CN116091272B - Campus abnormal activity monitoring method, device, equipment and medium - Google Patents
Campus abnormal activity monitoring method, device, equipment and medium Download PDFInfo
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Abstract
The invention relates to the technical field of campus activity monitoring, in particular to a method, a device, equipment and a medium for monitoring campus abnormal activity, wherein the method comprises the following steps: acquiring monitoring video data; obtaining a first monitoring data set based on the video data; based on the monitoring video, a first in-out inverse difference value is obtained; obtaining an abnormal data report of a corresponding date according to the corrected gate inhibition inverse difference value and the first in-out inverse difference; according to the invention, the total number of students which are abnormal and not normally studied is obtained by analyzing personnel difference values of entering a teaching building and entering and exiting the teaching building in a normal course, and the card reading records of the students which are walked in a normal course and a study period are combined with the card reading records of the entrance guard unit of the school, and the card reading records of the walked-in are corrected through a playfield heat value mimicry map, so that the total number of the students which are abnormal and not normally studied in the walked-in is obtained, and the number of abnormal students and the composition condition of the students which are not normally studied are obtained.
Description
Technical Field
The invention relates to the technical field of campus activity monitoring, in particular to a method, a device, equipment and a medium for monitoring campus abnormal activity.
Background
Along with the national floor of the related policies of reducing the weight of learning burden of students in middle and primary schools, the teaching habit is greatly improved, but the floor of the policies still exists at present, the main reasons are that the monitoring means are limited, so that the implementation effect of the related policies cannot be effectively monitored, and along with the development of big data, artificial intelligence and image recognition technology, a new solution is provided for the monitoring of the abnormal teaching activities.
Disclosure of Invention
The invention aims to provide a campus abnormal activity monitoring method, device and equipment and a readable storage medium, so as to solve the problems.
In order to achieve the above purpose, the embodiment of the present application provides the following technical solutions:
in one aspect, an embodiment of the present application provides a campus abnormal activity monitoring method, where the method includes: acquiring a monitoring video of each teaching building gate in a first monitoring period during each course, wherein the course is a normal course learning time meeting a rule, and the monitoring video is video data shot by monitoring equipment arranged at each teaching building gate; classifying a plurality of monitoring data according to dates based on video data shot by monitoring equipment at the gate of each teaching building to obtain large monitoring data at the gate of each daily teaching building, and recording the large monitoring data as a first monitoring data set; sequentially analyzing the monitoring video of each teaching building gate in the first monitoring data to obtain a first in-out inverse difference value, wherein the first in-out inverse difference value is the difference value between the number of people entering and the number of people exiting during the course of all the teaching building gates in one day; acquiring a gate entrance guard inverse difference value corresponding to a first gate entrance guard inverse difference value and a corresponding playground heat value mimicry map, wherein the gate entrance guard inverse difference value is a difference value between the number of large gate entrance guard persons and the number of exiting persons in a school period on the same day as the first gate entrance guard inverse difference value, the period of school is a time period from learning to learning, which accords with a regulation, the playground heat value mimicry map is a playground population activity heat mimicry map in a second time period after learning on the same day as the first gate entrance guard inverse difference value, and the activity heat mimicry map is formed by shooting and combining a plurality of infrared cameras arranged on a playground and is used for reflecting the density of playground activity personnel in the second time period after learning; correcting the corresponding gate inhibition inverse difference value according to the corresponding playground heat value mimicry diagram to obtain a corrected gate inhibition inverse difference value, wherein the corrected gate inhibition inverse difference value is an inverse difference value of a gate inhibition caused by teacher's lesson or tutorial after the corresponding gate inhibition inverse difference value deducts the factors of the playground activity; obtaining an abnormal data report of a corresponding date according to the corrected entrance guard inverse difference value and the first entrance guard inverse difference value, wherein the abnormal data report comprises the total number of abnormal towns or supplementary courses, the walking birth duty ratio in the total number and the resident school birth duty ratio in the total number; and statistically analyzing abnormal data reports corresponding to each day in the first monitoring period, and grading abnormal activities to generate abnormal activity grading reports corresponding to each school.
Optionally, according to the analyzing the monitoring video of each teaching building gate in the first monitoring data in turn, obtaining a first in-out inverse difference value, including:
slicing the first monitoring data according to a preset time interval, and taking at least two continuously adjacent slices as vector analysis raw materials;
taking a slice with a first time sequence in the vector analysis raw material as a target locking slice for locking the number of personnel targets appearing on a current time slice, marking the personnel targets, and then analyzing a slice behind the slice with the first time sequence to obtain a motion vector of each marked target personnel;
determining the type of the motion vector of each marked target person based on the motion vector of the target person, and classifying the marked target person into an entering person or an exiting person according to the type of the motion vector;
continuously counting the entering personnel and the leaving personnel respectively until all slices of the first monitoring data are analyzed and counted to obtain the total entering personnel number and the leaving personnel number corresponding to the first monitoring data;
and calculating a first in-out inverse difference value according to the total number of entering people and the number of leaving people, wherein the first in-out inverse difference value is used for reflecting the total number of students possibly participating in a supplementary lesson or being towed hall on the day corresponding to the first monitoring data.
Optionally, correcting the corresponding gate inhibition inverse difference value according to the corresponding playfield calorific value mimicry chart includes:
constructing a correction model, and acquiring training parameters, wherein the training parameters comprise a playground heat value mimicry chart within 1 hour after learning in the last 3 months and a human value of activities of a walking and reading student in the last three months still in the playground after learning;
training the correction model through training parameters to obtain a trained correction model, wherein the correction model is used for calculating the number of people moving on the playground after learning according to the playground calorific value mimicry diagram of the same day, and the number of the first correction people is calculated;
the method comprises the steps of calculating the gate inhibition inverse difference value of the current day through a campus gate inhibition system, correcting the gate inhibition inverse difference value based on a first corrected number of people, obtaining the inverse difference value of the gate inhibition caused by that a reading person is not on time and is not on study due to the course supplement of a teacher or a tug hall, and recording the corrected gate inhibition inverse difference value.
Optionally, statistically analyzing the abnormal data report corresponding to each day in the first monitoring period, and classifying the abnormal activity, including:
and carrying out weighted analysis on the abnormal activity factor coefficient of each abnormal data report based on the total number of abnormal towns or supplementary lessons, the walking birth proportion in the total number and the resident school birth proportion in the total number, and carrying out evaluation analysis on the overall abnormal activity of the school in a first monitoring period by combining the occurrence frequency of the abnormal data report in the first monitoring period, and obtaining a corresponding abnormal activity evaluation grade and an abnormal activity rating report, wherein the abnormal activity rating report comprises the number of students involved in the abnormal activity of the school, the constitution condition of the students and the occurrence frequency of the abnormal activity.
In a second aspect, an embodiment of the present application provides a campus abnormal activity monitoring device, where the device includes:
the first acquisition module is used for acquiring a monitoring video of each teaching building gate in a daily class period in a first monitoring period, wherein the class period is a normal class learning time conforming to a rule, and the monitoring video is video data shot by monitoring equipment arranged at each teaching building gate;
the first classification module classifies a plurality of monitoring data according to dates based on video data shot by monitoring equipment at the gate of each teaching building to obtain large monitoring data at the gate of each teaching building every day, and records the large monitoring data as a first monitoring data set;
the first calculation module is used for sequentially analyzing the monitoring video of each teaching building gate in the first monitoring data to obtain a first in-out inverse difference value, wherein the first in-out inverse difference value is a difference value between the number of people entering and the number of people exiting during the course of all the teaching building gates in one day;
the second acquisition module is used for acquiring a gate entrance guard inverse difference value corresponding to the first gate entrance guard inverse difference value and a corresponding playground heat value mimicry map, wherein the corresponding gate entrance guard inverse difference value is a difference value between the number of entrance guard persons and the number of exit persons in a period of time of school, which is the same day as the first gate entrance guard inverse difference value, the period of time of learning to be learned is in accordance with a stipulation, the corresponding playground heat value mimicry map is a playground population activity heat mimicry map in a second period of time of learning to be learned, which is the same day as the first gate entrance guard inverse difference value, and the activity heat mimicry map is formed by shooting and combining a plurality of infrared cameras arranged on a playground and is used for reflecting the density of playground activity persons in the second period of time of learning;
The first correction module corrects the corresponding gate inhibition inverse difference value according to the corresponding playground heat value mimicry diagram to obtain a corrected gate inhibition inverse difference value, wherein the corrected gate inhibition inverse difference value is a gate inhibition inverse difference value of a gate inhibition caused by teacher's lesson supplement or tutorial after the corresponding gate inhibition inverse difference value deducts the factors of the playground activity;
the second calculation module is used for obtaining an abnormal data report of a corresponding date according to the corrected gate entrance guard inverse difference value and the first gate entrance guard inverse difference value, wherein the abnormal data report comprises the total number of abnormal towns or supplementary lessons, the walking-reading living occupation ratio in the total number and the resident school living occupation ratio in the total number;
and the third calculation module is used for statistically analyzing the abnormal data report corresponding to each day in the first monitoring period, classifying the abnormal activity grades, generating an abnormal activity grade report corresponding to each school, and sending the abnormal activity grade report to the city education data monitoring platform.
Optionally, the first computing module includes:
the first calculation unit is used for carrying out slicing processing on the first monitoring data according to a preset time interval, and taking at least two continuously adjacent slices as vector analysis raw materials;
The second calculation unit takes a slice with a first time sequence in the vector analysis raw material as a target locking slice, is used for locking the number of personnel targets appearing on the current time slice, marks the personnel targets, and then analyzes a slice behind the slice with the first time sequence to obtain a motion vector of each marked target personnel;
a third calculation unit that determines a type of a motion vector of each marked target person based on the motion vector of the target person, and classifies the marked target person as an entering person or an exiting person according to the type of the motion vector;
a fourth calculation unit for respectively and continuously counting the entering personnel and the leaving personnel until all the slices of the first monitoring data are analyzed and counted to obtain the total entering personnel number and the leaving personnel number corresponding to the first monitoring data;
and a fifth calculation unit for calculating a first in-out inverse difference value according to the total number of entering people and the number of leaving people, wherein the first in-out inverse difference value is used for reflecting the total number of students possibly participating in a course supplement or being towed in the day corresponding to the first monitoring data.
Optionally, the first correction module includes:
the first construction unit is used for constructing a correction model and acquiring training parameters, wherein the training parameters comprise a playground heat value mimicry chart within 1 hour after the study of nearly 3 months and a person number of a student who walks and goes for reading in nearly three months and still moves in the playground after the study;
The first training unit is used for training the correction model through training parameters to obtain a trained correction model, wherein the correction model is used for calculating the number of people who still move in the playground after learning by the reading student on the same day according to the playground heat value mimicry chart on the same day, and the number of the first correction people;
and the sixth calculation unit calculates the gate inhibition inverse difference value of the current day through the campus gate inhibition system, corrects the gate inhibition inverse difference value based on the first corrected number of people, and obtains the inverse difference value of the gate inhibition caused by the fact that the reading student is not in time for learning due to the course supplement of a teacher or the tug hall, and records the inverse difference value as the corrected gate inhibition inverse difference value.
Optionally, the third computing module includes:
the seventh calculation unit is used for carrying out weighted analysis on the abnormal activity factor coefficient of each abnormal data report based on the total number of abnormal towns or supplementary lessons, the walking birth proportion in the total number and the resident school birth proportion in the total number, carrying out evaluation analysis on the overall abnormal activity of the school in the first monitoring period by combining the occurrence frequency of the abnormal data report in the first monitoring period, and obtaining a corresponding abnormal activity evaluation grade and an abnormal activity rating report, wherein the abnormal activity rating report comprises the number of students involved in the abnormal activity of the school, the constitution condition of the students and the occurrence frequency of the abnormal activity. .
In a third aspect, an embodiment of the present application provides a campus abnormal activity monitoring device, where the device includes a memory and a processor.
The memory is used for storing a computer program; the processor is used for implementing the steps of the campus abnormal activity monitoring method when executing the computer program.
In a fourth aspect, an embodiment of the present application provides a medium, where a computer program is stored, where the computer program when executed by a processor implements the steps of the campus abnormal activity monitoring method described above.
The beneficial effects of the invention are as follows:
according to the invention, the total number of students who enter the teaching building and go into and out of the teaching building in the normal course is obtained by analyzing the difference value of the personnel entering the teaching building and getting out of the teaching building, the card-swiping records of the walkers in the normal course and the course time period are combined with the card-swiping records of the university entrance guard unit, the card-swiping records of the walkers in the course are corrected through the playfield calorific value mimicry map, the total number of the students who go out of the school abnormally is obtained, the number of abnormal students who go out of the school in the course and the composition condition of the students are obtained, and the abnormal course-repairing tugging activities of the school are rated by continuously monitoring the frequency of the conditions and the number of the personnel involved in a period at regular intervals, so that the abnormal teaching activities of the school are effectively monitored.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for monitoring abnormal campaigns according to the embodiment of the invention;
FIG. 2 is a schematic diagram of a campus abnormal activity monitoring device according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a campus abnormal activity monitoring device according to an embodiment of the present invention.
Description of the embodiments
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals or letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Before the explanation, a brief description needs to be made on the distribution of collectors and the distribution situation of the power equipment, a fixed number of test specimens are randomly selected from the power equipment in an area, corresponding collectors are arranged on the specimens to realize the implementation detection of the belt equipment, and then the specific types of the power equipment to be monitored are the same, such as transformers, cables, transformer boxes and the like, wherein the embodiment takes a high-voltage transformer as an explanation object, and the other power equipment can realize the identification of the partial discharge type of the same power equipment according to the related principles disclosed by the embodiment, and the description is not related.
Examples
As shown in fig. 1, the embodiment provides a campus abnormal activity monitoring method, which includes:
Step S1, acquiring a monitoring video of each teaching building gate in a daily class period in a first monitoring period, wherein the class period is normal class learning time conforming to regulations, and the monitoring video is video data shot by monitoring equipment arranged at each teaching building gate;
step S2, classifying a plurality of monitoring data according to dates based on video data shot by monitoring equipment at the gate of each teaching building to obtain large monitoring data at the gate of each teaching building every day, and recording the large monitoring data as a first monitoring data set;
secondly, it should be noted that, after the first monitoring data set is obtained, invalid data in the data needs to be removed, and a specific removing manner is as follows:
step S21, removing an invalid first monitoring data set based on first historical weather data in a first monitoring period, wherein the first historical weather data is located in weather data in a second time period in each day in the first monitoring period; specifically, if the weather data in the second time period of each day is heavy rain, it is determined that the abnormality of the first monitoring data due to the weather is not due to the tutorial or the supplementary lesson of the teacher, and the abnormal first monitoring data set does not have an analytical meaning, so that the first monitoring data set needs to be removed.
By monitoring weather conditions, especially in rainy days, for a certain period of time, for example, within 1 hour after school, a large number of students will stay in the teaching building for the reason that they do not take an umbrella, and this will inevitably lead to abnormal travel of the monitored data, which is not considered to be the cause, and therefore the data needs to be rejected.
Step S3, sequentially analyzing the monitoring video of each teaching building gate in the first monitoring data to obtain a first in-out inverse difference value, wherein the first in-out inverse difference value is a difference value between the number of people entering and the number of people exiting during the course of all the teaching building gates in one day;
s4, acquiring a gate entrance guard inverse difference value corresponding to the first gate entrance guard inverse difference value and a corresponding playground heat value mimicry map, wherein the gate entrance guard inverse difference value is a difference value between the number of entrance guard persons and the number of exit persons in a period of time of school, which is the same as the first gate entrance guard inverse difference value, and the period of time of school is a time period from learning to learning, which accords with a rule, and the playground heat value mimicry map is a playground population activity heat mimicry map in a second time period after learning, which is the same as the first gate entrance guard inverse difference value, and the activity heat mimicry map is formed by shooting and combining a plurality of infrared cameras arranged on a playground and is used for reflecting the density of playground activity persons in the second time period after learning;
S5, correcting the corresponding gate inhibition inverse difference value according to the corresponding playground heat value mimicry diagram to obtain a corrected gate inhibition inverse difference value, wherein the corrected gate inhibition inverse difference value is an inverse difference value of a gate inhibition caused by teacher' S lesson supplement or tutorial after the corresponding gate inhibition inverse difference value deducts the factors of the playground activity;
s6, obtaining an abnormal data report of a corresponding date according to the corrected gate entrance guard inverse difference value and the first gate entrance inverse difference value, wherein the abnormal data report comprises the total number of abnormal towns or supplementary courses, the walking-reading living occupation ratio in the total number and the resident living occupation ratio in the total number;
and S7, statistically analyzing abnormal data reports corresponding to each day in the first monitoring period, classifying the abnormal activity grades, generating abnormal activity grade reports corresponding to each school, and sending the abnormal activity grade reports to the city education data monitoring platform.
In summary, the overall idea of steps S1-S7 is to obtain the video of the entrance and exit of the teaching building in the normal course through the cameras at each teaching place of the school, and then count the total entrance and exit population difference in the course of each day, the teacher severely towns or supplements the course to cause the entrance population in the normal course to be larger than the exit population, and further obtain the total number of students in the course of each day towning or supplementing the course, and then analyze the personnel constitution of the students in the towning or supplementing course, specifically divide the students in the part into remaining school students and free school students, the remaining school students leave the school because the monitoring and counting difficulty is large in 1 hour after the school, and the free school students leave the school in most 1 hour after the school, so the statistics is more convenient, the way is to count the inverse difference in the period from the school time to the school through the entrance guard unit, obtaining total number of the free radicals which do not leave the school normally, wherein the free radicals comprise free radicals which do not leave the school normally caused by being towed or the supplementary lessons and free radicals which play on the playground after being learned, so that the free radicals which do not leave the school normally need to be counted, the total number of the free radicals which do not leave the school normally is corrected, the free radicals become feasible along with the development of big data and artificial intelligence, specifically, the occupied number of the free radicals in playground activity personnel in 1 hour after being learned by artificial sampling and researching the recent march, meanwhile, the density mimicry diagram of the playground personnel in three months is generated, the constructed neural network model is trained by the data, the neural network model can have more daily activity thermal mimicry diagrams, how much free radicals do not leave the school in 1 hour after being learned today is obtained, and the system can evaluate the abnormal activity level of the school by a weighted rating mode according to various factors such as the number of students, the composition of the students, the frequency of occurrence of abnormality in one month or one quarter and the like designed in each class or class.
Next, in step S3, according to the monitoring video at each teaching building gate in the first monitoring data, a specific implementation manner of obtaining the first in-out inverse difference value may be:
s31, slicing the first monitoring data according to a preset time interval, and taking at least two continuously adjacent slices as vector analysis raw materials;
step S32, taking a slice with a first time sequence in the vector analysis raw material as a target locking slice, locking the number of personnel targets appearing on a current time slice, marking the personnel targets, and analyzing a slice behind the slice with the first time sequence to obtain a motion vector of each marked target personnel;
step S33, judging the type of the motion vector of each marked target person based on the motion vector of the target person, and classifying the marked target person into an entering person or an exiting person according to the type of the motion vector;
step S34, continuously counting the entering personnel and the exiting personnel respectively until all slices of the first monitoring data are analyzed and counted, and obtaining the total entering personnel number and the exiting personnel number corresponding to the first monitoring data;
And step S35, calculating a first business turn over reverse difference value according to the total number of entering people and the number of leaving people, wherein the first business turn over reverse difference value is used for reflecting the total number of students possibly participating in a course supplement or being towed in the day corresponding to the first monitoring data.
Next, in step S5, a specific implementation manner of correcting the corresponding gate inhibition inverse difference value according to the corresponding playfield calorific value mimicry map may be:
step S51, constructing a correction model, and acquiring training parameters, wherein the training parameters comprise a playground heat value mimicry chart within 1 hour after the study of nearly 3 months and a person number of a student who walks and goes for reading in nearly three months and still moves in the playground after the study;
step S52, training the correction model through training parameters to obtain a trained correction model, wherein the correction model is used for calculating the number of people who keep moving in the playground after learning on the same day according to the playground heat value mimicry diagram on the same day, and the number of the first correction people;
and step S53, calculating the gate inhibition inverse difference value of the current day through a campus gate inhibition system, correcting the gate inhibition inverse difference value based on the first corrected number of people to obtain an inverse difference value of the gate inhibition caused by the fact that a reading person is not on time and is released due to the course supplement of a teacher or the tug, and recording the inverse difference value as the corrected gate inhibition inverse difference value.
Next, in step S7, a specific implementation manner of statistically analyzing the abnormal data report corresponding to each day in the first monitoring period and classifying the abnormal activity level may be:
and step S71, carrying out weighted analysis on the abnormal activity factor coefficient of each abnormal data report based on the total number of abnormal towns or supplementary courses, the walking birth proportion in the total number and the resident school birth proportion in the total number, carrying out evaluation analysis on the overall abnormal activity of the school in a first monitoring period by combining the occurrence frequency of the abnormal data report in the first monitoring period, and obtaining a corresponding abnormal activity evaluation grade and an abnormal activity rating report, wherein the abnormal activity rating report comprises the number of students involved in the abnormal activity of the school, the constitution condition of the students and the occurrence frequency of the abnormal activity.
In the embodiment, the total number of students which are abnormally and abnormally studied is obtained by analyzing the difference value of the personnel entering the teaching building and entering the teaching building during normal teaching, the card swiping record of the students which are abnormally and abnormally studied in the normal learning and learning time period is combined with the card swiping record of the students which are walked and read in the normal learning and learning time period recorded by the entrance guard unit of the school, the card swiping record of the students which are walked and read in the learning is corrected through the playground heat value mimicry chart, the total number of the students which are abnormally and abnormally studied in the learning is obtained, the number of abnormal students and the composition condition of the students which are abnormally studied in the same day are obtained, and the abnormal teaching and teaching activities of the school are rated by continuously monitoring the frequency of the conditions and the number of the related personnel in a period regularly.
Examples
As shown in fig. 2, this embodiment provides a campus abnormal activity monitoring device, which includes:
the first obtaining module 71 obtains a monitoring video of each teaching building gate in a first monitoring period during each course, wherein the course is a normal course learning time conforming to a rule, and the monitoring video is video data shot by monitoring equipment arranged at each teaching building gate;
the first classification module 72 classifies the plurality of monitoring data according to dates based on the video data shot by the monitoring devices at the gates of the teaching buildings to obtain large monitoring data at the gates of the teaching buildings every day, and records the large monitoring data as a first monitoring data set;
the first calculation module 73 sequentially analyzes the monitoring video at each teaching building gate in the first monitoring data to obtain a first in-out inverse difference value, wherein the first in-out inverse difference value is a difference value between the number of people entering and the number of people exiting during the course of all the teaching building gates in one day;
a second obtaining module 74, configured to obtain a gate entrance guard inverse difference value corresponding to the first gate entrance guard inverse difference value and a corresponding playground heat value mimicry map, where the corresponding gate entrance guard inverse difference value is a difference value between a number of entrance persons and a number of exit persons of a large gate during a school period that is the same day as the first gate entrance guard inverse difference value, the period of school is a time period from learning to learning, and the corresponding playground heat value mimicry map is a playground population activity heat mimicry map in a second time period after learning and the first gate entrance guard inverse difference value is the same day, and the activity heat mimicry map is formed by shooting and combining a plurality of infrared cameras arranged on a playground, and is used for reflecting densities of playground activity staff in the second time period after learning;
A first correction module 75, configured to correct the corresponding gate inhibition inverse difference value according to the corresponding playground calorific value simulated graph, to obtain a corrected gate inhibition inverse difference value, where the corrected gate inhibition inverse difference value is an inverse difference value of a gate inhibition caused by a teacher's lesson or tutorial after the corresponding gate inhibition inverse difference value deducts a factor of going on a playground activity;
a second calculation module 76, according to the corrected gate entrance guard inverse difference value and the first gate entrance guard inverse difference value, obtaining an abnormal data report of a corresponding date, wherein the abnormal data report comprises the total number of abnormal towns or supplementary lessons, the reading going-in ratio in the total number and the resident school going-in ratio in the total number;
the third calculation module 77 statistically analyzes the abnormal data report corresponding to each day in the first monitoring period, classifies the abnormal activity report, generates an abnormal activity rating report corresponding to each school, and transmits the abnormal activity rating report to the city education data monitoring platform.
Optionally, the first computing module 73 includes:
a first calculation unit 731 that performs slicing processing on the first monitoring data at a preset time interval, and uses at least two continuously adjacent slices as vector analysis raw materials;
A second calculation unit 732, which uses the slice with the first time sequence in the vector analysis raw material as a target locking slice, is used for locking the number of personnel targets appearing on the current time slice, marks the number of personnel targets, and then analyzes the slice behind the slice with the first time sequence to obtain the motion vector of each marked target personnel;
a third calculation unit 733 that determines a type of a motion vector of each marked target person based on the motion vector of the target person, and classifies the marked target person as an entering person or an exiting person according to the type of the motion vector;
a fourth calculating unit 734, configured to continuously count the entering person and the exiting person respectively, until all slices of the first monitoring data are analyzed and counted, to obtain a total number of entering persons and a total number of exiting persons corresponding to the first monitoring data;
the fifth calculating unit 735 calculates a first in-out inverse difference value according to the total number of entering people and the number of leaving people, where the first in-out inverse difference value is used to reflect the total number of students possibly participating in a course or being towed in the course on the day corresponding to the first monitoring data.
Optionally, the first correction module 75 includes:
The first construction unit 751 is used for constructing a correction model and acquiring training parameters, wherein the training parameters comprise a playground heat value mimicry chart within 1 hour after the study of the last 3 months and the numerical values of people who walk and read for the last three months and still move in the playground after the study;
the first training unit 752 is used for training the correction model through training parameters to obtain a trained correction model, wherein the correction model is used for calculating the number of people of the practice life still moving in the playground after learning according to the playground heat value mimicry diagram of the same day, and the number of first correction people;
the sixth calculating unit 753 calculates the gate inhibition inverse difference value of the current day through the campus gate inhibition system, corrects the gate inhibition inverse difference value based on the first corrected number of people, and obtains the inverse difference value of the gate inhibition caused by the fact that the reading student is not on time and is released due to the teacher's lesson supplement or tug hall, and records the inverse difference value as the corrected gate inhibition inverse difference value.
Optionally, the third computing module 77 includes:
the seventh calculation unit 771 performs weighted analysis on the abnormal activity factor coefficient of each abnormal data report based on the total number of abnormal towns or supplementary lessons, the walking birth proportion in the total number of people and the resident school birth proportion in the total number of people, performs evaluation analysis on the overall abnormal activity of the school in the first monitoring period by combining the occurrence frequency of the abnormal data report in the first monitoring period, and obtains a corresponding abnormal activity evaluation grade and abnormal activity rating report, wherein the abnormal activity rating report comprises the number of students involved in the abnormal activity of the school, the constitution condition of the students and the occurrence frequency of the abnormal activity.
It should be noted that, regarding the apparatus in the above embodiments, the specific manner in which the respective modules perform the operations has been described in detail in the embodiments regarding the method, and will not be described in detail herein.
Examples
Corresponding to the above method embodiment, the embodiment of the present disclosure further provides a campus abnormal activity monitoring device, where a campus abnormal activity monitoring device described below and a campus abnormal activity monitoring method described above may be referred to correspondingly.
Fig. 3 is a block diagram illustrating a campus abnormal activity monitoring device 800, according to an example embodiment. As shown in fig. 3, the electronic device 800 may include: a processor 801, a memory 802. The electronic device 800 may also include one or more of a multimedia component 803, an i/O interface 804, and a communication component 805.
The processor 801 is configured to control overall operation of the electronic device 800 to perform all or part of the steps in the campus abnormal activity monitoring method described above. The memory 802 is used to store various types of data to support operation at the electronic device 800, which may include, for example, instructions for any application or method operating on the electronic device 800, as well as application-related data, such as contact data, messages sent and received, pictures, audio, video, and so forth. The Memory 802 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted through the communication component 805. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the electronic device 800 and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near FieldCommunication, NFC for short), 2G, 3G or 4G, or a combination of one or more thereof, the respective communication component 805 may thus comprise: wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the electronic device 800 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), digital signal processors (DigitalSignal Processor, abbreviated as DSP), digital signal processing devices (Digital Signal Processing Device, abbreviated as DSPD), programmable logic devices (Programmable Logic Device, abbreviated as PLD), field programmable gate arrays (Field Programmable Gate Array, abbreviated as FPGA), controllers, microcontrollers, microprocessors, or other electronic components for performing the campus abnormal activity monitoring method described above.
In another exemplary embodiment, a computer readable storage medium is also provided that includes program instructions that, when executed by a processor, implement the steps of the campus abnormal activity monitoring method described above. For example, the computer readable storage medium may be the memory 802 described above including program instructions executable by the processor 801 of the electronic device 800 to perform the campus abnormal activity monitoring method described above.
Examples
Corresponding to the above method embodiments, the present disclosure further provides a readable storage medium, where a readable storage medium described below and a campus abnormal activity monitoring method described above may be referred to correspondingly.
A readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the campus abnormal activity monitoring method of the above method embodiment.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, and the like.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A method for monitoring abnormal campaigns, the method comprising:
acquiring a monitoring video of each teaching building gate in a first monitoring period during each course, wherein the course is a normal course learning time meeting a rule, and the monitoring video is video data shot by monitoring equipment arranged at each teaching building gate;
Classifying a plurality of monitoring data according to dates based on video data shot by monitoring equipment at the gate of each teaching building to obtain large monitoring data at the gate of each daily teaching building, and recording the large monitoring data as a first monitoring data set;
analyzing the monitoring video of each teaching building gate in the first monitoring data in sequence to obtain a first in-out inverse difference value, comprising:
slicing the first monitoring data according to a preset time interval, and taking at least two continuously adjacent slices as vector analysis raw materials;
taking a slice with a first time sequence in the vector analysis raw material as a target locking slice for locking the number of personnel targets appearing on a current time slice, marking the personnel targets, and then analyzing a slice behind the slice with the first time sequence to obtain a motion vector of each marked target personnel;
determining the type of the motion vector of each marked target person based on the motion vector of the target person, and classifying the marked target person into an entering person or an exiting person according to the type of the motion vector;
continuously counting the entering personnel and the leaving personnel respectively until all slices of the first monitoring data are analyzed and counted to obtain the total entering personnel number and the leaving personnel number corresponding to the first monitoring data;
Calculating a first entrance-exit inverse difference value according to the total entrance personnel number and the exit personnel number, wherein the first entrance-exit inverse difference value is a difference value between the entrance number and the exit number of all teaching building gates in a class in one day and is used for reflecting the total number of students possibly participating in a supplementary class or being towed in the same day as the first monitoring data;
acquiring a gate entrance guard inverse difference value corresponding to a first gate entrance guard inverse difference value and a corresponding playground heat value mimicry map, wherein the gate entrance guard inverse difference value is a difference value between the number of large gate entrance guard persons and the number of exiting persons in a school period on the same day as the first gate entrance guard inverse difference value, the period of school is a time period from learning to learning, which accords with a regulation, the playground heat value mimicry map is a playground population activity heat mimicry map in a second time period after learning on the same day as the first gate entrance guard inverse difference value, and the activity heat mimicry map is formed by shooting and combining a plurality of infrared cameras arranged on a playground and is used for reflecting the density of playground activity personnel in the second time period after learning;
correcting the corresponding gate inhibition inverse difference value according to the corresponding playground heat value mimicry diagram to obtain a corrected gate inhibition inverse difference value, wherein the corrected gate inhibition inverse difference value is an inverse difference value of a gate inhibition caused by teacher's lesson or tutorial after the corresponding gate inhibition inverse difference value deducts the factors of the playground activity;
Obtaining an abnormal data report of a corresponding date according to the corrected entrance guard inverse difference value and the first entrance guard inverse difference value, wherein the abnormal data report comprises the total number of abnormal towns or supplementary courses, the walking birth duty ratio in the total number and the resident school birth duty ratio in the total number;
and statistically analyzing abnormal data reports corresponding to each day in the first monitoring period, and grading abnormal activities to generate abnormal activity grading reports corresponding to each school.
2. The campus abnormal activity monitoring method according to claim 1, wherein correcting the corresponding gate inhibition inverse difference value according to the corresponding playfield calorific value mimicry map comprises:
constructing a correction model, and acquiring training parameters, wherein the training parameters comprise a playground heat value mimicry chart within 1 hour after learning in the last 3 months and a human value of activities of a walking and reading student in the last three months still in the playground after learning;
training the correction model through training parameters to obtain a trained correction model, wherein the correction model is used for calculating the number of people moving on the playground after learning according to the playground calorific value mimicry diagram of the same day, and the number of the first correction people is calculated;
The method comprises the steps of calculating the gate inhibition inverse difference value of the current day through a campus gate inhibition system, correcting the gate inhibition inverse difference value based on a first corrected number of people, obtaining the inverse difference value of the gate inhibition caused by the fact that a student is read by a teacher for a lesson or a tug for a hall is not on time, and recording the inverse difference value as the corrected gate inhibition inverse difference value.
3. The method for monitoring abnormal activity of campus of claim 1, wherein statistically analyzing and grading abnormal activity reports corresponding to each day in the first monitoring period comprises:
and carrying out weighted analysis on the abnormal activity factor coefficient of each abnormal data report based on the total number of abnormal towns or supplementary lessons, the walking birth proportion in the total number and the resident school birth proportion in the total number, and carrying out evaluation analysis on the overall abnormal activity of the campus in a first monitoring period by combining the occurrence frequency of the abnormal data report in the first monitoring period, and obtaining a corresponding abnormal activity evaluation grade and an abnormal activity rating report, wherein the abnormal activity rating report comprises the number of students involved in the abnormal activity of the campus, the constitution condition of the students and the occurrence frequency of the abnormal activity.
4. The apparatus for monitoring the abnormal campus activity according to claim 1, wherein the apparatus for monitoring comprises:
The first acquisition module is used for acquiring a monitoring video of each teaching building gate in a daily class period in a first monitoring period, wherein the class period is a normal class learning time conforming to a rule, and the monitoring video is video data shot by monitoring equipment arranged at each teaching building gate;
the first classification module classifies a plurality of monitoring data according to dates based on video data shot by monitoring equipment at the gate of each teaching building to obtain large monitoring data at the gate of each teaching building every day, and records the large monitoring data as a first monitoring data set;
a first computing module comprising:
the first calculation unit is used for carrying out slicing processing on the first monitoring data according to a preset time interval, and taking at least two continuously adjacent slices as vector analysis raw materials;
the second calculation unit takes a slice with a first time sequence in the vector analysis raw material as a target locking slice, is used for locking the number of personnel targets appearing on the current time slice, marks the personnel targets, and then analyzes a slice behind the slice with the first time sequence to obtain a motion vector of each marked target personnel;
a third calculation unit that determines a type of a motion vector of each marked target person based on the motion vector of the target person, and classifies the marked target person as an entering person or an exiting person according to the type of the motion vector;
A fourth calculation unit for respectively and continuously counting the entering personnel and the leaving personnel until all the slices of the first monitoring data are analyzed and counted to obtain the total entering personnel number and the leaving personnel number corresponding to the first monitoring data;
a fifth calculation unit, configured to calculate a first entering and exiting inverse difference value according to the total number of entering people and the total number of exiting people, where the first entering and exiting inverse difference value is a difference value between the number of entering people and the number of exiting people of all teaching building gates in a course of a day, and is used to reflect a total number of students possibly participating in a supplementary course or being towed in a hall on a day corresponding to the first monitoring data;
the second acquisition module is used for acquiring a gate entrance guard inverse difference value corresponding to the first gate entrance guard inverse difference value and a corresponding playground heat value mimicry map, wherein the corresponding gate entrance guard inverse difference value is a difference value between the number of entrance guard persons and the number of exit persons in a period of time of school, which is the same day as the first gate entrance guard inverse difference value, the period of time of learning to be learned is in accordance with a stipulation, the corresponding playground heat value mimicry map is a playground population activity heat mimicry map in a second period of time of learning to be learned, which is the same day as the first gate entrance guard inverse difference value, and the activity heat mimicry map is formed by shooting and combining a plurality of infrared cameras arranged on a playground and is used for reflecting the density of playground activity persons in the second period of time of learning;
The first correction module corrects the corresponding gate inhibition inverse difference value according to the corresponding playground heat value mimicry diagram to obtain a corrected gate inhibition inverse difference value, wherein the corrected gate inhibition inverse difference value is a gate inhibition inverse difference value of a gate inhibition caused by teacher's lesson supplement or tutorial after the corresponding gate inhibition inverse difference value deducts the factors of the playground activity;
the second calculation module is used for obtaining an abnormal data report of a corresponding date according to the corrected gate entrance guard inverse difference value and the first gate entrance guard inverse difference value, wherein the abnormal data report comprises the total number of abnormal towns or supplementary lessons, the walking-reading living occupation ratio in the total number and the resident school living occupation ratio in the total number;
and the third calculation module is used for statistically analyzing the abnormal data report corresponding to each day in the first monitoring period, classifying the abnormal activity grades, generating an abnormal activity grade report corresponding to each school, and sending the abnormal activity grade report to the city education data monitoring platform.
5. The monitoring device of claim 4, wherein the first correction module comprises:
the first construction unit is used for constructing a correction model and acquiring training parameters, wherein the training parameters comprise a playground heat value mimicry chart within 1 hour after the study of nearly 3 months and a person number of a student who walks and goes for reading in nearly three months and still moves in the playground after the study;
The first training unit is used for training the correction model through training parameters to obtain a trained correction model, wherein the correction model is used for calculating the number of people who still move in the playground after learning by the reading student on the same day according to the playground heat value mimicry chart on the same day, and the number of the first correction people;
and the sixth calculation unit calculates the gate inhibition inverse difference value of the current day through the campus gate inhibition system, corrects the gate inhibition inverse difference value based on the first corrected number of people, and obtains the inverse difference value of the gate inhibition for reflecting the fact that the student is not on time to learn due to the course of a teacher or a tutor, and the inverse difference value is recorded as the corrected gate inhibition inverse difference value.
6. The monitoring device of claim 4, wherein the third computing module comprises:
the seventh calculation unit is used for carrying out weighted analysis on the abnormal activity factor coefficient of each abnormal data report based on the total number of abnormal towns or supplementary courses, the walking birth proportion in the total number and the resident school birth proportion in the total number, carrying out evaluation analysis on the overall abnormal activity of the campus in the first monitoring period by combining the occurrence frequency of the abnormal data report in the first monitoring period, and obtaining a corresponding abnormal activity evaluation grade and an abnormal activity rating report, wherein the abnormal activity rating report comprises the number of students involved in the abnormal activity of the campus, the constitution condition of the students and the occurrence frequency of the abnormal activity.
7. An electronic device comprising a memory and a processor,
the memory is used for storing a computer program;
the processor is configured to implement the method of any one of claims 1-3 when executing a program stored on the memory.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method of any of claims 1-3.
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