Visual landslide monitoring method and device

Abstract

The invention relates to a landslide visual monitoring method and device, and belongs to the technical field of landslide monitoring, and the method comprises the steps: obtaining the disaster scene data of a landslide monitoring area, constructing a view cone of the landslide monitoring area, dividing the disaster scene data through a plurality of LOD layers of the view cone, and obtaining the landslide scene data of the landslide monitoring area. Initial disaster scene data of each LOD level are obtained, error calculation and judgment can be carried out on the initial disaster scene data of each LOD level, and therefore multi-level visual analysis of the disaster scene data can be achieved; and the initial disaster scene data of each target LOD level can be subjected to adaptive scheduling and visual display, so that the intelligent degree and the adaptive capability of the disaster scene are improved, and the visualization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of landslide monitoring, and particularly to a landslide visualization monitoring method and device. Background Art

[0002] China has a vast territory and 70% of its territory belongs to mountainous areas. Landslide disasters are one of the most frequent geological disasters in mountainous areas, with characteristics such as a wide range of effects, a fast evolution speed, and strong destructive power, causing heavy losses to people's lives and property. Therefore, it is necessary to improve the emergency management level and disposal efficiency of landslide disasters and enhance the comprehensive disaster prevention and mitigation capabilities. Virtual three-dimensional scenes can support a series of complex large-system scientific experiments and management analyses in the virtual space. Combining it with disasters, the constructed virtual disaster scenes can support various management analyses of complex disaster systems in the virtual space. With the help of a landslide visualization model, the development trend of landslide disasters can be intuitively displayed. Once the monitoring data reaches the warning threshold, the model can quickly issue an alarm. Relevant departments can quickly respond based on the intuitive information provided by the model, timely notify the people in the threatened areas to evacuate, and strive for precious time for disaster prevention and mitigation, effectively reducing disaster losses.

[0003] However, the existing related research on disaster scene construction and visualization fails to focus on the requirements of disaster emergency tasks, and analyze the characteristics and interrelationships of elements such as users, terminals, and visualization tasks. Moreover, there is a lack of consistent semantic descriptions among disaster scene data, resulting in problems such as poor intelligence in disaster scene construction, weak adaptive terminal capabilities, and low visualization efficiency, and it is difficult to meet the multi-level visualization task requirements for a dynamic and complex landslide disaster environment.

[0004] Therefore, there is an urgent need to propose a landslide visualization monitoring method and device to solve the technical problems existing in the prior art, such as poor multi-level visualization analysis not based on task requirements, resulting in poor intelligence in disaster scene construction, weak adaptive terminal capabilities, and low visualization efficiency. Summary of the Invention

[0005] In view of this, it is necessary to provide a landslide visualization monitoring method and device to solve the problems existing in the prior art, such as poor intelligence in disaster scene construction, weak adaptive terminal capabilities, and low visualization efficiency, and it is difficult to meet the multi-level visualization task requirements for a dynamic and complex landslide disaster environment.

[0006] To solve the above problems, in a first aspect, the present invention provides a landslide visualization monitoring method, including: Obtain disaster scene data of a landslide monitoring area and construct a frustum of the landslide monitoring area; Divide the disaster scene data according to multiple LOD levels of the frustum to obtain initial disaster scene data for each LOD level; Calculate and judge the error of the initial disaster scenario data for each LOD level to obtain multiple target LOD levels; Perform adaptive scheduling and visual display on the initial disaster scenario data for each target LOD level to obtain a visual disaster scenario, and monitor the landslide monitoring area according to the visual disaster scenario.

[0007] In a possible implementation manner, the obtaining of the disaster scenario data of the landslide monitoring area includes: Determine a reference point and multiple deformation monitoring points; the reference point is located at a position outside the landslide monitoring area without strong interference, and the multiple deformation monitoring points are monitoring points set along the longitudinal section in the landslide direction within the landslide monitoring area; Obtain the digital elevation data, geological structure data, landslide body morphology data, and image materials of the multiple deformation monitoring points according to the reference point; Perform spatial integration according to the digital elevation data, the geological structure data, the landslide body morphology data, and the image materials to obtain disaster scenario data.

[0008] In a possible implementation manner, the dividing of the disaster scenario data according to the multiple LOD levels of the frustum to obtain the initial disaster scenario data for each LOD level includes: Eliminate the data in the disaster scenario data that does not belong to the frustum to obtain effective disaster scenario data; Determine an LOD division strategy according to the detail features of each LOD level in the frustum; Divide the effective disaster scenario data according to the LOD division strategy to obtain the initial disaster scenario data for each LOD level.

[0009] In a possible implementation manner, the calculating and judging the error of the initial disaster scenario data for each LOD level to obtain multiple target LOD levels includes: Construct a visual error evaluation model and set the geometric error value corresponding to each LOD level; Calculate the error of the geometric error value and the initial disaster scenario data corresponding to each LOD level according to the visual error evaluation model to obtain the visual error of each LOD level; Determine the LOD levels with visual errors less than a preset threshold among the multiple LOD levels as target LOD levels.

[0010] In a possible implementation, the adaptive scheduling and visual display of the initial disaster scenario data for each target LOD level to obtain a visual disaster scenario includes: Construct a multi-factor model; Fuse and schedule all the initial disaster scenario data according to the multi-factor model to obtain target disaster scenario data; Visually display the target disaster scenario data in the data format of 3D tiles to obtain a visual disaster scenario.

[0011] In a possible implementation, the construction of the multi-factor model includes: Set multiple terminal influencing factors and construct a network environment parameter set according to the multiple terminal influencing factors; Conduct hierarchical analysis on the network environment parameter set to obtain the weight of each terminal influencing factor; Construct a multi-factor model according to the weights of all the multiple terminal influencing factors.

[0012] In a possible implementation, the fusing and scheduling of all the initial disaster scenario data according to the multi-factor model to obtain target disaster scenario data includes: Fuse all the initial disaster scenario data according to the multi-factor model to obtain a spatial index; Determine the index strategy for each type according to the different types of the initial disaster scenario data and the spatial index; Adaptively schedule the initial disaster scenario data of the multiple target LOD levels according to the index strategy to obtain target disaster scenario data.

[0013] In a possible implementation, after the adaptive scheduling and visual display of the initial disaster scenario data for each target LOD level to obtain a visual disaster scenario, it further includes: Judge whether the visual frame rate of the visual disaster scenario is less than a preset frame rate threshold; If so, adjust the key preset parameters in the spatial index based on a dynamic parameter adjustment algorithm to obtain a new spatial index, and adaptively schedule and visually display the initial disaster scenario data of the multiple target LOD levels according to the new spatial index to obtain a visual disaster scenario.

[0014] In a possible implementation, the construction of the view frustum of the landslide monitoring area includes: Determine the viewing point according to the coordinates of the Beidou satellite navigation system; Simulate the viewing point according to the visible area analysis algorithm to generate the view frustum of the landslide monitoring area.

[0015] In a second aspect, the present invention further provides a landslide visualization monitoring device, including: A data acquisition module, configured to acquire disaster scenario data of a landslide monitoring area and construct a frustum of a pyramid for the landslide monitoring area; A hierarchical division module, configured to divide the disaster scenario data according to multiple LOD levels of the frustum of a pyramid to obtain initial disaster scenario data for each LOD level; An error calculation module, configured to calculate and judge errors of the initial disaster scenario data for each LOD level to obtain multiple target LOD levels; A regional monitoring module, configured to perform adaptive scheduling and visual display on the initial disaster scenario data for each target LOD level to obtain a visualized disaster scenario, and monitor the landslide monitoring area according to the visualized disaster scenario.

[0016] The beneficial effects of the present invention are as follows: A frustum of a pyramid for the landslide monitoring area is constructed, and the disaster scenario data is divided according to multiple LOD levels of the frustum of a pyramid to obtain initial disaster scenario data for each LOD level. Errors of the initial disaster scenario data for each LOD level can be calculated and judged, so as to realize multi-level visual analysis of the disaster scenario data. Adaptive scheduling and visual display can also be performed on the initial disaster scenario data for each target LOD level, improving the intelligence and adaptive ability of the disaster scenario, and also improving the visualization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flowchart of an embodiment of the landslide visualization monitoring method provided by the present invention; Figure 2 For the present invention Figure 1 It is a schematic flowchart of an embodiment of step S101 in the present invention; Figure 3 For the present invention Figure 1 It is a schematic flowchart of an embodiment of step S102 in the present invention; Figure 4 For the present invention Figure 1 It is a schematic flowchart of an embodiment of step S103 in the present invention; Figure 5 It is a schematic structural diagram of an embodiment of the landslide visualization monitoring device provided by the present invention; Figure 6 It is a schematic structural diagram of an embodiment of the electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The preferred embodiments of the present invention will be specifically described below with reference to the accompanying drawings. The accompanying drawings form a part of this application and are used together with the embodiments of the present invention to explain the principles of the present invention, rather than to limit the scope of the present invention.

[0019] The Beidou Navigation Satellite System (BDS) is a global satellite navigation system independently developed by China, aiming to provide high-precision positioning, navigation, and timing services for global users all-weather and all-time. BDS consists of a series of satellites, ground control centers, and user equipment, etc., and has the advantages of high precision, high reliability, and multiple frequency bands.

[0020] The LOD (Level of Detail) technology determines the resource allocation for object rendering according to the position and importance of the nodes of the object model in the display environment. By reducing the number of faces and detail level of unimportant objects, high-efficiency rendering operations can be obtained. This technology was first proposed by Clark in 1976, and its working principle is that when the viewing point is close to the object, the details of the model that can be observed are rich; when the viewing point is far from the model, the observed details gradually become blurred.

[0021] The spatial index is a data structure designed specifically for managing spatial data (such as geometric objects like points, lines, surfaces, or volumes). It divides, organizes, and indexes spatial data to accelerate operations such as spatial range queries, proximity queries, and nearest neighbor searches. The spatial index not only records the attributes of the data but also stores the position of the data in space and its geometric characteristics.

[0022] The Analytic Hierarchy Process (AHP) is a decision-making analysis method that combines qualitative and quantitative methods, proposed by Professor Saaty, an American operations researcher, in the 1970s. This method decomposes complex multi-objective decision-making problems into multiple levels, including objectives, criteria, and solutions, etc., and calculates the single-level ranking and overall ranking of the levels through the fuzzy quantification method of qualitative indicators, so as to provide a basis for the optimization decision-making of multiple indicators and multiple solutions.

[0023] As Figure 1 shown, a specific embodiment of the present invention discloses a landslide visualization monitoring method, including: S101. Obtain the disaster scene data of the landslide monitoring area and construct a frustum of the landslide monitoring area.

[0024] Embodiments of the present invention can be applied to a landslide visualization monitoring system. The landslide visualization monitoring system can be connected to the Beidou Satellite Navigation System (BDS) and SAR satellites. The landslide visualization monitoring system can receive time-series SAR satellite images. The landslide visualization monitoring system can be a software system running on a terminal device. The terminal device can be a server, a tablet computer, a vehicle-mounted device, an Augmented Reality (AR) / Virtual Reality (VR) device, a laptop computer, an Ultra-Mobile Personal Computer (UMPC), a netbook, a Personal Digital Assistant (PDA), a mobile phone, or other terminal devices. The specific type of the terminal device is not limited in the embodiments of the present application.

[0025] Among them, the landslide monitoring area can be determined according to the previous situation of the mountain body. In order to monitor the landslide monitoring area in real-time and visually, the disaster scene data of the landslide monitoring area can be obtained. The specific ways to obtain the disaster scene data can include but are not limited to satellite detection, video shooting, lidar, etc., and can be specifically set according to the actual situation. The disaster scene data can include digital elevation data, geological structure data, landslide body morphology data (including landslide boundaries, crack distributions, etc.), and related image materials, etc. The real-time position of the viewing point can also be determined according to the Beidou Satellite Navigation System, so as to construct a frustum of the landslide monitoring area.

[0026] S102. Divide the disaster scene data according to multiple LOD levels of the frustum to obtain the initial disaster scene data of each LOD level.

[0027] Among them, the frustum can include multiple LOD levels, and different LOD levels represent different levels of detail information. In order to adaptively schedule the disaster scene data according to different LOD levels, it is necessary to divide the disaster scene data through multiple LOD levels, so as to obtain the initial disaster scene data of each LOD level, providing a basis for subsequent adaptive scheduling.

[0028] S103. Calculate and judge the error of the initial disaster scene data of each LOD level to obtain multiple target LOD levels.

[0029] Among them, in the initial disaster scenario data at each LOD level, there is data that does not meet the requirements. In order to obtain more accurate data scheduling, it is necessary to calculate the initial disaster scenario data at each LOD level, and retain the initial disaster scenario data at the LOD levels that meet the requirements. The specific process is as follows: Calculate and judge the error of the initial disaster scenario data at each LOD level, so that the LOD levels whose calculated error meets the conditions can be retained, and multiple target LOD levels are obtained. Among them, the error calculation and judgment can also be carried out one by one for the initial disaster scenario data at each LOD level. For example, calculate and judge the error of the initial disaster scenario data at the first LOD level. If the error calculated at the first LOD level meets the conditions, the first LOD level can be determined as the target LOD level. Then, calculate and judge the error of the initial disaster scenario data at the second LOD level, and so on in a loop until all LOD levels are looped through, and multiple target LOD levels are obtained. If the error calculated at the first LOD level does not meet the conditions, the first LOD level is abandoned, and then the error of the initial disaster scenario data at the second LOD level is calculated and judged, and the loop continues until all LOD levels are looped through, and multiple target LOD levels are obtained. The specific error conditions can be set according to the actual situation, and the embodiments of the present invention do not limit this here.

[0030] S104. Perform adaptive scheduling and visual display on the initial disaster scenario data at each target LOD level to obtain a visual disaster scenario, and monitor the landslide monitoring area based on the visual disaster scenario.

[0031] Among them, in order to perform adaptive visual monitoring on the landslide monitoring area, an adaptive scheduling strategy can be set. According to this strategy, the initial disaster scenario data at each target LOD level can be adaptively scheduled, and then visual display is performed based on the scheduled data to obtain a visual disaster scenario, so that the staff can monitor the landslide monitoring area through the visual disaster scenario.

[0032] Compared with the prior art, the disaster scenario data for the landslide monitoring area provided in this embodiment constructs a frustum for the landslide monitoring area, divides the disaster scenario data through multiple LOD levels of the frustum to obtain the initial disaster scenario data at each LOD level, and can calculate and judge the error of the initial disaster scenario data at each LOD level, thereby realizing multi-level visual analysis of the disaster scenario data; it can also perform adaptive scheduling and visual display on the initial disaster scenario data at each target LOD level, improving the intelligence and adaptive ability of the disaster scenario, as well as the visualization efficiency.

[0033] In some embodiments of the present invention, as Figure 2 shown, step S101 includes: S201. Determine a reference point and multiple deformation monitoring points; the reference point is located at a position outside the landslide monitoring area without strong interference, and the multiple deformation monitoring points are monitoring points arranged along the longitudinal section in the landslide direction within the landslide monitoring area.

[0034] Among them, based on the Beidou Satellite Navigation System (BDS), a stable position without strong interference is selected in a safe area far from the landslide monitoring area to establish a reference point. Along the landslide direction in the landslide monitoring area, a longitudinal section is selected to establish n deformation monitoring points. Based on the reference point, the monitoring point data is collected, and the landslide digital elevation data is obtained through a BDS receiver, specifically, the discrete data of n BDS monitoring points at a fixed time is obtained. When establishing the landslide reference point, the following factors should be comprehensively considered: considering the stability of the reference point, the reference point should be arranged at a stable part at a certain distance from the landslide area, preferably on the outcrop of bedrock; from the perspective of eliminating correlation errors, the reference point should not be too far away from the monitoring points to avoid affecting the monitoring accuracy. Try to avoid the existence of strong interference sources and obstacles near the point position to ensure the reliability of the obtained landslide data.

[0035] S202. According to the reference point, obtain the digital elevation data, geological structure data, landslide body morphology data, and image materials of the multiple deformation monitoring points.

[0036] Among them, while using the Beidou Satellite Navigation System for monitoring to obtain the digital elevation data, the geological structure data, landslide body morphology data (including landslide boundaries, crack distributions, etc.), and relevant image materials of the landslide monitoring area are also collected. These data can be collected through satellite, pictures, videos, geological equipment, etc., and can be specifically set according to the actual situation.

[0037] S203. Perform spatial integration on the digital elevation data, geological structure data, landslide body morphology data, and image materials to obtain disaster scenario data.

[0038] Among them, the digital elevation data, geological structure data, landslide body morphology data, and image materials can be integrated into a unified spatial data framework to ensure the spatial matching and correlation between the data.

[0039] In some embodiments of the present invention, step S101 includes: Determine the viewing point according to the coordinates of the Beidou Satellite Navigation System; Simulate the viewing point according to the visible area analysis algorithm to generate a visual cone of the landslide monitoring area.

[0040] Among them, since the human eye has a perspective law when observing things, that is, the detailed information obtained for the farther scene is relatively less, and the detailed information obtained for the closer scene is relatively more, therefore, the embodiment of the present invention adopts a method of scheduling spatial data in a three-dimensional scene based on the user's viewpoint related information. In each rendering frame, multi-LOD hierarchical data is dynamically scheduled based on the viewpoint position. Specifically, the coordinate position of the Beidou satellite navigation system is determined as the viewpoint, and then the view cone corresponding to the current viewpoint can be accurately generated according to the real-time position of the viewpoint using an advanced visual field analysis algorithm. The view cone follows the principle of perspective projection, simulates the observation range of the human eye, and is in the shape of a four-sided pyramid. Through efficient spatial geometry calculation methods, the spatial position relationship between the view cone and the disaster scene data is deeply analyzed, which serves as the key basis for automatic data scheduling.

[0041] In some embodiments of the present invention, Figure 3 As shown, step S102 includes: S301. Eliminate the data that does not belong to the visual cone in the disaster scene data to obtain valid disaster scene data.

[0042] Among them, there may be invisible data in the disaster scene data, resulting in invalid scheduling, so the disaster scene data needs to be screened. The positional relationship between the disaster scene data and the view frustum clipping surface can be accurately calculated to determine whether the disaster scene data is within the view frustum, that is, whether it is within the current field of view. Only valid disaster scene data whose spatial position is exactly within the view frustum will be screened out and enter the subsequent rendering pipeline, thereby effectively avoiding invalid scheduling of invisible data and greatly improving data processing efficiency and rendering performance.

[0043] S302: Determine the LOD division strategy according to the detail features of each LOD level in the viewing cone.

[0044] In a specific embodiment of the present invention, there is a lot of disaster scene data. Different data, different types, different locations, and different relationships make the disaster scene data very complex. In order to facilitate scheduling, a LOD division strategy can be set. The specific LOD division strategy can be set according to actual conditions, and the embodiment of the present invention is not limited here. Among them, the LOD (Level of Detail) division strategy refers to rendering objects at different levels of detail at different observation distances to optimize performance and visual effects. The following are several common LOD division strategies: ‌Distance-based LOD‌: Dynamically adjusts the level of detail of an object based on the distance between the viewer and the object. The farther away, the lower the level of detail used; the closer the distance, the higher the level of detail used‌.

[0045] Screen Space-based LOD: Adjust the detail level of an object according to the number of pixels it occupies on the screen. The fewer pixels an object occupies, the lower the detail level used; the more pixels it occupies, the higher the detail level used.

[0046] Curvature-based LOD: Adjust the detail level of an object according to the curvature of its surface. Areas with smaller curvature can use models with lower detail levels, while areas with larger curvature require models with higher detail levels.

[0047] S303. Divide the effective disaster scenario data according to the LOD division strategy to obtain the initial disaster scenario data for each LOD level.

[0048] Among them, the effective disaster scenario data can be divided according to the LOD division strategy to generate data versions with different levels of detail, that is, the initial disaster scenario data for each LOD level. For example, for high-detail level data, maintain a high-precision representation of terrain and landslide features; for low-detail level data, reduce the complexity of the data through methods such as resampling, simplifying geometries, and merging textures to obtain multiple sets of data with different resolutions and precisions, and each set of data corresponds to a specific level of detail.

[0049] In some embodiments of the present invention, as Figure 4 shown, step S103 includes: S401. Build a visual error evaluation model and set the geometric error value corresponding to each LOD level.

[0050] Among them, a visual error evaluation model can be built. The visual error evaluation model comprehensively considers multiple pieces of information closely related to the viewpoint, such as the viewpoint distance, viewing angle, surface roughness, and screen error. Through this model, the visual error of the initial disaster scenario data at the corresponding LOD level can be calculated. The specific method for building the visual error evaluation model can be set according to the actual situation, and the embodiments of the present invention do not limit this here. The geometric error value corresponding to each LOD level can also be set according to the actual situation.

[0051] S402. Calculate the error between the geometric error value corresponding to each LOD level and the initial disaster scenario data according to the visual error evaluation model to obtain the visual error of each LOD level.

[0052] Among them, the visual error evaluation model uses screen pixel error to measure the visual error. When building a spatial index for multi-LOD level data, the user can write the geometric error value of each LOD level data as the level selection threshold. The calculation formula of the visual error evaluation model is shown in formula (1): (1) In the formula, is the viewpoint, is the center of the bounding box of the current LOD level data, is the screen pixel error; is the screen height; is the surface roughness; is the field of view angle; is the angle between the line of sight direction and the central axis of the field of view; The LOD level is L The preset geometric error of the data. For data of the same LOD level, the same geometric error value will be set. The numerical relationship between the LOD level and the preset geometric error value is shown in formula (2): (2) In the formula, The LOD level is L The preset geometric error of the data with +1.

[0053] Therefore, after determining the preset geometric error of the data of one LOD level, the preset geometric errors of the data of other LOD levels can be known. Thus, the visual error of each LOD level can be calculated according to formula (1) .

[0054] S403. Determine the LOD levels with visual errors less than the preset threshold among multiple LOD levels as the target LOD levels.

[0055] In a specific embodiment of the present invention, then it can be judged whether the visual error is less than the preset threshold. If so, it means that the rendering accuracy of the initial disaster scene data of the corresponding LOD level on the screen has met the visual quality standard preset by the system, and the corresponding LOD level is determined as the target LOD level. At this time, the data of this LOD level can be directly scheduled to the rendering pipeline to efficiently realize the visual rendering of the data. If not, it means that the level of detail of the current LOD level data is not sufficient to meet the visual quality requirements. Based on the current LOD level data, along the refinement direction, continuously retrieve data of a more refined level. After each new level of data is retrieved, the visual errors of other LOD levels can be judged. When the visual error judgment of all LOD levels is completed, all target LOD levels with visual errors less than the preset threshold can enter the rendering pipeline, so as to ensure that the visual effect of the disaster scene data is always maintained at a high quality during the visual presentation.

[0056] In some embodiments of the present invention, step S104 includes: Construct a multi-factor model; In some embodiments of the present invention, this process can be: Set multiple terminal influencing factors and construct a network environment parameter set according to the multiple terminal influencing factors; Conduct hierarchical analysis on the network environment parameter set to obtain the weight of each terminal influencing factor; Construct a multi-factor model according to the weights of all the multiple terminal influencing factors.

[0057] Among them, traditional data scheduling establishes a data dynamic scheduling mechanism driven by the viewpoint position, which can selectively schedule data at an appropriate LOD level in real time according to the viewpoint information, effectively improving the network data transmission efficiency and reducing the rendering pressure on the visualization terminal. The embodiments of the present invention innovatively propose to achieve adaptive data scheduling by considering influencing factors such as the terminal performance and network environment related to tasks, and use parameters such as the viewpoint information, user terminal performance configuration and screen size, user network environment, and real-time frame rate during the visualization process as terminal influencing factors. The multiple terminal influencing factors may include factors such as terminal performance configuration, terminal screen size, network environment, and real-time frame rate, and establish an adaptive scheduling model for disaster scenario data comprehensively influenced by multiple factors. The model is shown in formula (3), avoiding the limitation of relying only on the viewpoint information for data scheduling.

[0058] (3) In the formula, The function represents the overall disaster scenario data hierarchy rendered by the visualization terminal in each rendering frame, and the parameter represents the LOD levels of various disaster scenario data, and the parameter respectively represent the terminal performance configuration, terminal screen size, network environment, and real-time frame rate, and the parameter represents the current viewpoint information parameter; respectively represent the data at each LOD level, represents that under the influence of multiple parameters, the proportion of the data with the LOD level of in the entire disaster scenario data.

[0059] The specific scheduling process may be as follows: In the system initialization stage, through precise terminal identification technology, quickly obtain the type of the visualization terminal. Based on this, comprehensively sort out the inherent parameters such as the rendering performance and screen size of the terminal, and at the same time use network monitoring tools to deeply analyze the environmental conditions of the network accessed by the current terminal, and obtain key quantitative indicators such as bandwidth and network latency, so as to construct a network environment parameter set. To integrate the influence of multiple factors, the analytic hierarchy process (AHP) is used to determine the weights of each influencing factor, and then a comprehensive multi-factor model is established. The multi-factor model can accurately quantify the influence degree of each factor on data scheduling and visualization effect, providing a theoretical basis for subsequent operations.

[0060] Fuse and schedule all initial disaster scenario data according to the multi-factor model to obtain the target disaster scenario data; In some embodiments of the present invention, this process may be: Fuse all initial disaster scenario data according to the multi-factor model to obtain a spatial index; Determine the index strategy for each type according to the different types of initial disaster scenario data and the spatial index; Adaptively schedule the initial disaster scenario data at multiple target LOD levels according to the index strategy to obtain the target disaster scenario data.

[0061] Among them, based on the constructed multi-factor model, deeply fuse the multi-factor features of all initial disaster scenario data and customize a targeted spatial index. Implement different index strategies for different types of disaster scenario data and spatial indexes, that is, the index strategy for each type. For example, for the realistic terrain disaster scenario data in the affected area, the index strategy is to accurately specify the tile data scheduling rules with appropriate size resolution in the spatial index. Among them, the tile data scheduling rules mainly include the scheduling strategy of tiles and related technical details. The scheduling rules of tile data mainly involve the loading, unloading, and replacement strategies of tiles to ensure the rendering efficiency of the scene and memory management. For the landslide disaster simulation data, the index strategy is to intelligently schedule the disaster body model with appropriate LOD level using the spatial index; for the entity building model data in the affected area, the index strategy is to write the preset geometric error threshold into the initial spatial index to accurately plan the proportion of data at each LOD level in the total amount of the overall disaster scenario data at the start of rendering. Then, perform adaptive scheduling on the proportion through the adaptive scheduling model to obtain the target disaster scenario data, realize the adaptive selection of the LOD level, and ensure that the data scheduling highly matches the scene requirements.

[0062] Visually display the target disaster scenario data in the data format of 3D tiles to obtain a visual disaster scenario.

[0063] Among them, in the previous steps, by analyzing the differences in parameters such as the performance of the visualization terminal related to each stage task of the landslide disaster emergency and the network environment, on this basis, the scheduled disaster scenario data will be optimized and rendered into a disaster scenario next to achieve the efficient visualization of the landslide disaster emergency scenario and provide efficient and accurate scenario support for user interaction and exploration.

[0064] Specifically, the lightweight data format 3DTiles is used to store and represent the above disaster scenario data. It is a lightweight format for storing and transmitting three-dimensional geospatial data, supporting multi-level of detail (LOD) rendering, and capable of dynamically loading model data with different precisions according to the user's perspective and distance. Before transmitting the data to the client, 3DTiles will pre-calculate information such as the geometry, texture, and normal of each tile and save it in binary format for quick loading and rendering on the client. At the same time, the client usually sets up a caching mechanism to cache the already loaded tile data. When the user browses the same area again, the data can be directly retrieved from the cache to obtain the visualized disaster scenario without having to reload it from the server, further improving the loading speed and rendering performance. The lightweight format conversion is shown in Equation (4): (4) In the formula, represents the binary storage format, represents the geometry; represents the texture; represents the normal; respectively represent the target disaster scenario data at each LOD level; m represents the number of categories of the information set.

[0065] In some embodiments of the present invention, after step S104, it further includes: judging whether the visualization frame rate of the visualized disaster scenario is less than a preset frame rate threshold; If so, adjust the key preset parameters in the spatial index based on the dynamic parameter adjustment algorithm to obtain a new spatial index, and adaptively schedule and visually display the initial disaster scenario data at multiple target LOD levels according to the new spatial index to obtain the visualized disaster scenario.

[0066] In a specific embodiment of the present invention, disaster scenario data of a landslide monitoring area can be obtained in real time, and then the data can be scheduled in real time through the above process. In the whole process of continuous data scheduling and visualization, the built-in frame rate monitoring module is used to collect the visualization frame rate in real time and compare it with a preset frame rate threshold at high frequency. Once it is detected that the frame rate does not meet the expected standard, that is, the visualization frame rate is less than the preset frame rate threshold, the system will automatically trigger the intelligent optimization mechanism. With the help of the dynamic parameter adjustment algorithm, key preset parameters in each spatial index, such as tile size, geometric error threshold, etc., are adjusted in real time to obtain a new spatial index, so as to adaptively select more data at a lower LOD level for scheduling. This optimization process will continue to iterate until the real-time frame rate is stably maintained above the preset frame rate threshold, obtaining a visualized disaster scenario, realizing the adaptive scheduling of disaster scenario data considering multiple influencing factors, and ensuring the best visualization effect and ultimate user experience under the current visualization terminal and network environment conditions.

[0067] To better implement the landslide visualization monitoring method in the embodiments of the present invention, correspondingly, the embodiments of the present invention also provide a landslide visualization monitoring device, as Figure 5 shown. The landslide visualization monitoring device 500 includes: A data acquisition module 501, configured to acquire disaster scenario data of a landslide monitoring area and construct a frustum of a landslide monitoring area; A level division module 502, configured to divide the disaster scenario data according to multiple LOD levels of the frustum to obtain initial disaster scenario data for each LOD level; An error calculation module 503, configured to calculate and judge the error of the initial disaster scenario data for each LOD level to obtain multiple target LOD levels; A region monitoring module 504, configured to perform adaptive scheduling and visualization display on the initial disaster scenario data for each target LOD level to obtain a visualized disaster scenario, and monitor the landslide monitoring area according to the visualized disaster scenario.

[0068] The landslide visualization monitoring device 500 provided in the above embodiments can implement the technical solutions described in the above embodiments of the landslide visualization monitoring method. The specific implementation principles of the above modules or units can be referred to the corresponding content in the above embodiments of the landslide visualization monitoring method, which will not be elaborated here.

[0069] As Figure 6 shown, the present invention also correspondingly provides an electronic device 600. The electronic device 600 includes a processor 601, a memory 602, and a display 603. Figure 6Only some components of the electronic device 600 are shown, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively.

[0070] In some embodiments, the memory 602 can be an internal storage unit of the electronic device 600, such as the hard disk or memory of the electronic device 600. In some other embodiments, the memory 602 can also be an external storage device of the electronic device 600, such as a plug-in hard disk equipped on the electronic device 600, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc.

[0071] Furthermore, the memory 602 can also include both the internal storage unit of the electronic device 600 and the external storage device. The memory 602 is used to store the application software installed in the electronic device 600 and various types of data.

[0072] In some embodiments, the processor 601 can be a Central Processing Unit (CPU), a microprocessor or other data processing chips, and is used to run the program code stored in the memory 602 or process data, such as the landslide visualization monitoring method in the present invention.

[0073] In some embodiments, the display 603 can be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. The display 603 is used to display the information of the electronic device 600 and to display the visual user interface. The components 601 - 603 of the electronic device 600 communicate with each other through the system bus.

[0074] In some embodiments of the present invention, when the processor 601 executes the landslide visualization monitoring program in the memory 602, the following steps can be implemented: Obtain the disaster scene data of the landslide monitoring area and construct a frustum of the landslide monitoring area; Divide the disaster scene data according to multiple LOD levels of the frustum to obtain the initial disaster scene data of each LOD level; Calculate and judge the error of the initial disaster scene data of each LOD level to obtain multiple target LOD levels; Perform adaptive scheduling and visual display on the initial disaster scene data of each target LOD level to obtain a visual disaster scene, and monitor the landslide monitoring area according to the visual disaster scene.

[0075] It should be understood that when the processor 601 executes the landslide visualization monitoring program in the memory 602, in addition to the above functions, other functions can also be realized. For specific details, reference can be made to the description of the corresponding method embodiments above.

[0076] Furthermore, the embodiments of the present invention do not specifically limit the type of the mentioned electronic device 600. The electronic device 600 can be a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, or other portable electronic devices. Exemplary embodiments of the portable electronic device include, but are not limited to, portable electronic devices running IOS, android, microsoft, or other operating systems. The above portable electronic devices can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (such as a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 600 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (such as a touch panel).

[0077] Correspondingly, the embodiments of the present application also provide a computer-readable storage medium. The computer-readable storage medium is used to store computer-readable programs or instructions. When the programs or instructions are executed by a processor, the method steps or functions of the landslide visualization monitoring method provided by the above various method embodiments can be realized.

[0078] Those skilled in the art can understand that all or part of the processes of implementing the methods of the above embodiments can be completed by instructing relevant hardware (such as a processor, a controller, etc.) through a computer program. The computer program can be stored in a computer-readable storage medium. Among them, the computer-readable storage medium is a magnetic disk, an optical disk, a read-only memory, or a random access memory, etc.

[0079] The landslide visualization monitoring method and device provided by the present invention have been introduced in detail above. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those skilled in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A landslide visual monitoring method, characterized in that: include: Acquire disaster scene data of a landslide monitoring area and construct a viewing cone of the landslide monitoring area; Dividing the disaster scene data according to the multiple LOD levels of the view cone to obtain initial disaster scene data of each LOD level; Performing error calculation and judgment on the initial disaster scene data of each LOD level to obtain multiple target LOD levels; The initial disaster scenario data of each target LOD level is adaptively scheduled and visualized to obtain a visualized disaster scenario, and the landslide monitoring area is monitored according to the visualized disaster scenario.

2. The landslide visualization monitoring method according to claim 1, characterized in that: The obtaining of disaster scene data of the landslide monitoring area includes: Determine a reference point and a plurality of deformation monitoring points; the reference point is located outside the landslide monitoring area without strong interference, and the plurality of deformation monitoring points are monitoring points set along the longitudinal section of the landslide monitoring area in the direction of the landslide; According to the reference points, digital elevation data, geological structure data, landslide body morphology data and image data of the plurality of deformation monitoring points are obtained; The digital elevation data, the geological structure data, the landslide body morphology data and the image data are spatially integrated to obtain disaster scene data.

3. The landslide visualization monitoring method according to claim 1, characterized in that: The step of dividing the disaster scene data according to the multiple LOD levels of the view cone to obtain initial disaster scene data of each LOD level includes: Eliminate the data that does not belong to the visual cone from the disaster scene data to obtain valid disaster scene data; Determine an LOD division strategy according to detail features of each LOD level in the view cone; The effective disaster scene data is divided according to the LOD division strategy to obtain the initial disaster scene data of each LOD level.

4. The landslide visualization monitoring method according to claim 1, characterized in that: The error calculation and judgment of the initial disaster scene data of each LOD level is performed to obtain multiple target LOD levels, including: Constructing a visual error evaluation model and setting a geometric error value corresponding to each LOD level; Performing error calculation on the geometric error value corresponding to each LOD level and the initial disaster scene data according to the visual error evaluation model to obtain the visual error of each LOD level; The LOD level among the multiple LOD levels whose visual error is less than a preset threshold is determined as the target LOD level.

5. The landslide visualization monitoring method according to claim 1, characterized in that: The adaptive scheduling and visual display of the initial disaster scene data at each target LOD level to obtain a visual disaster scene includes: Constructing multi-factor models; According to the multi-factor model, all initial disaster scenario data are integrated and scheduled to obtain target disaster scenario data; The target disaster scene data is visualized in a three-dimensional tile data format to obtain a visualized disaster scene.

6. The landslide visualization monitoring method according to claim 5, characterized in that: The multi-factor model is constructed, comprising: Setting a plurality of terminal influencing factors, and constructing a network environment parameter set according to the plurality of terminal influencing factors; Performing hierarchical analysis on the network environment parameter set to obtain the weight of each terminal influencing factor; A multi-factor model is constructed according to all weights of the multiple terminal influencing factors.

7. The landslide visualization monitoring method according to claim 5, characterized in that: The method of fusing and scheduling all initial disaster scenario data according to the multi-factor model to obtain target disaster scenario data includes: All initial disaster scene data are integrated according to the multi-factor model to obtain a spatial index; Determining an indexing strategy for each type according to different types of the initial disaster scene data and the spatial index; The initial disaster scene data of the multiple target LOD levels are adaptively scheduled according to the indexing strategy to obtain target disaster scene data.

8. The landslide visualization monitoring method according to claim 7, characterized in that: After the initial disaster scene data of each target LOD level is adaptively scheduled and visualized to obtain a visualized disaster scene, the method further includes: Determining whether the visualization frame rate of the visualized disaster scene is less than a preset frame rate threshold; If so, the key preset parameters in the spatial index are adjusted based on a dynamic parameter adjustment algorithm to obtain a new spatial index, and the initial disaster scene data of the multiple target LOD levels are adaptively scheduled and visualized according to the new spatial index to obtain a visualized disaster scene.

9. The landslide visualization monitoring method according to claim 1, characterized in that: The constructing of the visual cone of the landslide monitoring area includes: Determine the viewpoint based on the coordinates of the Beidou satellite navigation system; The viewpoint is simulated according to a visual area analysis algorithm to generate a visual cone of the landslide monitoring area.

10. A landslide visual monitoring device, characterized in that: include: A data acquisition module, used to acquire disaster scene data of a landslide monitoring area and construct a visual cone of the landslide monitoring area; A hierarchical division module, used for dividing the disaster scene data according to the multiple LOD levels of the view cone to obtain initial disaster scene data of each LOD level; An error calculation module, used for performing error calculation and judgment on the initial disaster scene data of each LOD level to obtain multiple target LOD levels; The regional monitoring module is used to adaptively schedule and visualize the initial disaster scene data of each target LOD level to obtain a visualized disaster scene, and monitor the landslide monitoring area according to the visualized disaster scene.

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