Intelligent campus comprehensive management method and platform based on digital twinning

Abstract

The application provides a kind of wisdom campus integrated management method and platform based on digital twinning, belongs to digital twinning technical field, specifically includes: based on the distribution data of idle monitoring device in different image change period, when the interference state of idle monitoring device does not meet the requirements, determine the idle monitoring device of different regions of digital twinning model at different time in the image change period, and combine the composition data of idle monitoring device in the monitoring device of region, determine the setting area of screening image sub-module, according to the analysis result of monitoring image data of monitoring device in different period in setting area, determine the running mode of screening image sub-module in different period in setting area, improve the efficiency of update processing of digital twinning model.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of digital twinning, and particularly relates to a wisdom campus comprehensive management method and platform based on digital twinning. BACKGROUND

[0002] In order to utilize digital twinning technology to manage the wisdom campus, in the invention patent application CN202510322444.5 "A wisdom campus operation and maintenance management system based on digital twinning", the spatial conflict between devices is solved, the device cooperation work capability is improved, the path planning and task execution sequence are optimized, and the safety and stability of the wisdom campus operation and maintenance system are improved, but the following problems exist.

[0003] When the digital twinning technology is utilized to manage the campus, the image analysis of the monitoring device is crucial to the update processing of the digital twinning model, but since the number of monitoring devices inside the campus is large, when all the monitoring devices are analyzed, a large number of monitoring devices without image changes will inevitably affect the analysis timeliness of the image changes, thereby slowing down the update processing efficiency of the digital twinning model, so how to utilize the distribution data of the idle monitoring devices to determine the differential analysis processing strategy, and utilize the screening submodule to pre-screen the idle monitoring devices to improve the update processing efficiency of the digital twinning model becomes a technical problem to be solved.

[0004] To solve the above technical problems, the application provides a wisdom campus comprehensive management method and platform based on digital twinning. SUMMARY

[0005] To achieve the purpose of the application, the application adopts the following technical solutions:

[0006] Specifically, the application provides a wisdom campus comprehensive management method based on digital twinning, which specifically comprises:

[0007] S1, the digital twinning model is associated with the monitoring device inside the campus as the associated monitoring device, and the image monitoring data of different associated monitoring devices in different time periods is analyzed to determine the image change period in the time period;

[0008] S2, based on the analysis results of the image monitoring data of the associated monitoring devices in different image change periods, the distribution data of the idle monitoring devices in different image change periods is determined, and when the interference state of the idle monitoring devices does not meet the requirements based on the distribution data, the next step is entered;

[0009] S3 determines the idle monitoring devices of different regions of the digital twin model at different time points in the image variation period, and determines the setting region of the screening image sub-module in combination with the composition data of the idle monitoring devices in the monitoring devices of the region.

[0010] S4 determines the running mode of the screening image sub-module in the setting region at different time points according to the analysis result of the monitoring image data of the monitoring devices in the setting region at different time points.

[0011] The beneficial effects of the present application are:

[0012] The distribution data of the idle monitoring devices in different image variation periods are used to determine whether the interference state of the idle monitoring devices meets the requirements, and the number of idle monitoring devices in the image variation period and the distribution at different time points are fully considered, so as to realize the interference influence of the number and distribution of idle monitoring devices on the reading processing rate of the monitoring devices of the digital twin platform, and lay a foundation for realizing the differentiated running management mode of the monitoring devices of the digital twin platform.

[0013] According to the analysis result of the monitoring image data of the monitoring devices in the setting region at different time points, the running mode of the screening image sub-module in the setting region at different time points is determined, which not only ensures the efficiency of the update processing of the digital twin model in the image variation period, but also avoids the technical problem of low update processing efficiency of the setting region in the date with large flow variation caused by the fixed running mode.

[0014] Further technical solutions are that the associated monitoring devices are the monitoring devices required for the update processing of the digital twin model.

[0015] Further technical solutions are that the analysis result of the image monitoring data includes the flow at different time points.

[0016] Further technical solutions are that the determination method of the image variation period in the time period is:

[0017] The time points at which there is flow in the monitoring image of the different associated monitoring devices in the time period are determined as variation time points according to the analysis result of the image monitoring data of the different associated monitoring devices in the time period.

[0018] The variation value of different time points is determined according to the proportion of the number of associated monitoring devices belonging to variation time points at different time points.

[0019] The time period is determined to be an image variation period according to the variation value of different time points in the time period.

[0020] The further technical solution is that when the average value of the variation values at different time points is greater than the preset variation value threshold, the time period is determined as an image variation time period.

[0021] The further technical solution is that the method for determining the operation mode of the screening image submodule of the setting area is:

[0022] When in the image variation time period, the operation mode of the screening image submodule of the setting area is controlled as the first operation mode;

[0023] When not in the image variation time period, the analysis result of the monitoring image data of the setting area in the time period is used to determine the proportion of the number of idle monitoring devices at different time points in different historical dates in the time period;

[0024] The average value of the proportion of the number of idle monitoring devices at different time points in different historical dates is used to determine the idle number proportion at different time points;

[0025] The operation mode of the screening image submodule in the time period is determined according to the idle number proportion at different time points in the time period.

[0026] The further technical solution is that the operation mode of the screening image submodule in the time period is determined according to the idle number proportion at different time points in the time period, and specifically includes:

[0027] When the idle number proportion at different time points in the time period is all greater than the preset number proportion threshold, the operation mode of the screening image submodule of the setting area is controlled as the first operation mode;

[0028] When the idle number proportion at different time points in the time period is not all greater than the preset number proportion threshold, when the number of time points at which the idle number proportion in the time period is all greater than the preset number proportion threshold is greater than the time point number preset value, the operation mode of the screening image submodule of the setting area is controlled as the third operation mode, and when the number of time points at which the idle number proportion in the time period is all greater than the preset number proportion threshold is not greater than the time point number preset value, the operation mode of the screening image submodule of the setting area is controlled as the second operation mode.

[0029] In a second aspect, the present application provides a smart campus comprehensive management platform based on digital twinning, which adopts the above-mentioned smart campus comprehensive management method based on digital twinning, and specifically includes:

[0030] a variation time period screening module, an interference state evaluation module, and an operation mode control module;

[0031] The variation time period screening module is responsible for determining the image variation time period in the time period.

[0032] The interference state evaluation module is responsible for determining whether the interference state of the idle monitoring device meets the requirements.

[0033] The operation mode control module is responsible for determining the operation mode of the screening image sub-module in the setting area in different time periods.

[0034] Other features and advantages will be set forth in the following description of the specification, and the objectives and other advantages of the present application will be achieved and obtained in the structure specifically pointed out in the specification and drawings.

[0035] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the following preferred embodiments are specifically described below, and the accompanying drawings are described in detail. BRIEF DESCRIPTION OF DRAWINGS

[0036] The above and other features and advantages of the present application will become more apparent by describing in detail example embodiments thereof with reference to the attached drawings:

[0037] Figure 1 A flowchart of a comprehensive management method of a smart campus based on digital twinning is shown in the figure.

[0038] Figure 2 A flowchart of a method for determining image variation periods in time periods is shown in the figure.

[0039] Figure 3 A flowchart of a method for determining that the interference state of the idle monitoring device does not meet the requirements is shown in the figure.

[0040] Figure 4 A flowchart of a method for determining the setting area of the screening image sub-module is shown in the figure.

[0041] Figure 5 A framework diagram of a comprehensive management platform of a smart campus based on digital twinning is shown in the figure. DETAILED DESCRIPTION

[0042] In order to make the person skilled in the art better understand the technical solutions in the specification, the technical solutions in the specification will be described clearly and completely below in conjunction with the drawings in the specification. Obviously, the described embodiments are only part of the embodiments of the specification, not all. Based on the embodiments of the specification, all other embodiments obtained by the person skilled in the art without creative labor should belong to the protection scope of the specification.

[0043] When the updating process of the digital twin model is performed, in some moments, there is no people flow data in the monitoring image of the monitoring device, which causes that when the unified analysis process is performed by the updating management platform of the digital twin model, the monitoring device without people flow data may affect the analysis processing efficiency of the monitoring device with people flow data, and thus the updating efficiency is difficult to meet the requirements.

[0044] In the present application, according to the people flow monitoring data of the monitoring device in the specific region of the digital twin model and whether the current period is an image change period, it is determined whether the specific region needs to use the screening image submodule to screen the monitoring device without people flow data, thereby improving the updating processing efficiency of the digital twin model.

[0045] The image change period is a period in which the proportion of the number of monitoring devices with people flow in different moments is greater than 0.6 on average.

[0046] When in the image change period, in different image change periods, the proportion of the number of idle monitoring devices in different moments is greater than 0.2, it is determined that the interference state of the idle monitoring device does not meet the requirements.

[0047] The setting region of the screening image submodule is a region in which the number of idle monitoring devices in different moments in different image change periods is greater than 3, and the proportion of the number of idle monitoring devices in different moments is greater than 0.45.

[0048] When in the image change period, the first running mode is adopted;

[0049] When not in the image change period, the proportion of the number of idle monitoring devices in different moments in different historical dates in the setting region in the period is determined by analyzing the monitoring image data in the period.

[0050] The average value of the proportion of the number of idle monitoring devices in different moments in different historical dates is used to determine the idle number proportion in different moments, and when the idle number proportion in different moments in the period is greater than 0.3, the running mode of the screening image submodule of the setting region is controlled to be the first running mode, and when the idle number proportion in different moments in the period is not greater than 0.3, the running mode of the screening image submodule of the setting region is controlled to be the second running mode.

[0051] Embodiment 1

[0052] As Figure 1 shown, the present application provides a smart campus comprehensive management method based on digital twinning, specifically including:

[0053] S1 takes the associated monitoring device inside the campus as the associated monitoring device of the digital twin model, and determines the image change period in the period according to the analysis result of the image monitoring data of the different associated monitoring devices in different periods;

[0054] Further, the associated monitoring device is a monitoring device required for updating the digital twin model.

[0055] Specifically, the analysis result of the image monitoring data includes the passenger flow at different times.

[0056] Specifically, as shown in Figure 2 The method for determining the image change period in the period is:

[0057] According to the analysis result of the image monitoring data of the different associated monitoring devices in the period, the time when the passenger flow exists in the monitoring image of the different associated monitoring devices in the period is determined as the change time.

[0058] According to the proportion of the number of the associated monitoring devices belonging to the change time at different times, the change value of different times is determined.

[0059] According to the change value of different times in the period, it is determined whether the period is an image change period.

[0060] Further, when the average value of the change value at different times is greater than a preset change value threshold, it is determined that the period is an image change period.

[0061] In another possible embodiment, the method for determining the image change period in the period is:

[0062] According to the analysis result of the image monitoring data of the different associated monitoring devices in the period, the time when the passenger flow exists in the monitoring image of the different associated monitoring devices in the period is determined as the change time.

[0063] According to the proportion of the number of the associated monitoring devices belonging to the change time at different times, the change value of different times is determined.

[0064] According to the change value of different times in the period, it is determined whether the period is an image change period.

[0065] Further, when the average value of the change value at different times is greater than a preset change value threshold, it is determined that the period is an image change period.

[0066] In another possible embodiment, the method for determining the image change period in the period is:

[0067] S11 monitors the analysis result of the image monitoring data of the different associated monitoring devices in the time period, determines the time when the monitoring image of the different associated monitoring devices in the time period exists the people flow as the change time, and takes the proportion of the number of the change time of the different associated monitoring devices in the time period as the device change value of the different associated monitoring devices;

[0068] It should be noted that in the above steps, it is also necessary to determine whether the number of associated monitoring devices with change time is less than the preset associated monitoring device number threshold. Specifically, when the number of associated monitoring devices with change time is less than the preset associated monitoring device number threshold and the number of change time of different associated monitoring devices is within the preset time interval, it can be directly determined that the time period does not belong to the image change period. If the above conditions meet the requirements, it is also necessary to determine whether the average value of the device change value of the different associated monitoring devices meets the requirements, and whether the number of associated monitoring devices with device change value greater than the preset device change value meets the requirements. Specifically, the threshold is set to determine.

[0069] It can be understood that when the average value of the device change value of the different associated monitoring devices does not meet the requirements or the number of associated monitoring devices with device change value greater than the preset device change value does not meet the requirements, the change of the monitoring image of the associated monitoring device at this time is relatively severe, so it can be determined that the time period belongs to the image change period. Only when the above conditions meet the requirements, the next step of determining the change value of different time is needed.

[0070] S12 determines the change value of different time according to the proportion of the number of associated monitoring devices belonging to the change time of different time;

[0071] It should be further noted that before entering the next step, it is also necessary to further determine whether there is a time with a change value greater than the preset change value threshold. When there is no time with a change value greater than the preset change value threshold, it means that the change of the monitoring image of different time is small. Therefore, in this case, when the number of change time is within the preset time interval and the sum of the device change value of the different associated monitoring devices is less than the change value preset value, it can be directly determined that the time period does not belong to the image change period.

[0072] Further, it needs to be explained that if there is a time point when the variation value is greater than the preset variation value threshold or there is no time point when the variation value is greater than the preset variation value threshold, and it is not determined whether the period belongs to the image variation period, further determination is needed when the number of time points when the variation value is greater than the preset variation value threshold does not meet the requirement, that is, when the number of time points when the variation is more serious is more, the threshold value is used to determine, at this time the image variation in the period is more clustered, so it can be determined that the period belongs to the image variation period, and only when the number of time points when the variation value is greater than the preset variation value threshold meets the requirement, the determination of the image variation value needs to be performed.

[0073] S13 determines the image variation value of the period according to the variation values of different time points and the device variation values of different associated monitoring devices, determines whether the period is an image variation period based on the image variation value.

[0074] In a possible embodiment, the image variation value of the period can be determined according to the product of the average value of the variation values of different time points and the average value of the device variation values of different associated monitoring devices, or can be determined by the average value of the variation values of different time points and the average value of the device variation values of different associated monitoring devices, and the value range is between 0 and 1.

[0075] It needs to be further explained that when the image variation value of the period is greater than the preset image variation value threshold, for example, greater than 0.4, it is determined that the period is an image variation period.

[0076] S2 determines the distribution data of idle monitoring devices in different image variation periods based on the analysis results of the image monitoring data of the associated monitoring devices in the different image variation periods, and enters the next step when the interference state of the idle monitoring devices does not meet the requirement based on the distribution data.

[0077] Further, the idle monitoring device is a monitoring device without human flow at the time point, and specifically, when the monitoring device does not have human flow at the time point, the monitoring device is determined to be the idle monitoring device at the time point.

[0078] Table 1 is the idle situation of the associated monitoring devices of the digital twin model in a period

[0079] Time Area Idle monitoring number Non-idle monitoring number 08:15 Teaching building A door 2 5 Canteen pickup window 0 8 08:25 Laboratory corridor 3 2 Library gate 1 4 08:35 Administrative building elevator hall 5 0 Gym equipment room 6 0 08:40 Express center sorting area 0 5 Landscape lake ring road 4 0

[0080] Specifically, as shown in Figure 3 It is determined that the interference state of the idle monitoring device does not meet the requirement, and specifically includes:

[0081] determine the number of idle monitoring devices at different time in the image variation period based on the distribution data of the idle monitoring devices in different image variation periods;

[0082] determine the idle device interference value at different time according to the proportion of the number of idle monitoring devices at different time in the number of associated monitoring devices;

[0083] determine whether the interference state of the idle monitoring devices meets the requirement according to the idle device interference value at different time in different image variation periods.

[0084] Further, when the number of time points at which the idle device interference value is greater than the preset idle interference value threshold is greater than the preset interference time point threshold in different image variation periods, it is determined that the interference state of the idle monitoring devices does not meet the requirement.

[0085] It can be understood that when the interference state of the idle monitoring devices meets the requirement, there is no need to perform the setting processing of the screening image sub-module.

[0086] In another possible embodiment, determining that the interference state of the idle monitoring devices does not meet the requirement specifically includes:

[0087] determine the number of idle monitoring devices at different time in the image variation period based on the distribution data of the idle monitoring devices in different image variation periods;

[0088] determine the idle device interference value at different time according to the proportion of the number of idle monitoring devices at different time in the number of associated monitoring devices;

[0089] determine the comprehensive interference value according to the average value of the idle device interference value at different time in different image variation periods, and determine whether the interference state of the idle monitoring devices meets the requirement based on the comprehensive interference value.

[0090] Further, when the comprehensive interference value is greater than the preset comprehensive interference value threshold, it is determined that the interference state of the idle monitoring devices does not meet the requirement.

[0091] S3 determine the idle monitoring devices of different regions of the digital twin model at different time in the image variation period, and determine the setting region of the screening image sub-module in combination with the composition data of the idle monitoring devices in the monitoring devices of the region;

[0092] Specifically, as shown in Figure 4 the method for determining the setting region of the screening image sub-module is:

[0093] Determine the time when the monitoring device of the region belongs to the idle monitoring device according to the composition data of the idle monitoring device at different time in the image variation period, determine the idle time proportion of different monitoring devices based on the proportion of time belonging to the idle monitoring device, and take the average value of the idle time proportion of different monitoring devices as the idle device proportion;

[0094] Determine the idle device composition proportion at different time according to the composition data of the idle monitoring device in the monitoring device at different time in the image variation period;

[0095] Determine the idle influence value of the region based on the average value of the idle device composition proportion and the idle device proportion at different time in different image variation periods, and determine whether the region is the setting region of the screening image sub-module based on the idle influence value.

[0096] Further, when the idle influence value of the setting region is greater than the preset idle influence value threshold, it is determined that the region is the setting region of the screening image sub-module.

[0097] S4 determines the operation mode of the screening image sub-module in the setting region at different time according to the analysis result of the monitoring image data of the monitoring device in the setting region at different time.

[0098] Specifically, the method for determining the operation mode of the screening image sub-module is:

[0099] When it is in the image variation period, the operation mode of the screening image sub-module of the setting region is controlled to be the first operation mode;

[0100] When it is not in the image variation period, the number proportion of idle monitoring devices at different time in different historical dates in the period is determined according to the analysis result of the monitoring image data of the setting region in the period;

[0101] Determine the idle number proportion at different time according to the average value of the number proportion of idle monitoring devices at different time in different historical dates;

[0102] Determine the operation mode of the screening image sub-module in the period according to the idle number proportion at different time in the period.

[0103] Further, the operation mode of the screening image sub-module in the period is determined according to the idle number proportion at different time in the period, specifically including:

[0104] When the idle number proportion at different time in the period is greater than the preset number proportion threshold, the operation mode of the screening image sub-module of the setting region is controlled to be the first operation mode.

[0105] When the proportion of idle time at different times in the time period is not greater than a preset proportion threshold, and when the number of times when the proportion of idle time in the time period is greater than the preset proportion threshold is greater than a preset value for the number of times, the operation mode of the image filtering submodule in the setting area is controlled to be the third operation mode. When the number of times when the proportion of idle time in the time period is greater than the preset proportion threshold is not greater than the preset value for the number of times, the operation mode of the image filtering submodule in the setting area is controlled to be the second operation mode.

[0106] It is understood that the first operating mode involves using the image filtering submodule to analyze in real time the monitoring devices in the area that have pedestrian traffic data, and uploading the monitoring images of the monitoring devices that have pedestrian traffic data to the update management platform of the digital twin model. The update management platform then uses the monitoring images of the monitoring devices that have pedestrian traffic data to update the digital twin model.

[0107] It should be noted that the second operating mode does not require the use of the image filtering submodule to analyze the monitoring devices with pedestrian traffic data in the area in real time, and uploads the monitoring images of all monitoring devices in the area to the update management platform of the digital twin model. The update management platform uses the monitoring images of the monitoring devices with pedestrian traffic data to update the digital twin model.

[0108] Furthermore, the third operating mode is to determine the operating mode of the image filtering submodule in the next preset time period by using the average percentage of the number of idle monitoring devices at different times in the adjacent preset time period.

[0109] Specifically, the nearest preset time period is the preset time period closest to the current preset time period, and the preset time period is determined using a preset duration threshold.

[0110] It is understandable that when the average percentage of idle monitoring devices at different times in a nearby preset time period is less than the preset value of the idle number percentage, the image filtering submodule will operate in the second mode in the next preset time period; otherwise, the image filtering submodule will operate in the first mode in the next preset time period.

[0111] Example 2

[0112] Secondly, such as Figure 5 As shown, this invention provides a smart campus integrated management platform based on digital twins, employing the aforementioned smart campus integrated management method based on digital twins, specifically including:

[0113] a variable period screening module, an interference state evaluation module, and a running mode control module;

[0114] The variable period screening module is responsible for determining the image variable period in a period.

[0115] The interference state evaluation module is responsible for determining whether the interference state of the idle monitoring device meets the requirements.

[0116] The running mode control module is responsible for determining the running mode of the screening image sub-module in the setting area in different periods.

[0117] In another possible embodiment, the method for determining the setting area of the screening image sub-module is as follows:

[0118] S31 determines the idle time proportion of different monitoring devices based on the idle monitoring device data of the area at different times in the image variable period, and determines the idle device proportion of the monitoring device in different image variable periods as the device idle value of the area.

[0119] In a possible embodiment, the device idle value of the area can be determined by the average value of the idle device proportion in different image variable periods in different image variable periods, wherein the larger the device idle value is, the greater the probability that the same monitoring device belongs to the idle device in the image variable period is.

[0120] In addition, before entering step S32, it is also necessary to determine whether the number of monitoring devices in the area is less than the preset device number threshold. When the number of monitoring devices in the area is less than the preset device number threshold, the monitoring devices in the area are less, and therefore, it is not necessary to perform the setting of the screening image sub-module. Only when the number of monitoring devices in the area is not less than the preset device number threshold, it is necessary to determine whether the idle time proportion of different monitoring devices in different image variable periods is large.

[0121] It can be understood that when the monitoring device has a large average idle time proportion in different image variable periods, the average idle time proportion can be determined by a threshold. When the number of monitoring devices that are often in the idle state is large, the area can be directly determined to be the setting area of the screening image sub-module. Specifically, when the number is greater than the number threshold, it is determined that the number is large.

[0122] Specifically, in one of the cases, when the number of monitoring devices in idle state is not large, it is needed to determine whether the device idle value of the region is greater than the idle threshold value, if greater than the idle threshold value, different monitoring devices are in idle state, thus it can be determined that the region is the setting region of the screening image sub-module.

[0123] S32 determines the proportion of idle monitoring devices in different time periods of the region in the image variation period, and takes it as the idle device proportion, and determines the idle abnormal influence value of the region according to the idle device proportion in different time periods of different image variation periods.

[0124] It can be understood that, in one of the embodiments, the idle abnormal influence value is the average value of the idle device proportion in different time periods of different image variation periods.

[0125] In the above steps, it is further needed to determine that, when the idle device proportion in different time periods of the image variation period is greater than the preset idle device proportion threshold value, the region can be directly determined as the setting region of the screening image sub-module, and even when there is a time period in which the idle device proportion is not greater than the preset idle device proportion threshold value, it is needed to determine that, when the number of time periods in which the idle device proportion is not greater than the preset idle device proportion threshold value is greater than the preset time period threshold value, when the device idle value of the region is less than the preset device idle value threshold value, the region is determined not to belong to the setting region of the screening image sub-module.

[0126] And only when the device idle value of the region is not less than the preset device idle value threshold value or the number of time periods in which the idle device proportion is not greater than the preset idle device proportion threshold value is not greater than the preset time period threshold value, it is further needed to determine that, when the idle abnormal influence value of the region is greater than the preset abnormal influence value threshold value, the region is determined not to belong to the setting region of the screening image sub-module, and when the idle abnormal influence value of the region is not greater than the preset abnormal influence value threshold value, the idle influence value determination is needed.

[0127] S33 determines the idle influence value of the region based on the idle influence abnormal value and the device abnormal idle value, and determines whether the region is the setting region of the screening image sub-module based on the idle influence value.

[0128] In one of the possible embodiments, the idle influence value of the setting region is determined according to the average value of the idle influence abnormal value and the device abnormal idle value, and when the idle influence value is large, the region is determined as the setting region of the screening image sub-module.

[0129] The various embodiments in this specification describe the application in progressive stages. Each stage builds upon the previous stages, and each stage can be described in terms of the differences between that stage and the previous stage. For example, the device, apparatus, and non-transitory computer storage medium embodiments are described more quickly because they are substantially similar to the method embodiments. The relevant portions of the method embodiments are referenced.

[0130] The above description describes certain embodiments of the application. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown or sequential order in order to achieve the desired results. In some implementations, multitasking and parallel processing can be advantageous.

[0131] The above description only describes one or more embodiments of the application and is not meant to limit the application. One or more embodiments of the application can be modified and changed in various ways by those skilled in the art. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of one or more embodiments of the application should be included in the scope of the claims of the application.

Claims

1. A digital-twin-based intelligent campus integrated management method, characterized in that, Specifically comprising: The digital twin model is associated with the monitoring device inside the campus as an associated monitoring device, and the analysis results of the image monitoring data of different associated monitoring devices in different time periods are used to determine the image change time period in the time period; Based on the analysis results of the image monitoring data of the associated monitoring devices in different image change time periods, the distribution data of the idle monitoring devices in different image change time periods is determined, and if the interference state of the idle monitoring devices does not meet the requirements based on the distribution data, the next step is entered; The idle monitoring devices of different regions of the digital twin model in the image change time period are determined, and the setting region of the screening image sub-module is determined in combination with the composition data of the idle monitoring devices in the monitoring devices of the region; According to the analysis results of the monitoring image data of the monitoring devices in the setting region in different time periods, the running mode of the screening image sub-module in the setting region in different time periods is determined; If the interference state of the idle monitoring devices does not meet the requirements, specifically comprising: Based on the distribution data of the idle monitoring devices in different image change time periods, the number of idle monitoring devices at different times in the image change time period is determined; According to the proportion of the number of idle monitoring devices at different times in the number of associated monitoring devices, the idle device interference value at different times is determined; According to the idle device interference value at different times in different image change time periods, it is determined whether the interference state of the idle monitoring devices meets the requirements; When the number of times when the idle device interference value is greater than the preset idle interference value threshold in different image change time periods is greater than the preset interference time threshold, it is determined that the interference state of the idle monitoring devices does not meet the requirements; The method for determining the running mode of the screening image sub-module is: When in the image change time period, the running mode of the screening image sub-module in the setting region is controlled to be the first running mode; When not in the image change time period, the proportion of the number of idle monitoring devices at different times in different historical dates in the time period is determined based on the analysis results of the monitoring image data of the setting region in the time period; The average value of the proportion of the number of idle monitoring devices at different times in different historical dates is used to determine the idle number proportion at different times; According to the idle number proportion at different times in the time period, the running mode of the screening image sub-module in the time period is determined. 2.The digital-twin-based intelligent campus comprehensive management method of claim 1, wherein, The associated monitoring device is a monitoring device required for updating the digital twin model. 3.The digital-twin-based intelligent campus comprehensive management method of claim 1, wherein, The analysis results of the image monitoring data include the number of people at different times. 4.The digital-twin-based intelligent campus comprehensive management method of claim 1, wherein, The method for determining the image change time period in the time period is: The time when the monitoring image of the different associated monitoring devices in the time period has the number of people is determined based on the analysis results of the image monitoring data of the different associated monitoring devices in the time period, and is used as the change time; According to the proportion of the number of associated monitoring devices belonging to the change time at different times, the change value at different times is determined; The change value at different times in the time period is used to determine whether the time period is an image change time period. 5.The digital-twin-based intelligent campus comprehensive management method of claim 4, wherein, When the average value of the variation values at different time points is greater than the preset variation value threshold, it is determined that the time period is an image variation period. 6.The digital-twin-based intelligent campus comprehensive management method of claim 1, wherein, The method for determining the set area of the screening image sub-module is: Based on the composition data of the idle monitoring devices at different time points in the image variation period, the idle device composition proportion in the idle monitoring devices at different time points is determined, and the idle device composition proportion is taken as the idle device proportion; Based on the composition data of the idle monitoring devices at different time points in the image variation period, the idle device composition proportion in the idle monitoring devices at different time points is determined, and the idle device composition proportion is taken as the idle device proportion; Based on the average value of the idle device composition proportion and the idle device proportion at different time points in different image variation periods, the idle influence value of the set area is determined, and whether the area is a set area of the screening image sub-module is determined based on the idle influence value. 7.The digital-twin-based intelligent campus comprehensive management method of claim 1, wherein, According to the idle quantity proportion at different time points in the period, the running mode of the screening image sub-module in the period is determined, specifically including: When the idle quantity proportion at different time points in the period is greater than the preset quantity proportion threshold, the running mode of the screening image sub-module of the set area is controlled to be the first running mode; When the idle quantity proportion at different time points in the period is not greater than the preset quantity proportion threshold, when the number of time points with the idle quantity proportion greater than the preset quantity proportion threshold in the period is greater than the preset time point number, the running mode of the screening image sub-module of the set area is controlled to be the third running mode, and when the number of time points with the idle quantity proportion greater than the preset quantity proportion threshold in the period is not greater than the preset time point number, the running mode of the screening image sub-module of the set area is controlled to be the second running mode. 8.The digital-twin-based intelligent campus comprehensive management method of claim 1, wherein, The first running mode is to use the screening image sub-module to analyze the monitoring device with people flow data in the area in real time, and upload the monitoring image of the monitoring device with people flow data to the update management platform of the digital twin model, and the update management platform uses the monitoring image of the monitoring device with people flow data to update the digital twin model. 9.A digital-twin-based intelligent campus comprehensive management device, adopting the digital-twin-based intelligent campus comprehensive management method of any one of claims 1-8, characterized in that, Specifically including: Variation period screening module, interference state evaluation module, running mode control module; The variation period screening module is responsible for determining the image variation period in the period. The interference state evaluation module is responsible for determining whether the interference state of the idle monitoring device meets the requirements. The running mode control module is responsible for determining the running mode of the screening image sub-module of the set area at different time points.

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