, C }, h b ∈{U 1 ，U 2 ，...，U w }；
The intelligent regulation and control unit is used for outputting a standard attitude characteristic parameter set corresponding to the maximum similarity, acquiring a regulation and control instruction judgment probability value corresponding to the standard attitude characteristic parameter set corresponding to the maximum similarity, and controlling the light switch to be on if the regulation and control instruction judgment probability value is greater than or equal to 1, or else, controlling the light switch to be off.
An integrated management method of electrified education equipment based on the Internet comprises the following steps:
step S100: the light intensity sensor collects light intensity data in the teaching room, and constructs an integrated management model of the electrified education equipment according to the light intensity data, wherein the integrated management model of the electrified education equipment comprises a light regulation model and a light dark regulation model;
step S200: calculating a regulation and control instruction judgment probability value based on the electrified education equipment integrated management model;
step S300: acquiring historical gesture information data of a teacher in a classroom, extracting gesture feature parameters of the historical gesture information data, generating a gesture feature parameter set, training the gesture feature parameters under different gestures based on a regulation and control instruction judgment probability value, and outputting a regulation and control instruction judgment probability value and a standard gesture feature parameter set corresponding to the regulation and control instruction judgment probability value;
step S400: acquiring light intensity data in a current teaching room in real time, and judging whether to start an electrified teaching equipment integrated management model based on the light intensity data in the current teaching room; if the integrated management model of the electrified education equipment is started, further starting gesture monitoring of a current teacher, acquiring gesture information data of the current teacher, calculating similarity of gesture feature parameters of the current teacher based on a standard gesture feature parameter set, and outputting a standard gesture feature parameter set corresponding to the maximum similarity; according to the standard attitude characteristic parameter set corresponding to the maximum output similarity, intelligent regulation and control are carried out on the electrified education equipment;
according to the method, in the electrified education process, the lamplight needs to be intelligently controlled due to the problem of light environment; when the intensity of the light is too bright, the brightness of the projection equipment is covered, so that the projection content is hard to see clearly, and the light needs to be turned off at the moment; when the intensity of the light is too dark, the writing content on the blackboard is hard to see, and the light needs to be turned on at the moment; furthermore, the teacher is inevitably required to continuously switch the light control in the course of lessons, and sometimes forgets to adjust the light switch, so that precious lesson time is wasted, and meanwhile, students cannot see the blackboard or the projected content in time.
Further, the specific implementation process of the step S100 includes:
step S101: acquiring light intensity historical data within a preset time range to generate a light intensity historical data set which is marked as X= { X 1 ，X 2 ，...，X n -n is an integer, calculating an expected value and a standard deviation of the light intensity data in the teaching chamber from the light intensity history data, and recording the expected value as μ and the standard deviation as σ; according to the expected value and standard deviation of the light intensity historical data, a clustering model of the light intensity historical data is established, and a specific calculation formula is as follows:
wherein X is i ∈X，f(X i ) X represents i Cluster values of (2);
step S102: preset the light intensity data threshold, if f (X i ) If the light intensity data threshold is greater than or equal to the light intensity data threshold, f (X i ) Corresponding light intensity data X i Classifying the data into a brightness control model data set, and recording the brightness control model data set as GL; if f (X) i ) Less than the intensity data threshold, f (X i ) Corresponding light intensity data X i Classifying the data into a light and dark modulation model data set, and marking the light and dark modulation model data set as GA;
according to the above method, since lighting positions in each classroom are different, it is necessary to perform cluster analysis on the light intensity data.
Further, the specific implementation process of the step S200 includes:
step S201: acquiring historical light intensity data in a corresponding teaching room when the electrified education equipment integrated management model needs to be started at any time, and generating a starting model data set GS= { Y 1 ，Y 2 ，...，Y m M is an integer, and a regulation instruction judgment probability value is calculated according to the following specific calculation formula:
wherein F is 1 (GS) represents a first evaluation value of the regulation instruction, F 2 (GS) represents a second evaluation value of the control instruction, Y j ∈GS；
Step S202: calculating a regulating instruction judging probability value F (GS) =F according to a first evaluating value of a regulating instruction and a second evaluating value of the regulating instruction corresponding to the starting of the electrified education equipment integrated management model 1 (GS)/F 2 (GS)；
Step S203: returning to step S201, calculating a regulating instruction judging probability value corresponding to each time the integrated management model of the electrified education equipment needs to be started, and generating a regulating instruction judging probability value set { F (GS) 1 ，F(GS) 2 ，...，F(GS) L -L is an integer;
according to the method, after clustering analysis is carried out on the light intensity data, the two modes of brightness and light darkness are also required to be compared and analyzed according to the historical data; and in a large amount of light intensity data, different regulation and control instructions are acquired by combining different classroom positions to judge the probability value.
Further, the implementation process of the step S300 includes:
step S301: acquiring historical posture information data of a teacher in a classroom, extracting posture characteristic parameters of the historical posture information data when the teacher writes a state on a blackboard through a neural network algorithm, and generating a judging probability value F (GS) of any regulating instruction V The lower corresponding set of attitude feature parameters, noted as f (GS) V ＝{U 1 ，U 2 ，...，U w W is an integer, where U 1 ，U 2 ，...，U w Any element represents a gesture feature parameter, f (GS) V ∈{f(GS) 1 ，f(GS) 2 ，...，f(GS) L -wherein f (GS) 1 ，f(GS) 2 ，...，f(GS) L F (GS) 1 ，F(GS) 2 ，...，F(GS) L Correspondingly generated gesture feature parameter sets;
step S302: constructing a posture characteristic parameter training model, training the posture characteristic parameters under different postures, and calculating any regulation and control instruction judgment probability value F (GS) V Conditional probability P [ f (GS) V |G]＝P[G|f(GS) V ]*P[f(GS) V ]P (G), and
wherein G represents the presence of a set of gesture feature parameters f (GS) V A set of attitude characteristic parameters of any element, and +.>
f(GS) 2 ，...，f(GS) L }-{f(GS) V }，P[G|f(GS) V ]A conditional probability representing G;
step S303: respectively training and generating a gesture characteristic parameter set f (GS) V Corresponding optimal attitude characteristic parameters, presetting an attitude characteristic parameter training threshold, and when P [ f (GS) V |G]More than or equal to the poseThe training is completed when the state characteristic parameter trains the threshold value; integrate training results, and F (GS) V A set of data corresponding to the training is denoted as a standard posture feature parameter set, denoted as F (GS) V ＝{R 1 ，R 2 ，...，R w (wherein R is) 1 ，R 2 ，...，R w Respectively representing a standard attitude characteristic parameter;
according to the method, firstly, the illumination intensity data is required to be used as first analysis data, and only under different illumination conditions, people can not visually see the blackboard or projection content; secondly, when the regulation and control instruction judgment under different illumination corresponds to the occurrence, the gesture characteristics of a teacher are analyzed in detail; in the application, the brightness regulation model corresponds to the state that a teacher writes on a blackboard, the light control needs to be turned on during writing, and the light dark regulation model corresponds to the state that the teacher is teaching by combining projection contents, and the light control needs to be turned off; if the regulation command judges the probability value F (GS) V And the corresponding brightness regulation model is used for training and analyzing the gesture written by a teacher, otherwise, the teacher used for training and analyzing teaches the gesture when projecting the content, and the two gesture data are necessarily completely different.
Further, the specific implementation process of the step S400 includes:
step S401: acquiring light intensity data in a current teaching room in real time, recording the light intensity data as Q, and starting an electrified education equipment integrated management model if Q is smaller than GL U GA, or not starting the electrified education equipment integrated management model;
step S402: if the integrated management model of the electrified education equipment is started, further starting gesture monitoring of the current teacher, acquiring gesture information data of the current teacher, and extracting gesture characteristic parameters of the current teacher; the similarity of the gesture characteristic parameters of the current teacher is calculated, and a specific calculation formula is as follows:
wherein H is a Posture feature parameter representing any current teacherThe number C is the total number of the gesture characteristic parameters of the current teacher, a epsilon {1, 2.,. C }, h b ∈{U 1 ，U 2 ，...，U w }；
Step S403: outputting a standard attitude characteristic parameter set corresponding to the maximum similarity, and acquiring a regulation and control instruction judging probability value corresponding to the standard attitude characteristic parameter set corresponding to the maximum similarity, wherein if the regulation and control instruction judging probability value is greater than or equal to 1, the light switch is controlled to be turned on, otherwise, the light switch is controlled to be turned off;
according to the method, under multi-level data analysis, the judgment probability value of the regulation and control instruction is greater than or equal to 1, the intelligent control is indicated to be directed to the control under the light and dark model, otherwise, the intelligent control is indicated to be directed to the control under the light and dark model.
Compared with the prior art, the invention has the following beneficial effects: according to the integrated management system and method for the electrified education equipment based on the Internet, due to the fact that lighting positions of each classroom are different, a control mode is divided into two modes of brightness control and light and dark control through clustering analysis of light intensity historical data, and then a large amount of data are effectively integrated by combining regulation and control instruction judging probability values in the two modes, so that gesture data are required to be further analyzed only under the condition that proper regulation and control instruction judging probability values are achieved, and due to the fact that gestures of teachers in the two modes are completely different, a standard gesture feature data set is output through model training; and then combine big data analysis through internet technique, at the in-process of audio-visual education programme, can guarantee the intelligent of light and the accurate state of giving lessons of laminating mr of automated control, avoid the waste of time of giving lessons, make the student in time see the content of blackboard or projection clearly, liberated mr's both hands.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of an integrated management system for an electrified education equipment based on the Internet;
fig. 2 is a schematic diagram of steps of an integrated management method of an electrified education equipment based on the internet.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions:
referring to fig. 1, in a first embodiment: provided is an integrated management system of an electrified education equipment based on Internet, comprising: the system comprises an electrified education equipment integrated management model module, a regulation and control instruction judgment probability value calculation module, a standard attitude characteristic parameter training module and an intelligent regulation and control module;
the integrated management model module of the electrified education equipment is used for collecting light intensity data in the electrified education equipment, constructing an integrated management model of the electrified education equipment according to the light intensity data, wherein the integrated management model of the electrified education equipment comprises a light regulation and control model and a light dark regulation and control model; wherein, the light intensity data is collected by a light intensity sensor;
the integrated management model module of the electrified education equipment further comprises a light intensity historical data clustering model unit and a light intensity data classifying unit;
the light intensity historical data clustering model unit is used for acquiring light intensity historical data within a preset time range to generate a light intensity historical data set which is marked as X= { X 1 ，X 2 ，...，X n N is an integer, the expected value and standard deviation of the light intensity data in the teaching chamber are calculated according to the light intensity history data, the expected value is recorded as mu, and the standard deviation is recorded as sigma; according to the expected value and standard deviation of the light intensity historical data, a clustering model of the light intensity historical data is established
Wherein X is i ∈X，f(X i ) X represents i Cluster values of (2);
the light intensity data classifying unit is used for presetting a light intensity data threshold value, if f (X) i ) If the light intensity data threshold is greater than or equal to the light intensity data threshold, f (X i ) Corresponding light intensity data X i Classifying the data into a brightness control model data set, and recording the brightness control model data set as GL; if f (X) i ) Less than the intensity data threshold, f (X i ) Corresponding light intensity data X i Classifying the data into a light and dark modulation model data set, and marking the light and dark modulation model data set as GA;
the regulation and control instruction judgment probability value calculation module is used for calculating a regulation and control instruction judgment probability value based on the electrified education equipment integrated management model;
the regulation command evaluation probability value calculation module further comprises a regulation command evaluation value calculation unit and a regulation command evaluation probability value calculation unit;
the regulation and control instruction evaluation value calculation unit is used for acquiring historical light intensity data in the corresponding teaching room when the electrified education equipment integrated management model needs to be started at any time and generating a starting model data set GS= { Y 1 ，Y 2 ，...，Y m M is an integer, and a regulation instruction judgment probability value is calculated according to the following specific calculation formula:
wherein F is 1 (GS) represents a first evaluation value of the regulation instruction, F 2 (GS) represents a second evaluation value of the control instruction, Y j ∈GS；
The regulation instruction judgment probability value calculation unit is used for calculating a regulation instruction judgment probability value according to a first evaluation value of a corresponding regulation instruction and a second evaluation value of the regulation instruction when the electrified education equipment integrated management model is started as required
F(GS)＝F 1 (GS)/F 2 (GS); calculating a corresponding regulation and control instruction when the electrified education equipment integrated management model needs to be started every timeEvaluating the probability value and generating a regulating instruction evaluating probability value set { F (GS) 1 ，F(GS) 2 ，...，F(GS) L -L is an integer;
the standard gesture feature parameter training module is used for acquiring historical gesture information data of a teacher in a classroom, extracting gesture feature parameters of the historical gesture information data, generating a gesture feature parameter set, training the gesture feature parameters under different gestures based on a regulation and control instruction judgment probability value, and outputting the regulation and control instruction judgment probability value and a standard gesture feature parameter set corresponding to the regulation and control instruction judgment probability value;
the standard attitude characteristic parameter training module further comprises an attitude characteristic parameter training model unit and a standard attitude characteristic parameter set generating unit;
the gesture characteristic parameter training model unit is used for acquiring historical gesture information data of a teacher in a classroom, extracting gesture characteristic parameters of the historical gesture information data when the teacher writes a state on a blackboard through a neural network algorithm, and generating a judging probability value F (GS) of any regulating instruction V The lower corresponding set of attitude feature parameters, noted as f (GS) V ＝{U 1 ，U 2 ，...，U w W is an integer, where U 1 ，U 2 ，...，U w Any element represents a gesture feature parameter, f (GS) V ∈{f(GS) 1 ，f(GS) 2 ，...，f(GS) L -wherein f (GS) 1 ，f(GS) 2 ，...，f(GS) L F (GS) 1 ，F(GS) 2 ，...，F(GS) L Correspondingly generated gesture feature parameter sets; constructing a posture characteristic parameter training model, training the posture characteristic parameters under different postures, and calculating any regulation and control instruction judgment probability value F (GS) V Conditional probability P [ f (GS) V |G]＝P[G|f(GS) V ]*P[f(GS) V ]P (G), and
wherein G represents the presence of a set of gesture feature parameters f (GS) V A set of attitude characteristic parameters of any element, and +.>
f(GS) 2 ，...，f(GS) L }-{f(GS) V }，P[G|f(GS) V ]A conditional probability representing G;
a standard attitude feature parameter set generating unit for respectively training and generating an attitude feature parameter set f (GS) V Corresponding optimal attitude characteristic parameters, presetting an attitude characteristic parameter training threshold, and when P [ f (GS) V |G]When the training threshold value of the gesture characteristic parameter is larger than or equal to the training threshold value, the training is completed; integrate training results, and F (GS) V A set of data corresponding to the training is denoted as a standard posture feature parameter set, denoted as F (GS) V ＝{R 1 ，R 2 ，...，R w (wherein R is) 1 ，R 2 ，...，R w Respectively representing a standard attitude characteristic parameter;
the intelligent regulation and control module is used for collecting light intensity data in the current teaching room in real time and judging whether to start the integrated management model of the electrified teaching equipment based on the light intensity data in the current teaching room; if the integrated management model of the electrified education equipment is started, further starting gesture monitoring of a current teacher, acquiring gesture information data of the current teacher, calculating similarity of gesture feature parameters of the current teacher based on a standard gesture feature parameter set, and outputting a standard gesture feature parameter set corresponding to the maximum similarity;
according to the standard attitude characteristic parameter set corresponding to the maximum output similarity, intelligent regulation and control are carried out on the electrified education equipment;
the intelligent regulation and control module further comprises a similarity calculation unit and an intelligent regulation and control unit;
the similarity calculation unit is used for collecting the light intensity data in the current teaching room in real time, recording the light intensity data as Q, starting the integrated management model of the electrified education equipment if Q is E GL U GA, and not starting the integrated management model of the electrified education equipment if Q is E GL U GA; if the integrated management model of the electrified education equipment is started, further starting gesture monitoring of the current teacher, acquiring gesture information data of the current teacher, and extracting gesture characteristic parameters of the current teacher; the similarity of the gesture characteristic parameters of the current teacher is calculated, and a specific calculation formula is as follows:
wherein H is a Representing the gesture feature parameters of any current teacher, wherein C is the total number of the gesture feature parameters of the current teacher, and a is {1, 2.. The main, C }, h b ∈{U 1 ，U 2 ，...，U w }；
The intelligent regulation and control unit is used for outputting a standard attitude characteristic parameter set corresponding to the maximum similarity, acquiring a regulation and control instruction judgment probability value corresponding to the standard attitude characteristic parameter set corresponding to the maximum similarity, and controlling the light switch to be on if the regulation and control instruction judgment probability value is greater than or equal to 1, or else, controlling the light switch to be off.
Referring to fig. 2, in the second embodiment: the integrated management method of the electrified education equipment based on the Internet comprises the following steps:
acquiring light intensity historical data within a preset time range to generate a light intensity historical data set which is marked as X= { X 1 ，X 2 ，...，X n N is an integer, the expected value and standard deviation of the light intensity data in the teaching chamber are calculated according to the light intensity history data, the expected value is recorded as mu, and the standard deviation is recorded as sigma; according to the expected value and standard deviation of the light intensity historical data, a clustering model of the light intensity historical data is established, and a specific calculation formula is as follows:
wherein X is i ∈X，f(X i ) X represents i Cluster values of (2);
preset the light intensity data threshold, if f (X i ) If the light intensity data threshold is greater than or equal to the light intensity data threshold, f (X i ) Corresponding light intensity data X i Classifying the data into a brightness control model data set, and recording the brightness control model data set as GL; if f (X) i ) Less than the intensity data threshold, f (X i ) Corresponding light intensity data X i Classifying the data into a light and dark modulation model data set, and marking the light and dark modulation model data set as GA;
acquiring historical light intensity data in a corresponding teaching room when the electrified education equipment integrated management model needs to be started at any time, and generating a starting model data set GS= { Y 1 ，Y 2 ，...，Y m M is an integer, and a regulation instruction judgment probability value is calculated according to the following specific calculation formula:
wherein F is 1 (GS) represents a first evaluation value of the regulation instruction, F 2 (GS) represents a second evaluation value of the control instruction, Y j ∈GS；
Calculating a regulating instruction judging probability value F (GS) =F according to a first evaluating value of a regulating instruction and a second evaluating value of the regulating instruction corresponding to the starting of the electrified education equipment integrated management model 1 (GS)/F 2 (GS)；
Calculating a regulating instruction judging probability value corresponding to each time the electrified education equipment integrated management model is required to be started, and generating a regulating instruction judging probability value set { F (GS) 1 ，F(GS) 2 ，...，F(GS) L -L is an integer;
acquiring historical posture information data of a teacher in a classroom, extracting posture characteristic parameters of the historical posture information data when the teacher writes a state on a blackboard through a neural network algorithm, and generating a judging probability value F (GS) of any regulating instruction V The lower corresponding set of attitude feature parameters, noted as f (GS) V ＝{U 1 ，U 2 ，...，U w W is an integer, where U 1 ，U 2 ，...，U w Any element represents a gesture feature parameter, f (GS) V ∈{f(GS) 1 ，f(GS) 2 ，...，f(GS) L -wherein f (GS) 1 ，f(GS) 2 ，...，f(GS) L Respectively F%GS) 1 ，F(GS) 2 ，...，F(GS) L Correspondingly generated gesture feature parameter sets;
constructing a posture characteristic parameter training model, training the posture characteristic parameters under different postures, and calculating any regulation and control instruction judgment probability value F (GS) V Conditional probability P [ f (GS) V |G]＝P[G|f(GS) V ]*P[f(GS) V ]P (G), and
wherein G represents the presence of a set of gesture feature parameters f (GS) V A set of attitude characteristic parameters of any element, and +.>
f(GS) 2 ，...，f(GS) L }-{f(GS) V }，P[G|f(GS) V ]A conditional probability representing G;
respectively training and generating a gesture characteristic parameter set f (GS) V Corresponding optimal attitude characteristic parameters, presetting an attitude characteristic parameter training threshold, and when P [ f (GS) V |G]When the training threshold value of the gesture characteristic parameter is larger than or equal to the training threshold value, the training is completed; integrate training results, and F (GS) V A set of data corresponding to the training is denoted as a standard posture feature parameter set, denoted as F (GS) V ＝{R 1 ，
R 2 ，...，R w (wherein R is) 1 ，R 2 ，...，R w Respectively representing a standard attitude characteristic parameter;
acquiring light intensity data in a current teaching room in real time, recording the light intensity data as Q, and starting an electrified education equipment integrated management model if Q is smaller than GL U GA, or not starting the electrified education equipment integrated management model;
if the integrated management model of the electrified education equipment is started, further starting gesture monitoring of the current teacher, acquiring gesture information data of the current teacher, and extracting gesture characteristic parameters of the current teacher; the similarity of the gesture characteristic parameters of the current teacher is calculated, and a specific calculation formula is as follows:
wherein H is a Representing the gesture feature parameters of any current teacher, wherein C is the total number of the gesture feature parameters of the current teacher, and a is {1, 2.. The main, C }, h b ∈{U 1 ，U 2 ，...，U w }；
Outputting a standard attitude characteristic parameter set corresponding to the maximum similarity, and acquiring a regulation and control instruction judging probability value corresponding to the standard attitude characteristic parameter set corresponding to the maximum similarity, wherein if the regulation and control instruction judging probability value is greater than or equal to 1, the light switch is controlled to be turned on, otherwise, the light switch is controlled to be turned off; for example, light intensity data of a current classroom is collected in real time, the current light intensity data belongs to GL U GA, an integrated management model of the electrified education equipment is started, characteristic parameters of current gesture data of a teacher are collected, a standard gesture characteristic parameter set corresponding to the current gesture data characteristic parameter with the maximum similarity is output, a regulating instruction judgment probability value corresponding to the standard gesture characteristic parameter set is taken to be 2.8, and at the moment, the lighting switch is judged to be controlled to be turned on.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention and is not intended to limit the present invention, but although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the technical solutions described in the foregoing embodiments, or equivalents may be substituted for some of the technical features thereof. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An integrated management method of electrified education equipment based on the Internet is characterized by comprising the following steps:
step S100: the light intensity sensor collects light intensity data in the teaching room, and constructs an integrated management model of the electrified education equipment according to the light intensity data, wherein the integrated management model of the electrified education equipment comprises a light regulation model and a light dark regulation model;
step S200: calculating a regulation and control instruction judgment probability value based on the electrified education equipment integrated management model;
step S300: acquiring historical gesture information data of a teacher in a classroom, extracting gesture feature parameters of the historical gesture information data, generating a gesture feature parameter set, training the gesture feature parameters under different gestures based on a regulation and control instruction judgment probability value, and outputting a regulation and control instruction judgment probability value and a standard gesture feature parameter set corresponding to the regulation and control instruction judgment probability value;
step S400: acquiring light intensity data in a current teaching room in real time, and judging whether to start an electrified teaching equipment integrated management model based on the light intensity data in the current teaching room; if the integrated management model of the electrified education equipment is started, further starting gesture monitoring of a current teacher, acquiring gesture information data of the current teacher, calculating similarity of gesture feature parameters of the current teacher based on a standard gesture feature parameter set, and outputting a standard gesture feature parameter set corresponding to the maximum similarity; and performing intelligent regulation and control on the electrified education equipment according to the standard attitude characteristic parameter set corresponding to the maximum output similarity.
2. The method for integrated management of an audio-visual education programme equipment based on the internet as claimed in claim 1, wherein the specific implementation process of the step S100 comprises:
step S101: acquiring light intensity historical data within a preset time range to generate a light intensity historical data set which is marked as X= { X 1 ，X 2 ，...，X n -n is an integer, calculating an expected value and a standard deviation of the light intensity data in the teaching chamber from the light intensity history data, and recording the expected value as μ and the standard deviation as σ; according to the expected value and standard deviation of the light intensity historical data, a clustering model of the light intensity historical data is established, and a specific calculation formula is as follows:
wherein X is i ∈X，f(X i ) X represents i Cluster values of (2);
step S102: preset the light intensity data threshold, if f (X i ) If the light intensity data threshold is greater than or equal to the light intensity data threshold, f (X i ) Corresponding light intensity data X i Classifying the data into a brightness control model data set, and recording the brightness control model data set as GL; if f (X) i ) Less than the intensity data threshold, f (X i ) Corresponding light intensity data X i Classified as a light-dark modulation model data set, and the light-dark modulation model data set was noted as GA.
3. The method for integrated management of an electrified education equipment based on the internet as claimed in claim 2, wherein the implementation process of the step S200 includes:
step S201: acquiring historical light intensity data in a corresponding teaching room when the electrified education equipment integrated management model needs to be started at any time, and generating a starting model data set GS= { Y 1 ，Y 2 ，...，Y m M is an integer, and a regulation instruction judgment probability value is calculated according to the following specific calculation formula:
wherein F is 1 (GS) represents a first evaluation value of the regulation instruction, F 2 (GS) represents a second evaluation value of the control instruction, Y j ∈GS；
Step S202: calculating a regulating instruction judging probability value F (GS) =F according to a first evaluating value of a regulating instruction and a second evaluating value of the regulating instruction corresponding to the starting of the electrified education equipment integrated management model 1 (GS)/F 2 (GS)；
Step S203: returning to step S201, calculating the relation of each time the electrified education equipment integrated management model needs to be startedThe corresponding regulation command judges the probability value and generates a regulation command judging probability value set { F (GS) 1 ，F(GS) 2 ，...，F(GS) L And L is an integer.
4. The method for integrated management of internet-based audio-visual education equipment according to claim 3, wherein the specific implementation procedure of step S300 comprises:
step S301: acquiring historical posture information data of a teacher in a classroom, extracting posture characteristic parameters of the historical posture information data when the teacher writes a state on a blackboard through a neural network algorithm, and generating a judging probability value F (GS) of any regulating instruction V The lower corresponding set of attitude feature parameters, noted as f (GS) V ＝{U 1 ，U 2 ，...，U w W is an integer, where U 1 ，U 2 ，...，U w Any element represents a gesture feature parameter, f (GS) V ∈{f(GS) 1 ，f(GS) 2 ，...，f(GS) L -wherein f (GS) 1 ，f(GS) 2 ，...，f(GS) L F (GS) 1 ，F(GS) 2 ，...，F(GS) L Correspondingly generated gesture feature parameter sets;
step S302: constructing a posture characteristic parameter training model, training the posture characteristic parameters under different postures, and calculating any regulation and control instruction judgment probability value F (GS) V Conditional probability P [ f (GS) V |G]＝P[G|f(GS) V ]*P[f(GS) V ]P (G), and
wherein G represents the presence of a set of gesture feature parameters f (GS) V A set of attitude characteristic parameters of any element, and +.>
P[G|f(GS) V ]A conditional probability representing G;
step S303: respectively training and generating a gesture characteristic parameter set f (GS) V Corresponding optimal poseState characteristic parameters, preset attitude characteristic parameter training threshold, when P [ f (GS) V |G]When the training threshold value of the gesture characteristic parameter is larger than or equal to the training threshold value, the training is completed; integrate training results, and F (GS) V A set of data corresponding to the training is denoted as a standard posture feature parameter set, denoted as F (GS) V ＝{R 1 ，R 2 ，...，R w (wherein R is) 1 ，R 2 ，...，R w Respectively representing a standard attitude characteristic parameter.
5. The method for integrated management of internet-based audio-visual education equipment according to claim 4, wherein the specific implementation procedure of step S400 comprises:
step S401: acquiring light intensity data in a current teaching room in real time, recording the light intensity data as Q, and starting an electrified education equipment integrated management model if Q is smaller than GL U GA, or not starting the electrified education equipment integrated management model;
step S402: if the integrated management model of the electrified education equipment is started, further starting gesture monitoring of the current teacher, acquiring gesture information data of the current teacher, and extracting gesture characteristic parameters of the current teacher; the similarity of the gesture characteristic parameters of the current teacher is calculated, and a specific calculation formula is as follows:
wherein H is a Representing the gesture feature parameters of any current teacher, wherein C is the total number of the gesture feature parameters of the current teacher, and a is {1, 2.. The main, C }, h b ∈{U 1 ，U 2 ，...，U w }；
Step S403: outputting a standard attitude characteristic parameter set corresponding to the maximum similarity, acquiring a regulation and control instruction judging probability value corresponding to the standard attitude characteristic parameter set corresponding to the maximum similarity, and controlling the light switch to be on if the regulation and control instruction judging probability value is more than or equal to 1, otherwise, controlling the light switch to be off.
6. An integrated management system for an electrified education equipment based on internet, the system comprising: the system comprises an electrified education equipment integrated management model module, a regulation and control instruction judgment probability value calculation module, a standard attitude characteristic parameter training module and an intelligent regulation and control module;
the integrated management model module of the electrified education equipment is used for collecting light intensity data in the electrified education equipment, constructing an integrated management model of the electrified education equipment according to the light intensity data, wherein the integrated management model of the electrified education equipment comprises a light regulation model and a light dark regulation model; wherein, the light intensity data is collected by a light intensity sensor;
the regulation and control instruction judgment probability value calculation module is used for calculating a regulation and control instruction judgment probability value based on the electrified education equipment integrated management model;
the standard gesture feature parameter training module is used for acquiring historical gesture information data of a teacher in a classroom, extracting gesture feature parameters of the historical gesture information data, generating a gesture feature parameter set, training the gesture feature parameters under different gestures based on a regulation and control instruction judging probability value, and outputting the regulation and control instruction judging probability value and a standard gesture feature parameter set corresponding to the regulation and control instruction judging probability value;
the intelligent regulation and control module is used for collecting light intensity data in the current teaching room in real time and judging whether to start the integrated management model of the electrified teaching equipment based on the light intensity data in the current teaching room; if the integrated management model of the electrified education equipment is started, further starting gesture monitoring of a current teacher, acquiring gesture information data of the current teacher, calculating similarity of gesture feature parameters of the current teacher based on a standard gesture feature parameter set, and outputting a standard gesture feature parameter set corresponding to the maximum similarity; and performing intelligent regulation and control on the electrified education equipment according to the standard attitude characteristic parameter set corresponding to the maximum output similarity.
7. The internet-based integrated management system for electrified education equipment according to claim 6, wherein: the integrated management model module of the electrified education equipment further comprises a light intensity historical data clustering model unit and a light intensity data classifying unit;
the light intensity historical data clustering model unit is used for acquiring light intensity historical data within a preset time range to generate a light intensity historical data set which is marked as X= { X 1 ，X 2 ，...，X n -n is an integer, calculating an expected value and a standard deviation of the light intensity data in the teaching chamber from the light intensity history data, and recording the expected value as μ and the standard deviation as σ; according to the expected value and standard deviation of the light intensity historical data, a clustering model of the light intensity historical data is established
Wherein X is i ∈X，f(X i ) X represents i Cluster values of (2);
the light intensity data classifying unit is used for presetting a light intensity data threshold value, if f (X) i ) If the light intensity data threshold is greater than or equal to the light intensity data threshold, f (X i ) Corresponding light intensity data X i Classifying the data into a brightness control model data set, and recording the brightness control model data set as GL; if f (X) i ) Less than the intensity data threshold, f (X i ) Corresponding light intensity data X i Classified as a light-dark modulation model data set, and the light-dark modulation model data set was noted as GA.
8. The internet-based integrated management system for electrified education equipment according to claim 7, wherein: the regulation and control instruction evaluation probability value calculation module further comprises a regulation and control instruction evaluation value calculation unit and a regulation and control instruction evaluation probability value calculation unit;
the regulation and control instruction evaluation value calculation unit is used for acquiring historical light intensity data in the corresponding teaching room when the electrified education equipment integrated management model needs to be started at any time and generating a starting model data set GS= { Y 1 ，Y 2 ，...，Y m M is an integer, and a regulation instruction judgment probability value is calculated according to the following specific calculation formula:
wherein F is 1 (GS) represents a first evaluation value of the regulation instruction, F 2 (GS) represents a second evaluation value of the control instruction, Y j ∈GS；
The regulation instruction judgment probability value calculation unit is used for calculating a regulation instruction judgment probability value F (GS) =F according to a first evaluation value of a corresponding regulation instruction and a second evaluation value of the regulation instruction when the electrified education equipment integrated management model is started as required 1 (GS)/F 2 (GS); calculating a regulating instruction judging probability value corresponding to each time the electrified education equipment integrated management model is required to be started, and generating a regulating instruction judging probability value set { F (GS) 1 ，F(GS) 2 ，...，F(GS) L And L is an integer.
9. The internet-based integrated management system for electrified education equipment according to claim 8, wherein: the standard attitude characteristic parameter training module further comprises an attitude characteristic parameter training model unit and a standard attitude characteristic parameter set generating unit;
the gesture characteristic parameter training model unit is used for acquiring historical gesture information data of a teacher in a classroom, extracting gesture characteristic parameters of the historical gesture information data when the teacher writes on a blackboard through a neural network algorithm, and generating a judging probability value F (GS) of any regulating instruction V The lower corresponding set of attitude feature parameters, noted as f (GS) V ＝{U 1 ，U 2 ，...，U w W is an integer, where U 1 ，U 2 ，...，U w Any element represents a gesture feature parameter, f (GS) V ∈{f(GS) 1 ，f(GS) 2 ，...，f(GS) L -wherein f (GS) 1 ，f(GS) 2 ，...，f(GS) L F (GS) 1 ，F(GS) 2 ，...，F(GS) L Correspondingly generated gesture feature parameter sets; constructing a posture characteristic parameter training model, and training posture characteristic parameters under different posturesCalculating any regulating instruction judging probability value F (GS) V Conditional probability P [ f (GS) V |G]＝P[G|f(GS) V ]*P[f(GS) V ]P (G), and
wherein G represents the presence of a set of gesture feature parameters f (GS) V A set of attitude characteristic parameters of any element, and +.>
P[G|f(GS) V ]A conditional probability representing G;
the standard attitude feature parameter set generating unit is used for respectively training and generating an attitude feature parameter set f (GS) V Corresponding optimal attitude characteristic parameters, presetting an attitude characteristic parameter training threshold, and when P [ f (GS) V |G]When the training threshold value of the gesture characteristic parameter is larger than or equal to the training threshold value, the training is completed; integrate training results, and F (GS) V A set of data corresponding to the training is denoted as a standard posture feature parameter set, denoted as F (GS) V ＝{R 1 ，R 2 ，...，R w (wherein R is) 1 ，R 2 ，...，R w Respectively representing a standard attitude characteristic parameter.
10. The internet-based electrified education equipment integrated management system according to claim 9, wherein: the intelligent regulation and control module further comprises a similarity calculation unit and an intelligent regulation and control unit;
the similarity calculation unit is used for collecting light intensity data in the current teaching room in real time, marking the light intensity data as Q, starting an integrated management model of the electrified education equipment if Q is E GLU GA, and not starting the integrated management model of the electrified education equipment if Q is E GLU GA; if the integrated management model of the electrified education equipment is started, further starting gesture monitoring of the current teacher, acquiring gesture information data of the current teacher, and extracting gesture characteristic parameters of the current teacher; the similarity of the gesture characteristic parameters of the current teacher is calculated, and a specific calculation formula is as follows:
wherein H is a Representing the gesture feature parameters of any current teacher, wherein C is the total number of the gesture feature parameters of the current teacher, and a is {1, 2.. The main, C }, h b ∈{U 1 ，U 2 ，...，U w }；
The intelligent regulation and control unit is used for outputting a standard attitude characteristic parameter set corresponding to the maximum similarity, acquiring a regulation and control instruction judgment probability value corresponding to the standard attitude characteristic parameter set corresponding to the maximum similarity, and controlling the light switch to be on if the regulation and control instruction judgment probability value is greater than or equal to 1, or else, controlling the light switch to be off.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310130332.0A CN116193678A (en) | 2023-02-17 | 2023-02-17 | Internet-based integrated management system and method for electrified education equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310130332.0A CN116193678A (en) | 2023-02-17 | 2023-02-17 | Internet-based integrated management system and method for electrified education equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116193678A true CN116193678A (en) | 2023-05-30 |
Family
ID=86436146
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310130332.0A Pending CN116193678A (en) | 2023-02-17 | 2023-02-17 | Internet-based integrated management system and method for electrified education equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116193678A (en) |
-
2023
- 2023-02-17 CN CN202310130332.0A patent/CN116193678A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108537705A (en) | A kind of digital teaching material management system based on wisdom Teaching System | |
| CN111796752B (en) | Interactive teaching system based on PC | |
| CN111027486A (en) | Auxiliary analysis and evaluation system and method for big data of teaching effect of primary and secondary school classroom | |
| CN111461937A (en) | Automatic tracking method and system for autism intervention teaching course | |
| CN111144321B (en) | Concentration detection method, device, equipment and storage medium | |
| CN110275987A (en) | Intelligent tutoring consultant generation method, system, equipment and storage medium | |
| CN111610862A (en) | Online teaching mode switching method based on eye movement signal | |
| CN111523646B (en) | Intelligent perception network and management method for remote education learning center based on Internet of things | |
| CN112131483A (en) | Personalized course content recommendation method for online vocational education | |
| Saxena et al. | Structure, Objectives, and Operational Framework for Ethical Integration of Artificial Intelligence in Educational | |
| CN114219224A (en) | Teaching quality detection method and system for intelligent classroom | |
| CN111858968A (en) | Artificial intelligence learning device and system with pen-holding sitting posture correction function | |
| CN111399647A (en) | Artificial intelligence self-adaptation interactive teaching system | |
| CN111027941A (en) | Teaching experiment platform based on STM32 singlechip | |
| US20230334862A1 (en) | Construction method and system of descriptive model of classroom teaching behavior events | |
| CN106803890A (en) | A kind of Autoconducting method and device | |
| CN113282840A (en) | Comprehensive training acquisition management platform | |
| CN116193678A (en) | Internet-based integrated management system and method for electrified education equipment | |
| CN115482397A (en) | Action scoring system | |
| CN111857338A (en) | Method suitable for using mobile application on large screen | |
| CN112861809A (en) | Classroom new line detection system based on multi-target video analysis and working method thereof | |
| CN112714174A (en) | Intelligent education data acquisition system based on wireless communication | |
| CN116800919A (en) | Intelligent touch screen interaction teaching equipment | |
| CN112634096A (en) | Classroom management method and system based on intelligent blackboard | |
| CN111667128B (en) | Teaching quality assessment method, device and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |