Dynamic updating method, system and equipment of mountain and water three-dimensional holographic model and medium

Abstract

The invention discloses a dynamic updating method, system and device for a mountain-water three-dimensional holographic model and a medium, and the method comprises the steps: dividing a target region into a plurality of basic regions which are mutually independent in space through a Thiessen polygon algorithm according to a plot center point of the target region; monitoring the normalized vegetation index of each basic area according to a preset monitoring frequency to obtain a total NDVI change amount; performing overlay analysis on the total NDVI change amount and the population thermodynamic cumulative map of the target area to obtain a vegetation change area; when the total NDVI change amount meets a preset updating condition, performing real-time data acquisition based on the vegetation change area to obtain new image data; and updating the mountain-water three-dimensional holographic model of the target area through the new image data. According to the method, the updating cost is reduced through local accurate updating of the mountain-water three-dimensional holographic model.

Description

Technical Field

[0001] The present application belongs to the field of electronic information technology, and specifically relates to a method, system, device and medium for dynamically updating a three-dimensional holographic model of a landscape. Background Art

[0002] The landscape 3D model is a basic geographic entity in a 3D expression form, with semantic, structural and material characteristics. Rapidly identifying areas with significant changes and updating the landscape 3D model can help users to timely understand the current topography of the model-related areas and achieve efficient supervision of natural resources.

[0003] Currently, the existing landscape 3D model is often updated globally by collecting the terrain data of the target plot in real time. However, this update method requires processing a large amount of data, which increases the update cost. If the local update method is used directly, it is difficult to accurately distinguish which plots have undergone significant changes and need to be updated, and which plots do not need to be updated, and the update accuracy cannot be guaranteed. Summary of the invention

[0004] The present application proposes a method, system, device and medium for dynamically updating a landscape three-dimensional holographic model, which reduces the update cost by accurately updating the local part of the landscape three-dimensional holographic model.

[0005] A first aspect of the present application provides a method for dynamically updating a landscape three-dimensional holographic model, the method comprising:

[0006] According to the center point of the plot in the target area, the target area is divided into several basic areas that are independent of each other in space by using the Thiessen polygon algorithm;

[0007] According to a preset monitoring frequency, the normalized difference vegetation index of each base area is monitored to obtain the total NDVI change;

[0008] Performing a superposition analysis on the total NDVI change and the population thermal accumulation map of the target area to obtain the vegetation change area;

[0009] When the total amount of NDVI change meets the preset update condition, real-time data collection is performed based on the vegetation change area to obtain new image data;

[0010] The three-dimensional holographic model of the landscape in the target area is updated using new image data.

[0011] The above scheme first divides the target area into several spatially independent basic plots according to different land types and plot boundaries, so as to facilitate the subsequent careful monitoring of vegetation changes in each plot; then the total NDVI change is obtained by monitoring the normalized vegetation index of the basic area, and the degree of vegetation change over a period of time is obtained. The total NDVI change and the population thermal accumulation map of the target area are superimposed and analyzed to identify areas with significant vegetation changes caused by frequent human activities and improve the accuracy of the data. Then, when the total NDVI change meets the preset update conditions, it means that the vegetation change is obvious enough to update the landscape three-dimensional holographic model in time, so real-time data collection is carried out on the vegetation change area to obtain new image data for model update.

[0012] In a possible implementation method of the first aspect, the center point of the plot is specifically:

[0013] According to the acquired remote sensing image data and the preset land cover type, the target area is classified into land categories to obtain several land category areas;

[0014] According to the plot boundary information and plot management information, each land type area is divided into several independent basic plots;

[0015] The minimum bounding rectangle of each of the basic plots is calculated, and the center point of the minimum bounding rectangle is used as the center point of the plot.

[0016] The above scheme first divides the target area according to the difference of the surface, and then divides it according to the boundary lines of the plots to obtain independent basic plots with little difference in surface vegetation, which is convenient for subsequent observation of vegetation changes. In addition, the center point of the plot obtained provides data support for the subsequent division of monitoring units.

[0017] In a possible implementation method of the first aspect, the normalized difference vegetation index of each base area is monitored according to a preset monitoring frequency to obtain a total NDVI change, specifically:

[0018] According to a preset monitoring frequency, the normalized difference vegetation index of each base area is monitored to collect NDVI data in a time series format;

[0019] Cumulative calculation is performed based on the NDVI data to obtain the total NDVI change of each base area within a preset time period.

[0020] The above scheme monitors the normalized difference vegetation index in each basic area, and accumulates the NDVI data in different time periods to obtain the degree of regional vegetation change over a period of time, which can intuitively reflect whether there are significant changes in vegetation at different times.

[0021] In a possible implementation method of the first aspect, the total NDVI change amount and the population heat accumulation map of the target area are overlaid and analyzed to obtain the vegetation change area, specifically:

[0022] Based on the collected multi-source data, construct population heat maps of the target area at different time periods;

[0023] All population heat maps are accumulated in chronological order to generate a population heat accumulation map;

[0024] The total NDVI change amount and the population thermal accumulation map are time-aligned and overlaid to identify vegetation change areas in the target area.

[0025] The above scheme also constructs a population heat accumulation map to characterize the impact of human activities on vegetation changes in the same period of time, identifies areas with significant vegetation changes due to frequent human activities, improves data accuracy, and can update the model more accurately.

[0026] In a possible implementation method of the first aspect, when the total amount of NDVI change meets a preset update condition, real-time data collection is performed based on the vegetation change area to obtain new image data, specifically:

[0027] When the total amount of NDVI change is greater than or equal to the first threshold or the data update time of each of the basic areas exceeds the set second threshold, the UAV is controlled to collect real-time data of the vegetation change area to obtain new image data;

[0028] The second threshold is related to the vegetation growth period of each of the basic areas.

[0029] The above scheme ensures that the model is updated in time when the vegetation has changed significantly or the model has not been updated for a long time, and determines the second threshold value based on the vegetation growth cycle to ensure that the model can be updated in time in different growth cycles.

[0030] In a possible implementation method of the first aspect, the second threshold is related to a vegetation growth period of each of the base areas, specifically:

[0031] A type of vegetation in each of the basic areas is designated as a representative vegetation, and a second threshold of each of the basic areas is set according to a growth cycle of the representative vegetation.

[0032] In a possible implementation method of the first aspect, the method further includes:

[0033] Calibrate the updated landscape 3D holographic model according to the preset inspection frequency;

[0034] The update conditions and the data collection parameters of the UAV are adjusted according to the accuracy requirements obtained.

[0035] The above solution ensures the accuracy of the model by regularly calibrating the updated model limits. It also adjusts the update conditions and the data collection parameters of the drone according to the accuracy requirements to optimize the model update strategy and achieve efficient model update.

[0036] The second aspect of the present application provides a dynamic update system for a three-dimensional holographic model of mountains and rivers, the system comprising: a land parcel division module, a vegetation change total amount acquisition module, a change area determination module, an update data acquisition module and a model update module;

[0037] The plot division module is used to divide the target area into a number of spatially independent basic areas according to the plot center point of the target area through the Thiessen polygon algorithm;

[0038] The vegetation change total amount acquisition module is used to monitor the normalized vegetation index of each basic area according to a preset monitoring frequency to obtain the total NDVI change;

[0039] The change area determination module is used to perform superposition analysis on the total NDVI change and the population thermal accumulation map of the target area to obtain the vegetation change area;

[0040] The update data acquisition module is used to perform real-time data acquisition based on the vegetation change area to obtain new image data when the total amount of NDVI change meets the preset update condition;

[0041] The model updating module is used to update the landscape three-dimensional holographic model of the target area through new image data.

[0042] A third aspect of the present application provides a terminal device, which includes: a terminal device including a processor and a memory, the memory storing a computer program, and the processor implementing the steps of a method for dynamically updating a three-dimensional landscape holographic model as described in any one of the embodiments of the present application when executing the computer program.

[0043] A fourth aspect of the present application provides a storage medium storing computer-readable program code, which, when executed, implements the steps of a method for dynamically updating a landscape three-dimensional holographic model as described in any one of the embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the implementation manner will be briefly introduced below. Obviously, the drawings described below are only some implementation manners of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 It is a specific flow chart of a method for dynamically updating a landscape three-dimensional holographic model provided in a certain embodiment of the present application;

[0046] Figure 2 It is a specific structural diagram of a dynamic updating system of a landscape three-dimensional holographic model provided by a certain embodiment of the present application;

[0047] Figure 3 A structural diagram of a terminal device is provided for a certain embodiment of the present application. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0049] It should be understood that the step numbers used in this article are only for the convenience of description and are not intended to limit the order in which the steps are executed.

[0050] First embodiment

[0051] In order to better control natural resources, 3D models are often used to display holographic data of landscape projects. Because landscape resources are constantly changing, the corresponding 3D models need to be constantly updated to provide accurate and powerful data support for the protection, management and utilization of landscape projects. However, because the 3D landscape model involves a lot of data, and the vegetation changes are often not significant, high-precision observations are required to determine whether the model should be updated. In addition, updating the landscape model too frequently and blindly updating the landscape model globally will increase the cost of drone photography.

[0052] Because the embodiments of the present application mainly study how to observe with high precision whether the vegetation in the target area has changed significantly, and perform local updates on the areas where significant changes have occurred, thereby reducing the update cost while ensuring the accuracy of the three-dimensional landscape model.

[0053] like Figure 1 As shown, Figure 1A specific flow chart of a method for dynamically updating a landscape 3D holographic model is provided for a certain embodiment of the present application. The method for dynamically updating a landscape 3D holographic model of the present embodiment includes steps S1 to S5, which are described in detail as follows:

[0054] Step S1, according to the center point of the plot of the target area, the target area is divided into a number of basic areas which are independent of each other in space by using the Thiessen polygon algorithm.

[0055] In the embodiment of the present application, remote sensing image data is first obtained through remote sensing technology means such as satellite images and GIS data. Then, the target area of ​​the entire landscape project is classified into land types using the remote sensing image data and existing land cover types, and the different land cover types in the target area are clarified to obtain several different land type areas, such as forests, grasslands, water bodies, and buildings.

[0056] Then, within each land type area, each land type area is divided into several independent basic plots according to the existing plot boundary information and plot management information.

[0057] Optionally, in the embodiment of the present application, the plot management information mainly refers to the vegetation restoration areas that require special attention as stipulated, or vegetation restoration projects based on plots, and it is necessary to regularly monitor the project progress and the restoration effect of the construction plots.

[0058] The minimum enclosing rectangle of each basic plot is calculated, and the center point of the minimum enclosing rectangle is extracted as the center point of the plot to provide data support for subsequent area division.

[0059] Then, based on the center points of these plots, the Thiessen polygon algorithm is used to divide the entire target area into several spatially independent basic areas, each of which represents a monitoring unit.

[0060] The Thiessen polygon algorithm is a method for dividing geographic space into non-overlapping areas, each of which consists of a center point and other points closest to the center point. In the embodiment of the present application, the basic area is divided by the Thiessen polygon algorithm, and plots with similar geographical features and NDVI change trends can be divided into one basic area, making subsequent vegetation change monitoring more convenient and comprehensive.

[0061] Step S2, monitoring the normalized difference vegetation index of each base area according to a preset monitoring frequency to obtain the total NDVI change.

[0062] In an embodiment of the present application, the normalized difference vegetation index of each of the base areas is monitored regularly according to a preset monitoring frequency, and NDVI data in a time series format is collected.

[0063] The full name of the NDVI is Normalized Difference Vegetation Index, which is an important parameter reflecting vegetation growth and nutritional information. It mainly quantifies vegetation by measuring the difference between near-infrared (strong reflection from vegetation) and red light (absorption from vegetation), and the value range is fixed to [-1, +1]. When the NDVI value is negative, it means that the land cover currently measured is likely to be water; when the NDVI value is close to +1, it means that the land cover currently measured is likely to be dense green leaves; when the NDVI value is close to 0, it means that the land cover currently measured has no green leaves and may even be an urbanized area.

[0064] In the embodiment of the present application, the degree of vegetation change within each basic area is measured by measuring the NDVI data within the area, so as to timely update the areas with significant vegetation changes.

[0065] The NDVI data in time series format are cumulatively calculated to obtain the total NDVI change of each basic area within the preset period.

[0066] Optionally, the total amount of NDVI change described in the embodiment of the present application is mainly the cumulative change or relative change rate of NDVI within a certain period of time.

[0067] In addition, because NDVI data may change significantly at different times of the year, it is also necessary to examine other data from the same period, such as population heat maps, to make more detailed observations on vegetation changes.

[0068] Step S3, performing overlay analysis on the total NDVI change and the population thermal accumulation map of the target area to obtain the vegetation change area.

[0069] In the embodiment of the present application, multi-source data is first collected, such as mobile communication base station data, social media activity data, public transportation usage data, etc., to construct a population heat map of the target area. Among them, the population heat map is mainly used to show the distribution and flow of population in a certain area, and in the embodiment of the present application, it provides data support for studying the impact of human activities on vegetation changes.

[0070] The population heat maps of multiple periods are accumulated in chronological order to generate a population heat accumulation map, which is used to reflect areas with dense population activities in the long term. The population heat accumulation map is then superimposed and analyzed with the total NDVI change to identify areas with significant vegetation changes caused by frequent human activities.

[0071] It is worth mentioning that the population heat map has different weights in different land types, that is, human activities have different impacts on different land types. Therefore, the land type corresponding to the total NDVI change should be taken into account when conducting overlay analysis.

[0072] Step S4: when the total amount of NDVI change meets a preset update condition, real-time data collection is performed based on the vegetation change area to obtain new image data.

[0073] In the embodiment of the present application, in order to reduce the frequency of dispatching drones to collect landscape data as much as possible within the permitted range, an update condition is set to determine whether it is currently necessary to control the drone to go to the target area for high-resolution photography.

[0074] Specifically, when the total NDVI change of a certain basic area is greater than or equal to the first threshold, or the data update time of the basic area exceeds the set second threshold, the update mechanism is triggered, and a drone is dispatched to the basic area for high-resolution photography to obtain new image data. Among them, because the drone data collection is charged according to the shooting area, the cost can be saved by reducing the shooting area and frequency. In the embodiment of the present application, only the area where the vegetation has changed significantly is photographed to reduce the area that needs to be updated each time.

[0075] Furthermore, judging whether the update is timely by the second threshold is a measure taken to prevent update omissions, ensure that the details of normal data fluctuations are not lost, and increase the accuracy and credibility of the data. Specifically, it is done by recording the length of time since the last update of each basic area, that is, the data update time, and then setting a reasonable non-updated time threshold as the second threshold for judging whether the basic area needs to be updated.

[0076] Among them, for a certain basic area, a reasonable non-updated time threshold can be divided according to the growth stage of the dominant forest species in the basic area within a year, that is, the vegetation growth cycle, such as the growth period, the leaf fall period, etc., to ensure that there is at least one data update in each stage. If there is no obvious dominant forest species in the basic area, the different growth cycles of several dominant forest species can be superimposed, and the minimum number of updates can be used to cover all the superimposed growth cycles, so that the length of time between two adjacent updates is the time threshold.

[0077] Optionally, the dominant forest species may be the forest species with the largest coverage area within the basic area.

[0078] Step S5, updating the landscape 3D holographic model of the target area through the new image data.

[0079] In an embodiment of the present application, the texture of the vegetation change area is updated according to the new image data obtained by drone photography, and the updated content is integrated into the original landscape three-dimensional holographic model, so as to achieve partial update of the target area and the landscape three-dimensional holographic model, while maintaining the invariance of the unchanged part of the original landscape three-dimensional holographic model.

[0080] Compared with the original method of using fixed-point cameras to shoot and update the target area, using drones for updating makes the dynamic update of the model more comprehensive.

[0081] Furthermore, after the landscape 3D holographic model is updated, the quality of the model will be checked regularly, the accuracy of NDVI monitoring and drone photography will be evaluated, and the model will be calibrated in a timely manner. The first threshold, second threshold, and drone data collection parameters will be adjusted according to actual conditions and accuracy requirements to optimize the model update strategy.

[0082] In addition, we will regularly collect feedback on model usage to understand users’ satisfaction and specific needs for model updates and further improve the model update method.

[0083] Implementing the embodiments of the present application has the following beneficial effects:

[0084] The embodiment of the present application first divides the target area into several spatially independent basic plots according to different land types and plot boundaries, so as to facilitate the subsequent careful monitoring of vegetation changes in each plot; then the total NDVI change is obtained by monitoring the normalized vegetation index of the basic area, and the degree of vegetation change over a period of time is obtained, and the total NDVI change and the population thermal accumulation map of the target area are superimposed and analyzed to identify areas with significant vegetation changes due to frequent human activities, improve the accuracy of the data, and clarify that the model is updated only when the vegetation changes significantly, and reduce the model update frequency to reduce the model update cost. Then, when the total NDVI change meets the preset update conditions, it means that the vegetation change is obvious enough and the landscape three-dimensional holographic model needs to be updated in time. At this time, a drone is dispatched to collect accurate real-time data of the vegetation change area, that is, the local area, without shooting the entire target area, new image data can be obtained for local update of the model, thereby reducing the update cost by reducing the frequency of sending drones to shoot and the area of ​​each shooting.

[0085] Second embodiment

[0086] Furthermore, in order to implement the dynamic updating system of the landscape three-dimensional holographic model corresponding to the above method embodiment to achieve corresponding functions and technical effects, Figure 2 A structural diagram of a dynamic updating system for a landscape three-dimensional holographic model is provided. For ease of explanation, only the parts related to this embodiment are shown. The dynamic updating system for a landscape three-dimensional holographic model provided by the embodiment of the present application includes:

[0087] The land parcel division module 201 is used to divide the target area into a plurality of spatially independent basic areas according to the land parcel center point of the target area by using the Thiessen polygon algorithm.

[0088] In an embodiment of the present application, the target area is classified into several land class areas based on the acquired remote sensing image data and the preset surface cover type; each land class area is divided into several independent basic plots based on the plot boundary information and the plot management information; the minimum enclosing rectangle of each of the basic plots is calculated, and the center point of the minimum enclosing rectangle is used as the center point of the plot.

[0089] The vegetation change total amount acquisition module 202 is used to monitor the normalized difference vegetation index of each base area according to a preset monitoring frequency to obtain the total amount of NDVI change.

[0090] In an embodiment of the present application, the normalized vegetation index of each of the basic areas is monitored according to a preset monitoring frequency, and NDVI data in a time series format is collected; cumulative calculation is performed based on the NDVI data to obtain the total NDVI change of each of the basic areas within a preset time period.

[0091] The change area determination module 203 is used to perform superposition analysis on the total NDVI change amount and the population thermal accumulation map of the target area to obtain the vegetation change area.

[0092] In an embodiment of the present application, based on the collected multi-source data, population heat maps of the target area at several different time periods are constructed; all population heat maps are accumulated in chronological order to generate a population heat accumulation map; the total NDVI change and the population heat accumulation map are time-aligned and overlaid for analysis to identify vegetation change areas in the target area.

[0093] The update data collection module 204 is used to collect real-time data based on the vegetation change area to obtain new image data when the total amount of NDVI change meets the preset update condition.

[0094] In an embodiment of the present application, when the total amount of NDVI change is greater than or equal to the first threshold or the data update time of each of the basic areas exceeds the set second threshold, the drone is controlled to collect real-time data on the vegetation change area to obtain new image data;

[0095] The second threshold is related to the vegetation growth period of each of the basic areas.

[0096] The model updating module 205 is used to update the landscape 3D holographic model of the target area through new image data.

[0097] In an embodiment of the present application, the texture of the vegetation change area is updated according to the new image data obtained by drone photography, and the updated content is integrated into the original landscape three-dimensional holographic model to achieve partial update of the target area and the landscape three-dimensional holographic model, while maintaining the invariance of the unchanged part of the original landscape three-dimensional holographic model. Compared with the original use of fixed-point cameras to shoot and update the target area, the use of drones for updating makes the dynamic update of the model more comprehensive. Furthermore, after the landscape three-dimensional holographic model is updated, the quality of the model will be regularly checked, the accuracy of NDVI monitoring and drone photography will be evaluated, and the model will be calibrated in a timely manner. And the first threshold, the second threshold and the data acquisition parameters of the drone will be adjusted according to the actual situation and accuracy requirements to optimize the model update strategy. In addition, feedback on model use will be collected regularly to understand the user's satisfaction and specific needs for model updates, and further improve the model update method.

[0098] In some embodiments, the land parcel division module 201 further includes:

[0099] First, remote sensing image data is obtained through remote sensing technologies such as satellite images and GIS data. Then, the target area of ​​the entire landscape project is classified by using the remote sensing image data and existing land cover types, and the different land cover types in the target area are clarified to obtain several different land area types, such as forests, grasslands, water bodies, and buildings.

[0100] Then, within each land type area, each land type area is divided into several independent basic plots according to the existing plot boundary information and plot management information.

[0101] Optionally, in the embodiment of the present application, the plot management information mainly refers to the vegetation restoration areas that require special attention as stipulated, or vegetation restoration projects based on plots, and it is necessary to regularly monitor the project progress and the restoration effect of the construction plots.

[0102] The minimum enclosing rectangle of each basic plot is calculated, and the center point of the minimum enclosing rectangle is extracted as the center point of the plot to provide data support for subsequent area division.

[0103] Then, based on the center points of these plots, the Thiessen polygon algorithm is used to divide the entire target area into several spatially independent basic areas, each of which represents a monitoring unit.

[0104] The Thiessen polygon algorithm is a method for dividing geographic space into non-overlapping areas, each of which consists of a center point and other points closest to the center point. In the embodiment of the present application, the basic area is divided by the Thiessen polygon algorithm, and plots with similar geographical features and NDVI change trends can be divided into one basic area, making subsequent vegetation change monitoring more convenient and comprehensive.

[0105] In some embodiments, the vegetation change total amount acquisition module 202 further includes:

[0106] According to the preset monitoring frequency, the normalized difference vegetation index of each of the base areas is monitored regularly, and NDVI data in a time series format are collected.

[0107] The full name of the NDVI is Normalized Difference Vegetation Index, which is an important parameter reflecting vegetation growth and nutritional information. It mainly quantifies vegetation by measuring the difference between near-infrared (strong reflection from vegetation) and red light (absorption from vegetation), and the value range is fixed to [-1, +1]. When the NDVI value is negative, it means that the land cover currently measured is likely to be water; when the NDVI value is close to +1, it means that the land cover currently measured is likely to be dense green leaves; when the NDVI value is close to 0, it means that the land cover currently measured has no green leaves and may even be an urbanized area.

[0108] In the embodiment of the present application, the degree of vegetation change within each basic area is measured by measuring the NDVI data within the area, so as to timely update the areas with significant vegetation changes.

[0109] The NDVI data in time series format are cumulatively calculated to obtain the total NDVI change of each basic area within the preset period.

[0110] Optionally, the total amount of NDVI change described in the embodiment of the present application is mainly the cumulative change or relative change rate of NDVI within a certain period of time.

[0111] In addition, because NDVI data may change significantly at different times of the year, it is also necessary to examine other data from the same period, such as population heat maps, to make more detailed observations on vegetation changes.

[0112] In some embodiments, the change region determination module 203 further includes:

[0113] First, collect multi-source data, such as mobile communication base station data, social media activity data, public transportation usage data, etc., to build a population heat map of the target area. Among them, the population heat map is mainly used to show the distribution and flow of population in a certain area. In the embodiment of this application, it provides data support for studying the impact of human activities on vegetation changes.

[0114] The population heat maps of multiple periods are accumulated in chronological order to generate a population heat accumulation map, which is used to reflect areas with dense population activities in the long term. The population heat accumulation map is then superimposed and analyzed with the total NDVI change to identify areas with significant vegetation changes caused by frequent human activities.

[0115] It is worth mentioning that the population heat map has different weights in different land types, that is, human activities have different impacts on different land types. Therefore, the land type corresponding to the total NDVI change should be taken into account when conducting overlay analysis.

[0116] In some embodiments, the update data collection module 204 further includes:

[0117] In order to reduce the frequency of dispatching drones to collect landscape data within the permitted range as much as possible, an update condition is set to determine whether it is necessary to control the drone to go to the target area for high-resolution photography.

[0118] Specifically, when the total NDVI change of a certain basic area is greater than or equal to the first threshold, or the data update time of the basic area exceeds the set second threshold, the update mechanism is triggered, and a drone is dispatched to the basic area for high-resolution photography to obtain new image data. Among them, because the drone data collection is charged according to the shooting area, the cost can be saved by reducing the shooting area and frequency. In the embodiment of the present application, only the area where the vegetation has changed significantly is photographed to reduce the area that needs to be updated each time.

[0119] Furthermore, judging whether the update is timely by the second threshold is a measure taken to prevent update omissions, ensure that the details of normal data fluctuations are not lost, and increase the accuracy and credibility of the data. Specifically, it is done by recording the length of time since the last update of each basic area, that is, the data update time, and then setting a reasonable non-updated time threshold as the second threshold for judging whether the basic area needs to be updated.

[0120] Among them, for a certain basic area, a reasonable non-updated time threshold can be divided according to the growth stage of the dominant forest species in the basic area within a year, that is, the vegetation growth cycle, such as the growth period, the leaf fall period, etc., to ensure that there is at least one data update in each stage. If there is no obvious dominant forest species in the basic area, the different growth cycles of several dominant forest species can be superimposed, and the minimum number of updates can be used to cover all the superimposed growth cycles, so that the length of time between two adjacent updates is the time threshold.

[0121] Optionally, the dominant forest species may be the forest species with the largest coverage area within the basic area.

[0122] Implementing the embodiments of the present application has the following beneficial effects:

[0123] The embodiment of the present application first divides the target area into several spatially independent basic plots according to different land types and plot boundaries, so as to facilitate the subsequent careful monitoring of vegetation changes in each plot; then the total NDVI change is obtained by monitoring the normalized vegetation index of the basic area, and the degree of vegetation change over a period of time is obtained, and the total NDVI change and the population thermal accumulation map of the target area are superimposed and analyzed to identify areas with significant vegetation changes due to frequent human activities, thereby improving the accuracy of the data. Then, when the total NDVI change meets the preset update conditions, it means that the vegetation change is obvious enough and the three-dimensional holographic model of the landscape needs to be updated in time, so real-time data collection is performed on the vegetation change area to obtain new image data for model update.

[0124] Furthermore, Figure 3 This is a structural diagram of a terminal device provided in one embodiment of the present application. Figure 3 As shown, the terminal device 3 of this embodiment includes: at least one processor 30 (in Figure 3 Only one is shown) and a memory 31 and a computer program 32 stored in the memory 31 and executable on the at least one processor. When the processor 30 executes the computer program 32, the steps of a method for dynamically updating a landscape three-dimensional holographic model as described in any one of the embodiments of the present application can be implemented.

[0125] The terminal device 3 may be a computing device such as a desktop computer, a cloud server, or a laptop computer. The computing device may include but is not limited to a processor 30 and a memory 31 . Figure 3 This is merely an example of the terminal device 3 and does not constitute a limitation on the terminal device 3 , which may include more or fewer components than those shown in the figure.

[0126] An embodiment of the present application provides a storage medium, which stores computer-readable program codes. When the computer-readable program codes are executed, the steps of the above-mentioned method for dynamically updating a three-dimensional landscape holographic model are implemented.

[0127] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A method for dynamically updating a landscape three-dimensional holographic model, characterized in that: include: According to the center point of the plot in the target area, the target area is divided into several basic areas that are independent of each other in space by using the Thiessen polygon algorithm; According to a preset monitoring frequency, the normalized difference vegetation index of each base area is monitored to obtain the total NDVI change; Performing a superposition analysis on the total NDVI change and the population thermal accumulation map of the target area to obtain the vegetation change area; When the total amount of NDVI change meets the preset update condition, real-time data collection is performed based on the vegetation change area to obtain new image data; The three-dimensional holographic model of the landscape in the target area is updated using new image data.

2. The method for dynamically updating a landscape three-dimensional holographic model according to claim 1, characterized in that: The center point of the plot is specifically: According to the acquired remote sensing image data and the preset land cover type, the target area is classified into land categories to obtain several land category areas; According to the plot boundary information and plot management information, each land type area is divided into several independent basic plots; The minimum bounding rectangle of each of the basic plots is calculated, and the center point of the minimum bounding rectangle is used as the center point of the plot.

3. The method for dynamically updating a landscape three-dimensional holographic model according to claim 1, characterized in that: According to the preset monitoring frequency, the normalized difference vegetation index of each basic area is monitored to obtain the total NDVI change, which is specifically: According to a preset monitoring frequency, the normalized difference vegetation index of each base area is monitored to collect NDVI data in a time series format; Cumulative calculation is performed based on the NDVI data to obtain the total NDVI change of each base area within a preset time period.

4. The method for dynamically updating a landscape three-dimensional holographic model according to claim 1, characterized in that: The total amount of NDVI change and the population thermal accumulation map of the target area are superimposed and analyzed to obtain the vegetation change area, specifically: Based on the collected multi-source data, construct population heat maps of the target area at different time periods; All population heat maps are accumulated in chronological order to generate a population heat accumulation map; The total NDVI change amount and the population thermal accumulation map are time-aligned and overlaid to identify vegetation change areas in the target area.

5. The method for dynamically updating a landscape three-dimensional holographic model according to claim 1, characterized in that: When the total amount of NDVI change meets the preset update condition, real-time data collection is performed based on the vegetation change area to obtain new image data, specifically: When the total amount of NDVI change is greater than or equal to the first threshold or the data update time of each of the basic areas exceeds the set second threshold, the UAV is controlled to collect real-time data of the vegetation change area to obtain new image data; The second threshold is related to the vegetation growth period of each of the basic areas.

6. The method for dynamically updating a landscape three-dimensional holographic model according to claim 5, characterized in that: The second threshold is related to the vegetation growth cycle of each of the basic areas, specifically: A type of vegetation in each of the basic areas is designated as a representative vegetation, and a second threshold of each of the basic areas is set according to a growth cycle of the representative vegetation.

7. The method for dynamically updating a landscape three-dimensional holographic model according to any one of claims 1 to 6, characterized in that: Also includes: Calibrate the updated landscape 3D holographic model according to the preset inspection frequency; The update conditions and the data collection parameters of the UAV are adjusted according to the accuracy requirements obtained.

8. A dynamic updating system for a landscape three-dimensional holographic model, characterized in that: include: Plot division module, vegetation change total amount acquisition module, change area determination module, update data acquisition module and model update module; The plot division module is used to divide the target area into a number of spatially independent basic areas according to the plot center point of the target area through the Thiessen polygon algorithm; The vegetation change total amount acquisition module is used to monitor the normalized vegetation index of each basic area according to a preset monitoring frequency to obtain the total NDVI change; The change area determination module is used to perform superposition analysis on the total NDVI change and the population thermal accumulation map of the target area to obtain the vegetation change area; The update data acquisition module is used to perform real-time data acquisition based on the vegetation change area to obtain new image data when the total amount of NDVI change meets the preset update condition; The model updating module is used to update the landscape three-dimensional holographic model of the target area through new image data.

9. A terminal device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and when the processor executes the computer program, the method implements the steps of a method for dynamically updating a landscape three-dimensional holographic model as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores computer-readable program codes, which, when executed, implement the steps of a method for dynamically updating a landscape three-dimensional holographic model according to any one of claims 1 to 7.

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