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CN-121999436-A - Campus three-dimensional visual intelligent management method based on digital twinning

CN121999436ACN 121999436 ACN121999436 ACN 121999436ACN-121999436-A

Abstract

The application discloses a digital twin-based campus three-dimensional visual intelligent management method, which relates to the technical field of campus management and comprises the steps of monitoring each area in a campus to obtain a monitoring picture; the method comprises the steps of counting a monitoring picture to obtain initial monitoring data, carrying out element judgment on the initial monitoring data to obtain element data, matching area attributes of the initial monitoring data with the element data to obtain area labels, carrying out area label judgment on picture data to be analyzed, judging that no security problem exists in the picture data to be analyzed if the area labels are met, carrying out entropy judgment on the picture data to be analyzed according to the area labels if the area labels are not met, determining entropy change of the picture data to be analyzed, judging that no security problem exists in the picture data to be analyzed if the entropy of the picture data to be analyzed is reduced, and judging that the security problem exists and outputting an alarm signal if the entropy is increased. The intelligent campus security performance improvement method has the effect of improving the intelligent campus security performance.

Inventors

  • LIAO JIDONG

Assignees

  • 河南省总工会干部学校

Dates

Publication Date
20260508
Application Date
20260128

Claims (7)

  1. 1. The campus three-dimensional visual intelligent management method based on digital twinning is characterized by comprising the following steps of: Monitoring each area in the campus based on the high-definition camera to obtain a monitoring picture with a time attribute, marking the monitoring picture at the current time point as picture data to be analyzed according to the time attribute, and marking the monitoring picture at the historical time point as historical picture data; Based on a preset time length, counting the monitoring pictures under initial monitoring to obtain initial monitoring data; element judgment is carried out on the initial monitoring data, and element data in the initial monitoring data is determined, wherein the element data comprises dynamic elements and static elements; Acquiring the region attribute corresponding to the initial monitoring data, and matching the region attribute of the initial monitoring data with the element data to obtain a region label of a region corresponding to the initial monitoring data; Judging the region label of the picture data to be analyzed, determining whether the content in the picture data to be analyzed meets the corresponding region label, and if so, judging that the picture data to be analyzed has no security problem; If the content in the picture data to be analyzed does not meet the corresponding area label, carrying out entropy judgment on the picture data to be analyzed according to the area label, comparing the entropy value of the picture data to be analyzed with the entropy value of the historical picture data, and determining the entropy value change of the picture data to be analyzed; if the entropy value of the picture data to be analyzed is reduced, judging that the security problem does not exist in the picture data to be analyzed; If the entropy value of the picture data to be analyzed is increased, judging that the security problem exists in the picture data to be analyzed, and outputting a corresponding alarm signal according to the position of the area corresponding to the picture data to be analyzed.
  2. 2. The campus three-dimensional visualized intelligent management method based on digital twinning according to claim 1, wherein the matching the region attribute of the initial monitoring data with the element data to obtain the region label of the region corresponding to the initial monitoring data comprises the following steps: Based on the region attribute, matching the static element appearing in the initial monitoring data with the region attribute, and determining whether the static element appearing in the initial monitoring data meets the region characteristic of the region attribute; If the static element in the initial monitoring data meets the regional characteristics of the regional attribute, carrying out magnitude matching on the dynamic element according to the regional attribute, and determining whether the magnitude of the dynamic element in the initial monitoring data meets the regional characteristics of the regional attribute; If the magnitude of the dynamic element accords with the regional characteristic of the regional attribute, the regional attribute is used as a regional label of the region; If the magnitude of the dynamic element is judged to be not in accordance with the regional characteristics of the regional attribute, marking the regional characteristics according to the magnitude of the dynamic element, and correcting to obtain a regional label of the region; if the static element in the initial monitoring data cannot meet the regional characteristics of the regional attribute, reconstructing the regional attribute of the region according to the static element and the dynamic element to obtain the regional label of the region.
  3. 3. The method for three-dimensional visualized intelligent campus management based on digital twinning according to claim 2, wherein if the magnitude of the dynamic element is determined not to be in accordance with the regional characteristics of the regional attribute, the regional characteristics are marked according to the magnitude of the dynamic element, and the regional label of the region is obtained by correction, comprising: If the magnitude of the dynamic element does not accord with the regional characteristics of the regional attribute, determining the numerical relation between the magnitude of the dynamic element and the regional characteristics of the regional attribute; If the region characteristic corresponding to the region attribute is that the magnitude increases to be positive, and when the magnitude of the dynamic element is smaller than the region characteristic of the region attribute, determining that the numerical relationship between the magnitude of the dynamic element and the region characteristic of the region attribute is a first inverse relationship; if the region characteristic corresponding to the region attribute is that the magnitude is reduced to be positive, and when the magnitude of the dynamic element is larger than the region characteristic of the region attribute, judging that the numerical relation between the magnitude of the dynamic element and the region characteristic of the region attribute is a second inverse relation; And adding data information to the regional characteristics of the region according to the first inverse relation and the second inverse relation in the numerical relation to obtain the regional label of the region.
  4. 4. The campus three-dimensional visualized intelligent management method based on digital twinning is characterized in that if it is determined that a static element in initial monitoring data cannot meet the regional characteristics of the regional attribute, the regional attribute of the region is reconstructed according to the static element and a dynamic element to obtain a regional label of the region, the method comprises the steps of constructing a basic attribute of the region based on the static element, constructing an active attribute of the region based on the dynamic element, superposing the basic attribute and the active attribute, and determining the regional label of the region.
  5. 5. The method for three-dimensional visualized intelligent campus management based on digital twinning of claim 4, wherein the determining the region label of the picture data to be analyzed, determining whether the content in the picture data to be analyzed meets the corresponding region label, if yes, determining that the picture data to be analyzed has no security problem comprises: When the content in the picture data to be analyzed meets the regional label, selecting the picture data to be analyzed corresponding to the regional label with the built-in label according to the label information built in the regional label, and marking the selected picture data to be analyzed as second picture data to be analyzed; Performing security monitoring on the second data to be analyzed, if the numerical relation contained in the regional label of the second data to be analyzed is a first inverse relation, performing human identification on the dynamic elements, and if the dynamic elements are identified to be human, judging the number of individuals of the human; If the number of individuals of the human beings is smaller than or equal to a preset threshold, performing independence judgment on the individuals of the human beings, performing limb behavior judgment on the individuals with the independence, if the limb behavior of the individuals with the independence does not meet the built-in normal behavior standard, judging that the security problem exists in the second data to be analyzed, and outputting an alarm signal; judging the response behaviors of the human individuals without independence, if the response behaviors of the human individuals without independence exceed the built-in response standard, judging that the security problem exists in the second data to be analyzed, and outputting an alarm signal; if the number of individuals of the human beings is larger than a preset threshold, entropy judgment is carried out on the human population in the area.
  6. 6. The method for three-dimensional visualized intelligent campus management based on digital twinning according to claim 5, wherein the step of independently judging the human individuals is characterized by comprising the following steps: Based on the identified human individuals, determining the visual range of each human individual in the picture data to be analyzed; Marking human individuals in the picture data to be analyzed according to the visual range, if the human individuals are not in the visual range of each human individual, judging that the human individuals have independence, otherwise, judging that the human individuals do not have independence.
  7. 7. The campus three-dimensional visualized intelligent management method based on digital twinning according to claim 1, wherein the determining that the security problem does not exist in the picture data to be analyzed if the entropy value of the picture data to be analyzed is reduced comprises the following steps: When the entropy value of the picture data to be analyzed is reduced, carrying out magnitude identification on the dynamic elements in the picture data to be analyzed to obtain first magnitude data; Comparing the first magnitude data with the second magnitude data, and if the first magnitude data is not smaller than the second magnitude data, judging that the security problem does not exist in the picture data to be analyzed; If the first magnitude data is smaller than the second magnitude data, the corresponding dynamic element magnitude random selection is carried out on the historical picture data according to the first magnitude data, so that the historical data to be judged is obtained; Entropy judgment is carried out on the historical data to be judged, and second historical entropy value data are obtained; comparing the entropy value of the picture data to be analyzed with the second historical entropy value data, and if the entropy value of the picture data to be analyzed is larger than the second historical entropy value data, judging that the reason for the entropy value reduction of the picture data to be analyzed is that the dynamic element magnitude is reduced; if the entropy value of the picture data to be analyzed is not greater than the second historical entropy value data, judging that the reason for the entropy value reduction of the picture data to be analyzed is that the dynamic element distribution is more ordered; When judging that the entropy value of the picture data to be analyzed is reduced due to the dynamic element value, judging that the picture data to be analyzed has a safety problem, and outputting an alarm signal; When the reason for the entropy reduction of the picture data to be analyzed is judged to be that the dynamic element distribution is more ordered, the picture data to be analyzed is judged to have no safety problem.

Description

Campus three-dimensional visual intelligent management method based on digital twinning Technical Field The application relates to the technical field of campus management, in particular to a digital twinning-based campus three-dimensional visual intelligent management method. Background In the current age, digitization and intellectualization technologies represent a rapidly developing situation. Under the background, the defects of the traditional campus management mode are more remarkable, the management mode and means are relatively lagged, and the modern universities are difficult to adapt to the operation demands of the modern universities, which are increasingly complex and diversified in the development process. The problems of low management efficiency, unreasonable resource allocation, poor experience of teachers and students and the like are caused. In order to effectively solve the problems, the overall management efficiency of a university campus is improved, and the digital twin technology realizes real-time monitoring of a real environment by constructing a real-time mapping relation between a physical entity and a virtual model. By virtue of the unique advantages and strong functions, the method gradually shows very wide application prospect and huge application potential in the brand-new corner of the campus integrated service management field. In the related technology, the inner space of the campus is monitored by utilizing the high-definition camera, the monitored monitoring picture is processed by utilizing the digital twin technology, a three-dimensional visualized model of the campus is constructed, and the conditions of the campus, such as resource consumption, personnel flow and the like, are intuitively observed by utilizing the three-dimensional visualized model of the campus, so that the campus is managed in a targeted mode. And when the safety problem analysis is carried out on the monitored monitoring picture according to the high-definition camera, the rule is directly matched through the built-in fixed problem rule, so that when the safety problem in the monitoring picture exceeds the problem rule, whether the safety problem exists on the monitoring picture cannot be accurately identified, the safety performance of the intelligent campus is reduced, and improvement exists. Disclosure of Invention In order to improve security performance of an intelligent campus, the application provides a digital twinning-based campus three-dimensional visual intelligent management method. The application provides a campus three-dimensional visual intelligent management method based on digital twinning, which adopts the following technical scheme: A campus three-dimensional visual intelligent management method based on digital twinning comprises the following steps: Monitoring each area in the campus based on the high-definition camera to obtain a monitoring picture with a time attribute, marking the monitoring picture at the current time point as picture data to be analyzed according to the time attribute, and marking the monitoring picture at the historical time point as historical picture data; Based on a preset time length, counting the monitoring pictures under initial monitoring to obtain initial monitoring data; element judgment is carried out on the initial monitoring data, and element data in the initial monitoring data is determined, wherein the element data comprises dynamic elements and static elements; Acquiring the region attribute corresponding to the initial monitoring data, and matching the region attribute of the initial monitoring data with the element data to obtain a region label of a region corresponding to the initial monitoring data; Judging the region label of the picture data to be analyzed, determining whether the content in the picture data to be analyzed meets the corresponding region label, and if so, judging that the picture data to be analyzed has no security problem; If the content in the picture data to be analyzed does not meet the corresponding area label, carrying out entropy judgment on the picture data to be analyzed according to the area label, comparing the entropy value of the picture data to be analyzed with the entropy value of the historical picture data, and determining the entropy value change of the picture data to be analyzed; if the entropy value of the picture data to be analyzed is reduced, judging that the security problem does not exist in the picture data to be analyzed; If the entropy value of the picture data to be analyzed is increased, judging that the security problem exists in the picture data to be analyzed, and outputting a corresponding alarm signal according to the position of the area corresponding to the picture data to be analyzed. Preferably, based on the region attribute, matching the static element appearing in the initial monitoring data with the region attribute, and determining whether the static element appearing in th