CN113469157A - Student attendance management system and method based on big data - Google Patents
Student attendance management system and method based on big data Download PDFInfo
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Abstract
The invention discloses a student attendance management system and method based on big data, relating to the technical field of big data student management, wherein the system comprises a monitoring terminal, a student attendance analysis module, a student abnormal behavior analysis module and an attendance rate collection module; the monitoring terminal acquires monitoring terminals distributed at different positions, and acquires student data information to adjust a shooting angle and set the size of a shooting area; the student attendance analysis module is used for acquiring the distribution quantity of the head areas of the students at different time periods of the monitoring terminal so as to analyze the attendance rate of the students; the abnormal student behavior analysis module is used for acquiring the difference number of the head numbers of students in different time periods, and analyzing the difference number generated due to the fact that abnormal student behaviors cover the heads of the students or the abnormal distribution of the queues causes the reduction of the attendance number of the students; the attendance rate collection and analysis module is used for sending the compared attendance summary table to a teacher terminal and publishing attendance scores in time; to prevent the student from intentionally being absent.
Description
Technical Field
The invention relates to the technical field of big data student management, in particular to a student attendance management system and method based on big data.
Background
The attendance of students refers to the number of students participating in activities or taking class as a means for restricting the class taking of students; when students participate in class, teachers can monitor the class attendance records of the students in a roll call mode or a fingerprint input mode, and the number of students on duty can be monitored and screened; but for students taking part in activities, for example: students participate in national flag raising activities, hundred-year school celebration activities and the like, and the number of the students who actually participate in the activities cannot be monitored in time due to a large number of the students who participate in the activities, so that a plurality of students do not participate in the activities and do not go out at the same time;
the in-process that the student queued up in the queue, because the student can have actions such as lacing, bowing, waist support and lead to the quantity that can't accurately discern the student head, can produce the deviation at the in-process that detects, consequently only can produce the error through the rate of attendance of once assay student, consequently need provide a new technical scheme and solve the problem of the rate of attendance when the student activities of attendance.
Disclosure of Invention
The invention aims to provide a student attendance management system and method based on big data, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a student attendance management system based on big data comprises a monitoring terminal, a student attendance analysis module, a student abnormal behavior analysis module and an attendance rate collection module;
the monitoring terminal acquires monitoring terminals distributed at different positions, and acquires student data information to adjust a shooting angle and set the size of a shooting area;
the student attendance analysis module is used for acquiring the distribution quantity of the head areas of the students at different time periods of the monitoring terminal so as to analyze the attendance rate of the students;
the abnormal behavior analysis module of the student obtains the difference number of the heads of the students in different time periods and analyzes whether the generated difference is the reason that the students shield the heads of the students or the number of the students on duty is reduced due to abnormal distribution of the queues;
the attendance rate collection and analysis module is used for sending the compared attendance summary table to a teacher terminal and publishing attendance scores in time;
the attendance rate collection module is connected with the monitoring terminal, the student attendance analysis module and the student abnormal behavior analysis module.
Furthermore, the monitoring terminal comprises an angle shooting adjusting unit, a data acquisition unit, a data analysis unit and an area size setting unit;
the angle shooting adjusting unit is used for calling monitoring terminals distributed in the activity places, controlling the monitoring terminals and shooting students from different angles; therefore, the attendance number of students can be better acquired;
the data acquisition unit analyzes height distribution and student spacing distribution in the student queue according to the images shot by the monitoring terminal; therefore, whether the students are uniformly distributed can be judged, so that the head images of the heads of the students can be better intercepted, and the head images can be clearly identified;
the data analysis unit is used for analyzing and comparing the heights of the students and the distances among the students and judging whether the arrangement of the students is uniform;
the area size setting unit intercepts shot images according to the arrangement condition of students and analyzes the attendance condition of the students.
Furthermore, the student attendance analysis module comprises a first monitoring analysis unit, a student head framing processing unit, a student head quantity summarizing unit, a second monitoring analysis unit and a head quantity comparison analysis unit;
the first monitoring and analyzing unit is used for controlling the monitoring terminal to intercept the student queue distribution image for the first time according to the distribution condition of the student queue and analyzing the number of the students on attendance;
the student head framing processing unit frames student head units according to the intercepted images and summarizes the number of the students' heads;
the second monitoring and analyzing unit is used for controlling the monitoring terminal to intercept the student queue distribution image for the second time according to the distribution condition of the student queue and analyzing the number of the students on attendance;
the head number comparison and analysis unit is used for analyzing the difference value between the number of the persons who attendance and are analyzed by the first intercepted image and the number of the persons who attendance and are analyzed by the second intercepted image, judging whether the difference value result is larger than a first preset difference value and smaller than a second preset value or not, and analyzing whether the difference value result is larger than the second preset difference value or not when the difference value result does not meet the conditions;
the output end of the head quantity comparison and analysis unit is connected with the input ends of the first monitoring and analysis unit, the image partition detection unit, the student head framing processing unit, the student head quantity summarizing unit and the second monitoring and analysis unit.
Furthermore, the student abnormal behavior analysis module comprises an image area abnormal comparison unit, a monitoring end calling and monitoring unit, a queue abnormal change analysis unit, a queue included angle presentation unit, a queue familiarity analysis unit and a student attendance rate verification unit;
the image area abnormity comparison unit analyzes whether an abnormal image area exists in the intercepted image or not when detecting that the difference value between the number of people who work out and is analyzed by the first intercepted image and the number of people who work out and is analyzed by the second intercepted image is larger than a first preset value and smaller than a second preset value, and sends the abnormal image area to the monitoring end calling and monitoring unit for monitoring;
the monitoring end calls the monitoring unit, calls the average area of the continuous blank area and the head area of the student, compares the average area with the average area of the head area of the student, and analyzes whether the head area is shielded by the action of the student;
the queue abnormal change analysis unit is used for analyzing whether the number of people on duty changes when detecting that the difference value between the number of people on duty analyzed by the first intercepted image and the number of people on duty analyzed by the second intercepted image is larger than a second preset difference value;
the queue included angle presenting unit analyzes the included angle number between adjacent queues according to the queue distribution condition of the first intercepted image and the second intercepted image;
the queue familiarity analyzing unit analyzes whether the familiarity among the queues is greater than a third preset value according to the actions of students in the queues so as to analyze whether the queues are originally sub-queues disassembled from an integral queue;
the student attendance verification unit verifies the attendance of students according to the actions of the students in the queue;
the output end of the student attendance rate analysis unit is connected with the input ends of the image area abnormity comparison unit, the monitoring end calling monitoring unit, the queue abnormity change analysis unit, the queue included angle presentation unit and the queue familiarity analysis unit.
Furthermore, the attendance rate collecting and analyzing module comprises a teacher terminal verification unit, an attendance score publishing unit and a reminding and warning unit;
the teacher terminal verification unit receives and verifies the attendance rates of students and collects and analyzes the attendance rates;
the attendance score publishing unit is used for analyzing attendance scores of students according to the attendance summary results of the students;
the reminding warning unit sends the attendance scores to students and warns the deduction results of students who do not attend the attendance.
A student attendance management method based on big data comprises the following steps:
step Z01: the method comprises the following steps that a calling monitoring terminal shoots students, the positions and the sizes of shooting areas are adjusted according to the heights of the students and arrangement intervals in queues, head areas of the students in each queue in an image are selected in a frame mode, and the number of the heads of the students in the queues is analyzed according to first monitoring;
step Z02: setting the head number of students in a second monitoring analysis queue, judging the difference value of the head numbers of the students in the first monitoring period and the second monitoring period, analyzing whether the blank area of the continuous students in the queue is the average area formed by the head areas of the student numbers or not when the head number difference value of the students in the first monitoring period and the second monitoring period is larger than a first preset value and smaller than a second preset value, and analyzing the actions of the students when the condition is not met; when the difference value of the number of the heads of the students between the first monitoring period and the second monitoring period is larger than a second preset value, whether the degree of an included angle between two adjacent queues meets an included angle condition is analyzed, and when the degree of the included angle between two adjacent queues meets the included angle condition, the familiarity between the students is further analyzed, and whether the students are on duty is judged;
step Z03: and obtaining an attendance summary sheet according to the attendance condition of the students, and publishing attendance scores of the students to warn the students. In the step Z01, the set of the distribution of the heights of the students in the queue is obtained as W = { W = { (W) }1,w2,w3...wnN is the number of students, and the distance between students in the queue is D = { D =12,d23,d34...dn（n-1）}，d(n-1)nThe distance between the nth-1 student and the nth student is obtained and compared when the nth student queues up;
when d is detected(n+1)n-d(n+1)(n+2)When =0, the student spacing representing the queue is evenly distributed, and when d is detected(n+1)n-d(n+1)(n+2)When the number is not equal to 0, the non-uniform distribution of the student intervals of the queue is shown;
optionally numerical analysis
When the condition is met, indicating that the students in the queue are distributed in a sequential increasing mode; when the condition is not met, indicating that the arrangement states of the students in the queue are uneven;
when detecting that the queuing intervals are uniformly distributed and the distribution conditions of the students in the queue are sequentially increased, framing the queue area to analyze the number of the heads of the students, wherein d(n+1)nDistance between the nth student and the (n + 1) th student, d(n+1)(n+2)The distance between the (n + 1) th student and the (n + 2) th student; w is aiIs the height, w, of the ith studenti+1、wi-1Means the height of the (i + 1) th student and the height of the (i-1) th student;
when students do not meet sequential increment or uneven arrangement conditions, if the arrangement combination of the students is randomly distributed, the students higher in front can influence the detection of the images of the tops of the students lower in front, and therefore the judgment of the attendance rate of the whole students is influenced.
In the step Z02, the head images of the students in the queue are obtained according to the selected area, and the set of the areas of the head images of the students is L = { L = { L =1,l2,l3...ln};
The average area of the student's head image is
；
When the head detection areas of the students in the queue contain continuous blank area areas, the blank area areas are not equal to the average area sum of the head images of the students, and the number of the students in the queue is smaller than the number of the actual students, the fact that the number of the head areas of the students in the blank area is not detected is detected;
when the number of the student head areas in the blank area is equal to the actual number in the second monitoring analysis, the blank area indicates that the student does not detect the student head areas due to abnormal actions in the first monitoring analysis time period and the second monitoring analysis time period;
in the step Z02, when the difference between the numbers of the students distributed in the adjacent queues in the first monitoring analysis is greater than a third preset value, and the number of the students distributed in the adjacent queues in the second monitoring analysis is less than the third preset value; the direction of the students facing the direction in the adjacent queues is taken as a starting direction point, the direction of the students facing the direction in the adjacent queues is taken as an ending direction point, and data vectors formed by the adjacent queues are respectively
And
；
analyzing data vectors
And a data vector
Number of included angles therebetween
Specifically, the following formula is provided:
wherein the content of the first and second substances,
to represent
And
the included angle between the two parts is included,
and
respectively representing the modulus of the queue vector;
when in use
When =0 ° or 180 °, it means that no angle is generated between adjacent queues and adjacent queues do not tend to approach each other, and when
The time, the included angle is generated between the adjacent queues, and the adjacent queues contain the trend of mutual approaching.
When a data vector is detected
And
included angle therebetween is
When the familiarity degree in the adjacent queues is detected to be higher than a third preset value, the familiarity degree between the adjacent queues is high, and the two branch queues are divided by the whole queue; when the familiarity degree of the adjacent teams is detected to be lower than a third preset value, two teams which are low in familiarity degree and are not formed by splitting of the whole team are indicated, and the fact that the queues deviate and the number of students not going out of the queues is indicated;
the judgment is made by the following formula:
k refers to the number of action interactions between adjacent queues,
it is referred to as the familiar coefficient,
refers to the overall familiarity between adjacent queues,
it refers to the degree of static familiarity,
refers to dynamic familiarity.
Compared with the prior art, the invention has the following beneficial effects:
the set monitoring respectively analyzes the attendance rate of the students in the first monitoring time period and the second monitoring time period so as to prevent part of the students from not detecting the attendance of the students due to behavior actions; when the difference value of the number of the heads of the students between the first monitoring period and the second monitoring period is larger than a first preset value and smaller than a second preset value, whether the area of the blank areas of the continuous students in the queue is the average area formed by the head areas of the different students is analyzed; when first control and second control period student head quantity difference are greater than the second default, whether contained angle degree between adjacent two queues satisfies the contained angle condition, when contained angle degree between adjacent two queues satisfies the contained angle condition, further analysis student's familiarity degree to analysis student's the condition of attendance can reduce the lazy circumstances of student, thereby strengthens the management to the student, actively participates in school's activity.
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 schematic diagram of student attendance detection steps of a big data-based student attendance management system of the present invention;
FIG. 2 is a schematic diagram of the module composition of a big data-based student attendance management system of the present invention;
FIG. 3 is a schematic diagram of the distribution of the original queues of the student attendance management system based on big data according to the present invention;
fig. 4 is a schematic diagram of queue change of a big data-based student attendance management system according to the present invention.
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. 1-4, the present invention provides a technical solution:
a student attendance management system based on big data comprises a monitoring terminal, a student attendance analysis module, a student abnormal behavior analysis module and an attendance rate collection module;
the monitoring terminal acquires monitoring terminals distributed at different positions, and acquires student data information to adjust a shooting angle and set the size of a shooting area;
the student attendance analysis module is used for acquiring the distribution quantity of the head areas of the students at different time periods of the monitoring terminal so as to analyze the attendance rate of the students;
the abnormal behavior analysis module of the student obtains the difference number of the heads of the students in different time periods and analyzes whether the generated difference is the reason that the students shield the heads of the students or the number of the students on duty is reduced due to abnormal distribution of the queues;
the attendance rate collection and analysis module is used for sending the compared attendance summary table to a teacher terminal and publishing attendance scores in time;
the attendance rate collection module is connected with the monitoring terminal, the student attendance analysis module and the student abnormal behavior analysis module.
Furthermore, the monitoring terminal comprises an angle shooting adjusting unit, a data acquisition unit, a data analysis unit and an area size setting unit;
the angle shooting adjusting unit is used for calling monitoring terminals distributed in the activity places, controlling the monitoring terminals and shooting students from different angles;
the data acquisition unit analyzes height distribution and student spacing distribution in the student queue according to the images shot by the monitoring terminal;
the data analysis unit is used for analyzing and comparing the heights of the students and the distances among the students and judging whether the arrangement of the students is uniform;
when the height of a student is detected, in order to judge whether the queuing condition of the current queue is distributed in sequence or randomly; the random distribution refers to the distribution which is not carried out according to the height from low to high or from high to low;
the area size setting unit intercepts shot images according to the arrangement condition of students and analyzes the attendance condition of the students, so that the current attendance condition is displayed more clearly, the identification definition is increased, and if the area size is not adjusted during identification, the number of the heads of the identified students cannot be accurate;
furthermore, the student attendance analysis module comprises a first monitoring analysis unit, a student head framing processing unit, a student head quantity summarizing unit, a second monitoring analysis unit and a head quantity comparison analysis unit;
the first monitoring and analyzing unit is used for controlling the monitoring terminal to intercept the student queue distribution image for the first time according to the distribution condition of the student queue and analyzing the number of the students on attendance;
the student head framing processing unit frames student head units according to the intercepted images and summarizes the number of the students' heads;
the second monitoring and analyzing unit is used for controlling the monitoring terminal to intercept the student queue distribution image for the second time according to the distribution condition of the student queue and analyzing the number of the students on attendance;
the head number comparison and analysis unit is used for analyzing the difference value between the number of the persons who attendance and are analyzed by the first intercepted image and the number of the persons who attendance and are analyzed by the second intercepted image, judging whether the difference value result is larger than a first preset difference value and smaller than a second preset value or not, and analyzing whether the difference value result is larger than the second preset difference value or not when the difference value result does not meet the conditions;
the first preset value is smaller than the second preset value, and the preset value is the number of attendance people;
the third preset value is standard familiarity;
the fourth preset value is a standard student number difference value in the adjacent queues;
the output end of the head quantity comparison and analysis unit is connected with the input ends of the first monitoring and analysis unit, the image partition detection unit, the student head framing processing unit, the student head quantity summarizing unit and the second monitoring and analysis unit.
Furthermore, the student abnormal behavior analysis module comprises an image area abnormal comparison unit, a monitoring end calling and monitoring unit, a queue abnormal change analysis unit, a queue included angle presentation unit, a queue familiarity analysis unit and a student attendance rate verification unit;
the image area abnormity comparison unit analyzes whether an abnormal image area exists in the intercepted image or not when detecting that the difference value between the number of people who work out and is analyzed by the first intercepted image and the number of people who work out and is analyzed by the second intercepted image is larger than a first preset value and smaller than a second preset value, and sends the abnormal image area to the monitoring end calling and monitoring unit for monitoring;
the monitoring end calls the monitoring unit, calls the average area of the continuous blank area and the head area of the student, compares the average area with the average area of the head area of the student, and analyzes whether the head area is shielded by the action of the student;
the continuous blank area refers to the area of a continuous undetected area in the team; the continuous blank area refers to areas of the student with stoop and the like;
the student head region average area sum refers to the average area sum of the student head regions remaining except for the areas of the continuous undetected regions;
and calling the average area of the continuous blank area and the student head area and comparing the average area, namely judging the number of the areas of the student head areas which can be included in the blank area.
The queue abnormal change analysis unit is used for analyzing whether the number of people on duty changes when detecting that the difference value between the number of people on duty analyzed by the first intercepted image and the number of people on duty analyzed by the second intercepted image is larger than a second preset difference value;
the queue included angle presenting unit analyzes the included angle number between adjacent queues according to the queue distribution condition of the first intercepted image and the second intercepted image;
the queue familiarity analyzing unit analyzes whether the familiarity among the queues is greater than a third preset value according to the actions of students in the queues so as to analyze whether the queues are originally sub-queues disassembled from an integral queue;
the student attendance verification unit verifies the attendance of students according to the actions of the students in the queue;
the output end of the student attendance rate analysis unit is connected with the input ends of the image area abnormity comparison unit, the monitoring end calling monitoring unit, the queue abnormity change analysis unit, the queue included angle presentation unit and the queue familiarity analysis unit.
Furthermore, the attendance rate collecting and analyzing module comprises a teacher terminal verification unit, an attendance score publishing unit and a reminding and warning unit;
the teacher terminal verification unit receives and verifies the attendance rates of students and collects and analyzes the attendance rates;
the attendance score publishing unit is used for analyzing attendance scores of students according to the attendance summary results of the students;
the reminding warning unit sends the attendance scores to students and warns the scoring results of students not attendance;
when verifying the attendance condition of students, wherein the total score-attendance score is the number of times of attendance not deducted at each time, the attendance score of the students in the school period can be obtained and displayed for the students, and when the attendance number of the school period is greater than the preset number, the students are punished.
A student attendance management method based on big data comprises the following steps:
step Z01: the method comprises the following steps that a calling monitoring terminal shoots students, the positions and the sizes of shooting areas are adjusted according to the heights of the students and arrangement intervals in queues, head areas of the students in each queue in an image are selected in a frame mode, and the number of the heads of the students in the queues is analyzed according to first monitoring;
step Z02: setting the head number of students in a second monitoring analysis queue, judging the difference value of the head numbers of the students in the first monitoring period and the second monitoring period, analyzing whether the blank area of the continuous students in the queue is the average area formed by the head areas of the student numbers or not when the head number difference value of the students in the first monitoring period and the second monitoring period is larger than a first preset value and smaller than a second preset value, and analyzing the actions of the students when the condition is not met; when the difference value of the number of the heads of the students between the first monitoring period and the second monitoring period is larger than a second preset value, whether the degree of an included angle between two adjacent queues meets an included angle condition is analyzed, and when the degree of the included angle between two adjacent queues meets the included angle condition, the familiarity between the students is further analyzed, and whether the students are on duty is judged;
step Z03: and obtaining an attendance summary sheet according to the attendance condition of the students, and publishing attendance scores of the students to warn the students. In the step Z01, the set of the distribution of the heights of the students in the queue is obtained as W = { W = { (W) }1,w2,w3...wnN isThe number of students is indicated, and the distance between the students in the queue is D = { D = { (D)12,d23,d34...dn（n-1）}，d(n-1)nThe distance between the nth-1 student and the nth student is obtained and compared when the nth student queues up;
when d is detected(n+1)n-d(n+1)(n+2)When =0, the student spacing representing the queue is evenly distributed, and when d is detected(n+1)n-d(n+1)(n+2)When the number is not equal to 0, the non-uniform distribution of the student intervals of the queue is shown;
optionally numerical analysis
When the condition is met, indicating that the students in the queue are distributed in a sequential increasing mode; when the condition is not met, indicating that the arrangement states of the students in the queue are uneven;
when the uniform distribution of the queuing intervals is detected and the distribution conditions of the students in the queue are sequentially increased, the queue area is selected in a frame mode to analyze the number of the heads of the students; wherein d is(n+1)nDistance between the nth student and the (n + 1) th student, d(n+1)(n+2)The distance between the (n + 1) th student and the (n + 2) th student; wi refers to the height of the ith student, and wi +1 and wi-1 refer to the height of the (i + 1) th student and the height of the (i-1) th student;
in the step Z02, the head images of the students in the queue are obtained according to the selected area, and the set of the areas of the head images of the students is L = { L = { L =1,l2,l3...ln};
The average area of the student's head image is
；
When the head detection areas of the students in the queue contain continuous blank area areas, the blank area areas are not equal to the average area sum of the head images of the students, and the number of the students in the queue is smaller than the number of the actual students, the fact that the number of the head areas of the students in the blank area is not detected is detected;
when the number of the student head areas in the blank area is equal to the actual number in the second monitoring analysis, the blank area indicates that the student does not detect the student head areas due to abnormal actions in the first monitoring analysis time period and the second monitoring analysis time period;
the set average area of the head images is used for judging that the area of the head images formed by a plurality of continuous students is equal to the area of the blank area, and analyzing whether the area of the blank area is formed by the areas of the plurality of head areas or not so as to analyze the number of the students in the blank area; the average area of the image is set to better analyze the number of students contained in the blank area because the size of the head area of each student is not uniform.
In the step Z02, when the difference between the numbers of the students distributed in the adjacent queues in the first monitoring analysis is greater than a fourth preset value, and the number of the students distributed in the adjacent queues in the second monitoring analysis is less than the fourth preset value; taking the direction pointed by the student in the adjacent queue as a starting direction point, the direction pointed by the student in the back direction of the student in the adjacent queue as an ending direction point, taking the direction pointed by the student in the back direction of the student as an ending point O, and forming data vectors of the adjacent queue as
And
；
analyzing data vectors
And a data vector
Number of included angles therebetween
Specifically, the following formula is provided:
wherein the content of the first and second substances,
to represent
And
the included angle between the two parts is included,
and
respectively representing the modulus of the queue vector;
when in use
When =0 ° or 180 °, it means that no angle is generated between adjacent queues and adjacent queues do not tend to approach each other, and when
When the queue is in a closed state, the adjacent queues contain the trend of being close to each other;
the set included angle degree is used for analyzing whether an intersection is generated between the two queues or not, and when the included angle is included, the included angle is shown in the two queues; and when the included angle is larger, the represented included angle degree is closer.
When a data vector is detected
And
included angle therebetween is
When the familiarity in the adjacent queue is detected, the familiarity in the adjacent queue is acquiredWhen the familiarity between adjacent teams is higher than a third preset value, the two branch teams which are high in familiarity and are separated from the whole team are represented; when the familiarity degree of the adjacent teams is detected to be lower than a third preset value, two teams which are low in familiarity degree and are not formed by splitting of the whole team are indicated, and the fact that the queues deviate and the number of students not going out of the queues is indicated;
the judgment is made by the following formula:
k refers to the number of action interactions between adjacent queues,
it is referred to as the familiar coefficient,
refers to the overall familiarity between adjacent queues,
it refers to the degree of static familiarity,
refers to the degree of dynamic familiarity;
the actions between adjacent queues can be actions of shoulder-to-shoulder chatting at equal distances and close to each other, and the actions are set in a formula
The basic familiarity among students is defined, and the students are familiar with each other although not handed over, so that the familiarity among the students is defined; when the students chat close to each other, only the students can act and communicate with each other, and the familiarity changes accordingly, therefore, the method is provided
Is also indispensable.
Example 1:
students in the same year in the activity room participate in listening to the speech, the students' queues are from low to high to enter and listen to the speech, the area is photographed according to the first monitoring end, the area is photographed by the second monitoring end, photographing verification is carried out according to adjacent teams of the first monitoring end and the second monitoring end, the number of people among the adjacent teams in the area is detected to be changed, the vector of the adjacent team is A = (1, 2, 5), and the vector of the other vector team is B = (2, 2, 7);
analyzing data vectors
And a data vector
Number of included angles therebetween
；
When in use
When the queue is in a closed state, the adjacent queues contain the trend of being close to each other;
example 2, as shown in fig. 3 and 4, the trend graphs of the adjacent queues are close to each other, wherein the left vector is
Right side is vector
。
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 changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. 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. The utility model provides a student management system that attendance based on big data which characterized in that: the system comprises a monitoring terminal, a student attendance analysis module, a student abnormal behavior analysis module and an attendance rate collection module;
the monitoring terminal acquires monitoring terminals distributed at different positions, and acquires student data information to adjust a shooting angle and set the size of a shooting area;
the student attendance analysis module is used for acquiring the distribution quantity of the head areas of the students at different time periods of the monitoring terminal so as to analyze the attendance rate of the students;
the abnormal behavior analysis module of the student obtains the difference number of the heads of the students in different time periods and analyzes whether the generated difference is the reason that the students shield the heads of the students or the number of the students on duty is reduced due to abnormal distribution of the queues;
the attendance rate collection and analysis module is used for sending the compared attendance summary table to a teacher terminal and publishing attendance scores in time;
the attendance rate collecting module is connected with the monitoring terminal, the student attendance analysis module and the student abnormal behavior analysis module;
the student abnormal behavior analysis module comprises an image area abnormal comparison unit, a monitoring end calling monitoring unit, a queue abnormal change analysis unit, a queue included angle presentation unit, a queue familiarity analysis unit and a student attendance rate verification unit;
the image area abnormity comparison unit analyzes whether an abnormal image area exists in the intercepted image or not when detecting that the difference value between the number of people who work out and is analyzed by the first intercepted image and the number of people who work out and is analyzed by the second intercepted image is larger than a first preset value and smaller than a second preset value, and sends the abnormal image area to the monitoring end calling and monitoring unit for monitoring;
the monitoring end calls the monitoring unit, calls the average area of the continuous blank area and the head area of the student, compares the average area with the average area of the head area of the student, and analyzes whether the head area is shielded by the action of the student;
the queue abnormal change analysis unit is used for analyzing whether the number of people on duty changes when detecting that the difference value between the number of people on duty analyzed by the first intercepted image and the number of people on duty analyzed by the second intercepted image is larger than a second preset difference value;
the queue included angle presenting unit analyzes the included angle number between adjacent queues according to the queue distribution condition of the first intercepted image and the second intercepted image;
the queue familiarity analyzing unit analyzes whether the familiarity among the queues is greater than a third preset value according to the actions of students in the queues so as to analyze whether the queues are originally sub-queues disassembled from an integral queue;
the student attendance verification unit verifies the attendance of students according to the actions of the students in the queue;
the output end of the student attendance rate analysis unit is connected with the input ends of the image area abnormity comparison unit, the monitoring end calling monitoring unit, the queue abnormity change analysis unit, the queue included angle presentation unit and the queue familiarity analysis unit.
2. The big data based student attendance management system as claimed in claim 1 wherein: the monitoring terminal comprises an angle shooting adjusting unit, a data acquisition unit, a data analysis unit and an area size setting unit;
the angle shooting adjusting unit is used for calling monitoring terminals distributed in the activity places, controlling the monitoring terminals and shooting students from different angles;
the data acquisition unit analyzes height distribution and student spacing distribution in the student queue according to the images shot by the monitoring terminal;
the data analysis unit is used for analyzing and comparing the heights of the students and the distances among the students and judging whether the arrangement of the students is uniform;
the area size setting unit intercepts shot images according to the arrangement condition of students and analyzes the attendance condition of the students.
3. The big data based student attendance management system as claimed in claim 1 wherein: the student attendance analysis module comprises a first monitoring analysis unit, a student head framing processing unit, a student head quantity summarizing unit, a second monitoring analysis unit and a head quantity comparison analysis unit;
the first monitoring and analyzing unit is used for controlling the monitoring terminal to intercept the student queue distribution image for the first time according to the distribution condition of the student queue and analyzing the number of the students on attendance;
the student head framing processing unit frames student head units according to the intercepted images and summarizes the number of the students' heads;
the second monitoring and analyzing unit is used for controlling the monitoring terminal to intercept the student queue distribution image for the second time according to the distribution condition of the student queue and analyzing the number of the students on attendance;
the head number comparison and analysis unit is used for analyzing the difference value between the number of the persons who attendance and are analyzed by the first intercepted image and the number of the persons who attendance and are analyzed by the second intercepted image, judging whether the difference value result is larger than a first preset difference value and smaller than a second preset value or not, and analyzing whether the difference value result is larger than the second preset difference value or not when the difference value result does not meet the conditions;
the output end of the head quantity comparison and analysis unit is connected with the input ends of the first monitoring and analysis unit, the image partition detection unit, the student head framing processing unit, the student head quantity summarizing unit and the second monitoring and analysis unit.
4. The big data based student attendance management system as claimed in claim 1 wherein: the attendance rate collecting and analyzing module comprises a teacher terminal verification unit, an attendance score publishing unit and a reminding and warning unit;
the teacher terminal verification unit receives and verifies the attendance rates of students and collects and analyzes the attendance rates;
the attendance score publishing unit is used for analyzing attendance scores of students according to the attendance summary results of the students;
the reminding warning unit sends the attendance scores to students and warns the deduction results of students who do not attend the attendance.
5. A student attendance management method based on big data is characterized in that: the method comprises the following steps:
step Z01: the method comprises the following steps that a calling monitoring terminal shoots students, the positions and the sizes of shooting areas are adjusted according to the heights of the students and arrangement intervals in queues, head areas of the students in each queue in an image are selected in a frame mode, and the number of the heads of the students in the queues is analyzed according to first monitoring;
step Z02: setting the head number of students in a second monitoring analysis queue, judging the difference value of the head numbers of the students in the first monitoring period and the second monitoring period, analyzing whether the blank area of the continuous students in the queue is the average area formed by the head areas of the student numbers or not when the head number difference value of the students in the first monitoring period and the second monitoring period is larger than a first preset value and smaller than a second preset value, and analyzing the actions of the students when the condition is not met; when the difference value of the number of the heads of the students between the first monitoring period and the second monitoring period is larger than a second preset value, whether the degree of an included angle between two adjacent queues meets an included angle condition is analyzed, and when the degree of the included angle between two adjacent queues meets the included angle condition, the familiarity between the students is further analyzed, and whether the students are on duty is judged;
step Z03: and obtaining an attendance summary sheet according to the attendance condition of the students, and publishing attendance scores of the students to warn the students.
6. The big data based student attendance management method according to claim 5, wherein: in the step Z01, the set distributed by the heights of the students in the queue is obtained
,
The distance between students in the queue is D = { D = { (D) }12,d23,d34...dn（n-1）}，d(n-1)nThe distance between the nth-1 student and the nth student is obtained and compared when the nth student queues up;
when d is detected(n+1)n-d(n+1)(n+2)When =0, the student spacing representing the queue is evenly distributed, and when d is detected(n+1)n-d(n+1)(n+2)When the number is not equal to 0, the non-uniform distribution of the student intervals of the queue is shown;
optionally numerical analysis
When the condition is met, indicating that the students in the queue are distributed in a sequential increasing mode; when the condition is not met, indicating that the arrangement states of the students in the queue are uneven;
when the uniform distribution of the queuing intervals is detected and the distribution conditions of the students in the queue are sequentially increased, the queue area is selected in a frame mode to analyze the number of the heads of the students; wherein d is(n+1)nDistance between the nth student and the (n + 1) th student, d(n+1)(n+2)The distance between the (n + 1) th student and the (n + 2) th student; wi is the height of the ith student, wi +1 and wi-1 areRefers to the height of the (i + 1) th student and the height of the (i-1) th student.
7. The big data based student attendance management method according to claim 5, wherein: in the step Z02, the head images of the students in the queue are obtained according to the selected area, and the area set of the head images of the students is
The average area of the student's head image is
；
When the head detection areas of the students in the queue contain continuous blank area areas, the blank area areas are not equal to the average area sum of the head images of the students, and the number of the students in the queue is smaller than the number of the actual students, the fact that the number of the head areas of the students in the blank area is not detected is detected;
and when the number of the student head areas in the blank area is equal to the actual number in the second monitoring analysis, the blank area indicates that the students do not detect the student head areas due to abnormal actions in the first monitoring analysis time period and the second monitoring analysis time period.
8. The big data based student attendance management method according to claim 5, wherein: in the step Z02, when the difference between the numbers of the students distributed in the adjacent queues in the first monitoring analysis is greater than a third preset value, and the number of the students distributed in the adjacent queues in the second monitoring analysis is less than the third preset value; the direction of the students facing the direction in the adjacent queues is taken as a starting direction point, the direction of the students facing the direction in the adjacent queues is taken as an ending direction point, and data vectors formed by the adjacent queues are respectively
And
；
analyzing data vectors
And a data vector
Number of included angles therebetween
Specifically, the following formula is provided:
wherein the content of the first and second substances,
to represent
And
the included angle between the two parts is included,
and
respectively representing the modulus of the queue vector;
when in use
When =0 ° or 180 °, it means that no angle is generated between adjacent queues and adjacent queues do not tend to approach each other, and when
The time, the included angle is generated between the adjacent queues, and the adjacent queues contain the trend of mutual approaching.
9. The big data based student attendance management method according to claim 5 or 8, wherein: when a data vector is detected
And
included angle therebetween is
When the familiarity degree in the adjacent queues is detected to be higher than a third preset value, the familiarity degree between the adjacent queues is high, and the two branch queues are divided by the whole queue; when the familiarity degree of the adjacent teams is detected to be lower than a third preset value, two teams which are low in familiarity degree and are not formed by splitting of the whole team are indicated, and the fact that the queues deviate and the number of students not going out of the queues is indicated;
the judgment is made by the following formula:
k refers to the number of action interactions between adjacent queues,
it is referred to as the familiar coefficient,
refers to the overall familiarity between adjacent queues,
it refers to the degree of static familiarity,
refers to dynamic familiarity.
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