CN114997739B - Electronic student identity card information management system and method based on Internet of things - Google Patents
Electronic student identity card information management system and method based on Internet of things Download PDFInfo
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Abstract
The invention discloses an electronic student identity card information management system and method based on the Internet of things, and belongs to the technical field of electronic student identity card information management. The system comprises an electronic student identity card information acquisition module, a three-dimensional campus construction module, a trajectory analysis processing module, a comprehensive judgment module and a feedback module; the output end of the electronic student identity card information acquisition module is connected with the input end of the three-dimensional campus building module; the output end of the three-dimensional campus building module is connected with the input end of the track analysis processing module; the output end of the track analysis processing module is connected with the input end of the comprehensive judgment module; the output end of the comprehensive judgment module is connected with the input end of the feedback module. The invention can timely master the track data of students in the campus, and can simultaneously acquire the cross track data of the students and other students, comprehensively judge the situation that the students are integrated into a group, and further master the psychological states of the students.
Description
Technical Field
The invention relates to the technical field of electronic student identity card information management, in particular to an electronic student identity card information management system and method based on the Internet of things.
Background
The electronic student identity card is a new technology, is a product of the internet of things technology under a new generation of information technology, can be bound with mobile phones, telecom phones, unicom phones and other mobile phones, and parents can receive jobs and school notifications arranged by teachers in a short message mode. Meanwhile, an infrared scanning device is arranged at the entrance of a school, so that students can hang electronic student certificates in front of their breasts and can automatically scan and verify the electronic student certificates when the electronic student certificates pass the electronic student certificates, and parents can receive a short message in time, and thus, the parents can know the electronic student certificates at a glance when the electronic student certificates arrive at the school and are separated from the school. Meanwhile, the electronic student card has a track positioning function, so that the position information of students can be checked in time for schools of primary and middle schools, and whether the students are dangerous or escape is judged.
Meanwhile, in campus education, psychological conditions frequently appear, as teachers, it is difficult to know the psychological states of each student and the conditions of integration in time, and it is well known that juvenile education often affects the life of people, so that psychological education is extremely important, and at present, no system can analyze the psychological states of students by using electronic student cards.
Disclosure of Invention
The invention aims to provide an electronic student identity card information management system and method based on the Internet of things, and aims to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
an electronic student identity card information management method based on the Internet of things comprises the following steps:
s1, acquiring the action track of any student A through an electronic student card, and acquiring the class schedule information data of the student A at the same time;
s2, acquiring campus building data, constructing a three-dimensional campus model, and realizing coordinate positioning on students in a campus;
s3, acquiring activity class data of the student A, wherein the activity class comprises a physical education class and a social practice class, and constructing activity class track data of the student A; acquiring a non-lesson-going track route of the student A, and generating non-lesson-going track data of the student A;
s4, establishing time periods, analyzing the track change differences of the students A in different time periods, establishing a judgment model, judging the psychological states of the students A, setting a threshold value, and feeding back to a teacher port to remind a teacher to pay attention if the psychological states of the students A are lower than the threshold value.
According to the technical scheme, the building of the three-dimensional campus model comprises the following steps:
acquiring a vertical image and an oblique image of a campus by using unmanned aerial equipment;
the vertical image requires that the inclination angle of the photo is not more than 2 degrees; the oblique image requires that the overlapping degree of photos is not less than 65 percent, and the difference of adjacent visual angles is within 15 degrees;
respectively processing the vertical image and the oblique image;
constructing a three-dimensional model based on the oblique image, and constructing a digital positive image map based on the vertical image;
constructing a three-dimensional campus model by combining a three-dimensional model and a digital orthographic projection through a WebGIS system;
and accessing the electronic student identity card information data in the campus into the three-dimensional campus model, and accurately positioning each student wearing the electronic student identity card to generate coordinate data.
According to the above technical solution, the analyzing the differences of the track changes of the student a in different time periods includes:
acquiring activity course track data of student A, constructing a time period T, and generating a time region set
Said
Respectively, a time period point location, exists
；
Respectively acquiring activity class track data and non-class time track data of the student A at each time period point in the time region set;
acquiring student data which is in cross coincidence with the student A in each activity class and student data which is in cross coincidence with the student A in the non-class time trajectory data of the student A; recording the two data as a data sample set, and setting the same group data characteristics of each data in the data sample set;
clustering the data by using K-means, and taking a K value:
wherein, the first and the second end of the pipe are connected with each other,
is the ith cluster; p is
The sample point of (1);
is that
Center of mass of (i.e.
Mean of all samples in (1); SSE is the clustering error of all samples and represents the good or bad clustering effect;
taking SSE as a y-axis value and a K value as an X-axis value, constructing a coordinate system, generating a curve graph, and selecting the corresponding K value with the highest curve curvature as output;
as the clustering number K increases, the sample division becomes finer, the aggregation degree of each cluster gradually increases, and then the sum of squared errors SSE naturally becomes smaller.
When K is smaller than the real clustering number, the descending amplitude of SSE is large because the aggregation degree of each cluster is greatly increased by increasing K, and when K reaches the real clustering number, the returning of the aggregation degree obtained by increasing K is rapidly reduced, so the descending amplitude of SSE is rapidly reduced and then tends to be gentle along with the continuous increase of the K value, and the K value corresponding to the point with the highest curvature is the real clustering number of the data.
Initializing K cluster centers:
wherein the content of the first and second substances,
representing initialized K cluster centers;
for each piece of data j in the data sample set;
v (j) represents the average distance of data j from other data in the group;
t (j) represents the average distance of data j from the data of the neighboring group;
constructing a contour coefficient:
wherein the content of the first and second substances,
a contour coefficient representing data j;
if present, is
Then, then
Indicating that data j is at the boundary of two clusters; if it is
Then, then
If yes, the data j is indicated to be classified into other categories; if present, is
Then, then
If so, indicating that the clustering of the data j is reasonable;
dividing data in the data sample into K clusters through the contour coefficient;
constructing an average contour coefficient, and generating a nearest cluster:
wherein the content of the first and second substances,
is a cluster
The sample point of (1);
represents any sample point; e represents a cluster
The number of samples in (1);
represent
Reach cluster
Distance of (2) means
Reach cluster
Average distance of all samples in;
distributing the data samples in the K clusters to a cluster with the closest cluster center by utilizing iteration, setting the iteration frequency as Z, and acquiring a new cluster center as a final clustering result when the iteration reaches Z;
and obtaining the clusters stored in the maximum number of samples according to the clustering result and outputting the clusters to be used as the cross track student data of the student A.
According to the above technical solution, the judgment model includes:
acquiring cross track student data of a student A, labeling activity class data and non-class time data, and constructing a judgment model:
wherein the content of the first and second substances,
a collective integration probability index representing students;
representing the student serial number of the cross track with the student A in the student data of the cross track with the student A;
representing the total number of students in the cross track of the student A in the cross track student data of the student A;
representing activity class impact coefficients;
representing a non-class time impact coefficient;
representing the number of students with the crossed track of the student A and the number of the students with the crossed track of the student A in the crossed track student data of the student A;
representing the number of students in the cross track with the student A in the non-lesson time in the cross track student data of the student A;
the students in the cross track of the activity class and the student A in the cross track student data representing the student A are serial numbers
The number of student crossings;
the students with the crossed track of the student A are serial numbers in the crossed track student data representing the student A in the non-class time
The number of student crossings;
setting a probability index threshold, if any
If the probability index is lower than the threshold value of the probability index, the fact that the communication frequency between the student A and the students is low, certain psychological problems possibly exist or the students cannot be merged into a group in time is judged, and the psychological problems are fed back to a teacher port to remind the teacher of paying attention.
In the technical scheme, if a large number of different persons are gathered in one cluster in the cross data of the students, the students are attached with the same data characteristics, namely that the student A is related to a plurality of students and has strong collective integration capacity; the two influence coefficients are set to balance track crossing caused by association conditions specified by the teacher possibly occurring in the course of the activity, for example, two persons are divided into the same group by the teacher to perform the activity and the like;
an electronic student identity card information management system based on the Internet of things comprises an electronic student identity card information acquisition module, a three-dimensional campus construction module, a trajectory analysis processing module, a comprehensive judgment module and a feedback module;
the electronic student identity card information acquisition module is used for acquiring the action track of any student through the electronic student identity card and acquiring the class schedule information data of the student; the three-dimensional campus construction module is used for acquiring campus building data, constructing a three-dimensional campus model and realizing coordinate positioning on students in a campus; the track analysis processing module is used for capturing activity class data of students and non-class time track data of the students, constructing time periods and analyzing the track change differences of the students in different time periods; the comprehensive judgment module constructs a judgment model and judges the psychological state of the student; the feedback module is used for setting a threshold value, and if the threshold value is lower than the threshold value, the feedback module feeds back the threshold value to a teacher port to remind a teacher of paying attention;
the output end of the electronic student identity card information acquisition module is connected with the input end of the three-dimensional campus building module; the output end of the three-dimensional campus building module is connected with the input end of the track analysis processing module; the output end of the track analysis processing module is connected with the input end of the comprehensive judgment module; the output end of the comprehensive judgment module is connected with the input end of the feedback module.
According to the technical scheme, the electronic student identity card information acquisition module comprises a motion track acquisition unit and a class schedule information acquisition unit;
the action track acquisition unit is used for acquiring action track data of students in the campus according to the electronic student certificates; the school timetable information acquisition unit is used for acquiring activity course information data of students and acquiring non-school time data of the students;
the output end of the action track acquisition unit is connected with the input end of the three-dimensional campus building module; the output end of the school timetable information acquisition unit is connected with the input end of the three-dimensional campus building module.
According to the technical scheme, the three-dimensional campus building module comprises an image processing unit and a coordinate positioning unit;
the image processing unit acquires a vertical image and an oblique image of a campus by using unmanned aerial equipment, respectively performs image processing on the vertical image and the oblique image, and constructs a three-dimensional campus model by combining a three-dimensional model and a digital orthophoto map through a WebGIS system; the coordinate positioning unit is used for accessing electronic student identity card information data in a campus into the three-dimensional campus model, accurately positioning each student wearing the electronic student identity card, and generating coordinate data;
the output end of the image processing unit is connected with the input end of the coordinate positioning unit; and the output end of the coordinate positioning unit is connected with the input end of the track analysis processing module.
According to the technical scheme, the track analysis processing module comprises a track crossing unit and a track analysis unit;
the track crossing unit is used for acquiring activity course data of students and non-class time track data of the students and judging the students having tracks crossed with the students; the track analysis unit is used for constructing a model and carrying out clustering analysis;
the output end of the track crossing unit is connected with the input end of the track analysis unit; and the output end of the track analysis unit is connected with the input end of the comprehensive judgment module.
According to the technical scheme, the comprehensive judgment module comprises a time period construction unit and a comprehensive judgment unit;
the time period construction unit is used for constructing time periods and analyzing the variation of the track changes of students in different time periods; the comprehensive judgment unit is used for constructing a judgment model, judging the psychological states of the students and generating a collective integration probability index of the students;
the output end of the time period construction unit is connected with the input end of the comprehensive judgment unit; and the output end of the comprehensive judgment unit is connected with the input end of the feedback module.
According to the technical scheme, the feedback module comprises a threshold setting unit and a feedback unit;
the threshold setting unit is used for setting a threshold and analyzing the collective integration probability index of the students; the feedback unit is used for feeding back the collective integration probability index of the students to a teacher port to remind the teacher of paying attention when the collective integration probability index of the students is lower than a threshold value;
the output end of the threshold setting unit is connected with the input end of the feedback unit.
Compared with the prior art, the invention has the following beneficial effects:
the invention utilizes the electronic student identity card information acquisition module to acquire the action track of any student through the electronic student identity card and simultaneously acquire the class schedule information data of the student; acquiring campus building data by using a three-dimensional campus construction module, constructing a three-dimensional campus model, and realizing coordinate positioning on students in a campus; capturing activity class data of students and non-class time track data of the students by using a track analysis processing module, constructing time periods, and analyzing the track change differences of the students in different time periods; a comprehensive judgment module is utilized to construct a judgment model, and the psychological state of the student is judged; setting a threshold value by using a feedback module, and if the threshold value is lower than the threshold value, feeding back the threshold value to a teacher port to remind a teacher of paying attention; the invention can timely acquire the track data of students in active class and non-class, and can realize the analysis of the cross track of the students and judge the interaction degree between the students on the premise of ensuring the safety of the students, thereby achieving the purposes of laterally acquiring the condition that the students are integrated into a group, sensing the psychological state of the students, timely feeding back to a teacher port, and leading the students to form good psychological health in the juvenile period by the teacher for gradual guidance.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flow chart of an electronic student identity card information management system and method based on the internet of things.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, in the first embodiment:
in a wisdom campus, constructing a three-dimensional campus model comprises:
acquiring a vertical image and an oblique image of a campus by using unmanned aerial equipment;
the vertical image requires that the inclination angle of the photo is not more than 2 degrees; the oblique image requires that the overlapping degree of photos is not less than 65 percent, and the difference of adjacent visual angles is within 15 degrees;
respectively processing the vertical image and the oblique image;
constructing a three-dimensional model based on the oblique image, and constructing a digital positive image mapping chart based on the vertical image;
constructing a three-dimensional campus model by combining a three-dimensional model and a digital orthographic projection through a WebGIS system;
and accessing the electronic student identity card information data in the campus into a three-dimensional campus model, and accurately positioning each student wearing the electronic student identity card to generate coordinate data.
Acquiring the action track of any student A through an electronic student certificate, and acquiring the class schedule information data of the student A;
acquiring activity course data of a student A, wherein the activity course comprises a physical education course and a social practice course, and constructing activity course track data of the student A; acquiring a non-lesson-going track route of the student A, and generating non-lesson-going track data of the student A;
constructing time periods, and analyzing the track change differences of the student A in different time periods;
the analyzing the differences of the track changes of the student A in different time periods comprises the following steps:
acquiring activity course track data of student A, constructing a time period T, and generating a time region set
Said
Respectively, a time period point location, exists
；
For example, one day for a time period T;
respectively acquiring activity class trajectory data and non-class time trajectory data of the student A of each time period point in the time region set;
acquiring student data which is in cross coincidence with the student A in each activity class and student data which is in cross coincidence with the student A in the non-class time trajectory data of the student A; recording the two data as a data sample set, and setting the same group data characteristics of each data in the data sample set;
clustering the data by using K-means, and taking a K value:
wherein the content of the first and second substances,
is the ith cluster; p is
The sample point of (1);
is that
Of center of mass, i.e.
Mean of all samples in (1); SSE is the clustering error of all samples and represents the good or bad clustering effect;
taking SSE as a y-axis value and a K value as an X-axis value, constructing a coordinate system, generating a curve graph, and selecting the corresponding K value with the highest curve curvature as output;
initializing K clustering centers:
wherein the content of the first and second substances,
representing initialized K clustering centers;
for each piece of data j in the data sample set;
v (j) represents the average distance of data j from other data in the group;
t (j) represents the average distance of data j from the data of the neighboring group;
constructing a contour coefficient:
wherein the content of the first and second substances,
a contour coefficient representing data j;
if present, is
Then, then
Indicating that data j is at the boundary of two clusters; if it is
Then, then
If yes, indicating that the data j should be classified into other categories; if present, is
Then, then
If so, indicating that the clustering of the data j is reasonable;
dividing data in the data sample into K clusters through the contour coefficient;
constructing an average contour coefficient, and generating a nearest cluster:
wherein, the first and the second end of the pipe are connected with each other,
is a cluster
The sample point of (1);
represents any sample point; e represents a cluster
The number of samples in (1);
represents
Reach cluster
Distance of (2) means
Reach cluster
Average distance of all samples in;
distributing data samples in the K clusters to a cluster with the closest cluster center by using iteration, setting the iteration frequency as Z, and acquiring a new cluster center as a final clustering result when the iteration reaches Z;
and obtaining the clusters stored in the maximum number of samples according to the clustering result and outputting the clusters to be used as the cross track student data of the student A.
E.g. there are 10 groups of samples in the center of the output cluster, i.e.
Wherein the content of the first and second substances,
representing activity class data;
represents non-class time data, and B, C represents students;
and judging the psychological state of the student A, setting a threshold value, and if the psychological state of the student A is lower than the threshold value, feeding back the psychological state to a teacher port to remind the teacher of paying attention.
The judgment model comprises:
acquiring cross track student data of a student A, labeling activity class data and non-class time data, and constructing a judgment model:
wherein the content of the first and second substances,
a collective integration probability index representing students;
representing the student serial number of the cross track with the student A in the student data of the cross track with the student A;
representing the total number of students in the cross track of the student A in the cross track student data of the student A;
representing activity class impact coefficients;
representing a non-class time impact coefficient;
representing the number of students with the crossed track of the student A and the number of the students with the crossed track of the student A in the crossed track student data of the student A;
representing the number of students in the cross track with the student A in the non-lesson time in the cross track student data of the student A;
the students in the cross track of the activity class and the student A in the cross track student data representing the student A are serial numbers
The number of student crossings;
the students with the crossed track of the student A are serial numbers in the crossed track student data representing the student A in the non-class time
The number of student crossings;
based on the above data, it can be seen that
When the value of B is not less than the predetermined value,
=5；
=6；
=2；
=4；
setting a probability index threshold, if any
If the probability index is lower than the threshold value of the probability index, the fact that the communication frequency between the student A and the students is low, certain psychological problems possibly exist or the students cannot be merged into a group in time is judged, and the psychological problems are fed back to a teacher port to remind the teacher of paying attention.
In the second embodiment, an electronic student identity card information management system based on the internet of things is provided, and the system comprises an electronic student identity card information acquisition module, a three-dimensional campus construction module, a trajectory analysis processing module, a comprehensive judgment module and a feedback module;
the electronic student identity card information acquisition module is used for acquiring the action track of any student through the electronic student identity card and acquiring the class schedule information data of the student; the three-dimensional campus construction module is used for acquiring campus building data, constructing a three-dimensional campus model and realizing coordinate positioning on students in a campus; the track analysis processing module is used for capturing activity class data of students and non-class time track data of the students, constructing time periods and analyzing the track change differences of the students in different time periods; the comprehensive judgment module constructs a judgment model and judges the psychological state of the student; the feedback module is used for setting a threshold value, and if the threshold value is lower than the threshold value, the feedback module feeds back the threshold value to a teacher port to remind a teacher of paying attention;
the output end of the electronic student identity card information acquisition module is connected with the input end of the three-dimensional campus building module; the output end of the three-dimensional campus building module is connected with the input end of the track analysis processing module; the output end of the track analysis processing module is connected with the input end of the comprehensive judgment module; the output end of the comprehensive judgment module is connected with the input end of the feedback module.
The electronic student identity card information acquisition module comprises a motion track acquisition unit and a class schedule information acquisition unit;
the action track acquisition unit is used for acquiring action track data of students in the campus according to the electronic student certificates; the school timetable information acquisition unit is used for acquiring activity course information data of students and acquiring non-school time data of the students;
the output end of the action track acquisition unit is connected with the input end of the three-dimensional campus building module; the output end of the school timetable information acquisition unit is connected with the input end of the three-dimensional campus building module.
The three-dimensional campus building module comprises an image processing unit and a coordinate positioning unit;
the image processing unit acquires a vertical image and an oblique image of a campus by using unmanned flight equipment, respectively performs image processing on the vertical image and the oblique image, and constructs a three-dimensional campus model by combining a three-dimensional model and a digital orthographic image through a WebGIS (web geographic information system); the coordinate positioning unit is used for accessing electronic student identity card information data in a campus into the three-dimensional campus model, accurately positioning each student wearing the electronic student identity card, and generating coordinate data;
the output end of the image processing unit is connected with the input end of the coordinate positioning unit; and the output end of the coordinate positioning unit is connected with the input end of the track analysis processing module.
The track analysis processing module comprises a track crossing unit and a track analysis unit;
the track crossing unit is used for acquiring activity class data of students and non-class time track data of the students and judging the students crossed with the tracks of the students; the track analysis unit is used for constructing a model and carrying out clustering analysis;
the output end of the track crossing unit is connected with the input end of the track analysis unit; and the output end of the track analysis unit is connected with the input end of the comprehensive judgment module.
The comprehensive judgment module comprises a time period construction unit and a comprehensive judgment unit;
the time period construction unit is used for constructing time periods and analyzing the variation of the track changes of students in different time periods; the comprehensive judgment unit is used for constructing a judgment model, judging the psychological states of the students and generating a collective integration probability index of the students;
the output end of the time period construction unit is connected with the input end of the comprehensive judgment unit; and the output end of the comprehensive judgment unit is connected with the input end of the feedback module.
The feedback module comprises a threshold setting unit and a feedback unit;
the threshold setting unit is used for setting a threshold and analyzing the collective integration probability index of the students; the feedback unit is used for feeding back the collective integration probability index of the students to a teacher port to remind the teacher of paying attention when the collective integration probability index of the students is lower than a threshold value;
the output end of the threshold setting unit is connected with the input end of the feedback unit.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: 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 embodiments described above, or equivalents may be substituted for elements thereof. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. An electronic student identity card information management method based on the Internet of things is characterized by comprising the following steps: the method comprises the following steps:
s1, a school issues an electronic student certificate for a student, a student guardian authorizes the school to perform positioning record analysis on the student, and the school acquires the action track of any student A through the electronic student certificate and acquires the class schedule information data of the student A at the same time;
s2, acquiring campus building data, constructing a three-dimensional campus model, and realizing coordinate positioning on students in the campus;
s3, acquiring activity course data of the student A, wherein the activity course comprises a physical education course and a social practice course, and constructing activity course track data of the student A; acquiring a non-lesson-going trajectory route of the student A, and generating non-lesson-going trajectory data of the student A;
s4, establishing time periods, analyzing the track change differences of the students A in different time periods, establishing a judgment model, judging the psychological states of the students A, setting a threshold value, and if the psychological states are lower than the threshold value, feeding back the psychological states to a teacher port to remind a teacher of paying attention;
the analyzing the differences of the track changes of the student A in different time periods comprises the following steps:
acquiring activity course track data of student A, constructing time period T, and generating time region set
Said
Respectively, a time period point location, exists
；
Respectively acquiring activity class track data and non-class time track data of the student A at each time period point in the time region set;
acquiring student data which is in cross coincidence with the student A in each activity class and student data which is in cross coincidence with the student A in the non-class-time trajectory data of the student A; recording the two data as a data sample set, and setting the same group data characteristics of each data in the data sample set;
clustering the data by using K-means, and taking a K value:
wherein the content of the first and second substances,
is the ith cluster; p is
The sample point of (1);
is that
Of center of mass, i.e.
Mean of all samples in (1); SSE is the clustering error of all samples and represents the good or bad clustering effect;
taking SSE as a y-axis value and a K value as an X-axis value, constructing a coordinate system, generating a curve graph, and selecting the corresponding K value with the highest curve curvature as output;
initializing K clustering centers:
wherein the content of the first and second substances,
representing initialized K cluster centers;
for each piece of data j in the data sample set;
v (j) represents the average distance of data j from other data in the group;
t (j) represents the average distance of data j from the data of the neighboring group;
constructing a contour coefficient:
wherein the content of the first and second substances,
a contour coefficient representing data j;
if present, is
Then, then
Indicating that data j is at the boundary of two clusters; if it is
Then, then
If yes, the data j is indicated to be classified into other categories; if present, is
Then, then
If so, indicating that the clustering of the data j is reasonable;
dividing data in the data sample into K clusters through the contour coefficient;
constructing an average contour coefficient, and generating a nearest cluster:
wherein, the first and the second end of the pipe are connected with each other,
is a cluster
The sample point of (1);
represents any sample point; e represents a cluster
The number of samples in (1);
represents
Reach cluster
Distance of (2) means
Reach cluster
Average distance of all samples in;
distributing the data samples in the K clusters to a cluster with the closest cluster center by utilizing iteration, setting the iteration frequency as Z, and acquiring a new cluster center as a final clustering result when the iteration reaches Z;
and obtaining the clusters stored in the maximum number of samples according to the clustering result and outputting the clusters to be used as the cross track student data of the student A.
2. The electronic student identity card information management method based on the Internet of things according to claim 1, wherein: the building of the three-dimensional campus model comprises the following steps:
acquiring a vertical image and an inclined image of a campus by using unmanned flight equipment;
the vertical image requires that the inclination angle of the photo is not more than 2 degrees; the oblique image requires that the overlapping degree of photos is not less than 65 percent, and the difference of adjacent visual angles is within 15 degrees;
respectively processing the vertical image and the inclined image;
constructing a three-dimensional model based on the oblique image, and constructing a digital positive image mapping chart based on the vertical image;
constructing a three-dimensional campus model by combining a three-dimensional model and a digital orthographic projection through a WebGIS system;
and accessing the electronic student identity card information data in the campus into a three-dimensional campus model, and accurately positioning each student wearing the electronic student identity card to generate coordinate data.
3. The electronic student identity card information management method based on the internet of things according to claim 2, wherein: the judgment model comprises:
acquiring cross track student data of a student A, labeling activity class data and non-class time data, and constructing a judgment model:
wherein the content of the first and second substances,
a collective integration probability index representing students;
representing the student serial number of the cross track with the student A in the student data of the cross track with the student A;
representing the total number of students crossing with the student A in the student data of the crossing track of the student A;
representing activity class impact coefficients;
when representing non-class hoursAn influence coefficient;
representing the number of students in the cross track of the student A and the number of students in the cross track of the student A in the activity class;
representing the number of students in the cross track with the student A in the non-lesson time in the cross track student data of the student A;
the students in the cross track of the activity class and the student A in the cross track student data representing the student A are serial numbers
The number of student crossings;
the students with the crossed track of the student A are serial numbers in the crossed track student data representing the student A in the non-class time
The number of student crossings;
setting a probability index threshold, if any
If the probability index is lower than the threshold value of the probability index, the fact that the communication frequency of the student A and the students is low, certain psychological problems possibly exist or the students cannot be integrated in the group in time is judged, and the psychological problems are fed back to a teacher port to remind the teacher of paying attention.
4. An electronic student identity card information management system based on the internet of things, applying the electronic student identity card information management method based on the internet of things of claim 1, is characterized in that: the system comprises an electronic student identity card information acquisition module, a three-dimensional campus construction module, a track analysis processing module, a comprehensive judgment module and a feedback module;
the electronic student identity card information acquisition module is used for acquiring the action track of any student through the electronic student identity card and acquiring the class schedule information data of the student; the three-dimensional campus construction module is used for acquiring campus building data, constructing a three-dimensional campus model and realizing coordinate positioning on students in a campus; the track analysis processing module is used for capturing activity class data of students and non-class time track data of the students, constructing time periods and analyzing the track change differences of the students in different time periods; the comprehensive judgment module constructs a judgment model and judges the psychological state of the student; the feedback module is used for setting a threshold value, and if the threshold value is lower than the threshold value, the feedback module feeds back the instruction port to remind an instruction teacher to pay attention;
the output end of the electronic student identity card information acquisition module is connected with the input end of the three-dimensional campus building module; the output end of the three-dimensional campus building module is connected with the input end of the track analysis processing module; the output end of the track analysis processing module is connected with the input end of the comprehensive judgment module; the output end of the comprehensive judgment module is connected with the input end of the feedback module.
5. The electronic student identity card information management system based on the internet of things according to claim 4, wherein: the electronic student identity card information acquisition module comprises a motion track acquisition unit and a class schedule information acquisition unit;
the action track acquisition unit is used for acquiring action track data of students in the campus according to the electronic student certificates; the school timetable information acquisition unit is used for acquiring activity course information data of students and acquiring non-school time data of the students;
the output end of the action track acquisition unit is connected with the input end of the three-dimensional campus building module; the output end of the school timetable information acquisition unit is connected with the input end of the three-dimensional campus building module.
6. The electronic student identity card information management system based on the internet of things according to claim 4, wherein: the three-dimensional campus building module comprises an image processing unit and a coordinate positioning unit;
the image processing unit acquires a vertical image and an oblique image of a campus by using unmanned flight equipment, respectively performs image processing on the vertical image and the oblique image, and constructs a three-dimensional campus model by combining a three-dimensional model and a digital orthographic image through a WebGIS (web geographic information system); the coordinate positioning unit is used for accessing electronic student identity card information data in the campus into the three-dimensional campus model, accurately positioning each student wearing the electronic student identity card, and generating coordinate data;
the output end of the image processing unit is connected with the input end of the coordinate positioning unit; and the output end of the coordinate positioning unit is connected with the input end of the track analysis processing module.
7. The electronic student identity card information management system based on the internet of things according to claim 4, wherein: the track analysis processing module comprises a track crossing unit and a track analysis unit;
the track crossing unit is used for acquiring activity course data of students and non-class time track data of the students and judging the students having tracks crossed with the students; the track analysis unit is used for constructing a model and carrying out clustering analysis;
the output end of the track crossing unit is connected with the input end of the track analysis unit; and the output end of the track analysis unit is connected with the input end of the comprehensive judgment module.
8. The electronic student identity card information management system based on the internet of things according to claim 4, wherein: the comprehensive judgment module comprises a time period construction unit and a comprehensive judgment unit;
the time period construction unit is used for constructing time periods and analyzing the variation of the track changes of students in different time periods; the comprehensive judgment unit is used for constructing a judgment model, judging the psychological states of the students and generating a collective integration probability index of the students;
the output end of the time period construction unit is connected with the input end of the comprehensive judgment unit; and the output end of the comprehensive judgment unit is connected with the input end of the feedback module.
9. The electronic student identity card information management system based on the internet of things according to claim 4, wherein: the feedback module comprises a threshold setting unit and a feedback unit;
the threshold setting unit is used for setting a threshold and analyzing the collective integration probability index of the students; the feedback unit is used for feeding back the collective integration probability index of the students to a teacher port to remind the teacher of paying attention when the collective integration probability index of the students is lower than a threshold value;
the output end of the threshold setting unit is connected with the input end of the feedback unit.
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| CN106912019A (en) * | 2017-03-01 | 2017-06-30 | 安徽大智睿科技技术有限公司 | A kind of campus student behavior analysis method and system based on wifi hotspot |
| CN107067160A (en) * | 2017-03-13 | 2017-08-18 | 同济大学 | A kind of assembled architecture allocation of the labour force method based on BIM |
| CN107423563A (en) * | 2017-07-25 | 2017-12-01 | 深信服科技股份有限公司 | A kind of students psychology analysis method, equipment and its storage medium |
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